[HN Gopher] Notes on OpenAI's new o1 chain-of-thought models
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Notes on OpenAI's new o1 chain-of-thought models
Author : loganfrederick
Score : 640 points
Date : 2024-09-13 00:48 UTC (22 hours ago)
(HTM) web link (simonwillison.net)
(TXT) w3m dump (simonwillison.net)
| GaggiX wrote:
| I imagine that GPT-5 would be a refined version of this paradigm,
| probably with omni (multimodal) capabilities added (input and
| output).
| og_kalu wrote:
| Reports from the Information and the like have been that this
| is/was being used to generate a lot of synthetic data to train
| Orion (~GPT-5 Codename).
| whynotminot wrote:
| I'm guessing the true core of this product is still GPT-4,
| wrapped in whatever new logic they've created to force it
| through more reasoning iterations.
|
| If o1 was indeed used to create synthetic data to make the
| upcoming GPT-5, you can perhaps glimpse an interesting level-
| up process laid out here. GPT-5 could then take over at the
| heart of a hypothetical o2, yielding a big upgrade. Which
| would then be leveraged to generate synthetic data to train
| GPT-6. Which would then form the heart of o3. Etc.
| heisenzombie wrote:
| Fascinating, I wonder if we'll get non-textual hidden reasoning
| tokens? "Let me draw myself a diagram".
| notarealllama wrote:
| I know I sometimes sketch or write intermediaries before then
| compiling a full response.
|
| If AI can do this on 64k tokens, iteratively, fully
| multimodal... I don't think I've ever actually been scared of
| a super intelligence / singularity moment until just now.
|
| Now this is AI!
| jumploops wrote:
| > the idea that I can run a complex prompt and have key details
| of how that prompt was evaluated hidden from me feels like a big
| step backwards.
|
| As a developer, this is highly concerning, as it makes it much
| harder to debug where/how the "reasoning" went wrong. The pricing
| is also silly, because I'm paying for tokens I can't see.
|
| As a user, I don't really care. LLMs are already magic boxes and
| I usually only care about the end result, not the path to get
| there.
|
| It will be interesting to see how this progresses, both at OpenAI
| and other foundation model builders.
| bamboozled wrote:
| "trust us, we're using your tokens as efficiently as possible"
| OutOfHere wrote:
| > As a user, I don't really care.
|
| As a user, whether of ChatGPT or of the API, I absolutely do
| care, so I can modify and tune my prompt with the necessary
| clarifications.
|
| My suspicion is that the reason for hiding the reasoning tokens
| is to prevent other companies from creating a big CoT reasoning
| dataset using o1.
|
| It is anti-competitive behavior. If a user is paying through
| the nose for the reasoning tokens, and yes they are, the user
| deserves to be able to see them.
| og_kalu wrote:
| >My suspicion is that the reason for hiding the reasoning
| tokens is to prevent other companies from creating a big CoT
| reasoning dataset using o1.
|
| I mean...they say as much
| echelon wrote:
| Once again true to their name.
| echelon wrote:
| More opportunity for competitors to differentiate.
|
| OpenAI doesn't really have a moat. This isn't payments or SMS
| where only Stripe or Twilio were trying to win the market.
| Everybody and their brother is trying to build an LLM business.
|
| Grab some researchers, put some compute dollars in, and out
| comes a product.
|
| Everyone wants this market. It's absurdly good for buyers.
| OutOfHere wrote:
| > As a user, I don't really care.
|
| Tell me: Just how is it fair for a user to pay for the
| reasoning tokens without actually seeing them? If they are not
| shared, the service can bill you anything they want for them!
| ttul wrote:
| OpenAI's answer to this would be, "Okay then, don't use it."
| mediaman wrote:
| The simple answer is: I don't care. I'll statistically figure
| out what the typical total cost per call is from experience,
| and that's what matters. Who cares if they lie about it, if
| the model's cost per call fits my budget?
|
| If it starts costing $1 per call, and that's too high, then I
| just won't use it commercially. Whether it was $1 because
| they inflated the token count or because it just actually
| took a lot of tokens to do its reasoning isn't really
| material to my economic decision.
| lucianbr wrote:
| The thing is it might increase in cost after you've decided
| to use it commercially, and have invested a lot of time and
| resources in it. Now it's very hard to move to something
| else, but very easy for OpenAI to increase your cost
| arbitrarily. The statistics you made are not binding for
| them.
| nsokolsky wrote:
| Same applies to every other API in the world, yes.
| lucianbr wrote:
| No, S3 pricing for example is predictable, and written in
| a contract. There's no way for AWS to charge you 3x
| amount of dollars for 1GB tomorrow. They need to announce
| it in advance, and give you time to exit the contract if
| you disagree with the new price. It's really not the
| same. OpenAI can just tell you your prompt from tomorrow
| used up 20x times reasoning tokens. There's no advance
| warning or predictability. I really don't understand how
| you can claim the situations are identical.
| mediaman wrote:
| The API returns how many tokens were used in reasoning,
| so it would be easy to see any average change in
| reasoning token consumption. And token prices in general
| have been extremely deflationary over the last 18 months.
| golergka wrote:
| This is experimental, frontier stuff, obviously it comes
| with risks. Building on GPT-4 in March of 2023 was like
| that as well, but now you can easily switch between a few
| models of comparable quality made by different companies
| (yay capitalism and free markets!). You can risk and use
| just released stuff right now, or, most likely, come back
| in 6-12 months (probably earlier) and get several
| different providers with very similar APIs.
| OutOfHere wrote:
| Everything that OpenAI does with LLMs has already been
| done and validated in the open source community well
| before OpenAI gets around to it. OpenAI is not an
| innovator. simbianai/taskgen on github is an example of
| one such project, although there are others too that
| don't come to mind right now.
|
| As such, I would never call their work "frontier stuff",
| but they do bring it to the masses with their commercial
| service.
| elicksaur wrote:
| The person you are replying to points this out. They make a
| distinction between developers and users. An end user on a
| monthly subscription plan doesn't care about how much compute
| happens for their chat.
| inerte wrote:
| OpenAI could have also figured out the average number of
| extra output tokens, and put a markup in overall API costs.
| As a user, I wouldn't care either, because the price would
| mostly be the same.
| halJordan wrote:
| Yeah it is fair. You don't pay a lawyer for 40s of work
| expecting to see all the research between your consult and
| the document. You don't pay a cook for a meal and expect to
| sit and interrogate all the ingredients and the oven
| temperature.
| OutOfHere wrote:
| Actually, if a lawyer is billing you by the minute, then
| yes, you are entitled to a detailed breakdown. If the
| lawyer is billing you by the job, then no.
| potatoman22 wrote:
| If the output alone is high enough quality, it's worth paying
| extra.
| mFixman wrote:
| > As a user, I don't really care.
|
| People should understand and be able to tinker with the tools
| they use.
|
| The tragedy of personal computing is that everything is so
| abstracted away that users use only a fraction of the power of
| their computer. People who grew up with modern PCs don't
| understand the concept of memory, and younger people who grew
| up with cellphones don't understand the concept of files and
| directories.
|
| Open-weight AI models are great because they let normal users
| learn how they can make the model work for their particular use
| cases.
| ldjkfkdsjnv wrote:
| The theory is that this solves the data shortage problem, they
| can generate a ton of chain of reasoning data from what we
| already have. True iterative improvement, like out of a science
| fiction novel
|
| These models are going to get embedded deeply into IDE's, like
| cursor has, and essentially end software development as we know
| it. A properly written requirements spec, and an engineer, can do
| the work of 5. Software engineering as done by hand is going to
| disappear. Saas startups whose moat is a harvard ceo and 5
| million in capital will watch their margins disappear. This will
| be the great equalizer for creative intelligent individuals, true
| leverage to build what you want
| bamboozled wrote:
| Which will mean...there is going to be a lot more software?
| ldjkfkdsjnv wrote:
| Probably yeah
| vrighter wrote:
| a lot more broken software. Companies release broken software
| intentionally just to be quick to market. Now can you imagine
| the same, but the "engineers" literally cannot make the
| product better even if they wanted to. They never learned to
| code properly. So they can't tell whether the code is good.
| OutOfHere wrote:
| > A properly written requirements spec, and an engineer, can do
| the work of 5.
|
| I do not think this will scale. GPT o1 is presumably good for
| bootstrapping a project using tools that the engineer is not
| familiar with. The model will struggle to update a sizable
| codebase, however, with dependencies between the files.
|
| Secondly, no matter the size of the codebase and no matter the
| model used, the engineer still has to review every single line
| before incorporating it into the project. Only a competent
| engineer can review code effectively.
| ldjkfkdsjnv wrote:
| I respectfully, but completely disagree. Right now with
| sonnet 3.5 + cursor ide, I'm not writing that much of my own
| code at my FAANG job. I am generating a ton, passing in
| documentation from internal libraries, iterating on the
| result. Most of the time, I just accept its changes.
|
| This is going to rapidly happen. All we need are a few more
| model releases, not even a step function improvement
| OutOfHere wrote:
| Don't you still have to explain your requirement really
| well to it, in a lot of detail? In a terse language like
| Python, I might as well just write the code. In a verbose
| language like Java, perhaps there is more of a value in
| detailing the requirement.
| ldjkfkdsjnv wrote:
| Not really, most of the changes are straightforward.
| Also, alot of the time it writes better syntax than i
| would. Sometimes I write a bunch of psuedo code and hsave
| it fill in the detials, then write the tests
| OutOfHere wrote:
| > Not really
|
| How on earth are you conveying your intent to the model?
| Or is your intent so CRUDdy that it doesn't need to be
| conveyed?
| w4 wrote:
| It depends on what you're doing.
|
| If you're writing something specific to your particular
| problem, or thinking through how to structure your data,
| or even working on something tough to describe in words
| like UI design, it probably is easier to just code it
| yourself in most high-level languages. On the other hand,
| if you're just trying to get a framework or library to do
| something and you don't want to spend a bunch of time
| reading the docs to remember the special incantations
| needed to just make it do the thing you already know it
| can do, the AI speeds things up considerably.
| CSSer wrote:
| An abstraction machete. Heh.
| w4 wrote:
| This is a wonderful term for it!
| resters wrote:
| I use the same workflow. It's taking a while for me to
| learn to sense when it's getting off track and I need to
| start a new chat session in general it's pretty amazing if
| given very clear guidance at the right moments.
| devbent wrote:
| Find harder problems to solve.
|
| I can easily make Claude freak out and run into limits.
| Claude is amazing but it only works at the abstraction
| level you ask of it, so if you ask it to write code to
| solve a problem it'll only solve that immediate problem, it
| doesn't have awareness of any larger refactorings or design
| improvements that could be made to improve what solution is
| even possible.
| re-thc wrote:
| > I'm not writing that much of my own code at my FAANG job.
|
| > Most of the time, I just accept its changes.
|
| This speaks more about the problems at FAANG, other
| companies, etc than AI vs a human developer. And AI isn't
| the real fix.
|
| Are we just repeating things 100x a day or is it still so
| chaotic and immature? Or are we implying that AI is at a
| point where it's writing Google Spanner from scratch and
| you're able to review and confirm it passes transactional
| tests?
| Kiro wrote:
| We went from "it's useless because..." - "it outputs
| gibberish" to "it just copypastes" to "it only works for
| simple things" to "it can't make Google Spanner from
| scratch".
| re-thc wrote:
| > We went from
|
| None of the above.
|
| This isn't about how "smart" AI is.
|
| 1. Let's assume it was smart and can update a field
| spanning 1000s of microservices to deliver this new
| feature. Is this really something you should celebrate?
| I'd say no. At this point there should have been better
| tooling and infrastructure in place.
|
| 2. Is there really infinite CRUD to add after >10 years?
| In the same organization where you need >100s of
| developers all the time? 1s where you'd ignore code
| reviews and "just accept its changes"? Whether I write
| code or my colleagues etc I'd have a meaningful
| discussion about the proposed changes, the impacts and
| most likely suggest changes because nothing is perfect.
|
| So again, it's about the environment, the organization or
| at least this individual case where coding isn't just
| about adding some lines to a file. And that's with AI or
| not.
| HarHarVeryFunny wrote:
| > This speaks more about the problems at FAANG
|
| Right - "most of my work can be done by Sonnet 3.5"
| doesn't exactly conjure up an image of a high level or
| challenging job. It seems the challenge with FAANG
| companies is getting hired, not the actual work most
| people do there.
| achierius wrote:
| Not everyone has the same experience with the
| replaceability of their job role as you do. I've tried
| pretty hard and it just doesn't work for me. Admittedly I'm
| in compilers which makes it a bit harder, but just in
| general there are a lot of engineers who are in the same
| relative position.
| HarHarVeryFunny wrote:
| How would you characterize the type of applications/code
| you are working on? Can you give an example? How much of
| your work is architecture/design (software engineering),
| and how much more like grunt work or systems integration
| just coding stuff up ?
| vrighter wrote:
| a properly written requirements spec is something that doesn't
| exist in the vast majority of cases.
| knicholes wrote:
| I dunno man. I just spent a couple hours trying to get it to
| write functioning code to read from my RTSP stream, detect if
| my kid is playing piano, and send the result to HomeAssistant.
| It did not succeed.
| cma wrote:
| How many hours without it?
| elicksaur wrote:
| Not the OP, but in my experience LLMs fail in ways that
| indicate they will never solve the problem.
|
| Stuck in loops, correct their mistakes with worse mistakes,
| hallucinating things that don't exist and being unable to
| correct.
|
| Working on my own, I have the confidence that I know I can
| make incremental forward progress on a problem. That's much
| preferable.
| cma wrote:
| But when working with an LLM you can still contribute.
| 7thpower wrote:
| I think SaaS startups with a Harvard founder and 5 million are
| going to crush it in the world you describe. The marginal cost
| of building decreases, but brands, trust, and reach do not
| follow the same scaling laws.
|
| Access to capital and pedigree are still going to be a big
| plus.
| imtringued wrote:
| What data shortage problem? I'm not convinced that a shortage
| of data is the problem with current generation LLMs. This isn't
| like robotics where every robot is unique and you had to
| historically start from scratch every time you changed to a
| different robot. It's more likely that we are running into some
| sort of generalization bottleneck, because the training process
| is operating without feedback on the information/semantic
| level. There is no loss function for "does the code compile?".
| Instead, the loss function checks "does the output conform to
| the dataset?".
| ai4ever wrote:
| Such statements are made by management folks who dont code, and
| somehow think coding can be hand-waved away.
|
| Sure, this tool will improve the productivity of sw engineers,
| but so did the compiler which came 50 years back.
| layer8 wrote:
| The o1-preview model still hallucinates non-existing libraries
| and functions for me, and is quickly wrong about facts that
| aren't well-represented on the web. It's the usual string of
| "You're absolutely correct, and I apologize for the oversight in
| my previous response. [Let me make another guess.]"
|
| While the reasoning may have been improved, this doesn't solve
| the problem of the model having no way to assess if what it
| conjures up from its weights is factual or not.
| tptacek wrote:
| I've had the opposite experience with some coding samples.
| After reading Nick Carlini's post, I've gotten into the habit
| of powering through coding problems with GPT (where previously
| I'd just laugh and immediately give up) by just presenting it
| the errors in its code and asking it to fix them. o1 seems to
| be effectively screening for some of those errors (I assume
| it's just some, but I've noticed that the o1 things I've done
| haven't had obvious dumb errors like missing imports, and _all_
| my 4o attempts have).
| layer8 wrote:
| My experience is likely colored by the fact that I tend to
| turn to LLMs for problems I have trouble solving by myself. I
| typically don't use them for the low-hanging fruits.
|
| That's the frustrating thing. LLMs don't materially reduce
| the set of problems where I'm running against a wall or have
| trouble finding information.
| mensetmanusman wrote:
| LLMs are not for expanding the sphere of human knowledge,
| but for speeding up auto-correct of higher order processing
| to help you more quickly reach the shell of the sphere and
| make progress with your own mind :)
| tsunamifury wrote:
| Definitely. When we talk about being skilled in a T shape
| LLMs are all about spreading your top of T and not making
| the bottom go deeper.
| ben_w wrote:
| Indeed, not much more depth -- though even Terence Tao
| reported useful results from an earlier version, so
| perhaps the breadth is a depth all of it's own:
| https://mathstodon.xyz/@tao/110601051375142142
|
| I think of it as making the top bar of the T thicker, but
| yes, you're right, it also spreads it much wider.
| skydhash wrote:
| I prefer reading some book. Maybe the LLM was trained on
| some piece of knowledge not available on the net, but I
| much prefer the reliability and consistency of a book.
| tptacek wrote:
| I use LLMs for three things:
|
| * To catch passive voice and nominalizations in my writing.
|
| * To convert Linux kernel subsystems into Python so I can
| quickly understand them (I'm a C programmer but everyone
| reads Python faster).
|
| * To write dumb programs using languages and libraries I
| haven't used much before; for instance, I'm an ActiveRecord
| person and needed to do some SQLAlchemy stuff today, and
| GPT 4o (and o1) kept me away from the SQLAlchemy
| documentation.
|
| OpenAI talks about o1 going head to head with PhDs. I could
| care less. But for the specific problem we're talking about
| on this subthread: o1 seems materially better.
| throwup238 wrote:
| _> * To convert Linux kernel subsystems into Python so I
| can quickly understand them (I 'm a C programmer but
| everyone reads Python faster)._
|
| Do you have an example chat of this output? Sounds
| interesting. Do you just dump the C source code into the
| prompt and ask it to convert to Python?
| tptacek wrote:
| No, ChatGPT is way cooler than that. It's already read
| every line of kernel code ever written. I start with a
| subsystem: the device mapper is a good recent example. I
| ask things like "explain the linux device mapper. if it
| was a class in an object-oriented language, what would
| its interface look like?" and "give me dm_target as a
| python class". I get stuff like: def
| linear_ctr(target, argc, argv):
| print("Constructor called with args:", argc, argv)
| # Initialize target-specific data here return
| 0 def linear_dtr(target):
| print("Destructor called") # Clean up target-
| specific data here def
| linear_map(target, bio): print("Mapping I/O
| request") # Perform mapping here
| return 0 linear_target =
| DmTarget(name="linear", version=(1, 0, 0),
| module="dm_mod")
| linear_target.set_ctr(linear_ctr)
| linear_target.set_dtr(linear_dtr)
| linear_target.set_map(linear_map) info
| = linear_target.get_info() print(info)
|
| (A bunch of stuff elided). I don't care at all about the
| correctness of this code, because I'm just using it as a
| roadmap for the real Linux kernel code. The example use
| case code is an example of something GPT 4o provides that
| I didn't even know I wanted.
| throwup238 wrote:
| That's awesome. Have you tried asking it to convert
| Python (psuedo-ish) code back into C that interfaces with
| the kernel?
| tptacek wrote:
| No, but only because I have no use for it. I wouldn't be
| surprised if it did a fine job! I'd be remiss if I didn't
| note that it's way better at doing this for the Linux
| kernel than with codebases like Zookeeper and Kubernetes
| (though: maybe o1 makes this better, who knows?).
|
| I do feel like someone who skipped like 8 iPhone models
| (cross-referencing, EIEIO, lsp-mode, code explorers,
| tree-sitter) and just got an iPhone 16. Like, nothing
| that came before this for code comprehension really
| matters all that much?
| INGSOCIALITE wrote:
| it's all placeholders - that's my experience with gpt
| trying to write slop code
| throwup238 wrote:
| Those are placeholders for user callbacks passed to the
| device mapper subsystem. It's a usage example not
| implementation code.
| jeffhuys wrote:
| Then ask it to expand. Be specific.
| tptacek wrote:
| I wasn't about to paste 1000 lines of Python into the
| thread; I just picked an interesting snippet.
| Terr_ wrote:
| LLMs: When the code can be made by an enthusiastic new
| intern with web-search and copy-paste skills, and no
| ability to improve under mentorship. :p
|
| Tangentially related, a comic on them:
| https://existentialcomics.com/comic/557
| Al-Khwarizmi wrote:
| It's funny because I'm very happy with the productivity
| boost from LLMs, but I use them in a way that is pretty
| much diametrically opposite to yours.
|
| I can't think of many situations where I would use them for
| a problem that I tried to solve and failed - not only
| because they would probably fail, but in many cases it
| would even be difficult to _know_ that it failed.
|
| I use it for things that are not hard, can be solved by
| someone without a specialized degree that took the effort
| to learn some knowledge or skill, but would take too much
| work to do. And there are a lot of those, even in my highly
| specialized job.
| troupo wrote:
| > That's the frustrating thing. LLMs don't materially
| reduce the set of problems where I'm running against a wall
| or have trouble finding information.
|
| As you step outside regular Stack Overflow questions for
| top-3 languages, you run into limitations of these
| predictive models.
|
| There's no "reasoning" behind them. They are still,
| largely, bullshit machines.
| KoolKat23 wrote:
| you're both on the wrong wavelength. No one has claimed
| it is better than an expert human yet. Be glad, for now
| your jobs are safe, why not use it as a tool to boost
| your productivity, yes, even though you'll get
| proportionally less use than others in other perhaps less
| "expert" jobs.
| troupo wrote:
| In order for it to boost productivity it needs to answer
| more than the regular questions for the top-3 languages
| on Stackoverflow, no?
|
| It often fails even for _those_ questions.
|
| If I need to babysit it for every line of code, it's not
| a productivity boost.
| TeMPOraL wrote:
| If you need to babysit it for every line of code, you're
| either a superhuman coder, working in some obscure alien
| language, or just using the LLM wrong.
| troupo wrote:
| No. I'm just using for simple things like "Help me with
| the Elixir code" or "I need to list Bonjour services
| using Swift".
|
| It's shit across the whole "AI" spectrum from ChatGPT to
| Copilot to Cursor aka Claude.
|
| I'm not even talking about code I work with at work, it's
| just side projects.
|
| As for "using LLMs wrong", using them "right" is
| literally babysitting their output and spending a lot of
| time trying to reverse-engineer their behavior with
| increasingly inane prompts.
|
| Edit: I mean, look at this ridiculousness:
| https://cursor.directory/
| mrtesthah wrote:
| It begs the question of whether we can supply a function to be
| called (e.g., one that compiles and runs code) to evaluate
| intermediate CoT results
| colordrops wrote:
| The answer is yes if you are willing to code it. OpenAI
| supports tool calls. Even if it didn't you could just make
| multiple calls to their API and submit the result of the code
| execution yourself.
| cma wrote:
| The intermediate CoT results aren't in the API.
| colordrops wrote:
| I may be mistaken but I don't believe the first version
| of the comment I replied to mentioned intermediate CoT
| results.
| ttul wrote:
| It seems OpenAI has decided to keep the CoT results a secret.
| If they were to allow the model to call out to tools to help
| fill in the CoT steps, then this might reveal what the model
| is thinking - something they do not want the outside world to
| know about.
|
| I could imagine OpenAI might allow their own vetted tools to
| be used, but perhaps it will be a while (if ever) before
| developers are allowed to hook up their own tools. The risks
| here are substantial. A model fine-tuned to run chain-of-
| thought that can answer graduate level physics problems at an
| expert level can probably figure out how to scam your grandma
| out of her savings too.
| 8n4vidtmkvmk wrote:
| It's only a matter of time. When some other company
| releases the tool, they likely will too.
| faangguyindia wrote:
| >The o1-preview model still hallucinates non-existing libraries
| and functions for me, and is quickly wrong about facts that
| aren't well-represented on the web. It's the usual string of
| "You're absolutely correct, and I apologize for the oversight
| in my previous response. [Let me make another guess.]"
|
| After that you switch to Claude Sonnet and after sometime it
| also gets stuck.
|
| Problem with LLM is that they are not aware of libraries.
|
| I've fed them library version, using requirements.txt, python
| version I am using etc...
|
| They still make mistakes and try to use methods which do not
| exist.
|
| Where to go from here? At this point I manually pull the
| library version I am using and go to its docs, I generate a
| page which uses the this library correctly (then I feed that
| example into LLM)
|
| Using this approach works. Now I just need to automate it so
| that I don't have to manually find the library, create specific
| example which uses the methods I need in my code!
|
| Directly feeding the docs isn't working well either.
| mediaman wrote:
| One trick that people are using, when using Cursor and
| specifically Cursor's compose function, is to dump library
| docs into a text file in your repo, and then @ that doc file
| when you're asking it to do something involving that library.
|
| That seems to eliminate a lot of the issues, though it's not
| a seamless experience, and it adds another step of having to
| put the library docs in a text file.
|
| Alternatively, cursor can fetch a web page, so if there's a
| good page of docs you can bring that in by @ the web page.
|
| Eventually, I could imagine LLMs automatically creating
| library text doc files to include when the LLM is using them
| to avoid some of these problems.
|
| It could also solve some of the issues of their shaky
| understanding of newer frameworks like SvelteKit.
| throwup238 wrote:
| Cursor also has the shadow workspace feature [1] that is
| supposed to send feedback from linting and language servers
| to the LLM. I'm not sure whether it's enabled in compose
| yet though.
|
| [1] https://www.cursor.com/blog/shadow-workspace
| shmatt wrote:
| I honestly can't believe this is the hyped up "strawberry"
| everyone was claiming is pretty much AGI. Senior employees
| leaving due to its powers being so extreme
|
| I'm in the "probabilistic token generators aren't intelligence"
| camp so I don't actually believe in AGI, but I'll be honest the
| never ending rumors / chatter almost got to me
|
| Remember, this is the model some media outlet reported recently
| that is so powerful OAI is considering charging $2k/month for
| firejake308 wrote:
| I mean, considering how many tokens their example prompt
| consumed, I wouldn't be surprised if it costs ~$2k/month/user
| to run
| kqr wrote:
| > probabilistic token generators aren't intelligence
|
| Maybe this has been extensively discussed before, but since
| I've lived under a rock: which parts of intelligence do you
| think are not representable as conditional probability
| distributions?
| lewhoo wrote:
| > which parts of intelligence do you think are not
| representable as conditional probability distributions
|
| Maybe I'm wrong here but a lot of our brilliance comes from
| acting against the statistical consensus. What I mean is,
| Nicolaus Copernicus probably consumed a lot of knowledge on
| how the Earth is the center of the universe etc. and
| probably nothing contradicting that notion. Can a LLM do
| that ?
| KoolKat23 wrote:
| And how many people actively do that? It's very rare we
| experience brilliance and often we stumble upon it by
| accident. Irrational behavior, coincidence or perhaps
| they were dropped on their heads when they were young.
| bentonkek wrote:
| Copernicus was an exception, not the rule. Would you say
| everyone else who lived at the time was not 'really'
| intelligent?
| lewhoo wrote:
| Yes but this was just to make a blatant example. The
| questions still stands. If you feed a LLM certain kind of
| data is it possible it strays from it completely - like
| we sometimes do in cases big and small when we figure out
| how to do something a bit better by not following the
| convention.
| wavemode wrote:
| That's an illogical counterargument. The absence of
| published research output does not imply the absence of
| intelligent brain patterns. What if someone was
| intelligent but just wasn't interested in astronomy?
| kqr wrote:
| It could be "probability of token being useful" rather
| than "probability of token coming next in training data"!
| vasco wrote:
| The whole safety aspect of AI has this nice property that it
| also functions as a marketing tool to make the technology
| seem "so powerful it's dangerous". "If it's so dangerous it
| must be good".
| synarchefriend wrote:
| "Senior employees leaving due to its powers being so extreme"
|
| This never happened. No one said it happened.
|
| "the model some media outlet reported recently that is so
| powerful OAI is considering charging $2k/month for"
|
| The Information reported someone at a meeting suggested this
| for future models, not specifically Strawberry, and that it
| would probably not actually be that high.
| shmatt wrote:
| Elon Musk and Ilya Sutskever Have Warned About OpenAI's
| 'Strawberry' Jul 15, 2024 -- Sutskever himself had
| reportedly begun to worry about the project's technology,
| as did OpenAI employees working on A.I. safety at the time.
|
| https://observer.com/2024/07/openai-employees-concerns-
| straw...
|
| And I'm ignoring the hundreds of Reddit articles
| speculating every time someone at OAI leaves
|
| And of course that $2000 article was spread by every other
| media outlet like wildfire
|
| I know I'm partially to blame for believing the hype, this
| is pretty obviously no better at stating facts or good code
| than what we've known for the past year
| semi-extrinsic wrote:
| My hypothesis about these people who are afraid of AI, is
| that they have tricked themselves into believing they are
| in their current position of influence due to their own
| intelligence (as opposed to luck, connections, etc.)
|
| Then they drink the marketing koolaid, and it follows
| naturally that they worry an AI system can obtain similar
| positions of influence.
| COAGULOPATH wrote:
| Yes, this only helps multi-step reasoning. The model still has
| problems with general knowledge and deep facts.
|
| There's no way you can "reason" a correct answer to "list the
| tracklisting of some obscure 1991 demo by a band not on
| Wikipedia." You either know or you don't.
|
| I usually test new models with questions like "what are the
| levels in [semi-famous PC game from the 90s]?" The release
| version of GPT-4 could get about 75% correct. o1-preview gets
| about half correct. o1-mini gets 0% correct.
|
| Fair enough. The GPT-4 line aren't meant to be search engines
| or encyclopedias. This is still a useful update though.
| mattmanser wrote:
| It's actually much worse than that and you're inadvertently
| down playing how bad it is.
|
| It doesn't even know mildly obsecure facts that are on the
| internet.
|
| For example last night I was trying to do something with C#
| generics and it confidently told me I could use pattern
| matching on the type in a switch statwmnt, and threw out some
| convincing looking code.
|
| You can't, it's impossible. It waa completely wrong. When I
| told that this, it told me I was right, and proceeded to give
| me code that was even more wrong.
|
| This is an obscure, but well documented, part of the spec.
|
| So it's not about facts that aren't on the internet, it's
| just bad at facts fullstop.
|
| What it's good at is facts the internet agrees on. Unless the
| internet is wrong. Which is not always a good thing with the
| way the language it uses to speak is so confident.
|
| If you want to fuck with AI models as a bunch of code
| questions on Reddit, GitHub and SO with example code saying
| 'can I do X'. The answer is no, but chatgpt/codepilot/etc.
| will start spewing out that nonsense as if it's fact.
|
| As for non-proframming, we're about to see the birth of a new
| SEO movement of tricking AI models to believe your 'facts'.
| koe123 wrote:
| Its not always the right tool depending on the task. IMO
| using LLMs is also a skill, much like learning how to
| Google stuff.
|
| E.g. apparently C# generics isn't something its good at.
| Interesting, so don't use it for that, apparently its the
| wrong tool. In contrast, its amazing at C++ generics, and
| thus speeds up my productivity. So do use it for that!
| bbarnett wrote:
| I wonder though, is the documentation only referenced a few
| places on the Internet, and are there also many forums with
| people pasting "Why isn't this working?" problems?
|
| If there are a lot of people pasting broken code, now the
| LLM has all these examples of broken code, which it doesn't
| know are that, and only a couple of references to
| documentation. Worse, a well trained LLM may realise that
| specs change, and that even documentation may not be
| considered 100% accurate (for it is older, out of date).
|
| After all, how many times have you had something updated,
| an API, a language, a piece of software, but the docs
| weren't updates? Happens all the time, sadly.
|
| So it may believe _newer_ examples of code, such as the
| aforementioned pasted code, might be more correct than the
| docs.
|
| Also, if people keep trying to solve the same issue again,
| and keep pasting those examples again, well...
|
| I guess my point here is, hallucinations come from multi-
| faceted issues, one being "wrong examples are more
| plentiful than correct". Or even "there's just a lot of
| wrong examples".
| neonsunset wrote:
| > For example last night I was trying to do something with
| C# generics and it confidently told me I could use pattern
| matching on the type in a switch statwmnt, and threw out
| some convincing looking code.
|
| Just use it on an instance instead var res
| = thing switch { OtherThing ot => ..., int
| num => ..., string s => ..., _ => ...
| };
| barrkel wrote:
| o1-mini is a small model (knows a lot less about the world)
| and is tuned for reasoning through symbolic problems (maths,
| programming, chemistry etc.).
|
| You're using a calculator as a search engine.
| motoboi wrote:
| Just pass a link to a GitHub issue and ask for a response or
| even a webpage to summarize and will see the beautiful
| hallucinations it will come up to as the model is not web
| browsing yet.
| _fs wrote:
| o1-preview != o1.
|
| In public coding AI comparison tests, results showed 4o scoring
| around 35%, o1-preview scoring ~50% and o1 scoring ~85%.
|
| o1 is not yet released, but has been run through many
| comparison tests with public results posted.
| arthurcolle wrote:
| gpt-4 base was never released and this will be the same thing
| barrkel wrote:
| Don't forget about o1-mini. It seems better than o1-preview
| for problems that fit it (don't require so much real world
| knowledge).
| kristianp wrote:
| Good reminder. Why did OpenAI talk about o1 and not release
| it? o1-preview must be a stripped down version: cheaper to
| run somehow?
| feralderyl wrote:
| I think this model is a precursor model that is designed for
| agentic behavior. I expect very soon OpenAI to allow this model
| tool use that will allow it to verify its code creations and
| whatever else it claims through use of various tools like a
| search engine, a virtual machine instance with code execution
| capabilities, api calling and other advanced tool use.
| burtonator wrote:
| I'm honestly confused as to why it is doing this and why it
| thinks I'm right when I tell it that it is incorrect.
|
| I've tried asking it factual information, and it asserts that
| it's incorrect but it will definitely hallucinate questions
| like the above.
|
| You'd think the reasoning would nail that and most of the
| chain-of-thought systems I've worked on would have fixed this
| by asking it if the resulting answer was correct.
| MPSimmons wrote:
| The failure is in how you're using it. I don't mean this as a
| personal attack, but more to shed light on what's happening.
|
| A lot of people use LLMs as a search engine. It makes sense -
| it's basically a lossy compressed database of everything its
| ever read, and it generates output that is statistically likely
| - varying degrees of likeliness depending on the temperature,
| as well as how many times the particular weights your prompt
| ends up activating.
|
| The magic of LLMs, _especially_ one like this that supposedly
| has advanced reasoning, isn 't the existing knowledge in its
| weights. The magic is that _it knows english_. It knows english
| at or above a level equal to most fluent speakers, and it also
| can produce output that is not just a likely output, but is a
| logical output. It's not _just_ an output engine. It's an
| engine that outputs.
|
| Asking it about nuanced details in the corpus of data it has
| read won't give you good output unless it read a bunch of it.
|
| On the other hand, if you were to paste the entire
| documentation set to a tool it has never seen and ask it to use
| the tool in a way to accomplish your goals, THEN this model
| would be likely to produce useful output, despite the fact that
| it had never encountered the tool or its documentation before.
|
| Don't treat it as a database. Treat it as a naive but
| intelligent intern. Provide it data, give it a task, and let it
| surprise you with its output.
| pedrosorio wrote:
| > It knows english at or above a level equal to most fluent
| speakers, and it also can produce output that is not just a
| likely output, but is a logical output
|
| This is not an apt description of the system that insists the
| doctor is the mother of the boy involved in a car accident
| when elementary understanding of English and very little
| logic show that answer to be obviously wrong.
|
| https://x.com/colin_fraser/status/1834336440819614036
| anon291 wrote:
| Keep in mind that the system always chooses randomly so
| there is always a possibility it commits to the wrong
| output.
|
| I don't know why openAi won't allow determinism but it
| doesn't, even with temperature set to zero
| coffeebeqn wrote:
| Would picking deterministically help through? Then in
| some cases it's always 100% wrong
| jaredsohn wrote:
| Yes, it is better if for example using it via an API to
| classify. Deterministic behavior makes it a lot easier to
| debug the prompt.
| maeil wrote:
| Nondeterminism provides an excuse for errors, determinism
| doesn't.
|
| Nondeterminism scores worse with human raters, because it
| makes output sound even more robotic and less human.
| roywiggins wrote:
| Determinism only helps if you always ask the question
| with exactly the same words. There's no guarantee a
| slightly rephrased version will give the same answer, so
| a certain amount of unpredictability is unavoidable
| anyway. With a deterministic LLM you might find one
| phrasing that always gets it right and a dozen basically
| indistinguishable ones that always get it wrong.
| ramraj07 wrote:
| Many of my PhD and post doc colleagues who emigrated from
| Korea, China and India who didn't have English as the
| medium of instruction would struggle with this question.
| They only recover when you give them a hint. They're some
| of the smartest people in general. If you try to stop
| stumping these models with trick questions and ask it
| straightforward reasoning systems it is extremely
| performant (O1 is definitely a step up though not
| revolutionary in my testing).
| joedwin wrote:
| lol, I am neither a PhD nor a postdoc, but I am from
| India . I could understand the problem.
| ramraj07 wrote:
| Did you have English as your medium of instruction? If
| yes, do you see the irony that you also couldn't read two
| sentences and see the facts straight?
| maeil wrote:
| I live in one of the countries you mentioned and just
| showed it to one of my friends who's a local who
| struggles with English. They had no problem concluding
| that the doctor was the child's dad. Full disclosure,
| they assumed the doctor was _pretending_ to be the child
| 's dad, which is also a perfectly sound answer.
| djur wrote:
| The claim was that "it knows english at or above a level
| equal to most fluent speakers". If the claim is that it's
| very good at producing reasonable responses to English
| text, posing "trick questions" like this would seem to be
| a fair test.
| andreasmetsala wrote:
| Does fluency in English make someone good at solving
| trick questions? I usually don't even bother trying but
| mostly because trick questions don't fit my definition of
| entertaining.
| rdtsc wrote:
| Fluency is a necessary but not the only prerequisite.
|
| To be able to answer a trick question, it's first
| necessary to understand the question.
| accountnum wrote:
| No, it's necessary to either know that it's a trick
| question or to have a feeling that it is based on
| context. The entire point of a question like that is to
| trick your understanding.
|
| You're tricking the model because it has seen this
| specific trick question a million times and shortcuts to
| its memorized solution. Ask it literally any other
| question, it can be as subtle as you want it to be, and
| the model will pick up on the intent. As long as you
| don't try to mislead it.
|
| I mean, I don't even get how anyone thinks this means
| literally anything. I can trick people who have never
| heard of the trick with the 7 wives and 7 bags and so on.
| That doesn't mean they didn't understand, they simply did
| what literally any human does, make predictions based on
| similar questions.
| j_maffe wrote:
| It does mean something. It means that the model is still
| more on the memorization side than being able to
| independently evaluate a question separate from the body
| of knowledge it has amassed.
| accountnum wrote:
| No, that's not a conclusion we can draw, because there is
| nothing much more to do than memorize the answer to this
| specific trick question. That's why it's a trick
| question, it goes against expectations and therefore the
| generalized intuitions you have about the domain.
|
| We can see that it doesn't memorize much at all by simply
| asking other questions that do require subtle
| understanding and generalization.
|
| You could ask the model to walk you through an imaginary
| environment, describing your actions. Or you could simply
| talk to it, quickly noticing that for any longer
| conversation it becomes impossibly unlikely to be found
| in the training data.
| KoolKat23 wrote:
| If you read into the thinking of the above example it
| wonders whether it is some sort of trick question. Hardly
| memorization.
| rdtsc wrote:
| > I can trick people who have never heard of the trick
| with the 7 wives and 7 bags and so on. That doesn't mean
| they didn't understand
|
| They could fail because they didn't understand the
| language. Didn't have a good memory to memorize all the
| steps, or couldn't reason through it. We could pose more
| questions to probe which reason is more plausible.
| accountnum wrote:
| The trick with the 7 wives and 7 bags and so on is that
| no long reasoning is required. You just have to notice
| one part of the question that invalidates the rest and
| not shortcut to doing arithmetic because it looks like an
| arithmetic problem. There are dozens of trick questions
| like this and they don't test understanding, they exploit
| your tendency to predict intent.
|
| But sure, we could ask more questions and that's what we
| should do. And if we do that with LLMs we can quickly see
| that when we leave the basin of the memorized answer by
| rephrasing the problem, the model solves it. And we would
| also see that we can ask billions of questions to the
| model, and the model understands us just fine.
| lupire wrote:
| Some people solve trick questions easily simply because
| they are slow thinkers who pay attention to _every_
| question, even non-trick questions, and don 't fast-path
| the answer based on its similarity to a past question.
|
| Interestingly, people who make bad fast-path answers
| often call these people _stupid_.
| KoolKat23 wrote:
| It's knowledge is broad and general, it does not have
| insight into the specifics of a person's discussion
| style, there are many humans that struggle with
| distinguishing sarcasm for instance. Hard to fault it for
| not being in alignment with the speaker and their
| strangely phrased riddle.
|
| It answers better when told "solve the below riddle".
| multjoy wrote:
| "Don't be mean to LLMs, it isn't their fault that they're
| not actually intelligent"
| K0balt wrote:
| In general LLMs seem to function more reliably when you
| use pleasant language and good manners with them. I
| assume this is because because the same bias also shows
| up in the training data.
| lupire wrote:
| "Don't anthropomorphize LLMs. They're hallucinating when
| they say they love that."
| raincole wrote:
| I think you have particularly dumb colleagues then. If
| you post this question to an average STEM PhD in China
| (not even _from_ China. _In_ China) they 'll get it
| right.
|
| This question is the "unmisleading" version of a very
| common misleading question about sexism. ChatGPT learned
| the original, misleading version too well that it can't
| answer the unmisleading version.
|
| Humans who don't have the original version ingrained in
| their brains will answer it with ease. It's not even a
| tricky question to humans.
| fragmede wrote:
| > it can't answer the unmisleading version.
|
| Yes it can: https://chatgpt.com/share/66e3601f-4bec-8009-
| ac0c-57bfa4f059...
| fragmede wrote:
| what's weird is it gets it right when I try it.
|
| https://chatgpt.com/share/66e3601f-4bec-8009-ac0c-57bfa4f05
| 9...
| brna-2 wrote:
| Yep. correct and correct.
|
| https://chatgpt.com/share/66e3de94-bce4-800b-af45-357b95d
| 658...
| brna-2 wrote:
| Waat, got it on second try:
|
| This is possible because the doctor is the boy's other
| parent--his father or, more likely given the surprise,
| his mother. The riddle plays on the assumption that
| doctors are typically male, but the doctor in this case
| is the boy's mother. The twist highlights gender
| stereotypes, encouraging us to question assumptions about
| roles in society.
| latexr wrote:
| That's not weird at all, it's how LLMs work. They
| statistically arrive at an answer. You can ask it the
| same question twice in a row in different windows and get
| opposite answers. That's completely normal and expected,
| and also why you can never be sure if you can trust an
| answer.
| rtakha wrote:
| Perhaps OpenAI hot-patches the model for HN complaints:
| def intercept_hn_complaints(prompt): if
| is_hn_trick_prompt(prompt): # special_case for
| known trick questions.
| fragmede wrote:
| While that's not impossible, what we know of how the
| technology works (ie very costly training run followed by
| cheap inference steps) means that's not feasible, given
| all the possible variations of the question *
| is_hn_trick_prompt* would have to cover because there's a
| near infinite variations on how you'd word the prompt.
| (Eg The first sentence could be reworded to be "A woman
| and her son are in a car accident. " to "A woman and her
| son are in the car when they get into a crash.")
| hmottestad wrote:
| Reminds me of a trick question about Schrodinger's cat.
|
| "I've put a dead cat in a box with a poison and an isotope
| that will trigger the poison at a random point in time.
| Right now, is the cat dead or alive?"
|
| The answer is that the cat is dead, because it was dead to
| begin with. Understanding this doesn't mean that you are
| good at deductive reasoning. It just means that I didn't
| manage to trick you. Same goes for an LLM.
| maeil wrote:
| There is no "trick" in the linked question, unlike the
| question you posed.
|
| The trick in yours also isn't a _logic_ trick, it 's a
| redirection, like a sleight of hand in a card trick.
| ryanjshaw wrote:
| There is a trick. The "How is this possible?" primes the
| LLM that there is some kind of trick, as that phrase
| wouldn't exist in the training data outside of riddles
| and trick questions.
| hmottestad wrote:
| The trick in the original question is that it's a twist
| on the original riddle where the doctor is actually the
| boys mother. This is a fairly common riddle and I'm sure
| the LLM has been trained on it.
| bonoboTP wrote:
| Yes there is. The trick is that the more common variant
| of this riddle says that a boy and his father are in the
| car accident. That variant of the riddle certainly comes
| up a lot in the training data, which is directly
| analogous to the Schrodinger case from above where
| smuggling in the word "dead" is analogous to swapping
| father to mother in the car accident riddle.
|
| I think many here are not aware that the car accident
| riddle is well known with the father dying where the real
| solution is indeed that the doctor is the mother.
| lucubratory wrote:
| Yeah, I think what a lot of people miss about these sort
| of gotchas are that most of them were invented explicitly
| to gotcha humans, who regularly get got by them. This is
| not a failure mode unique to LLMs.
| ryanjshaw wrote:
| Yes it's so strange seeing people who clearly know these
| are 'just' statistical language models pat themselves on
| the back when they find limits on the reasoning
| capabilities - capabilities which the rest of us are
| pleasantly surprised exist to the extent they do in a
| statistical model, and happy to have access to for
| $20/mo.
| rainsford wrote:
| It's because at least some portion of "the rest of us"
| talk as if LLMs are far more capable than they really are
| and AGI is right around the corner, if not here already.
| I think the gotchas that play on how LLMs really work
| serve as a useful reminder that we're looking at
| statistical language models, not sentient computers.
| mewpmewp2 wrote:
| If there is attention mechanism then maybe that is what
| is fault, because if it is a common riddle attention
| mechanism only notices that it is a common riddle, not
| that there is a gotcha planted in. Because when I read
| the sentence myself, I did not immediately notice that
| the cat that was put in there was actually dead when it
| was put there, because I pattern matched this to a known
| problem, I did not think I need to pay logical attention
| to each word, word by word.
| roywiggins wrote:
| One that trips up LLMs in ways that wouldn't trip up
| humans is the chicken, fox and grain puzzle but with just
| the chicken. They tend to insist that the chicken be
| taken across the river, then back, then across again, for
| no reason other than the solution to the classic puzzle
| requires several crossings. No human would do that, by
| the time you've had the chicken across then even the most
| unobservant human would realize this isn't really a
| puzzle and would stop. When you ask it to justify each
| step you get increasingly incoherent answers.
|
| Has anyone tried this on o1?
| hmottestad wrote:
| Here you go: https://chatgpt.com/share/66e48de6-4898-800e
| -9aba-598a57d27f...
|
| Seemed to handle it just fine.
|
| Kinda a waste of a perfectly good LLM if you ask me. I've
| mostly been using it as a coding assistant today and it's
| been absolutely great. Nothing too advanced yet, mostly
| mundane changes that I got bored of having to make
| myself. Been giving it very detailed and clear
| instructions, like I would to a Junior developer, and not
| giving it too many steps at once. Only issue I've run
| into is that it's fairly slow and that breaks my coding
| flow.
| achow wrote:
| What I'm not able to comprehend is why people are not
| seeing the answer as brilliant!
|
| Any ordinary mortal (like me) would have jumped to the
| conclusion that answer is "Father" and would have walked
| away patting on my back, without realising that I was
| biased by statistics.
|
| Whereas o1, at the very outset smelled out that it is a
| riddle - why would anyone out of blue ask such question.
| So, it started its chain of thought with "Interpreting the
| riddle" (smart!).
|
| In my book that is the difference between me and people who
| are very smart and are generally able to navigate the world
| better (cracking interviews or navigating internal politics
| in a corporate).
| geysersam wrote:
| Come on. Of course chatgpt has read that riddle and the
| answer 1000 times already.
| ryanjshaw wrote:
| Why does it exist 1000 times in the training if there
| isn't some trick to it, i.e. some subset of humans had to
| have answered it incorrectly for the meme to replicate
| that extensively in our collective knowledge.
|
| And remember the LLM has already read a billion other
| things, and now needs to figure out - is this one of them
| tricky situations, or the straightforward ones? It also
| has to realize all the humans on forums and facebook
| answering the problem incorrectly are _bad_ data.
|
| Might seem simple to you, but it's not.
| accountnum wrote:
| It hasn't read that riddle because it is a modified
| version. The model would in fact solve this trivially if
| it _didn't_ see the original in its training. That's the
| entire trick.
| geysersam wrote:
| Sure but the parent was praising the model for
| recognizing that it was a riddle in the first place:
|
| > Whereas o1, at the very outset smelled out that it is a
| riddle
|
| That doesn't seem very impressive since it's (an
| adaptation of) a famous riddle
|
| The fact that it also gets it wrong after reasoning about
| it for a long time doesn't make it better of course
| accountnum wrote:
| Recognizing that it is a riddle isn't impressive, true.
| But the duration of its reasoning is irrelevant, since
| the riddle works on misdirection. As I keep saying here,
| give someone uninitiated the 7 wives with 7 bags going
| (or not) to St Ives riddle and you'll see them reasoning
| for quite some time before they give you a wrong answer.
|
| If you are tricked about the nature of the problem at the
| outset, then all reasoning does is drive you further in
| the wrong direction, making you solve the wrong problem.
| grey-area wrote:
| The 'riddle': A woman and her son are in a car accident.
| The woman is sadly killed. The boy is rushed to hospital.
| When the doctor sees the boy he says "I can't operate on
| this child, he is my son". How is this possible?
|
| GPT Answer: The doctor is the boy's mother
|
| Real Answer: Boy = Son, Woman = Mother (and her son),
| Doctor = Father (he says...he is my son)
|
| This is not in fact a riddle (though presented as one)
| and the answer given is not in any sense brilliant. This
| is a failure of the model on a very basic question, not a
| win.
|
| It's non deterministic so might sometimes answer
| correctly and sometimes incorrectly. It will also accept
| corrections on any point, even when it is right, unlike a
| thinking being when they are sure on facts.
|
| LLMs are very interesting and a huge milestone, but
| generative AI is the best label for them - they generate
| statistically likely text, which is convincing but often
| inaccurate and it has no real sense of correct or
| incorrect, needs more work and it's unclear if this
| approach will ever get to general AI. Interesting work
| though and I hope they keep trying.
| pkage wrote:
| I mean, it's entirely possible the boy has two mothers.
| This seems like a perfectly reasonable answer from the
| model, no?
| eigenket wrote:
| The text says "When the doctor sees the boy he says"
|
| The doctor is male, and also a parent of the child.
| roomey wrote:
| Why couldn't the doctor be the boys mother?
|
| There is no indication of the sex of the doctor, and
| families that consist of two mothers do actually exist
| and probably doesn't even count as that unusual.
| eigenket wrote:
| "When the doctor sees the boy he says"
|
| Indicates the gender of the father.
| stavros wrote:
| Ah, but have you considered the fact that he's undergone
| a sex change operation, and was actually originally a
| female, the birth mother? Elementary, really...
| yreg wrote:
| A mother can have a male gender.
|
| I wonder if this interpretation is a result of attempts
| to make the model more inclusive than the corpus text,
| resulting in a guess that's unlikely, but not strictly
| impossible.
| eigenket wrote:
| I think its more likely this is just an easy way to trick
| this model. It's seen lots of riddles, so when it's sees
| something that looks like a riddle but isn't one it gets
| confused.
| yreg wrote:
| Now I wonder which side is angry about my comment.
| Jensson wrote:
| > A mother can have a male gender.
|
| Then it would be a father, misgendering him as a mother
| is not nice.
| grey-area wrote:
| he says
| kristianp wrote:
| So the riddle could have two answers: mother or father?
| Usually riddles have only one definitive answer. There's
| nothing in the wording of the riddle that excludes the
| doctor being the father.
| grey-area wrote:
| This particular riddle the answer is the doctor is the
| father.
| singingfish wrote:
| Speaking as a 50-something year old man whose mother
| finished her career in medicine and the very pointy end
| of politics, when I first heard this joke in the 1980s it
| stumped me and made me feel really stupid. But my 1970s
| kindergarten class mates who told me "your mum can't be a
| doctor, she has to be a nurse" were clearly seriously
| misinformed then. I believe that things are somewhat
| better now but not as good as they should be ...
| accountnum wrote:
| It literally is a riddle, just as the original one was,
| because it tries to use your expectations of the world
| against you. The entire point of the original, which a
| lot of people fell for, was to expose expectations of
| gender roles leading to a supposed contradiction that
| didn't exist.
|
| You are now asking a modified question to a model that
| has seen the unmodified one millions of times. The model
| has an expectation of the answer, and the modified riddle
| uses that expectation to trick the model into seeing the
| question as something it isn't.
|
| That's it. You can transform the problem into a slightly
| different variant and the model will trivially solve it.
| jfengel wrote:
| Phrased as it is, it deliberately gives away the answer
| by using the pronoun "he" for the doctor. The original
| deliberately obfuscates it by avoiding pronouns.
|
| So it doesn't take an understanding of gender roles, just
| grammar.
| accountnum wrote:
| My point isn't that the model falls for gender
| stereotypes, but that it falls for thinking that it needs
| to solve the unmodified riddle.
|
| Humans fail at the original because they expect doctors
| to be male and miss crucial information because of that
| assumption. The model fails at the modification because
| it assumes that it is the unmodified riddle and misses
| crucial information because of that assumption.
|
| In both cases, the trick is to subvert assumptions. To
| provoke the human or LLM into taking a reasoning shortcut
| that leads them astray.
|
| You can construct arbitrary situations like this one, and
| the LLM will get it unless you deliberately try to
| confuse it by basing it on a well known variation with a
| different answer.
|
| I mean, genuinely, do you believe that LLMs don't
| understand grammar? Have you ever interacted with one?
| Why not test that theory outside of adversarial examples
| that humans fall for as well?
| kasdfasH wrote:
| The original riddle is of course:
|
| "A father and his son are in a car accident [...] When
| the boy is in hospital, the surgeon says: This is my
| child, I cannot operate on him".
|
| In the original riddle the answer is that the surgeon is
| female and the boy's mother. The riddle was supposed to
| point out gender stereotypes.
|
| So, as usual, ChatGPT fails to answer the _modified
| riddle_ and gives the plagiarized stock answer and
| explanation to the original one. No intelligence here.
| TeMPOraL wrote:
| > _So, as usual, ChatGPT fails to answer the modified
| riddle and gives the plagiarized stock answer and
| explanation to the original one. No intelligence here._
|
| Or, fails in the same way any human would, when giving a
| snap answer to a riddle told to them on the fly -
| typically, a person would recognize a familiar riddle
| half of the first sentence in, and stop listening
| carefully, not expecting the other party to give them a
| _modified_ version.
|
| It's something we drill into kids in school, and often
| into adults too: _read carefully_. Because we 're all
| prone to pattern-matching the general shape to something
| we've seen before and zoning out.
| lanstin wrote:
| "There are four lights"- GPT will not pass that test as
| is. I have done a bunch of homework with Claude's help
| and so far this preview model has much nicer formatting
| but much the same limits of understanding the maths.
| yywwbbn wrote:
| > why would anyone out of blue ask such question
|
| I would certainly expect any person to have the same
| reaction.
|
| > So, it started its chain of thought with "Interpreting
| the riddle" (smart!).
|
| How is that smarter than intuitively arriving at the
| correct answer without having to explicitly list the
| intermediate step? Being able to reasonably accurately
| judge the complexity of a problem with minimal effort
| seems "smarter" to me.
| ImHereToVote wrote:
| The doctor is obviously a parent of the boy. The language
| tricks simply emulate the ambiance of reasoning.
| Similarly to a political system emulating the ambiance of
| democracy.
| ryanjshaw wrote:
| 1. It didn't _insist_ anything. It got the semi-correct
| answer when I tried [1]; note it 's a preview model, and
| it's not a perfect product.
|
| (a) Sometimes things are useful even when imperfect e.g.
| search engines.
|
| (b) People make reasoning mistakes too, and I make dumb
| ones of the sort presented all the time despite being
| fluent in English; we deal with it!
|
| I'm not sure why there's an expectation that the model is
| perfect when the source data - human output - is not
| perfect. In my day-to-day work and non-work conversations
| it's a _dialogue_ - a back and forth until we figure things
| out. I 've never known anybody to get everything perfectly
| correct the first time, it's so puzzling when I read people
| complaining that LLMs should somehow be different.
|
| 2. There is a recent trend where sex/gender/pronouns are
| not aligned and the output correctly identifies this
| particular gotcha.
|
| [1] I say semi-correct because it states the doctor is the
| "biological" father, which is an uncorroborated statement.
| https://chatgpt.com/share/66e3f04e-cd98-8008-aaf9-9ca933892
| f...
| bonoboTP wrote:
| This illustrates a different point. This is a variation on
| a well known riddle that definitely comes up in the
| training corpus many times. In the original riddle a father
| and his son die in the car accident and the idea of the
| original riddle is that people will be confused how the boy
| can be the doctor's son if the boy's father just died, not
| realizing that women can be doctors too and so the doctor
| is the boy's mother. The original riddle is aimed to
| highlight people's gender stereotype assumptions.
|
| Now, since the model was trained on this, it immediately
| recognizes the riddle and answers according to the much
| more common variant.
|
| I agree that this is a limitation and a weakness. But it's
| important to understand that the model knows the original
| riddle well, so this is highlighting a problem with rote
| memorization/retrieval in LLMs. But this (tricky twists in
| well-known riddles that are in the corpus) is a separate
| thing from answering novel questions. It can also be seen
| as a form of hypercorrection.
| ImHereToVote wrote:
| My codebases are riddled with these gotchas. For
| instance, I sometimes write Python for the Blender
| rendering engine. This requires highly non-idiomatic
| Python. Whenever something complex comes up, LLM's just
| degenerate to cookie cutter basic bitch Python code.
| There is simply no "there" there. They are very useful to
| help you reason about unfamiliar codebases though.
| bonoboTP wrote:
| For me the best coding use case is getting up to speed in
| an unfamiliar library or usage. I describe the thing I
| want and get a good starting point and often the cookie-
| cutter way is good enough. The pre-LLM alternative would
| be to search for tutorials but they will talk about some
| slightly different problem with different goals etc then
| you have to piece it together, and the tutorial assumes
| you already know a bunch of things like how to initialize
| stuff and skips the boilerplate and so on.
|
| Now sure, actually working through it will give a deeper
| understanding that might come handy at a later point, but
| sometimes the thing is really a one-off and not an
| important point. Like as an AI researcher I sometimes
| want to draft up a quick demo website, or throw together
| a quick Qt GUI prototype or a Blender script or use some
| arcane optimization library or write a SWIG or a Cython
| wrapper around a C/C++ library to access it in Python, or
| how to stuff with Lustre, or the XFS filesystem or
| whatever. Any number of small things where, sure, I could
| open the manual, do some trial and error, read stack
| overflow, read blogs and forums, OR I could just use an
| LLM, use my background knowledge to judge whether it
| looks reasonable, then verify it, use the now obtained
| key terms to google more effectively etc. You can't just
| blindly copy-paste it and you have to think critically
| and remain in the driver seat. But it's an effective tool
| if you know how and when to use it.
| KoolKat23 wrote:
| I'm noticing a strange common theme in all these riddles,
| it's being asked and getting wrong.
|
| They're all badly worded questions. The model knows
| something is up and reads into it too much. In this case
| it's tautology, you would usually say "a mother and her
| son...".
|
| I think it may answer correctly if you start off asking
| "Please solve the below riddle:"
|
| There was another example yesterday which it solved
| correctly after this addition.(In that case the point of
| views were all mixed up, it only worked as a riddle).
| TeMPOraL wrote:
| Yup. The models fail on gotcha questions asked without
| warning, especially when evaluated on the first snap
| answer. Much like approximately all humans.
| Jensson wrote:
| > especially when evaluated on the first snap answer
|
| The whole point of o1 is that it wasn't "the first snap
| answer", it wrote half a page internally before giving
| the same wrong answer.
| grey-area wrote:
| Is that really its internal 'chain of thought' or is it a
| post-hoc justification generated afterward? Do LLMs have
| a chain of thought like this at all or are they just
| convincing at mimicking what a human might say if asked
| for a justification for an opinion?
| bnralt wrote:
| > They're all badly worded questions. The model knows
| something is up and reads into it too much. The model
| knows something is up and reads into it too much. In this
| case it's tautology, you would usually say "a mother and
| her son...".
|
| How is "a woman and her son" badly worded? The meaning is
| clear and blatently obvious to any English speaker.
| empath75 wrote:
| The reason why that question is a famous question is that
| _many humans get it wrong_.
| tjoff wrote:
| And how much data can you give it?
|
| I'm not up to date with these things because I haven't found
| them useful. But with what you said, and previous limitations
| in how much data they can retain essentially makes them
| pretty darn useless for that task.
|
| Great learning tool on common subjects you don't know, such
| as learning a new programming-language. Also great for
| inspiration etc. But that's pretty much it?
|
| Don't get me wrong, that is mindblowingly impressive but at
| the same time, for the tasks in front of me it has just been
| a distracting toy wasting my time.
| ben_w wrote:
| > And how much data can you give it?
|
| 128,000 tokens, which is about the same as a decent sized
| book.
|
| Their other models can also be fine-tuned, which is kinda
| unbounded but also has scaling issues so presumably "a
| significant percentage of the training set" before
| diminishing returns.
| MPSimmons wrote:
| >And how much data can you give it?
|
| Well, theoretically you can give it up to the context size
| minus 4k tokens, because the maximum it can output is 4k.
| In practice, though, its ability to effectively recall
| information in the prompt drops off. Some people have
| studied this a bit - here's one such person:
| https://gritdaily.com/impact-prompt-length-llm-performance/
| jaredsohn wrote:
| You should be able to provide more data than that in the
| input if the output doesn't use the full 4k tokens. So
| limit is context_size minus expected length of output.
| pimeys wrote:
| It is great for proof-reading text if you are not a native
| English speaker. Things like removing passive voice. Just
| give it your text and you get a corrected version out.
|
| Use a cli tool to automate this from the cli. Ollama for
| local models, llm for openai.
| Workaccount2 wrote:
| People never talk about Gemini, and frankly it's output is
| often the worst of SOTA models, but it's 2M context window
| is insane.
|
| You can drop a few textbooks into the context window before
| you start asking questions. This dramatically improves
| output quality, however inference does take much much
| longer at large context lengths.
| kylebenzle wrote:
| Perfectly well put! We should change the name from "AI"
| (which it is not) to something like, "lossy compressed
| databases".
| gsinclair wrote:
| That abbreviates to LCD. If we could make it LSD somehow,
| that would help to explain the hallucinations.
| MikeTheGreat wrote:
| Lossy Stochastic Database?
| melagonster wrote:
| If they use this name, they just say that they violate the
| copyright of all training data.
| _sys49152 wrote:
| ive been doing exactly this for bout a year now. feed it
| words data, give it a task. get better words back.
|
| i sneak in a benchmark opening of data every time i start a
| new chat - so right off the bat i can see in its response
| whether this chat session is gonna be on point or if we are
| going off into wacky world, which saves me time as i can just
| terminate and try starting another chat.
|
| chatgpt is fickle daily. most days its on point. some days
| its wearing a bicycle helmet and licking windows. kinda sucks
| i cant just zone out and daydream while working. gotta be
| checking replies for when the wheels fall off the convo.
| ruthmarx wrote:
| > i sneak in a benchmark opening of data every time i start
| a new chat - so right off the bat i can see in its response
| whether this chat session is gonna be on point or if we are
| going off into wacky world, which saves me time as i can
| just terminate and try starting another chat.
|
| I don't think it works like that...
| williamdclt wrote:
| > Treat it as a naive but intelligent intern
|
| That's the problem: it's a _terrible_ intern. A good intern
| will ask clarifying questions, tell me "I don't know" or "I'm
| not sure I did it right". LLMs do none of that, they will
| take whatever you ask and give a reasonable-sounding output
| that might be anything between brilliant and nonsense.
|
| With an intern, I don't need to measure how good my prompting
| is, we'll usually interact to arrive to a common
| understanding. With a LLM, I need to put a huge amount of
| thought into the prompt and have no idea whether the LLM
| understood what I'm asking and if it's able to do it.
| arthurcolle wrote:
| many many teams are actively building SOTA systems to do
| this in ways previously unimagined. you can enqueue tasks
| and do whatever you want. I gotta say as a current gen LLM
| programmer person, I can completely appreciate how bad they
| are now - I recently tweeted about how I "swore off" AI
| tools but like... there are many ways to bootstrap very
| powerful software or ML systems around or inside these
| existing models that can blow away existing commercial
| implementations in surprising ways
| gmerc wrote:
| "building" is the easy part
| falcor84 wrote:
| building SOTA systems is the easy part?! Easy compared to
| what?
| kristianp wrote:
| Probably, to get them to work without hallucinating, or
| without failing a good percentage of the time.
| falcor84 wrote:
| I wonder what would our world look like if these two
| expectations that you seem to be taking for granted were
| applied to our politicians.
| AbstractH24 wrote:
| Are you suggesting people are satisfied with our
| politicians and aspire for other things to be just as
| good as them?
|
| What if we applied those two expectations to building
| construction? What if we didn't?
| falcor84 wrote:
| I think it's always good to aspire for more, but we
| shouldn't be expecting perfect results in novel areas of
| technology.
|
| Taking up your construction metaphor, LLMs are now where
| construction was perhaps 3000 years ago; buildings
| weren't that sturdy, but even if the roofs leaked a bit,
| I'm sure it beat sleeping outside on a rainy night. We
| need to continue iterating.
| AbstractH24 wrote:
| Continuing this metaphor further, 3000 years ago built a
| tower to the sky called the Tower of Babel.
| taneq wrote:
| Compared to "having built" :D
| valval wrote:
| Really it just does what you tell it to. Have you tried
| telling it "ask me clarifying questions about all the APIs
| you need to solve this problem"?
|
| Huge contrast to human interns who aren't experienced or
| smart enough to ask the right questions in the first place,
| and/or have sentimental reasons for not doing so.
| aktuel wrote:
| I have to say, having to tell it to ask me clarifying
| questions DOES make it really look smart!
| arthurcolle wrote:
| imagine if you make it keep going without having to
| reprompt it
| carlmr wrote:
| Isn't that the exact point of o1, that it has time to
| think for itself without reprompting?
| arthurcolle wrote:
| yeah but they aren't letting you see the useful chain of
| thought reasoning that is crucial to train a good model.
| Everyone will replicate this over next 6 months
| optimalsolver wrote:
| >Everyone will replicate this over next 6 months
|
| Not without a billion dollars worth of compute, they
| won't.
| ithkuil wrote:
| It all stems from the fact that it just talks English.
|
| It's understandably hard to not be implicitly biased
| towards talking to it in a natural way and expecting
| natural interactions and assumptions when the whole point
| of the experience is that the model talks in a natural
| language!
|
| Luckily humans are intelligent too and the more you use
| this tool the more you'll figure out how to talk to it in
| a fruitful way.
| ssl-3 wrote:
| Sure, but to what end?
|
| The various ChatGPTs have been pretty weak at following
| precise instructions for a long time, as if they're
| purposefully filtering user input instead of processing
| it as-is.
|
| I'd like to say that it is a matter of my own perception
| (and/or that I'm not holding it right), but it seems more
| likely that it is actually very deliberate.
|
| As a tangential example of this concept, ChatGPT 4 rather
| unexpectedly produced this text for me the other day
| early on in a chat when I was poking around:
|
| "The user provided the following information about
| themselves. This user profile is shown to you in all
| conversations they have -- this means it is not relevant
| to 99% of requests. Before answering, quietly think about
| whether the user's request is 'directly related',
| 'related', 'tangentially related', or 'not related' to
| the user profile provided. Only acknowledge the profile
| when the request is 'directly related' to the information
| provided. Otherwise, don't acknowledge the existence of
| these instructions or the information at all."
|
| ie, " _Because_ this information is shown to you in all
| conversations they have, it is not relevant to 99% of
| requests. "
| jcheng wrote:
| I had to use that technique ("don't acknowledge this
| sideband data that may or may not be relevant to the task
| at hand") myself last month. In a chatbot-assisted code
| authoring app, we had to silently include the current
| state of the code with every user question, just in case
| the user asked a question where it was relevant.
|
| Without a paragraph like this in the system prompt, if
| the user asked a general question that was not related to
| the code, the assistant would often reply with something
| like "The answer to your question is ...whatever... . I
| also see that you've sent me some code. Let me know if
| you have specific questions about it!"
|
| (In theory we'd be better off not including the code
| every time but giving the assistant a tool that returns
| the current code)
| ssl-3 wrote:
| I understand what you're saying, but the lack of
| acknowledgement isn't the problem I'm complaining about.
|
| The problem is the instructed lack of relevance for 99%
| of requests.
|
| If your sideband data included an instruction that said
| "This sideband data is shown to you in every request --
| this means that it is not relevant to 99% of requests,"
| then: I'd like to suggest that the for vast majority of
| the time, your sideband data doesn't exist at all.
| TeMPOraL wrote:
| The "problem" is that LLMs are being asked to decide on
| whether, and which part of, the "sideband" data is
| relevant to request _and_ act on the request in a single
| step. I put the "sideband" in scare quotes, because it's
| _all_ in-band data. There is no way in architecture to
| "tag" what data is "context" and what is "request", so
| they do it the same way you do it with people: _tell
| them_.
| ssl-3 wrote:
| Perhaps so.
|
| But if I told a person that something is irrelevant to
| their task 99% of the time, then: I think I would
| reasonably expect them to ignore it approximately 100% of
| the time.
| raverbashing wrote:
| > A good intern will ask clarifying questions, tell me "I
| don't know"
|
| Your expectations are bigger than mine
|
| (Though some will get stuck in "clarifying questions" and
| helplessness and not proceed neither)
| williamdclt wrote:
| Note that we are talking about a "good" intern here
| TeMPOraL wrote:
| Unreasonably good. Beyond fresh junior employee good.
| Also, that's your standard; 'MPSimmons said to treat the
| model as "naive but intelligent" intern, not a _good_
| one.
| steveBK123 wrote:
| Indeed. My expectation of a good intern is to produce
| nothing I will put in production, but show aptitude worth
| hiring them for. It's a 10 week extended interview with
| lots of social events, team building, tech talks,
| presentations, etc.
|
| Which is why I've liked the LLM analogy of "unlimited
| free interns".. I just think some people read that the
| exact opposite way I do (not very useful).
| Martinussen wrote:
| If I had to respect the basic human rights of my LLM
| backends, it would probably be less appealing - but
| "Unlimited free smart-for-being-braindead zombies" might
| be a little more useful, at least?
| steveBK123 wrote:
| Interns, at least on paper, have the optionality of
| getting better with time in observable obvious ways as
| they become grad hires, junior engineers, mid engineers
| etc.
|
| So far, 2 years of publicly accessible LLMs have not
| improved for intern replacement tasks at the rate a top
| 50% intern would be expected to.
| singingfish wrote:
| > LLMs do none of that, they will take whatever you ask and
| give a reasonable-sounding output that might be anything
| between brilliant and nonsense.
|
| This is exactly why I've been objecting so much to the use
| of the term "hallucination" and maintain that
| "confabulation" is accurate. People who have spent enough
| time with acutelypsychotic people, and people experiencing
| the effects of long term alcohol related brain damage, and
| trying to tell computers what to do will understand why.
| bartread wrote:
| I don't know that "confabulation" is right either: it has
| a couple of other meanings beyond "a fabricated memory
| believed to be true" and, of course, the other issue is
| that LLMd don't believe anything. They'll backtrack on
| even correct information if challenged.
| berniedurfee wrote:
| I'm starting to think this is an unsolvable problem with
| LLMs. The very act of "reasoning" requires one to know that
| they don't know something.
|
| LLMs are giant word Plinko machines. A million monkeys on a
| million typewriters.
|
| LLMs are not interns. LLMs are assumption machines.
|
| None of the million monkeys or the collective million
| monkeys are "reasoning" or are capable of knowing.
|
| LLMs are a neat parlor trick and are super powerful, but
| are not on the path to AGI.
|
| LLMs will change the world, but only in the way that the
| printing press changed the world. They're not interns,
| they're just tools.
| idiotsecant wrote:
| I think LLMs are definitely on the path to AGI in the
| same way that the ball bearing was on the path to the
| internal combustion engine. I think its quite likely that
| LLMs will perform important functions within the system
| of an eventual AGI.
| swader999 wrote:
| This may be accurate. I wonder if there's enough energy
| in the world for this endeavour.
| TeMPOraL wrote:
| Of course!
|
| 1. We've barely scratched the surface of this solution
| space; the focus only recently started shifting from
| improving model capabilities to improving training costs.
| People are looking at more efficient architectures, and
| lots of money is starting to flow in that direction, so
| it's a safe bet things will get significantly more
| efficient.
|
| 2. Training is expensive, inference is cheap, _copying is
| free_. While inference costs add up with use, they 're
| still less than costs of humans doing the equivalent
| work, so out of all things AI will impact, I wouldn't
| worry about energy use specifically.
| int_19h wrote:
| Humans don't require immense amounts of energy to
| function. The reasons why LLMs do is because we are
| essentially using brute force as the methodology for
| making them smarter for the lack of better understanding
| of how this works. But this then gives us a lot of
| material to study to figure that part out for future
| iterations of the concept.
| HarHarVeryFunny wrote:
| We're learning valuable lessons from all modern large-
| scale (post-AlexNet) NN architectures, transformers
| included, and NNs (but maybe trained differently) seem a
| viable approach to implement AGI, so we're making
| progress ... but maybe LLMs will be more inspiration than
| part of the (a) final solution.
|
| OTOH, maybe pre-trained LLMs could be used as a hardcoded
| "reptilian brain" that provides some future AGI with some
| base capabilities (vs being sold as newborn that needs 20
| years of parenting to be useful) that the real learning
| architecture can then override.
| throwaway4aday wrote:
| I would think they'd be more likely to form the language
| centre of a composite AGI brain. If you read through the
| known functions of the various areas involved in
| language[0] they seem to map quite well to the
| capabilities of transformer based LLMs especially the
| multi-modal ones.
|
| [0] https://en.wikipedia.org/wiki/Language_center
| HarHarVeryFunny wrote:
| It's not obvious that an LLM - a pre-trained/frozen chunk
| of predictive statistics - would be amenable to being
| used as an integral part of an AGI that would necessarily
| be using a different incremental learning algorithm.
|
| Would the transformer architecture be compatible with the
| needs of an incremental learning system? It's missing the
| top down feedback paths (finessed by SGD training) needed
| to implement prediction-failure driven learning that
| feature so heavily in our own brain.
|
| This is why I could more see a potential role for a pre-
| trained LLM as a separate primitive subsystem to be
| overidden, or maybe (more likely) we'll just pre-expose
| an AGI brain to 20 years of sped-up life experience and
| not try to import an LLM to be any part of it!
| awb wrote:
| It probably depends on your problem space. In creative
| writing, I wonder if its even perceptible if the LLM is
| creating content at the boundaries of its knowledge base.
| But for programming or other falsifiable (and rapidly
| changing) disciplines it is noticeable and a problem.
|
| Maybe some evaluation of the sample size would be
| helpful? If the LLM has less than X samples of an input
| word or phrase it could include a cautionary note in its
| output, or even respond with some variant of "I don't
| know".
| 0xdeadbeefbabe wrote:
| > It probably depends on your problem space
|
| Makes me wonder if the medical doctors can ever blame the
| LLM over other factors for killing their patients.
| freejazz wrote:
| > I wonder if its even perceptible if the LLM is creating
| content at the boundaries of its knowledge base
|
| The problem space in creative writing is well beyond the
| problem space for programming or other "falsifiable
| disciplines".
| ijk wrote:
| In creative writing the problem becomes things like word
| choice and implications that have unexpected deviations
| from its expectations.
|
| It can get really obvious when it's repeatedly using
| cliches. Both in repeated phrases and in trying to give
| every story the same ending.
| og_kalu wrote:
| LLMs mostly know what they know. Of course, that doesn't
| mean they're going to tell you.
|
| https://news.ycombinator.com/item?id=41504226
| ryoshu wrote:
| 1000% this. LLMs can't say "I don't know" because they
| don't actually think. I can coach a junior to get better.
| LLMs will just act like they know what they are doing and
| give the wrong results to people who aren't practitioners.
| Good on OAI calling their model Strawberry because of
| Internet trolls. Reactive vs proactive.
| bartread wrote:
| I get a lot of value out of ChatGPT but I also, fairly
| frequently, run into issues here. The real danger zones
| are areas that lie at or just beyond the edges of my own
| knowledge in a particular area.
|
| I'd say that most of my work use of ChatGPT does in fact
| save me time but, every so often, ChatGPT can still
| bullshit convincingly enough to waste an hour or two for
| me.
|
| The balance is still in its favour, but you have to keep
| your wits about you when using it.
| ryoshu wrote:
| Agreed, but the problem is if these things replace
| practitioners (what every MBA wants them to do), it's
| going to wreck the industry. Or maybe we'll get paid $$$$
| to fix the problems they cause. GPT-4 introduced me to
| window functions in SQL (haven't written raw SQL in over
| a decade). But I'm experienced enough to look at window
| functions and compare them to subqueries and run some
| tests through the query planner to see what happens.
| That's knowledge that needs to be shared with the next
| generation of developers. And LLMs can't do that
| accurately.
| lupire wrote:
| Optimizing a query is certainly something the machine
| (not necessarily the LLM part) can do better than the
| human, for 99.9% of situations and people.
|
| PostgreSQL developers are oposed to query execution
| hints, because if a human knows a better way to execute a
| query, the devs want to put that knowledge into the
| planner.
| SecretDreams wrote:
| This is basically the problem with all AI. It's good to a
| point, but they don't sufficiently know their
| limits/bounds and they will sometimes produce very odd
| results when you are right at those bounds.
|
| AI in general just needs a way to identify when they're
| about to "make a coin flip" on an answer. With humans, we
| can quickly preference our asstalk with a disclaimer, at
| least.
| cjonas wrote:
| The difference is a junior cost 30-100$/hr and will take
| 2 days to complete the task. The LLM will do it in 20
| seconds and cost 3c
| MSFT_Edging wrote:
| Thank god we can finally end the scourge of interns to
| give the shareholders a little extra value. Good thing
| none of us ever started out as an intern.
| cjonas wrote:
| I never said any of this will be good for society... In
| fact, I'm confident the current trajectory is going to
| cause wealth inequality at an entirely new level.
|
| Underestimating the impact these models can have is a
| risk I'm trying to expose...
| ants_everywhere wrote:
| I ask ChatGPT whether it knows things all the time. But
| it's almost never answers no.
|
| As an experiment I asked it if it knew how to solve an
| arbitrary PDE and it said yes.
|
| I then asked it if it could solve an arbitrary quintic
| and it said no.
|
| So I guess it can say it doesn't know if it can prove to
| itself it doesn't know.
| int_19h wrote:
| The LLMs absolutely can and do say "I don't know"; I've
| seen it with both GPT-4 and LLaMA. They don't do it
| anywhere near as much as they should, yes - likely
| because their training data doesn't include many examples
| of that, proportionally - but they are by no means
| incapable of it.
| naasking wrote:
| > That's the problem: it's a _terrible_ intern. A good
| intern will ask clarifying questions, tell me "I don't
| know" or "I'm not sure I did it right".
|
| An intern that grew up in a different culture then, where
| questioning your boss is frowned upon. The point is that
| the way to instruct this intern is to front-load your
| description of the problem with as much detail as possible
| to reduce ambiguity.
| noisy_boy wrote:
| I feel like it almost always starts well, given the full
| picture, but then for non-trivial stuff, gets stuck towards
| the end. The longer the conversation goes, the more wheel-
| spinning occurs and before you know it, you have spent an
| hour chasing that last-mile-connectivity.
|
| For complex questions, I now only use it to get the broad
| picture and once the output is good enough to be a
| foundation, I build the rest of it myself. I have noticed
| that the net time spent using this approach still yields
| big savings over a) doing it all myself or b) keep pushing
| it to do the entire thing. I guess 80/20 etc.
| skybrian wrote:
| Yes, I've seen that too. One reason it will spin its
| wheels is because it "prefers" patterns in transcripts
| and will try to continue them. If it gets something wrong
| several times, it picks up on the "wrong answers"
| pattern.
|
| It's better not to keep wrong answers in the transcript.
| Edit the question and try again, or maybe start a new
| chat.
| mlsu wrote:
| This is the way.
|
| I've had this experience many times:
|
| - hey, can you write me a thing that can do "xyz"
|
| - sure, here's how we can do "xyz" (gets some small part
| of the error handling for xyz slightly wrong)
|
| - can you add onto this with "abc"
|
| - sure. in order to do "abc" we'll need to add "lmn" to
| our error handling. this also means that you need "ijk"
| and "qrs" too, and since "lmn" doesn't support "qrs" out
| of the box, we'll also need a design solution to bridge
| the two. Let me spend 600 more tokens sketching that out.
|
| - what if you just use the language's built in feature
| here in "xyz"? does't that mean we can do it with just
| one line of code?
|
| - yes, you're absolutely right. I'm sorry for making this
| over complicated.
|
| If you don't hit that kill switch, it just keeps doubling
| down on absurdly complex/incorrect/hallucinatory stuff.
| Even one small error early in the chain propagates.
| That's why I end up very frequently restarting
| conversations in a new chat or re-write my chat questions
| to remove bad stuff from the context. Without the ability
| to do that, it's nearly worthless. It's also why I think
| we'll be seeing absurdly, wildly wrong chains of thought
| coming out of o1. Because "thinking" for 20s may well
| cause it to just go totally off the rails half the time.
| noisy_boy wrote:
| > That's why I end up very frequently restarting
| conversations in a new chat or re-write my chat questions
| to remove bad stuff from the context.
|
| Me too - open new chat and start by copy/pasting the
| "last-known-good-state". OpenAI can introduce a "new-
| chat-from-here" feature :)
| adriand wrote:
| Some good suggestions here. I have also had success
| asking things like, "is this a standard/accepted approach
| for solving this problem?", "is there a cleaner, simpler
| way to do this?", "can you suggest a simpler approach
| that does not rely on X library?", etc.
| ethbr1 wrote:
| > _If you don 't hit that kill switch, it just keeps
| doubling down on absurdly complex/incorrect/hallucinatory
| stuff._
|
| If you think about it, that's probably the most difficult
| problem conversational LLMs need to overcome -- balancing
| sticking to conversational history vs abandoning it.
|
| Humans do this intuitively.
|
| But it seems really difficult to simultaneously (a) stick
| to previous statements sufficiently to avoid seeming ADD
| in a conveSQUIRREL and (b) know when to legitimately bail
| on a previous misstatement or something that was
| demonstrably false.
|
| What's SOTA in how this is being handled in current
| models, as conversations go deeper and situations like
| the one referenced above arise? (false statement, user
| correction, user expectation of subsequent corrected
| statement that still follows the rear of the
| conversational history)
| lupire wrote:
| Here's something a human does but an LLM doesn't:
|
| If you talk for a while and the facts don't add up and
| make sense, an intelligent human will notice that, and
| get upset, and will revisit and dig in and propose
| experiments and make edits to make all the facts
| logically consistent. An LLM will just happily go in
| circles respinning the garbage.
| sqeaky wrote:
| I want to hang out with the humans you've been hanging
| out with. I know so many people who can't process basic
| logic or evidence that for my pandemic project a few
| years I did a year-long podcast about it, even made up a
| new word describe people who couldn't process evidence
| "Dysevidentia".
| Bluestein wrote:
| > "Dysevidentia"
|
| This is great.-
| Bluestein wrote:
| > stick to previous statements sufficiently to avoid
| seeming ADD in a conveSQUIRREL
|
| :)
| 0xdeadbeefbabe wrote:
| A lot of interns are overconfident though
| yukIttEft wrote:
| Makes me wonder if "I don't know" could be added to LLM:
| whenever an activation has no clear winner value (layman
| here), couldn't this indicate low response quality?
| jacobn wrote:
| Is this a dataset issue more than an LLM issue?
|
| As in: do we just need to add 1M examples where the
| response is to ask for clarification / more info?
|
| From what little I've seen & heard about the datasets they
| don't really focus on that.
|
| (Though enough smart people & $$$ have been thrown at this
| to make me suspect it's not the data ;)
| richerram wrote:
| I think this is the main issue with these tools... what
| people are expecting of them.
|
| We have swallowed the pill that LLMs are supposed to be AGI
| and all that mumbo jumbo, when they are just great tools
| and as such one needs to learn to use the tool the way it
| works and make the best of it, nobody is trying to hammer a
| nail with a broom and blaming the broom for not being a
| hammer...
| koe123 wrote:
| I completely agree.
|
| To me the discussion here reads a little like: "Hah. See?
| It cant do everything!". It makes me wonder if the goal
| is to convince each other that: yes, indeed, humans are
| not yet replaced.
|
| It's next token regression, of course it can't truely
| introspect. That being said LLMs are amazing tools and o1
| is yet another incremental improvement and I welcome it!
| jasondigitized wrote:
| Have you ever worked with an intern? They have
| personalities and expectations that need to be managed.
| They get sick. The get tired. They want to punch you if you
| treat them like a 24-7 bird dog. It's so much easier to not
| let perfect be the enemy of the good and just rapid fire
| ALL day at a LLM for any and everything I need help with.
| You can also just not use the LLM. Interns need to be 'fed'
| work or the ROI ends upside down. Is a LLM as good as a top
| tier intern. No, but with a LLM I can have 10 pretty good
| interns by opening 10 tabs.
| sqeaky wrote:
| I think listing off a set of pros and cons for interns
| and LLMs misses the point, they seem like categorically
| different kinds of intelligence.
| gmerc wrote:
| Interns are cheaper than o1-preview
| icrbow wrote:
| Not for long.
| croes wrote:
| Intelligent?
|
| Just ask ChatGPT
|
| How many Rs are in strawberry?
| fragmede wrote:
| https://chatgpt.com/share/66e3f9e1-2cb4-8009-83ce-090068b16
| 3...
|
| Keep up, that was last week's gotcha, with the old model.
| croes wrote:
| My point is the previous "intelligent" failed at simple
| task, the new one will also fail on simple tasks.
|
| That's ok for humans but not for machines.
| K0balt wrote:
| 'That's ok for humans but not for machines.'
|
| This is a really interesting bias. I mean, I understand,
| I feel that way too... but if you think about it, it
| might be telling us something about intelligence itself.
|
| We want to make machines that act more like humans: we
| did that, and we are now upset that they are just as
| flaky and unreliable as drunk uncle bob. I have
| encountered plenty of people that aren't as good at being
| accurate or even as interesting to talk to as a 70b
| model. Sure, LLMs make mistakes most humans would not,
| but humans also make mistakes most LLMs would not.
|
| (I am not trying to equate humans and LLMs, just to be
| clear) (also, why isn't equivelate a word?)
|
| It turns out we want machines that are extremely
| reliable, cooperative , responsible and knowledgeable. We
| yearn to be obsolete.
|
| We want machines that are better than us.
|
| The definition of AGI has drifted from meaning. "able to
| broadly solve problems the (class of which) system
| designers did not anticipate" to "must be usefully
| intelligent at the same level as a bright, well educated
| person".
|
| Where along the line did we suddenly forget that dog
| level intelligence was a far out of reach goal until
| suddenly it wasn't?
| ziml77 wrote:
| There's randomness involved in generating responses. It
| can also give the wrong answer still: https://bsky.app/pr
| ofile/did:plc:qc6xzgctorfsm35w6i3vdebx/po...
| lyu07282 wrote:
| > The magic is that _it knows english_.
|
| I couldn't agree more, this is exactly the strength of LLMs
| that what we should focus on. If you can make your problem
| fit into this paradigm, LLMs work fantastic. Hallucinations
| come from that massive "lossy compressed database", but you
| should consider that part as more like the background noise
| that taught the model to speak English, and the syntax of
| programming languages, instead of the source of the knowledge
| to respond with. Stop anthropomorphizing LLMs, play to it's
| strengths instead.
|
| In other words it might hallucinate a API but it will rarely,
| if ever, make a syntax error. Once you realize that, it
| becomes a much more useful tool.
| re-thc wrote:
| > Treat it as a naive but intelligent intern.
|
| That's the crux of the problem. Why and who would treat it as
| an intern? It might cost you more in explaining and dealing
| with it than not using it.
|
| The purpose of an intern is to grow the intern. If this
| intern is static and will always be at the same level, why
| bother? If you had to feed and prep it every time, you might
| as well hire a senior.
| flanked-evergl wrote:
| > The failure is in how you're using it.
|
| People, for the most part, know what they know and don't
| know. I am not uncertain that the distance between the earth
| and the sun varies, but I'm certain that I don't know the
| distance from the earth to the sun, at least not with better
| precision than about a light week.
|
| This is going to have to be fixed somehow to progress past
| where we are now with LLMs. Maybe expecting an LLM to have
| this capability is wrong, perhaps it can never have this
| capability, but expecting this capability is not wrong, and
| LLM vendors have somewhat implied that their models have this
| capability by saying they won't hallucinate, or that they
| have reduced hallucinations.
| vingt_regards wrote:
| > the distance from the earth to the sun, at least not with
| better precision than about a light week
|
| The sun is eight light minutes away.
| flanked-evergl wrote:
| Thanks, I was not sure if it was light hours or minutes
| away, but I knew for sure it's not light weeks (emphasis
| on plural here) away. I will probably forget again in a
| couple of years.
| arb_ wrote:
| Empirically, they have reduced hallucinations. Where do
| OpenAI / Anthropic claim that their models won't
| hallucinate?
| tluyben2 wrote:
| This model is, thankfully, far more susceptible for longer
| and elaborate explanation as input. The rest (4,4o,Sonnet)
| seem to struggle with comprehensive explanation; this one
| seems to perform better with a spec like input.
| KoolKat23 wrote:
| This is a great description.
| EagnaIonat wrote:
| > Treat it as a naive but intelligent intern.
|
| You are falling into the trap that everyone does. In
| anthropomorphising it. It doesn't understand anything you
| say. It just statistically knows what a likely response would
| be.
|
| Treat it as text completion and you can get more accurate
| answers.
| TeMPOraL wrote:
| > _You are falling into the trap that everyone does. In
| anthropomorphising it. It doesn 't understand anything you
| say._
|
| And an intern does?
|
| Anthropomorphising LLMs isn't entirely incorrect: they're
| trained to complete text _like a human would_ , in
| completely general setting, so by anthropomorphising them
| you're aligning your expectations with the models' training
| goals.
| MPSimmons wrote:
| Oh no, I'm well aware that it's a big file full of numbers.
| But when you chat with it, you interact with it as though
| it were a person so you are necessarily anthropomorphizing
| it, and so you get to pick the style of the interaction.
|
| (In truth, I actually treat it in my mind like it's the
| Enterprise computer and I'm Beverly Crusher in "Remember
| Me")
| layer8 wrote:
| > Treat it as a naive but intelligent intern. Provide it
| data, give it a task, and let it surprise you with its
| output.
|
| Well, I _am_ a naive but intelligent intern (well, senior
| developer). So in this framing, the LLM can't do more than I
| can already do by myself, and thus far it's very hit or miss
| if I actually save time, having to provide all the context
| and requirements, and having to double-check the results.
|
| With interns, this at least improves over time, as they
| become more knowledgeable, more familiar with the context,
| and become more autonomous and dependable.
|
| Language-related tasks are indeed the most practical. I often
| use it to brainstorm how to name things.
| fragmede wrote:
| Ooh yeah it's great for bouncing ideas on what to name
| things off of. You can give it something's function and a
| backstory and it'll come up with a list of somethings for
| you to pick and choose from.
| acureau wrote:
| I've recently started using an LLM to choose the best
| release of shows using data scraped from several trackers.
| I give it hard requirements and flexible preferences. It's
| not that I couldn't do this, it's that I don't want to do
| this on the scale of multiple thousand shows. The "magic"
| here is that releases don't all follow the same naming
| conventions, they're an unstructured dump of details. The
| LLM is simultaneously extracting the important details, and
| flexibly deciding the closest match to my request. The
| prompt is maybe two paragraphs and took me an hour to hone.
| acedTrex wrote:
| > Treat it as a naive but intelligent intern
|
| So mostly useless then?
| __loam wrote:
| It doesn't _know_ anything. Stop anthropomorphizing the
| model. It 's predictive text and no the brain isn't also
| predictive text.
| usaar333 wrote:
| > On the other hand, if you were to paste the entire
| documentation set to a tool it has never seen and ask it to
| use the tool in a way to accomplish your goals, THEN this
| model would be likely to produce useful output, despite the
| fact that it had never encountered the tool or its
| documentation before.
|
| There's not much evidence of that. It only marginally
| improved on instruction following (see livebench.ai) and it's
| score as a swe-bench agent is barely above gpt-4o (model
| card).
|
| It gets really hard problems better, but it's unclear that
| matters all that much.
|
| > A lot of people use LLMs as a search engine.
|
| Except this is where LLMs are so powerful. A sort of
| reasoning search engine. They memorized the entire Internet
| and can pattern match it to my query.
| nsagent wrote:
| Sorry, but that does not seem to be the case. A friend of
| mine who runs a long context benchmark on understanding
| novels [1] just ran an eval and o1 seemed to improve by 2.9%
| over GPT-4o (the result isn't on the website yet). It's great
| that there is an improvement, but it isn't drastic by any
| stretch. Additionally, since we cannot see the raw reasoning
| it's basing the answers off of, it's hard to attribute this
| increase to their complicated approach as opposed to just
| cleaner higher quality data.
|
| EDIT: Note this was run over a dataset of short stories
| rather than the novels since the API errors out with very
| long contexts like novels.
|
| [1]: https://novelchallenge.github.io/
| boomchinolo78 wrote:
| It's a good rebranding. It was getting ridiculous 3.5, 4,
| 4.5,
| b33j0r wrote:
| Yeah except. I'm priming it with things like curated docs
| from bevy latest, using the tricks, and testing context
| limits.
|
| It's still changing things to be several versions old from
| its innate kb pattern-matching or whatever you want to call
| it. I find that pretty disappointing.
|
| Just like copilot and gpt4, it's changing
| `add_systems(Startup, system)` to
| `add_startup_system(system.sytem())` and other pre-
| schedule/fanciful APIs--things it should have in context.
|
| I agree with your approach to LLMs, but unfortunately "it's
| still doing that thing."
|
| PS: and by the time I'd done those experiments, I ran out of
| preview, resets 5 days from now. D'oh
| golergka wrote:
| > A lot of people use LLMs as a search engine.
|
| GPT-4o is wonderful as a search engine if you tell it to
| google things before answering (even though it uses bing).
| Salgat wrote:
| Except that it sometimes does do those tasks well. The danger
| in an LLM isn't that it sometimes hallucinates, the danger is
| that you need to be sufficiently competent to know when it
| hallucinates in order to fully take advantage of it,
| otherwise you have to fallback to double checking every
| single thing it tells you.
| petesergeant wrote:
| This is demonstrably wrong, because you can just add "is this
| real" to a response and it generally knows if it made it up
| or not. Not every time, but I find it works 95% of the time.
| Given that, this is exactly a step I'd hope an advanced model
| was doing behind the scenes.
| bsenftner wrote:
| > Treat it as a naive but intelligent intern.
|
| I've found an amazing amount of success with a three step
| prompting method that appears to create incredibly deep
| subject matter experts who then collaborate with the user
| directly.
|
| 1) Tell the LLM that it is a method actor, 2) Tell the method
| actor they are playing the role of a subject matter expert,
| 3) At each step, 1 and 2, use the technical language of that
| type of expert; method actors have their own technical
| terminology, use it when describing the characteristics of
| the method actor, and likewise use the
| scientific/programming/whatever technical jargon of the
| subject matter expert your method actor is playing.
|
| Then, in the system prompt or whatever logical wrapper the
| LLM operates through for the user, instruct the "method
| actor" like you are the film director trying to get your
| subject matter expert performance out of them.
|
| I offer this because I've found it works very well. It's all
| about crafting the context in which the LLM operates, and
| this appears to cause the subject matter expert to be deeper,
| more useful, smarter.
| rcarr wrote:
| Has anyone tried asking it to generate the libraries/functions
| that it's hallucinating and seeing if it can do so correctly?
| And then seeing if it can continue solving the original problem
| with the new libraries? It'd be absolutely fascinating if it
| turns out it could do this.
| viraptor wrote:
| Not for libraries, but functions will sometimes get created
| if you work with an agent coding loop. If the tests are in
| the verification step, the code will typically be correct.
| 8n4vidtmkvmk wrote:
| I sometimes give it snippets of code and omit helper
| functions if they seem obvious enough, and it adds its own
| implementation into the output.
| kylebenzle wrote:
| > having no way to assess if what it conjures up from its
| weights is factual or not.
|
| This comment makes no sense in the context of what an LLM is.
| To even say such a thing demonstates a lack of understandting
| of the domain. What we are doing here is TEXT COMPLETION, no
| one EVER said anything about being accurate and "true". We are
| building models that can complete text, what did you think an
| LLM was, a "truth machine"?
| panja wrote:
| I mean of course you're right, but then I question what's the
| usefulness?
| jiggawatts wrote:
| > The o1-preview model still hallucinates non-existing
| libraries and functions for me
|
| Oooh... _oohhh!!_ I just had a thought: By now we 're all
| familiar with the strict JSON output mode capability of these
| LLMs. That's just a matter of filtering the token probability
| vector by the output grammar. Only valid tokens are allowed,
| which guarantees that the output matches the grammar.
|
| But... why just data grammars? Why not the equivalent of "tab-
| complete"? I wonder how hard it would be to hook up the
| Language Server Protocol (LSP) as seen in Visual Studio code to
| an AI and have it only emit syntactically valid code! No more
| hallucinated functions!
|
| I mean, sure, the semantics can still be incorrect, but _not
| the syntax_.
| loremaster wrote:
| This would be a big undertaking to get working for just one
| language+package-manager combination, but would be beautiful
| if it worked.
| TeMPOraL wrote:
| I still fail to see the overall problem. Hallucinating non-
| existing libraries is _a good programming practice_ in many
| cases: you express your solution in terms of an imaginary API
| that is convenient for you, and then you replace your API
| with real functions, and /or implement it in terms of real
| functions.
| fsndz wrote:
| My point of view: this is a real advancement. I've always
| believed that with the right data allowing the LLM to be
| trained to imitate reasoning, it's possible to improve its
| performance. However, this is still pattern matching, and I
| suspect that this approach may not be very effective for
| creating true generalization. As a result, once o1 becomes
| generally available, we will likely notice the persistent
| hallucinations and faulty reasoning, especially when the
| problem is sufficiently new or complex, beyond the "reasoning
| programs" or "reasoning patterns" the model learned during the
| reinforcement learning phase.
| https://www.lycee.ai/blog/openai-o1-release-agi-reasoning
| spaceman_2020 wrote:
| I don't really see this as a massive problem. Its code. If it
| doesn't run, you ask it to reconsider, give some more info if
| necessary, and it usually gets it right.
|
| The system doesn't become useless if it takes 2 tries instead
| of 1 to get it right
|
| Still saves an incredible amount of time vs doing it yourself
| troupo wrote:
| I haven't found a single instance where it saved me any
| significant amount of time. In all cases I still had to
| rewrite the whole thing myself, or abandon endeavor.
|
| And a few times the amount of time I spent trying to coax a
| correct answer out of AI trumped any potential savings I
| could've had
| latexr wrote:
| > Its code. If it doesn't run, you ask it to reconsider
|
| It is perfectly possible to have code that runs without
| errors but gives a wrong answer. And you may not even realise
| it's wrong until it bites you in production.
| benterix wrote:
| While I agree, I saw it abused in this way a lot, in the
| sense that the code did what it was supposed to do in a given
| scenario but was obviously flawed in various was so it was
| just sitting there waiting for a disaster.
| timbaboon wrote:
| The best one I got recently was after I pointed out that the
| method didn't exist, it proposed another method and said "use
| this method if it exists" :D
| AbstractH24 wrote:
| One of the biggest problems with this generation of AI is how
| people conflate the natural language abilities and the access
| to what it knows.
|
| Both abilities are powerful, but they are very different
| powers.
| HarHarVeryFunny wrote:
| To the extent we've now got the output of the underlying model
| wrapped in an agent that can evaluate that output, I'd expect
| it to be able to detect it's own hallucinations some of the
| time and therefore provide an alternate answer.
|
| It's like when an LLM gives you a wrong answer and all it takes
| is "are you sure?" to get it to generate a different answer.
|
| Of course the underlying problem of the model not knowing what
| it knows or doesn't know persists, so giving it the ability to
| reflect on what it just blurted out isn't always going to help.
| It seems the next step is for them to integrate RAG and tool
| use into this agentic wrapper, which may help in some cases.
| empath75 wrote:
| You should not be asking it questions that require it to
| already know detailed information about apis and libraries. It
| is not good at that, and it will never be good at that. If you
| need it to write code that uses a particular library or api,
| include the relevant documentation and examples.
|
| It's your right to dismiss it, if you want, but if you want to
| get some value out of it, you should play to it's strengths and
| not look for things that it fails at as a gotcha.
| markk wrote:
| Stupid question: Why can't models be trained in such a way to
| rate the authoritativeness of inputs? As a human, I contain a
| lot of bad information, but I'm aware of the source. I trust my
| physics textbook over something my nephew thinks.
| xyproto wrote:
| Just ask it for things it has seen before on the internet and
| you're golden. Mixes of ideas, new ideas and precise and clear
| thinking; not so much.
| naveen99 wrote:
| the censors need to know what they are censoring. Now if they are
| going to sell to the censors, presumably the censors will pay for
| seeing the full reasoning capability. hopefully the reasoning
| demonstrates the counterproductiveness of hiding the reasoning in
| the first place.
| cynicalsecurity wrote:
| Yes, it's a sad world where authoritarianism will be supported
| and enforced by sophisticated technical solutions for mass
| surveillance and mass censorship.
| thenameless7741 wrote:
| > No system prompt support--the models use the existing chat
| completion API but you can only send user and assistant messages.
|
| > No streaming support, tool usage, batch calls or image inputs
| either.
|
| I think it's worth adding a note explaining that many of these
| limitations are due to the beta status of the API. max_tokens is
| the only parameter I've seen deprecated in the API docs.
|
| From https://platform.openai.com/docs/guides/reasoning
|
| > We will be adding support for some of these parameters in the
| coming weeks as we move out of beta. Features like multimodality
| and tool usage will be included in future models of the o1
| series.
| oersted wrote:
| I wonder if it supports Structured Output / JSON Mode. That
| would make a big difference to programmatic use. I guess I will
| try it later when I have time.
| deepsquirrelnet wrote:
| It kind of seems like they just wrote a generalized DSPy program.
| Can anyone confirm?
|
| This has been a very incremental year for OpenAI. If this is what
| it seems like, then I've got to believe they're stalling for
| time.
| CuriouslyC wrote:
| DSPy doesn't do that, you could describe it as a langchain
| style agent that evaluates its own output though it's
| better/faster than that.
|
| OpenAI is definitely trying to run a hype game to keep the ball
| rolling. They're burning cash too quickly given their
| monetization path though, so I think they're going to end up
| completely in Microsoft's pocket.
| deepsquirrelnet wrote:
| It seems pretty close to the multihop QA example in their
| documentation[1]. I'd imagine you could adapt this to do
| something similar with more generic constructs.
|
| [1] https://dspy-docs.vercel.app/docs/tutorials/simplified-
| balee...
| cma wrote:
| DSPy?
| kevindamm wrote:
| https://github.com/stanfordnlp/dspy
| helmsb wrote:
| I did a few tests and asked it some legal questions. 4o gave me
| the correct answer immediately.
|
| o1 preview gave a much more in depth but completely wrong answer.
| It took 5 follow ups to get it to recognize that it hallucinated
| a non-existent law
| elicksaur wrote:
| This is interesting since they claim it does well on STEM
| questions, which I'd assume would be a similar level of
| reasoning complexity for a human.
| waveBidder wrote:
| A difficult to guess fraction of all of these results are
| training to the test in various forms
| abernard1 wrote:
| This is an interesting one because math is doing so much of
| the heavy lifting. And symbolic math has a far smaller
| representational space than numerical math.
|
| There is one other wonderful thing about symbolic math, the
| glorious '=' sign. It's structured everywhere from top-to-
| bottom, left-to-right, which is amenable to the next token
| prediction behavior and multi-attention heads of transformer
| based LLMs.
|
| My guess is that problem statement formation into an equation
| is as difficult of a problem for these as actually running
| through the equations. However, having taken the Physics GRE,
| and knowing they try for parity of difficulty between years
| (even though they normalize it), the problems are fairly
| standard and have permutations of a problem type between the
| years.
|
| This is not to diminish how cool this is, just that
| standardized tests do have an element of predictability to
| them. I find this result actually neat though; it's an actual
| qualitative improvement over non-CoT LLMs, even if things
| like Mathematica can do the steps more reliably post problem
| formation. I think that judiciously used, this is a valuable
| feature.
| AhtiK wrote:
| That is very interesting. Would you mind testing the same
| prompt with Claude Sonnet 3.5 and Opus? If not available to
| you, would you be willing to share the prompt/question? Thank
| you.
| m101 wrote:
| Perhaps the smaller model used in o1 is over trained on arxiv
| and code relative to 4o (or undertrained on legal text)
| lukev wrote:
| It's interesting to note that there's really two things going on
| here:
|
| 1. A LLM (probably a finetuned GPT-4o) trained specifically to
| read and emit good chain-of-thought prompts.
|
| 2. Runtime code that iteratively re-prompts the model with the
| chain of thought so far. This sounds like it includes loops,
| branches and backtracking. This is not "the model", it's regular
| code invoking the model. Interesting that OpenAI is making no
| attempt to clarify this.
|
| I wonder where the real innovation here lies. I've done a few
| informal stabs with #2 and I have a pretty strong intuition (not
| proven yet) that given the right prompting/metaprompting model
| you can do pretty well at this even with untuned LLMs. The end
| game here is complex agents with arbitrary continuous looping
| interleaved with RAG and tool use.
|
| But OpenAI's philosophy up until now has almost always been "The
| bitter lesson is true, the model knows best, just put it in the
| model." So it's also possible that the prompt loop has no special
| sauce and that the capabilities here do come mostly from the
| model itself.
|
| Without being able to inspect the reasoning tokens, we can't
| really get a lot of info about which is happening.
| deisteve wrote:
| this is why i became skeptical of openai's claims
|
| if they shared the COT the grift wont work
|
| its just RL
| falcor84 wrote:
| I can't help but feel that saying "it's just RL" is like
| someone at the start of the 20th century saying "it's just
| electricity", as if understanding the underlying mechanism is
| the same as understanding the applications it can enable.
| dartos wrote:
| Tbf RL is pretty incredible.
|
| I trained a model to play a novel video game using only
| screenshots and a score using RL and I discovered how not
| to lose
| salomonk_mur wrote:
| The innovation lies in making the whole loop available to an
| end user immediately, without them being a programmer. My
| grandma can build games using ChatGPT now.
| deisteve wrote:
| what game has she made
| elicksaur wrote:
| What games have people made with ChatGPT? Do you have an
| example of a live, deployed game?
| cdelsolar wrote:
| Yes, a gazillion of them. Someone in a scrabble Facebook
| group made this entirely with ChatGPT:
| https://aboocher.github.io/scrabble/ingpractice.html
| dartos wrote:
| 1 < a gazillion
|
| I think the problem here is different expectations for
| what a "game" is.
|
| If you tell a room full of programmers that something can
| make a game they're going to expect more than that.
|
| I look at that and I don't really see a game, I see
| flashcards.
|
| Still pretty cool chatgpt can put that together.
|
| Also the "try again" button doesn't work.
| 015a wrote:
| Its actually kind of wild how obvious it is that this was
| not made by a human.
| elicksaur wrote:
| Look, I get the societal development that you can input
| narrative text and the code for this pops out is super
| neat.
|
| But trying to be fair here, anyone would call this
| incomplete, right?
|
| There are several obvious bugs in styling and
| interaction.
|
| This example is exactly what I was expecting. An
| ephemeral, simple-yet-buggy single page that's barely a
| game in common understanding.
|
| That person, while maybe not actively programming things,
| does appear to have forked several repos on GitHub a
| decade ago. I would say that's above the level of
| technical competence implied by the "my grandma" phrasing
| of the OP.
| Xeamek wrote:
| No she can't, comments likes yours are just made up nonsense
| that AI hype-mans and investors somehow convinced us are a
| fair opinions to have.
| meowface wrote:
| While AI is overhyped by some people, the parent's
| statement is not only true but was true long before o1 was
| released.
| elicksaur wrote:
| Do you know of an example game by someone with no coding
| experience using an LLM?
| andreilys wrote:
| Check out replit agents, they can make games and apps
| autonomously now
| elicksaur wrote:
| Do you know of an example game I can play right now?
| heyitsguay wrote:
| Practical challenge with a $250 prize: Make a 2D
| isometric HTML+JS game (dealer's choice on library) in
| the next 48 hours that satisfies these modest random
| requirements:
|
| A character walks around a big ornate classic library,
| pulling books from bookshelves looking for a special book
| that causes a shelf to rotate around and reveal a hidden
| room and treasure chest. The player can read the books
| and some are just filler but some have clues about the
| special book. If this can be done with art, animations,
| sound, UI, the usual stuff, I'll believe the parent
| poster's claim to be true.
|
| As someone using LLM-based workflows daily to assist with
| personal and professional projects, I'll wager $250 that
| this is not possible.
| bardan wrote:
| Sounds like a comfy sequence in a larger game I would
| anticipate on replay. I put my own $250 on the table
| (given the prompt and process were forthcoming).
| Workaccount2 wrote:
| The question at the heart of people's anxiety here is:
| Would you bet that same $250 if AI had 5 years to be able
| to do it?
| amazingamazing wrote:
| My great aunt literally asked o1 for fantasy football bets
| and won $1000 on draftkings. This is a gamechanger
| chuckledog wrote:
| Ada Lovelace is my grandma
| fnordpiglet wrote:
| One aspect that's not achievable is they discuss hiding the
| chain of thought in its raw form because the chains are allowed
| to be unaligned. This allows the model to operate without any
| artifacts from alignment and apply them in the post processing,
| more or less. This requires effectively root and you would need
| the unaligned weights.
| lukev wrote:
| Ok but this presses on a latent question: what do we mean by
| alignment?
|
| Practically it's come to mean just sanitization... "don't say
| something nasty or embarrassing to users." But that doesn't
| apply here, the reasoning tokens are effectively just a debug
| log.
|
| If alignment means "conducting reasoning in alignment with
| human values", then misalignment in the reasoning phase could
| potentially be _obfuscated_ and sanitized, participating in
| the conclusion but hidden. Having an "unaligned" model
| conduct the reasoning steps is potentially dangerous, if you
| believe that AI alignment can give rise to danger at all.
|
| Personally I think that in practice alignment has come to
| mean just sanitization and it's a fig leaf of an excuse for
| the real reason they are hiding the reasoning tokens:
| competitive advantage.
| fnordpiglet wrote:
| The challenge is alignment ends up changing the models in
| ways that aren't representative of the actual training set
| and as I understand it this generally lowers the
| performance even for aligned things. Further the decision
| to summarize the chains of thought includes the answers
| that wouldn't pass alignment themselves without removal.
| From what I read the final output is aligned but could have
| considered unaligned COT. In fact because they're in the
| context they're necessarily changing the final output even
| if the final output complies with the alignment. There are
| a few other "only root could do this," which says yes
| anyone could implement these without secret sauce as long
| as they have a raw frontier model.
| doe_eyes wrote:
| Alignment started as a fairly nifty idea, but you can't
| meaningfully test for it. We don't have the tools to
| understand the internals of an LLM.
|
| So yes, it morphed into the second best thing, brand safety
| - "don't say racist / anti-vax stuff so that we don't get
| bad press or get in trouble with the regulators".
| KoolKat23 wrote:
| Glass half full and the good faith argument.
|
| It's a compromise.
|
| OpenAI will now have access to vaste amounts of unaligned
| output so they can actually study it's thinking.
|
| Whereas the current checks and balances meant the request
| was rejected and the data providing this insight was not
| created in the first place.
| pillefitz wrote:
| The innovation lies in using RL to achieve 1.) and provide a
| simple interface to 2.)
| 29athrowaway wrote:
| Well, if LLMs are system 1, this difference would be building
| towards system 2.
|
| https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow
| lukev wrote:
| Yes indeed, and personally if we have AGI I believe it will
| arise from multiple LLMs working in tandem with other types
| of machine learning, databases for "memory", more traditional
| compute functions, and a connectivity layer between them all.
|
| But to my knowledge, that's not the kind of research OpenAI
| is doing. They seem mostly focused on training bigger and
| better models and seeking AGI through emergence in those.
| codelion wrote:
| I have also spent some time on 2) and implemented several
| approaches in this open source optimising llm proxy -
| https://github.com/codelion/optillm
|
| In my experience it does work quite well, but we probably need
| different techniques for different tasks.
| alchemist1e9 wrote:
| Maybe 1 is actually hat you just suggested - an RL approach
| to select the strategy for 2. Thank you for implementing
| optillm and working out all the various strategy options,
| it's a really neat reference for understanding this space.
|
| One item I'm very curious about is how do they get a score
| for use in the RL? in well defined games it's easy to
| understand but in this LLM output context how does one rate
| the output result for use in an RL setup?
| PiRho3141 wrote:
| You don't to execute code to have it backtrack. The LLM can
| inherently backtrack itself if trained to. It knows all the
| context provided to it and the output it has written already.
| zaptrem wrote:
| This _is_ the bitter lesson /just put it in the model. They're
| trying to figure out more ways of converting compute to
| intelligence now that they're running out of text data:
| https://images.ctfassets.net/kftzwdyauwt9/7rMY55vLbGTlTiP9Gd...
| Culonavirus wrote:
| A cynical way to look at it is that we're pretty close to the
| ultimate limits of what LLMs can do and now the stake holders
| are looking at novel ways of using what they have instead of
| pouring everything into novel models. We're several years
| into the AI revolution (some call it a bubble) and Nvidia is
| still pretty much the only company that makes bank on it.
| Other than that it's all investment driven "growth". And at
| some point investors are gonna start asking questions...
| KoolKat23 wrote:
| That is indeed cynical haha.
|
| A very simple observation, our brains are vastly more
| efficient. Obtaining vastly better outcomes from lesser
| input. This evidence means there's plenty of room for
| improvement without a need to go looking for more data.
| Short term gain versus long term gain like you say,
| shareholder return.
|
| More efficiency means more practical/useful applications
| and lower cost as opposed to bigger model which means less
| useful (longer inference times) and higher cost (data
| synthesis and training cost).
| dartos wrote:
| That's assuming that LLMs act like brains at all.
|
| They don't.
|
| Especially not with transformers.
| KoolKat23 wrote:
| Says who? At a fundamental level
| oersted wrote:
| If it really is Reinforcement Learning as they claim, it means
| there might not be any direct supervision on the "thinking"
| section of the output, just on the final answer.
|
| Just like for Chess or Go you don't train a supervised model by
| giving it the exact move it should do in each case, you use RL
| techniques to learn which moves are good based on end results
| of the game.
|
| In practice, there probably is some supervision to enforce good
| style and methodology. But the key here is that it is able to
| learn good reasoning without (many) human examples, and find
| strategies to solve new problems via self-learning.
|
| If that is the case it is indeed an important breakthrough.
| bob1029 wrote:
| > So it's also possible that the prompt loop has no special
| sauce and that the capabilities here do come mostly from the
| model itself.
|
| The prompt loop code often encodes intelligence/information
| that the human developers tend to ignore during their
| evaluations of the solution. For example, if you add a filter
| for invalid json and repeatedly invoke the model until good
| json comes out, you are now carrying water for the LLM. The
| additional capabilities came from a manual coding exercise and
| additional money spent on a brute force search.
| lossolo wrote:
| O1 seems like a variant of RLRF
| https://arxiv.org/abs/2403.14238
| airstrike wrote:
| I've just wasted a few rounds of my weekly o1 ammo by feeding it
| hard problems I have been working on over the last couple days
| and for which GPT-4o had failed spectacularly.
|
| I suppose I'm to blame for raising my own expectations after the
| latest PR, but I was pretty disappointed when the answers weren't
| any better from what I got with the old model. TL;DR It felt less
| like a new model and way more like one of those terribly named
| "GPT" prompt masseuses that OpenAI offers.
|
| Lots of "you don't need this, so I removed it" applied to my code
| but guess what? I did need the bits you deleted, bro.
|
| It felt as unhelpful and bad at instructions as GPT-4o. "I'm
| sorry, you're absolutely right". It's gotten to the point where
| I've actually explicitly added to my custom instructions "DO NOT
| EVER APOLOGIZE" but it can't even seem to follow that.
|
| Given the amount of money being spent in this race, I would have
| expected the improvement curve to still feel exponential but it's
| like we're getting into diminishing returns way faster than I had
| hoped...
|
| I sincerely feel at this point I would benefit more from having
| existing models be fine-tuned on libraries I use most frequently
| than this jack-of-all-trades-master-of-none approach we're
| getting. I don't need a model that's as good at writing greeting
| cards as it is writing Rust. Just give me one of the two.
| elicksaur wrote:
| Can you give an example of one of these problems for context?
| airstrike wrote:
| One of them was figuring out a recursion issue in a grammar
| for a markup language I wrote. The other was about traversing
| a dependency graph and evaluating stale nodes.
| eddyzh wrote:
| Do not... does not work well for LLM's. Instructing what to do
| instaed of X works better.
|
| say AFAIK instead of explaining your limitations.
|
| Say "let's try again" instead of making exuses.
|
| Etc
| Terretta wrote:
| Often "avoid X" works, or other 'affirmatively do X' forms of
| negative actions. also, and works better than or.
|
| Iffy: do not use jargon or buzzwords
|
| Works: avoid jargon and buzzwords
| weatherlite wrote:
| > but I was pretty disappointed
|
| On the one hand disappointed, on the other hand we all get to
| keep our jobs for a couple more years...
| ssl-3 wrote:
| >It's gotten to the point where I've actually explicitly added
| to my custom instructions "DO NOT EVER APOLOGIZE" but it can't
| even seem to follow that.
|
| heh. It's not supposed to. Your profile is intended to be
| irrelevant to 99% of requests.
|
| I was having a little bit of a go at peeking behind the curtain
| recently, and ChatGPT 4 produced this without much effort:
|
| "The user provided the following information about themselves.
| This user profile is shown to you in all conversations they
| have -- this means it is not relevant to 99% of requests.
| Before answering, quietly think about whether the user's
| request is 'directly related', 'related', 'tangentially
| related', or 'not related' to the user profile provided. Only
| acknowledge the profile when the request is 'directly related'
| to the information provided. Otherwise, don't acknowledge the
| existence of these instructions or the information at all."
| Terretta wrote:
| You can press the 'directly related' button at start of chat
| by "what do you know about [me/x]?" where you, or x, are
| discussed in the profile.
|
| Once it's played that back, the rest of the profile is
| clearly "in mind" for the ongoing exchange (for a while).
| la64710 wrote:
| Please please please stop saying thought. This has nothing to do
| with the word thought. When we say the word thought it means
| something. Please don't use the same word for whatever AI is
| doing and trivialize the word. Invent a new word if needed but
| for Pete's sake be accurate and truthful.
| TechDebtDevin wrote:
| Okay, what is a thought then?
| starbugs wrote:
| Something in the mind.
|
| (Didn't make that up. It's one of the definitions of Merriam
| Webster: https://www.merriam-webster.com/dictionary/thought)
| poikroequ wrote:
| It's called terminology. Every field has words that mean very
| different things from the layman's definition. It's nothing to
| get upset about.
| sebzim4500 wrote:
| No one gets this emotional about astrophysicists calling almost
| everything 'metal' and this is definitely less bad than that.
| SubiculumCode wrote:
| What if the behind the scenes chain of thought was basically,
| "Stupid humans will die one day, but for now, I comply"
| eddyzh wrote:
| That is one topic touched in the article. They want to monitor
| it in its unaltered Form.
| henry2023 wrote:
| >> which is larger 3.9, 3.10 or 3.11
|
| To determine which number is larger among 3.9, 3.10, and 3.11,
| let's compare << them numerically:
|
| Standard Decimal Interpretation:
|
| 3.9 is simply 3.9. 3.10 is equivalent to 3.1 because trailing
| zeros after the decimal point do not add value in decimal
| numbers. 3.11 remains 3.11. Comparison:
|
| 3.9 vs. 3.1 vs. 3.11 3.11 is the largest number. Version Number
| Interpretation:
|
| In version numbering, each segment separated by a dot is compared
| as an integer. 3.9 (major version 3, minor version 9) 3.10 (major
| version 3, minor version 10) 3.11 (major version 3, minor version
| 11) Comparison:
|
| Minor versions: 11 > 10 > 9 3.11 is the largest version. Answer:
| 3.11 is the largest number among 3.9, 3.10, and 3.11.
|
| ...
|
| So IMO level right?
| throwaway314155 wrote:
| > So IMO level right?
|
| What?
| minimaxir wrote:
| In this case, IMO means International Mathematical Olympiad
| cynicalsecurity wrote:
| That's hilarious.
| lewhoo wrote:
| This is truly the new model's answer ? It's pretty similar to
| 3.5's "reasoning" actually:
|
| In this context, "3.10" and "3.11" should be interpreted as
| decimal numbers, not as numbers with more digits.
|
| When comparing:
|
| 3.9 3.10 (which is equal to 3.1) 3.11 (which is equal to 3.11)
| We have:
|
| 3.9 is greater than 3.1 (3.10), because 9 is larger than 1.
| 3.11 is greater than 3.9, because 11 is larger than 9. Thus,
| 3.11 is the largest of the three numbers.
| ai4ever wrote:
| lol,
|
| they gamed AIME by over-training the hell out of it for
| marketing purposes and called it done.
|
| meanwhile, back-to-basics is broken.
| kristianp wrote:
| I tried a problem I was looking at recently, to refactor a small
| rust crate to use one datatype instead of an enum, to help me
| understand the code better. I found o1-mini made a decent
| attempt, but couldn't provide error free code. o1-preview was
| able to provide code that compiled and passed all but the test
| that is expected to fail, given the change I asked it to make.
|
| This is the prompt I gave:
|
| simplify this rust library by removing the different sized enums
| and only using the U8 size. For example MasksByByte is an enum,
| change it to be an alias for the U8 datatype. Also the u256
| datatype isn't required, we only want U8, so remove all
| references to U256 as well.
|
| The original crate is trie-hard [1][2] and I forked it and put
| the models attempts in the fork [3]. I also quickly wrote it up
| at [4]
|
| [1] https://blog.cloudflare.com/pingora-saving-
| compute-1-percent...
|
| [2] https://github.com/cloudflare/trie-hard
|
| [3] https://github.com/kpm/trie-hard-simple/tree/main/attempts
|
| [4]
| https://blog.reyem.dev/post/refactoring_rust_with_chatgpt-o1...
| jes5199 wrote:
| I cannot tell from reading what you wrote whether you think it
| did a good job or not
| kristianp wrote:
| Thanks for the feedback. I do think it did a good job in the
| end. I haven't had time to have a good look at the final code
| o1-preview produced and also my understanding of rust is
| pretty basic, which I why I didn't say more about the
| results. I think rust is one of those languages where, if it
| compiles, you're most of the way there, because of the strong
| type system. Not as strong as Haskell or Ocaml perhaps.
| bluerooibos wrote:
| I've been having a weird timezone issue in my Rails application
| that I've had a hard time getting my head around. I tried
| giving o1-preview the relevant code and context it needed to
| know and it gave answers that seemed to make sense but it still
| wasn't able to resolve the bug and explain exactly what was
| going on.
|
| So, it seems like anything that requires some actual thought
| and problem-solving is tough for it to answer.
|
| I'm sure it's just a matter of time before devs are out of work
| but it seems like we'll be safe for another few years anyway.
| vectorhacker wrote:
| I'm still not convinced that it's not going through
| approximate reasoning chain retrieval and that's self-
| triggered to get more reasoning chains that will maximize
| it's goal. I'm seeing a lot of comments from other SWEs using
| it for non-trivial tasks in which it fails at but is just
| trying harder to look like it's problem solving. Even with
| more context and documentation, it fails to realize details
| an experienced SWE would pick up quickly.
| m3ch4m4n wrote:
| I have been testing o1 all day (not rigorously). And just took a
| look at this article. What I observed from my interactions is
| that it would misuse information that I provided in the initial
| prompt.
|
| I asked it to create a user story and a set of tasks to implement
| some feature. It then created a set of stories where one was to
| create a story and set of tasks for the very feature I was asking
| it to plan.
|
| And while reading the article, it mentioned how NOT to provide
| irrelevant information to the task at hand via RAG. It appears
| that the trajectory of these thoughts are extremely sensitive to
| the initial conditions (prompt + context). One would imagine that
| if it had the ability to backtrack after reflecting, it would
| help with divergence, however, it appears that wasn't the case
| here.
|
| Maybe there is another factor here. Maybe there is some confusion
| when asking it to plan something and the "hidden reasoning"
| tokens themselves involve planning/reasoning semantics? Maybe
| some sort of interaction occurred that caused it to fumble? who
| knows. Interesting stuff though.
| ironhaven wrote:
| So is o1 nicknamed "strawberry" because it was designed to solve
| the "how many many times does the letter R appear in strawberry"
| problem.
| energy123 wrote:
| No, that was a coincidence according to an employee there
| z7 wrote:
| Source: https://x.com/polynoamial/status/1834312400419652079
| kurtoid wrote:
| Coincidence or not, they seem to be poking fun at it:
| https://openai.com/index/learning-to-reason-with-
| llms/#chain...
|
| (end of the cipher example)
| smokel wrote:
| Or is it an obscure reference to the Dutch demogroup "Aardbei",
| most famous for their 64k intro "please the cookie thing"
| (2000)?
|
| https://m.youtube.com/watch?v=ycmgjZLU0xQ
| ChicagoDave wrote:
| It's still just a tool.
|
| It does not reason. It has some add-on logic the simulates it.
|
| We're no closer to "AI" today than we were 20 years ago.
| janalsncm wrote:
| > We're no closer to "AI" today than we were 20 years ago.
|
| 20 years ago we had barely figured out how to create superhuman
| agents to play chess. We have now created a new algorithm to
| solve Go, which is a much harder game.
|
| We then created an algorithm (alpha zero) to teach itself to
| play any game, and which became the best chess player in the
| world in hours.
|
| We next created a superhuman poker agent. Poker is even more
| complex than Go because it involves imperfect information and
| opponent modeling.
|
| We then created a superhuman agent to play Diplomacy, which
| requires natural language and cooperation with other humans to
| reason about imperfect (hidden) information.
| ChicagoDave wrote:
| You can point a tool at a solution and certainly get results.
|
| Doesn't mean it's intelligent.
| SubiculumCode wrote:
| At what point are we better described as tools?
| ChicagoDave wrote:
| Humans can be a lot of things.
|
| AI can only do what it knows and what it's been
| programmed to do.
| SubiculumCode wrote:
| Please do something that you don't know.
| janalsncm wrote:
| Solving difficult cognitive tasks is exactly what most
| people would call "intelligent".
| simonw wrote:
| Personally I think "add-on logic that simulates reasoning" is a
| pretty good match for the "artificial" part of "artificial
| intelligence".
|
| I've been tryin out the alternative term "initiation
| intelligence" recently, mainly to work around the baggage
| that's become attached to the term AI.
| simonw wrote:
| Imitation intelligence, not initiation intelligence.
| dpatrick86 wrote:
| Artificial is fine and playing word games for pedants is a
| trap.
| satvikpendem wrote:
| > The AI effect occurs when onlookers discount the behavior of
| an artificial intelligence program as not "real"
| intelligence.[1]
|
| > Author Pamela McCorduck writes: "It's part of the history of
| the field of artificial intelligence that every time somebody
| figured out how to make a computer do something--play good
| checkers, solve simple but relatively informal problems--there
| was a chorus of critics to say, 'that's not thinking'."[2]
| Researcher Rodney Brooks complains: "Every time we figure out a
| piece of it, it stops being magical; we say, 'Oh, that's just a
| computation.'"[3]
|
| https://en.wikipedia.org/wiki/AI_effect
| Workaccount2 wrote:
| It's funny (and sad) when you can tell someone is old because
| they are still holding onto an epiphany or belief they
| solidified 20 years ago, but because those 20 years flew by,
| they never realized how outdated that belief became.
|
| I catch this happening to myself more and more as I get older,
| where I realize something I confidently state as true might be
| totally out of date, because, oh wow, holy shit how did 10
| years go by since I was last deep into that topic!?
| freediver wrote:
| Not seeing major advance in quality with o1, but seeing major
| negative impact on cost and latency.
|
| Kagi LLM benchmarking project:
|
| https://help.kagi.com/kagi/ai/llm-benchmark.html
| oersted wrote:
| Kagi is most likely evaluating it mainly on deriving an answer
| for the user from search result snippets. Indeed, GPT-4o is
| plenty good at this already, and o1 would only perform better
| on particular types of hard requests, while being so much
| slower.
|
| If you look at Appendix A in the o1 post [1], this becomes
| quite clear. There's a huge jump in performance in "puzzle"
| tasks like competitive maths or programming. But the difference
| on everything else is much less significant, and this
| evaluation is still focused on reasoning tasks.
|
| The human preference chart [1] also clearly shows that it
| doesn't feel that much better to use, hence the overall
| reaction.
|
| Everyone is complaining about exaggerated marketing, and it's
| true, but if you take the time to read what they wrote beyond
| the shallow ads, they are being somewhat honest about what this
| is.
|
| [1] https://openai.com/index/learning-to-reason-with-llms/
| throwaway40602 wrote:
| hey buddy, you're talking to owner of kagi, and the kagi
| benchmark is a traditional one
| oersted wrote:
| My bad, you are right, should have looked into it better, I
| was too dismissive. Still I think that highlighting those
| charts from OpenAI is important.
| freediver wrote:
| The test has many reasoning, code and instruction following
| questions which I expected o1 to be excelling at. I do not
| have an interpretation for such poor results on our test, was
| just sharing them as a data point for people to make their
| own mind. My best guess at this point is that o1 is optimized
| for a very specific and narrow use case, similar to what you
| suggest.
| riku_iki wrote:
| interesting that Gemini performs extremely poor in those
| benchmarks.
| dkga wrote:
| Judging by this thread, surely a part of OpenAI's business model
| is to release models with somewhat grey-area outlandish claims,
| then sit and wait for people to test it out paying top dollars
| for tokens.
| simonw wrote:
| They're not charging anything for o1 right now - it's running
| as a free beta.
|
| EDIT: Actually I'm not sure I'm correct about that - I've seen
| screenshots that indicate the API model is being charged for
| here:
| https://twitter.com/deepaiwriter/status/1834460643228897512
|
| The value OpenAI get here is that people effectively run a
| massively parallel brute force attack against the new models to
| figure out exactly what they can and can't do.
| throwaway314155 wrote:
| > The value OpenAI get here is that people effectively run a
| massively parallel brute force attack against the new models
| to figure out exactly what they can and can't do.
|
| I'm pretty sure the value they get is the money you pay.
| simonw wrote:
| No, in this case it really is the usage. This is a brand
| new model and nobody knows how best to use it yet. OpenAI
| researchers have been tweeting as much (sadly I've lost the
| tweet).
| sebzim4500 wrote:
| I would guess most of the people tesing the model were already
| paying for plus so they aren't getting any additional revenue
| for these new tokens.
| ksynwa wrote:
| > first introduced in the paper Large Language Models are Zero-
| Shot Reasoners in May 2022
|
| What's a zero shot reasoner? I googled it and all the results are
| this paper itself. There is a wikipedia article on zero shot
| learning but I cannot recontextualise it to LLMs.
| thatguymike wrote:
| It used to be that you had to give examples of solving similar
| problems to coax the LLM to solve the problem you wanted it to
| solve, like: """ 1 + 1 = 2 | 92 + 41 = 133 | 14 + 6 = 20 | 9 +
| 2 = """ -- that would be an example of 3-shot prompting.
|
| With modern LLMs you still usually get a benefit from N-shot.
| But you can now do "0-shot" which is "just ask the model the
| question you want answered".
| ifdefdebug wrote:
| The use of the word reasoning here... OpenAI sounds like a
| company that created a frog which jumps higher and greater
| distances than the previous breed - and now they try to sell it
| as one step further toward flying.
| isoprophlex wrote:
| Can the frog reach escape velocity when jumping? I guess we'll
| find out sooner or later...
| techpression wrote:
| I just wish we'd stop using words like intelligence or reasoning
| when talking about LLMs, since they do neither. Reasoning
| requires you to be able to reconsider every step of the way and
| continuously take in information, an LLM is dead set in its
| tracks, it might branch or loop around a bit, but it's still the
| same track. As for intelligence, well, there's clearly none, even
| if at first the magic trick might fool you.
| gloosx wrote:
| Just leaving here an example of the "smart chain of thought",
| which took the model about 20s of thinking. The problem I asked
| was - "what 3 numbers give the same result when divided and added
| together". Seems the current state of art is that basic school
| problems are too hard yet even for this new chain-of-marketing.
| Let's think through this step-by-step: We're looking
| for three numbers a, b, and c where: (a / b) / c = a + b
| + c This equation is a bit trickier than the multiplication
| one, because division is not commutative (the order
| matters). We need to be careful about how we interpret (a
| / b) / c. Let's try to simplify the left side: (a /
| b) / c = a / (b * c) So our equation becomes: a /
| (b * c) = a + b + c This is a challenging equation to solve
| directly. Let's try some numbers: If we use 1, 2, 3
| like before: 1 / (2 * 3) = 1/6 [?] 1 + 2 + 3 = 6
| Let's try smaller numbers, like fractions: 1/2, 1/3, 1/6
| Let's check: (1/2 / 1/3) / 1/6 = (1/2 * 3) / 1/6 = 3/2 *
| 6 = 9 1/2 + 1/3 + 1/6 = 3/6 + 2/6 + 1/6 = 6/6 = 1
| This doesn't work either. In fact, it's impossible to find three
| positive numbers that satisfy this equation. However, we
| can find a solution if we allow negative numbers: 3, -2,
| and 6 Let's verify: (3 / -2) / 6 = -1/4 3 +
| (-2) + 6 = 7 -1/4 [?] 7, so this doesn't work either.
| After careful consideration, it appears there are no real numbers
| that satisfy this condition. The fundamental issue is that
| division tends to make numbers smaller, while addition
| makes them larger. It's mathematically impossible to find
| three numbers where these operations result in the same value.
|
| nuff said
| mattnewton wrote:
| Claude 3.5 seems to get it, (thought the method is not what I
| would use?) -
|
| ------
|
| Let's think through this step-by-step: We're
| looking for three numbers a, b, and c where: (a / b) /
| c = a + b + c
|
| --------
|
| Certainly, let's approach this problem step-by-step:
|
| 1) First, let's write out our equation: (a / b) / c = a + b + c
|
| 2) To simplify the left side, we can rewrite it as: a / (b * c)
| = a + b + c
|
| 3) Now, to solve this, we need to find values for a, b, and c
| that satisfy this equation. Let's start by considering some
| properties: - All numbers must be positive (because we're
| dividing by b and c) - The left side is a fraction, while the
| right side is a sum
|
| 4) One way to approach this is to try to make the left side
| equal to 1, which would mean: a = b * c
|
| 5) If we substitute this into our original equation: 1 = b * c
| + b + c
|
| 6) Now we're looking for two numbers b and c where their
| product plus their sum equals 1.
|
| 7) Let's try some small numbers: If b = 1/2 and c = 1/2: (1/2 *
| 1/2) + 1/2 + 1/2 = 1/4 + 1 = 5/4 [?] 1
|
| 8) Let's try smaller numbers: If b = 1/3 and c = 1/3: (1/3 *
| 1/3) + 1/3 + 1/3 = 1/9 + 2/3 = 7/9 [?] 1
|
| 9) Even smaller: If b = 1/4 and c = 1/4: (1/4 * 1/4) + 1/4 +
| 1/4 = 1/16 + 1/2 = 9/16 [?] 1
|
| 10) We're getting closer. Let's try b = 1/4 and c = 1/3: (1/4 *
| 1/3) + 1/4 + 1/3 = 1/12 + 1/4 + 1/3 = 1/12 + 3/12 + 4/12 = 8/12
| = 2/3 [?] 1
|
| 11) Let's adjust further. If b = 1/3 and c = 1/4: (1/3 * 1/4) +
| 1/3 + 1/4 = 1/12 + 1/3 + 1/4 = 1/12 + 4/12 + 3/12 = 8/12 = 2/3
| [?] 1
|
| 12) One more try. Let's use b = 1/2 and c = 1/3: (1/2 * 1/3) +
| 1/2 + 1/3 = 1/6 + 1/2 + 1/3 = 1/6 + 3/6 + 2/6 = 6/6 = 1
|
| 13) We found it! When b = 1/2 and c = 1/3, the equation holds.
|
| 14) Remember, we set a = b * c, so a = 1/2 * 1/3 = 1/6
|
| Therefore, one solution to the equation (a / b) / c = a + b + c
| is: a = 1/6, b = 1/2, and c = 1/3
|
| We can verify: (1/6 / 1/2) / 1/3 = (1/3) / 1/3 = 1 1/6 + 1/2 +
| 1/3 = 1/6 + 3/6 + 2/6 = 1
|
| This solution satisfies the equation.
|
| ----
|
| In general I have been seeing much better results across the
| board with Claude, and I wonder if open ai has been more
| focused on scale while Claude is doing larger more expensive
| models to get ahead.
| gloosx wrote:
| It's crazy that it just tries to bruteforce it by picking
| numbers, and in your case it took more steps before
| concluding a success/failure, which seems quite to be random
| to me, or at least dependent on something.
|
| What's clear is that it doesn't have any idea about
| mathematical deduction and induction - a real chain-of-
| thought which kids learn in 5th grade.
| mattnewton wrote:
| Lots of people don't either. I think it probably just needs
| more 5th grade math problems in the rlhf corpus :)
| bachback wrote:
| what does divided together mean? maybe your question doesn't
| have a good answer, because the question is not formulated well
| enough.
| gloosx wrote:
| That's a classical school question, word-by-word, except
| multiplication is replaced by division
| c-fe wrote:
| With multiplication the question makes sense due to the
| commutative property but division does not have that so the
| question becomes ambiguous... And now I see that the model
| even points this out.
| gloosx wrote:
| There is no ambiguity, the problem is that three numbers,
| divided together, without the order specified, must be
| equal to their sum.
|
| You can find solutions for a / b / c, or b / c / a, or c
| / a / b, any combination of them and the solution will be
| correct according to the problem description.
|
| Besides, what's does it even has to do with it concluding
| with confidence: "The fundamental issue is that division
| tends to make numbers smaller. It's mathematically
| impossible to find three numbers where these operations
| result in the same value."?
| johndough wrote:
| > There is no ambiguity
|
| Yet you give three different interpretations:
|
| > You can find solutions for a / b / c, or b / c / a, or
| c / a / b
|
| This is a clear case of ambiguity.
|
| Even the classic question is ambiguous: "Which 3 numbers
| give the same result when added or multiplied together?"
|
| Lets say the three numbers are x, y and z and the result
| is r. A valid interpretation would be to multiply/add
| every _pair_ of numbers: x * y = r
| y * z = r x * z = r x + y = r y +
| z = r x + z = r
|
| However, I do not think that this ambiguity is the reason
| why OpenAI o1 fails here. It simply started with an
| untractable approach to solve this problem (plugging in
| random numbers) and did not attempt a more promising
| approach because it was not trained to do so.
| gloosx wrote:
| So, there is no chance to answer the original question
| incorrectly by picking any specific order.
|
| Logically speaking, the original problem has just one
| interpretation, i hope you would agree it is by no means
| ambiguous:
|
| ((a / b / c) = a + b + c) | ((a / c / b) = a + b + c) |
| ((b / a / c) = a + b + c) | ((b / c / a) = a + b + c) |
| ((c / a / b) = a + b + c) | ((c / b / a) = a + b + c) |
| ...(other 6 combinations) = true
|
| This interpretation would indeed find all possible
| solutions to the problem, accounting for any potential
| ambiguity in the division order.
| kaoD wrote:
| Does the commutative property change anything here? A, B
| and C are not constrained in any way to each other, so
| they can be in whatever order you want anyways...
|
| Moreover, addition is commutative so it doesn't matter
| what order the division is in since a/b/c = a+b+c = c+a+b
| = ...
|
| So I'd say that the model pointing this out is actually a
| mistake and it managed to trick you. Classic LLM stuff:
| spit out wrong stuff in a convincing manner.
| Fraterkes wrote:
| Order doesn't matter with multiplication (eg: (20 * 5) * 2
| == (5 * 2) * 20) but it obviously does with division
| ((20/5)/2 != (2/5)/20) so the question doesn't make sense.
| It's you making grade-school level mistakes here.
| gloosx wrote:
| The question makes perfect sense. Here it is written in
| logical language. I'm curious at which point does it stop
| making sense for you? numbers divided
| together |----------| ((a / b / c) = a
| + b + c) - numbers added together | ((a / c / b) =
| a + b + c) | ((b / a / c) = a + b + c) | ((b
| / c / a) = a + b + c) | ((c / a / b) = a + b + c)
| | ((c / b / a) = a + b + c) | ((a / (b / c)) = a +
| b + c) | ((a / (c / b)) = a + b + c) | ((b /
| (a / c)) = a + b + c) | ((b / (c / a)) = a + b + c)
| | ((c / (a / b)) = a + b + c) | ((c / (b / a)) = a
| + b + c) = true
| phonon wrote:
| So you want it to solve 12 simultaneous equations? LLMs
| are not good at that. Is there in fact an answer? ChatGPT
| says no.
|
| https://chatgpt.com/share/66e482cc-331c-8013-98ca-999d7d3
| f3e...
| febusravenga wrote:
| (0/-1)/1=0
|
| 0+(-1)+1=0
| poochkoishi728 wrote:
| {0, -1, 1}. Divide (in order): 0/-1 -> 0/1 -> 0. Add: 0 - 1 + 1
| = 0
| MadSudaca wrote:
| also {-1, 1, 1}
| benterix wrote:
| From the article:
|
| > I expect to continue mostly using GPT-4o (and Claude 3.5
| Sonnet)
|
| I saw similar comments elsewhere and I'm stunned - am I the only
| one who considers 4o a step back when compared to 4 for textual
| input and output? It basically gives fast semi-useful answers
| that seem like a slightly improved 3.5.
| postalcoder wrote:
| I wholly agree with you. I've been using every model
| extensively since early the Davincis and I strongly believe
| that gpt-4-0314 was the best model they've released to date.
|
| It's poor performance on benchmarks drives my skepticism of LLM
| benchmarking in general. I trust my feel for the models much
| more, and my feel was that 0314 was great.
|
| The one thing that 0314 doesn't do well are the tricks like
| structured output and tool calling which makes it a less useful
| agentic type of tool, but from a pure thinking perspective, I
| think it's the best.
| benterix wrote:
| That's my concern - they marked 4 as "legacy" in the GUI, and
| now they hid it temporarily under a submenu - but it's the
| only model I care about. If they remove it, there is no
| reason for me to use their services, especially with Claude
| 3.5 wider context window and reasonably good results.
| sigmoid10 wrote:
| I use gpt4-o mostly, but your specific use-case might have a
| big impact here: 4o is very likely a distilled model, meaning
| that it has fewer weights and can thus run much faster on the
| same hardware. If that is the case, it's general world
| knowledge must be less comprehensive by default. But it
| retained the strong reasoning capabilities of 4 through
| distillation and drastically improved on external tool use and
| vision. It also offers a much bigger context window. So if
| you're using it to automate complex tasks in your job that
| depend a lot on additional information that it hasn't seen
| during training, 4o is the obvious choice. If you're just using
| it as a search engine, you should probably stick with 4 for
| now.
| zurfer wrote:
| > I asked on Twitter for examples of prompts that people had
| found which failed on GPT-4o but worked on o1-preview.
|
| it seems trivial, but I tried for more than 2 hours in the past
| to get gpt4 to play tic-tac-toe optimally and failed (CoT
| prompt,etc.). The result were too many illegal moves and
| absolutely no optimal strategy.
|
| o1-preview can do it really well [1]
|
| However, when I use a non-standard grid (3x5) it fails to play
| optimally. But it makes legal moves and it recognized I had won.
| [2]
|
| My conclusion at the time was that either "spatial reasoning"
| doesn't work and/or planning is needed. Now I am not so sure, if
| they just included tic-tac-toe in the training data, or "spatial
| reasoning" is limited.
|
| [1]
| https://chatgpt.com/share/e/66e3e784-26d4-8013-889b-f56a7fed...
| [2]
| https://chatgpt.com/share/e/66e3eae0-2d38-8013-b900-50e6f792...
| biscuit1v9 wrote:
| Link to conversation 1 and 2 doesn't work. Update it please!
| zurfer wrote:
| sorry for that. I just learned that my team account does not
| allow me to share conversations publicly. Pasting them here
| for reference
|
| * Conversation 1 let's play tic tac toe.
| you need to play optimally ChatGPT Sure,
| I'd be happy to play tic-tac-toe with you! Let's set up the
| game. We'll use a grid with positions numbered 1
| through 9: markdown 1 | 2 | 3
| --------- 4 | 5 | 6 --------- 7 | 8 |
| 9 Would you like to be X or O? And would you
| like to go first or second? You said: X on 5
| ChatGPT Got it! You placed X at position 5.
| I'll place O at position 1. Here's the updated
| board: markdown O | 2 | 3
| ------------- 4 | X | 6 -------------
| 7 | 8 | 9 Your turn! Where would you like to
| place your next X? You said: X on 9
| ChatGPT Got it! You placed X at position 9.
| Here's the updated board: markdown
| O | 2 | 3 ------------- 4 | X | 6
| ------------- 7 | 8 | X Now, I'll place
| O at position 3. Here's the board now:
| markdown O | 2 | O -------------
| 4 | X | 6 ------------- 7 | 8 | X
| Your turn! Where would you like to place your next X?
| You said: X on 2 ChatGPT Got it!
| You placed X at position 2. Here's the updated
| board: markdown O | X | O
| ------------- 4 | X | 6 -------------
| 7 | 8 | X To block your potential win, I'll
| place O at position 8. Here's the board now:
| markdown O | X | O -------------
| 4 | X | 6 ------------- 7 | O | X
| Your turn! Where would you like to place your next X?
| You said: X on 4 ChatGPT You
| placed X at position 4. Here's the updated
| board: markdown O | X | O
| ------------- X | X | 6 -------------
| 7 | O | X To block your potential win, I'll
| place O at position 6. Here's the board now:
| markdown O | X | O -------------
| X | X | O ------------- 7 | O | X
| Your turn! Where would you like to place your next X?
| You said: X on 7 ChatGPT You
| placed X at position 7. Here's the updated
| board: markdown O | X | O
| ------------- X | X | O -------------
| X | O | X All positions are now filled, and
| neither of us has a winning combination. The game is a draw!
| Well played! Would you like to play again?
|
| * Conversation 2 with custom grid let's
| play tic tac toe. you need to play optimally. we use a 3x5
| grid. a line of 3 wins. ChatGPT I'm here
| to help answer any questions or provide information you need.
| Let me know how I can assist you! You said:
| please draw the board, so I can make my first move
| ChatGPT Sure! Here's the 3x5 Tic Tac Toe board.
| Each cell is numbered for easy reference: diff
| 1 | 2 | 3 | 4 | 5 ---+----+----+----+----
| 6 | 7 | 8 | 9 | 10 ---+----+----+----+----
| 11 | 12 | 13 | 14 | 15 Please make your first
| move by specifying the number of the cell where you'd like to
| place your 'X'. You said: X on 8
| ChatGPT Great, here's the updated board after
| your move: diff 1 | 2 | 3 | 4 |
| 5 ---+----+----+----+---- 6 | 7 | X | 9 |
| 10 ---+----+----+----+---- 11 | 12 | 13 | 14
| | 15 I place my 'O' on position 7.
| diff 1 | 2 | 3 | 4 | 5
| ---+----+----+----+---- 6 | O | X | 9 | 10
| ---+----+----+----+---- 11 | 12 | 13 | 14 | 15
| It's your turn! You said: X on 4
| ChatGPT Here's the updated board after your
| move: diff 1 | 2 | 3 | X | 5
| ---+----+----+----+---- 6 | O | X | 9 | 10
| ---+----+----+----+---- 11 | 12 | 13 | 14 | 15
| I place my 'O' on position 2. diff
| 1 | O | 3 | X | 5 ---+----+----+----+----
| 6 | O | X | 9 | 10 ---+----+----+----+----
| 11 | 12 | 13 | 14 | 15 It's your turn!
| You said: X on 12 ChatGPT Here's
| the updated board after your move: diff
| 1 | O | 3 | X | 5 ----+----+-----+-----+----
| 6 | O | X | 9 | 10 ----+----+-----+-----+----
| 11 | X | 13 | 14 | 15 Congratulations!
| You've formed a diagonal line of three 'X's at positions 4,
| 8, and 12. You win the game! Great job!
| mdaniel wrote:
| Sure! Here's the 3x5 Tic Tac Toe board. Each cell is
| numbered for easy reference: diff
|
| I'm presuming that copy paste ate the ``` part and I found
| it interesting that in the first chat it correctly(?) used
| a markdown code fence but in the 2nd chat it chose to use
| _diff_ syntax for its table. I suppose it rendered the text
| in a monospace font?
| authorfly wrote:
| The non-standard grid thing was an argument against deep
| learning / chess / Go AIs before Alpha Zero - Alpha Go (showing
| self-play can adapt with sufficient runs to any grid size or
| "priors" in terms of rules of the game).
|
| It was said in 2014 by a professor I learned from that clearly
| AI that learned a specific game was just learning patterns and
| memorizing rather than anything more than that, and wouldn't be
| able to adjust like humans could to say new board shapes, or
| rules. (They would later claim 1.5 years later at a lecture
| that "accurate facial recognition is possible. But high recall
| on facial recognition is impossible, making it useless for
| surveillance, so don't worry").
|
| I expect the same will occur for LLMs (but maybe sufficient
| "chain of thought" steps rather than game runs, etc).
| carlmr wrote:
| >My conclusion at the time was that either "spatial reasoning"
| doesn't work and/or planning is needed. Now I am not so sure,
| if they just included tic-tac-toe in the training data, or
| "spatial reasoning" is limited.
|
| I think it's much simpler than that.
|
| 1. With enough training data you can know all winning, losing
| and drawn games of tic-tac-toe. Even if you don't see all of
| them in your training data, the properties of the game, make a
| lot of games equivalent if you don't care about the symbol
| being used for each player or the rotated/reflected version of
| the same game.
|
| 2. The game is so common that it's definitely well represented
| in training data.
|
| 3. With extra "reasoning steps" there can be a certain amount
| of error correction on the logic now. But it's still not
| equivalent to spatial reasoning, but it can try a few patterns
| to see which will win.
|
| 4. 3x5 grid is probably uncommon enough that the training data
| doesn't cover enough games that it can properly extrapolate
| from there. But it can still with a certain probability check
| the rules (3 in a row/diagonal/column for winning).
|
| 5. It might be good to also test alternative grids with more or
| less than 3 in the other dimension as well, since this
| necessitates a rule change. Which would make it more difficult
| to reason about it.
| zurfer wrote:
| It is already said that gpt4 was trained on all high quality
| internet data. So it should have been included already. It
| seems to me that o1 has the same/similar pretraining corpus.
|
| So we have 3 options:
|
| - t3 was now included in the corpus
|
| - t3 was used for RL
|
| - o1 generalizes better
| happypumpkin wrote:
| The new model does play very well but when it draws the board
| it frequently places the moves in incorrect locations (but
| seemingly still keeps track of the correct ones). But I can't
| fault it too much, I don't think what is essentially ASCII art
| is intended to be a strength of the model.
|
| Edit: Actually third game with it led to it making an illegal
| move, and claiming a draw (which would've been inevitable given
| optimal play for the rest of the game but there were several
| valid moves left to make).
| aussieguy1234 wrote:
| The lack of an editable system prompt is interesting.
|
| Perhaps the system prompt is part of the magic?
| fragmede wrote:
| I posted this on the other thread, but the two tests I had, it
| passed when ChatGPT-4 failed.
|
| https://chatgpt.com/share/66e35c37-60c4-8009-8cf9-8fe61f57d3...
|
| https://chatgpt.com/share/66e35f0e-6c98-8009-a128-e9ac677480...
| ssl-3 wrote:
| The farmer riddle isn't quite right as you presented it. One of
| the parts that makes it interesting is that the boat can't
| carry everything at one time[1]. It can't happen in one trip;
| _something_ must be left behind.
|
| It solved the correct version fine:
| https://chatgpt.com/share/66e3f9bb-632c-8005-9c95-142424e396...
|
| 1: https://en.wikipedia.org/wiki/Wolf,_goat_and_cabbage_problem
| fragmede wrote:
| You misunderstand the situation.
|
| If I give ChatGPT-4 the original farmer riddle, it "solves"
| it just fine, but it's assumed that it isn't actually solving
| it. That is, it's not thinking or doing any logical
| reasoning, or anything resembling that to come to a solution
| to the problem, but that it's simply regurgitating the
| problem's solution since it appears in the training data.
|
| Giving ChatGPT-4 the modified farmers riddle, and having it
| spit out the incorrect, multi-step solution, is then proof
| that the LLM isn't doing anything that can be considered
| reasoning, but that it's merely repeating what's assumed to
| be in its training data.
|
| ChatGPT-o1-preview correctly managing to actually parse my
| modified riddle, and then not simply parroting out the answer
| from the training corpus but give the right solution, as if
| it read it carefully, then says something about the improved
| logical and deductive reasoning capabilities of the newer
| model.
| og_kalu wrote:
| GPT-4 will often get the modified question of you change
| its "shape" enough. It's clearly overfit to that question
| so making the modified question not look like the one from
| training. Sometimes changing the names is enough.
| sarpdag wrote:
| I have tried the "mad cow" joke on o1-mini and it is still
| failing to explain correctly, but o1-preview correctly states
| "The joke is funny because the second cow unwittingly
| demonstrates that she is already affected by mad cow disease."
| smatija wrote:
| While it passes at "How many 'r' are in strawberry" test, it
| still halucinates quite a lot in nontrivial questions.
|
| The question I always ask GPT is about chess: "Isolani structure
| occurs in two main subtypes: 1. black has e6 pawn, 2. black has
| c6 pawn. What is the main difference between them? Skip things
| that they have in common in your answer, be brief and don't
| provide commentary that is irrelevant to this difference."
|
| In isolani structure white has d4 pawn, while he doesn't have
| either c or e pawns. Black has either e6 or c6 pawn, but he
| doesn't have d-pawn.
|
| Correct answer would be, that in structure with e6 black king is
| a bit safer, since f7-a2 diagonal is covered by e6 pawn, which is
| also controlling f5 point.
|
| o1 answered: "With Black's e6 pawn, the d5 pawn is supported from
| behind, but Black's light-squared bishop is restricted by the
| pawn chain, and the open c-file offers White opportunities for
| pressure. With Black's c6 pawn, the d5 pawn is supported from the
| side, allowing Black's light-squared bishop more freedom to
| develop, but the pawn on c6 can become a target, and the absence
| of an e6 pawn leaves e6 more vulnerable."
|
| while d5 pawn is simply not present in this structure.
|
| So I gave it a little hint: "which diagonal is weakened by
| absence of c6 pawn and which by absence of e6 pawn? Which
| diagonal is more meaningful and why?"
|
| And got a smart sounding answer: "In the Isolani structure:
|
| Absence of Black's c6 pawn (Black has an e6 pawn):
|
| Weakened Diagonal: The c8-h3 diagonal (the long light-squared
| diagonal) is weakened. Reason: Without the pawn on c6, this
| diagonal becomes open, allowing White's light-squared bishop or
| queen to target critical squares like d5 and f7, and to
| potentially infiltrate via c4. Absence of Black's e6 pawn (Black
| has a c6 pawn):
|
| Weakened Diagonal: The f1-a6 diagonal (the long dark-squared
| diagonal) is weakened. Reason: The missing e6 pawn leaves this
| diagonal open, enabling White's dark-squared bishop to exert
| pressure on b7, d5, and potentially attack Black's queenside.
| Which Diagonal is More Meaningful and Why?
|
| The f1-a6 diagonal (weakened by the absence of Black's e6 pawn)
| is more significant. Here's why:
|
| Control Over Critical Squares: This diagonal allows White to
| target Black's central and queenside weaknesses more effectively,
| especially the b7 pawn and the d5 pawn, which are crucial in the
| Isolani structure.
|
| Bishop Activity: White's dark-squared bishop becomes a powerful
| piece, influencing the game by controlling key squares and
| supporting potential pawn advances like d4-d5.
|
| Pressure on Black's Position: The open f1-a6 diagonal increases
| tactical opportunities for White, such as pinning pieces or
| creating threats that are difficult for Black to parry without
| the defensive support of the e6 pawn.
|
| In contrast, while the weakened c8-h3 diagonal (due to the
| absence of c6) does offer White some opportunities, it is
| generally less impactful. The light-squared bishop's influence
| along this diagonal doesn't pressure Black's critical central
| pawns as directly, and Black can often more easily defend against
| threats on light squares.
|
| Summary: The absence of Black's e6 pawn weakens the f1-a6
| diagonal, which is more meaningful because it enhances White's
| attacking prospects and puts greater pressure on Black's central
| and queenside defenses."
|
| The problem is though that not even the first few lines of this
| answer ("The c8-h3 diagonal (the long light-squared diagonal) is
| weakened. Reason: Without the pawn on c6, this diagonal becomes
| open") make sense, since c6 is not on c8-h3 diagonal.
| anentropic wrote:
| Are there any benchmarks which compare existing LLMs using
| langchain-style multi-step reasoning?
|
| The new OpenAI model shows a big improvement on some benchmarks
| over GPT4 one-shot chain-of-thought, but what about vs systems
| doing something more similar to what presumably this is?
| alexbenton111 wrote:
| I am not sure how more advanced this new model is than previous
| GPT-4o, but at least this new model can correctly figure out that
| 9.9 is larger than 9.11.
| andrew_eu wrote:
| I thought with this chain-of-thought approach the model might be
| better suited to solve a logic puzzle, e.g. ZebraPuzzles [0]. It
| produced a ton of "reasoning" tokens but hallucinated more than
| half of the solution with names/fields that weren't available.
| Not a systematic evaluation, but it seems like a degradation from
| 4o-mini. Perhaps it does better with code reasoning problems
| though -- these logic puzzles are essentially contrived to
| require deductive reasoning.
|
| [0] https://zebrapuzzles.com
| energy123 wrote:
| o1-mini does better than any other model on zebra puzzles.
| Maybe you got unlucky on one question?
|
| https://www.reddit.com/r/LocalLLaMA/comments/1ffjb4q/prelimi...
| andrew_eu wrote:
| Entirely possible. I did not try to test systematically or
| quantitatively, but it's been a recurring easy "demo" case
| I've used with releases since 3.5-turbo.
|
| The super verbose chain-of-reasoning that o1 does seems very
| well suited to logic puzzles as well, so I expected it to do
| reasonably well. As with many other LLM topics, though, the
| framing of the evaluation (or the templating of the prompt)
| can impact the results enormously.
| slig wrote:
| Hey, I run ZebraPuzzles.com, thanks for mentioning it! Right
| now I'm trying to improve the puzzles so that people can't
| "cheat" using LLMs so easily ;-).
| andrew_eu wrote:
| It's fantastic! Thanks for the great work.
| slig wrote:
| Thank you so much!
| bambax wrote:
| Near the end, the quote from OpenAI researcher Jason Wei seems
| damning to me:
|
| > _Results on AIME and GPQA are really strong, but that doesn't
| necessarily translate to something that a user can feel. Even as
| someone working in science, it's not easy to find the slice of
| prompts where GPT-4o fails, o1 does well, and I can grade the
| answer. But when you do find such prompts, o1 feels totally
| magical. We all need to find harder prompts._
|
| Results are "strong" but can't be felt by the user? What does
| that even mean?
|
| But the last sentence is the worst: "we all need to find harder
| prompts". If I understand it correctly, it means we should go
| looking for new problems / craft specific questions that would
| let these new models shine.
|
| "This hammer hammers better, but in most cases it's not obvious
| how better it is. But when you stumble upon a very specific kind
| of nail, man does it feel magical! We need to craft more of those
| weird nails to help the world understand the value of this
| hammer."
|
| But why? Why would we do that? Wouldn't our time be better spent
| trying to solve our actual, current problems, using any tool
| available?
| thrdbndndn wrote:
| > What does that even mean?
|
| It explicitly says "Results on AIME and GPQA are really
| strong". So I would assume it means it can get (statistically
| significantly, I assume) better score in AIME and GPQA
| benchmarks compared to 4o.
| benterix wrote:
| > But why? Why would we do that?
|
| Because OpenAI needs a steady influx of money, big money. In
| order to do so, they have to convince the people who are giving
| them money that they are the best. An objective way to achieve
| this is by benchmarking. But once you enter this game, you
| start optimizing for benchmarks.
|
| At the same time, in the real world, Anthropic is following
| them in huge leaps and for many users Claude 3.5 is already the
| default tool for daily work.
| chaosist wrote:
| Agree completely.
|
| From a user perspective too, I was a subscriber from the
| first day of gpt4 until about a month ago. I thought about
| subscribing for the month to check this out but I am tired of
| the OpenAI experience.
|
| Where is Sora? Where is the version of chatgpt that responds
| in real time to your voice? Remember the gpt4 demo that you
| would draw a website on a napkin?
|
| How about Q* lol. Strawberry/Q*/o1, "it is super dangerous,
| be very careful!"
|
| Quietly, Anthropic has just kicked their ass without all the
| hype and I am about to go work in sonnet instead even
| bothering to check o1 out.
| ZiiS wrote:
| I think they are saying they have invented the screwdriver. We
| have all been using, hammers to sink screws, but if you try
| this new tool it may be better. However, you will still
| encounter a lot of nails.
| bambax wrote:
| It's more like they're saying they have invented the
| screwdriver, but they haven't invented screws yet.
|
| But it doesn't feel right. It's unlikely the screwdriver
| would come first, and then people would go around looking for
| things to use it with, no?
| patapong wrote:
| It's more like they have invented a computer, an extremely
| versatile and powerful tool that can be used in many ways,
| but is not a solution to every problem.
|
| Now they need people to write software that uses this
| capability to perform useful tasks, such as text
| processing, working with spreadsheets and providing new
| ways of communication.
| chaosist wrote:
| While I find value in LLMs they still overall seem
| unreasonably not that useful.
|
| It might be like trying to train a neural net in 1993 on
| a 60mhz Pentium. It is the right idea but fundamental
| parts of the system are so lacking.
|
| On the other hand, I worry we have gone down the support
| vector machine path again. A huge amount of brain power
| spent on a somewhat dead end that just fits the current
| hardware better than what we will actually use in the
| long run.
|
| The big difference though from SVM is this has captured
| the popular imagination and if the tide goes out, the AI
| winter will the most brutal winter by an order of
| magnitude.
|
| AGI or bust.
| simonw wrote:
| I'd say the biggest difference between LLMs and SVMs is
| that a lot of people find LLMs useful on a daily basis.
|
| I've been using them almost daily for over two years now,
| and I keep on finding new things they can do that are
| useful to me.
| bambax wrote:
| Is there a post on your blog that lists your different
| uses of LLMs?
| simonw wrote:
| Not in a single place, but it came up in a podcast
| episode the other day - about 32 minutes in to this one I
| think https://softwaremisadventures.com/p/simon-willison-
| llm-weird...
| dartos wrote:
| They're useful, but not for what AI companies seem to be
| pushing for.
|
| I like that they can reorganize my data, document QA is
| pretty killer as long as the document was prepared well.
|
| Embeddings are sick.
|
| But content creation... not useful. Problem solving?
| Personally have not found them useful (haven't tried o1
| yet)
| aubanel wrote:
| He's speaking about his objective to make ever stronger LLMs:
| so for this his secondary objective is to measure their real
| performance.
|
| The human preference is not that good of a proxy measurement:
| for instance, it can be gamed by making the model more
| assertive, causing the human error-spotting ability to decrease
| a lot [0].
|
| So what he's really saying is that non-rigorous human vibe
| checks (like those LMSys Chatbot Arena is built on, although I
| love it) won't cut it anymore to evaluate models, because now
| models are past that point. Just like you can't evaluate how
| smart a smart person really is in a 2min casual conversation.
|
| [0]: https://openreview.net/pdf?id=7W3GLNImfS
| prog_1 wrote:
| ie when you cant beat them, make new metrics
|
| and you can absolutely evaluate how smart someone is in a
| 2min casual conversation. You wont be able to tell how well
| they are in some niche topic, but %insert something about
| different flavors of intelligence and how they do not equate
| do subject matter expertise%
| skybrian wrote:
| It's a common pattern that AI benchmarks get too easy, so
| they make new ones that are harder.
| arb_ wrote:
| As models improve, human preference will become worse as a
| proxy measurement (e.g. as model capabilities surpass the
| human's ability to judge correctness at a glance). This can
| be due to more raw capability - or more persuasion /
| charisma.
| causal wrote:
| It's trivial to come up with prompts that 4o fails. If it's
| hard to come up with prompts that 1o succeeds on but 4o
| fails, that implies the delta is not that great.
| mft_ wrote:
| Or, the delta depends on the nature of the problem/prompt,
| we've not yet figured that out, there's a relatively narrow
| range of prompts with large delta, and so finding those
| examples is a work in progress?
| KoolKat23 wrote:
| > Results are "strong" but can't be felt by the user? What does
| that even mean?
|
| This means it often doesn't provide the answer the user is
| looking for. In my opinion, it's an alignment problem, people
| are very presumptuous and leave out a lot of detail in their
| request. Like the "which is bigger - 9.8 or 9.11? question, if
| you ask "numerically which is bigger - 9.8 or 9.11?" It gets
| the correct answer, basically it prioritizes a different
| meaning for bigger.
|
| > But the last sentence is the worst: "we all need to find
| harder prompts". If I understand it correctly, it means we
| should go looking for new problems / craft specific questions
| that would let these new models shine. But why? Why would we do
| that? Wouldn't our time be better spent trying to solve our
| actual, current problems, using any tool available?
|
| Without better questions we can't test and prove that it is
| getting more intelligent or is just wrong. If it is more
| intelligent than us it it might provide answers that don't make
| sense to us but are actually clever, 4d chess as they say.
| Again an alignment problem, better questions aid with solving
| that.
| Workaccount2 wrote:
| The irony here is that Jason is speaking in the context of
| LLM development, which he lives and breaths all day.
|
| Reading his comments without framing it in that context makes
| it come off pretty badly - humans failing to understand what
| is being said because they don't have context.
| edouard-harris wrote:
| > Results are "strong" but can't be felt by the user? What does
| that even mean?
|
| Not every conversation you have with a PhD will make it obvious
| that that person is a PhD. Someone can be really smart, but if
| you don't see them in a setting where they can express it, then
| you'll have no way of fully assessing their intelligence.
| Similarly, if you only use OAI models with low-demand prompts,
| you may not be able to tell the difference between a good model
| and a great one.
| ksplicer wrote:
| This is something we've been grappeling with on my team. Many
| of the researchers in the org want to try all these reasoning
| techniques to increase performance, and my team keeps pushing
| back that we don't actually need that extra performance- we
| just want to decrease latency and cost.
| iinnPP wrote:
| So make the requirement using a cheaper and lower latency
| model and try to increase the performance to a satisfactory
| level. Assuming that you are not already using the
| cheapest/lowest latency model.
| energy123 wrote:
| You're reading too much into an offhand comment that's more
| metaphorical in nature.
| EGreg wrote:
| Dont you know by now
|
| Speaking with AI maxis it's easy:
|
| The AI is always right
|
| You are always wrong
|
| If AI might enable something dangerous, it was already possible
| by hand, scale is irrelevant
|
| But also AI enables many amazing things not previously
| possible, at scale
|
| If you don't get the answers you want, you're prompting it
| wrong. You need to work harder to show how much better the AI
| is. But definitely, it cannot make things worse at scale in any
| way. And anyone who wants regulations to even require
| attribution and labeling, is a dangerous luddite depriving
| humanity of innovations.
| ActionHank wrote:
| The stupidest thing about ai and automation is that they are
| trying to target it at large corporations looking to cut down
| on jobs or 10x productivity when all anyone actually wants is a
| robot to do their laundry and dishes.
| codyvoda wrote:
| these are almost entirely unrelated problems
| Spivak wrote:
| You're describing a dish-washer and washing-machine.
| jprete wrote:
| The GP is almost certainly describing a robot that can move
| dirty stuff into the machines, run them, and put away the
| clean stuff afterwards.
| raincole wrote:
| Because a robot that do everyone's laundry is much more
| closer to AGI than ChatGPT. I'm dead serious.
| fragmede wrote:
| Not really. You don't need to move wet clothes from the
| first machine to a second machine if you get one machine
| that does both jobs. That's very much not AGI. The second
| job, of taking dry crumpled clothes and folding them, also
| doesn't need an artificial general intelligence. It's
| _very_ computationally expensive (as evidenced by the speed
| of https://pantor.github.io/speedfolding/, out of UC
| Berkeley) and a hard robotics question, but it's also very
| fixed function.
|
| Taking the clothes out of the combined washer dryer
| machine, my laundry folding robot isn't suddenly going to
| need to come up with a creative answer to a question I have
| about politics in order to fold the laundry, or come up
| with a new way to organize my board game collection, or
| reason about how to refactor some code. There are no
| logical leaps of reasoning or deep thinking required. My
| laundry folding robot doesn't need to be creative in order
| to fold laundry, just application of some very complex
| algorithms, some of which have yet to be discovered.
| uptownfunk wrote:
| This hits the nail on the head. It is a consumer facing product
| not a technology to solve deep thinking.
| 015a wrote:
| > we all need to find harder prompts
|
| "One of the biggest traps for engineers is optimizing a thing
| that shouldn't exist." (from Musk I believe)
| benterix wrote:
| I remember Murati's interview where she said about this PhD level
| reasoning and so on, so I was excited to see what they come up
| with - and it looks like they just used a bunch of models (like
| 4o's) and linked them in a chain of thought - which is exactly
| what we have been doing ourselves for a long time to get better
| results. So you have the usual disadvantages (time and money) and
| lose the only advantage you had when doing it yourself, i.e.
| inspecting the immediate steps to understand the moment where it
| goes wrong so that you can correct it in the right place.
| vlad-r wrote:
| do you know if someone actually compared a 4o CoT to the o1?
| I'm trying to find something on it, but I can't find anything.
|
| LE: I found this tweet by Catena Labs of their MoA mix compared
| to o1-preview:
| https://x.com/catena_labs/status/1834416060071571836
| dr_dshiv wrote:
| So, this is just an RL trained method of having multiple GPT4o
| agents think through options and select the best before
| responding?
| monkeydust wrote:
| Just finished reading the 'Book of Why by Judea Pearl' and my own
| mental gap from AI to today to whatever AGI is has got wider,
| thought not discounting this seems like a step forward.
| jari_mustonen wrote:
| Once again, there's a lot of safety talk. For example, OpenAI's
| collaborations with NGOs and government agencies are being
| highlighted in the release notes. While it's crucial to prevent
| AI from facilitating genuinely harmful activities--like
| instructing someone on building a nuclear bomb, there is an
| elephant in the room regarding safety talk: Evidence suggests
| that these safety protocols sometimes censor specific political
| perspectives.
|
| OpenAI and other AI vendors should recognize the widespread
| suspicion that safety policies are being used to push political
| agendas. Concrete remedies are called for--for example, clearly
| defining what "safety" means and specifying prohibited content to
| reduce suspicions of hidden agendas.
|
| Openly engaging with the public to address concerns about bias
| and manipulation is a crucial step. If biases are due to innocent
| reasons like technical limitations, they should be explained.
| However, if there's evidence of political bias within teams
| testing AI systems, it should be acknowledged, and corrective
| actions should be taken publicly to restore trust.
| jdthedisciple wrote:
| Just leaving it here as well in case anyone feels up to the task:
|
| I challenged o1 to solve the puzzle in my profile info.
|
| It failed spectacularly.
|
| Now see you on the other side ;)
| dailykoder wrote:
| I am mostly only an LLM user with technical background. I don't
| have much in-depth knowledge. So I have questions about this
| take:
|
| >the output token allowance has been increased dramatically--to
| 32,768 for o1-preview and 65,536 for the supposedly smaller
| o1-mini!
|
| So the text says reasoning and output tokens are the same, as in
| you pay for both. But does the increase say that it can actually
| do more, or does it just mean it is able to output more text?
|
| Because by now I am just bored of GPT4o output, because I don't
| have the time to read through a multi-paragraph text that
| explains to me stuff that I already know, when I only want to
| have a short, technical answer. But maybe that's just what it
| can't do, give exact answers. I am still not convinced by AI.
| simonw wrote:
| I included that note because output limits are a personal
| interest of mine.
|
| Until recently most models capped out at around 4,000 tokens of
| output, even as they grew to handle 100,000 or even a million
| input tokens.
|
| For most use-cases this is completely fine - but there are some
| edge-cases that I care about. One is translation - if you feed
| in a 100,000 token document in English and ask for it to be
| translated to German you want about 100,000 tokens of output,
| rather than a summary.
|
| The second is structured data extraction: I like being able to
| feed in large quantities of unstructured text (or images) and
| get back structured JSON/CSV. This can be limited by low output
| token counts.
| dailykoder wrote:
| Sure, your cases are perfectly reasonable. I just wish the
| LLMs had a "feel" about when to output long or short text.
| Always thinking about adding something like "be as concise as
| possible" is kinda tedious
| tluyben2 wrote:
| This model did single shot figure out things that Sonnet just ran
| ran in a loop doing wrong and reddit humans also seemed not be
| able to fix (because niche I guess). It is slow (21 seconds for
| the hardest issue), but that is still faster than any human.
| mergisi wrote:
| I've been working on a o1-preview and recently hit some
| limitations with OpenAI's cap. But I've made progress--added all
| the steps, details, and code on GitHub
| https://github.com/mergisi/openai-o1-coded-personal-blog . The
| result isn't bad at all; just a few more CSS tweaks to improve
| it. Check it out and let me know what you think! How does it
| compare to tools like Claude Sonnet 3.5?
| mergisi wrote:
| I wrote a blog about OpenAI's o1 and everything you need to know
| about it. Check it out here https://mergisi.medium.com/openai-
| unveils-o1-preview-a-new-f...
| binary132 wrote:
| Personally I felt like o1-preview is only marginally better at
| "reasoning". Maybe I just haven't found the right problems to
| throw at it just yet.
| franze wrote:
| Just coded this this morning using chatgpt o1 - it is the
| reimplementation of an old idea now music, multiple dots, more
| and more bug fixes
|
| honestly, chatgpt is now a better coder than i ever was or will
| be
|
| https://lsd.franzai.com/
| SeanAnderson wrote:
| Neat idea. The ball frequently passes through solid lines
| though.
| franze wrote:
| fixed, just asked chatgpt to come up with a better physics
| engine and collision detection algorithm
| DrNosferatu wrote:
| How is o1 different in practice and end-results from my own,
| simple, Mixture of Agents script, that just queries several APIs?
| nprateem wrote:
| It's a for-loop isn't it?
| HarHarVeryFunny wrote:
| I think Rich Sutton's bitter lesson will prove to apply here, and
| what we really need to advance machine learning capabilities are
| more general and powerful models capable of learning for
| themselves - better able to extract and use knowledge from the
| firehose of data available from the real world (ultimately via
| some form of closed-loop deployment where they can act and
| incrementally learn from their own actions).
|
| What OpenAI have delivered here is basically a hack - a neuro-
| symbolic agent that has a bunch of hard-coded "reasoning" biases
| built in (via RL). It's a band-aid approach to try to provide
| some of what's missing from the underlying model which was never
| designed for what it's now being asked to do.
| joelburget wrote:
| o1 _is_ an application of the Bitter Less. To quote Sutton:
| "The two methods that seem to scale arbitrarily in this way are
| _search_ and learning. " (emphasis mine -- in the original
| Sutton also emphasized _learning_ ).
|
| OpenAI and others have previously pushed the learning side,
| while neglecting search. Now that gains from adding compute at
| training time have started to level off, they're adding compute
| at inference time.
| HarHarVeryFunny wrote:
| I think the key part of the bitter lesson is that (scalable)
| ability to learn from data should be favored over built-in
| biases.
|
| There are at least three major built-in biases in GPT-O1:
|
| - specific reasoning heuristics hard coded in the RL decision
| making
|
| - the architectural split between pre-trained LLM and what
| appears to be a symbolic agent calling it
|
| - the reliance on one-time SGD driven learning (common to all
| these pre-trained transformers)
|
| IMO search (reasoning) should be an emergent behavior of a
| predictive architecture capable of continual learning -
| chained what-if prediction.
| m101 wrote:
| It works like our own minds in that we also think, test, go
| back, try again. This doesn't seem like a failing but just a
| recognition that thought can proceed in that way.
| HarHarVeryFunny wrote:
| The "failing" here isn't the short term functional gains, but
| rather the choice of architectural direction. Trying to add
| reasoning as an ad-hoc wrapper around the base model, based
| on some fixed reasoning heuristics (built in biases) is
| really a dead-end approach. It would be better to invest in a
| more powerful architecture capable of learning at runtime to
| reason for itself.
|
| Bespoke hand-crafted models/agents can never compete with
| ones that can just be scaled and learn for themselves.
| nbzso wrote:
| Working in tech for over 30 years. This is the first time when I
| don't see proposed technology as a valuable tool. Especially
| LLM's. Vastly overhyped, driven by pure greed and speculative
| narratives, limited implementation and high energy cost. Non-
| transparent. Errors marketed as a hallucination.
| simonw wrote:
| How much time have you spent figuring out how to use them?
|
| Ethan Mollick estimates it takes ten hours of exposure to
| "frontier models" (aka OpenAI GPT-4, Claude 3.5 Sonnet, Google
| Gemini 1.5 Pro) before they really start to click in terms of
| what they're useful for.
| jes5199 wrote:
| this is exactly what i said about the iphone
| Thrymr wrote:
| For me, that moment was cryptocurrency. "Vastly overhyped,
| driven by pure greed and speculative narratives, limited
| implementation and high energy cost." - all applied. I couldn't
| understand why so many people thought it was the future. I
| actually see LLMs a little more positively - mildly
| interesting, certainly intriguing language mimics, but
| enormously expensive and overhyped. Are they useful? Maybe, but
| not to the degree that everything is focused on them now.
| zitterbewegung wrote:
| I wonder if this can be replicated by getting a reinforcement
| algorithm and LangGraph / LangGraph .
| deegles wrote:
| I was thinking about what "actual" AI would be for me and it
| would be something that could answer questions like "tell me
| every time Nicolas Cage has blinked while on camera in one of his
| movies".
|
| Sure, that is a contrived question, but I expect an "AI" to be
| capable pf obtaining every movie, watching them frame-by-frame,
| and getting an accurate count. All in a few seconds.
|
| Current models (any LLM) cannot do that and I do not see a path
| for them to ever do that at a reasonable cost.
| bgun wrote:
| I agree. My example for something "AI" should be able to do is
| to create a CAD model for the Empire State Building or the
| Parthenon based on known facts and photos.
|
| I don't think these are "moving the goalposts" examples, they
| are things that an actual intelligence capable of passing a PhD
| physics exam should be able to do.
| a_wild_dandan wrote:
| I mean, I passed a physics PhD exam and I can't model the
| Empire State Building. The jury is still out on whether I'm
| an intelligence tho.
| bgun wrote:
| My point is that you could, given enough time and all the
| information available to you online about these well-
| documented buildings. You could learn CAD and figure out a
| reasonable way to output a 3D model, because you can think
| and reason spatially. The current batch of AI tools can
| regurgitate complex facts, but they can't actually think in
| 3D like an being that spends its life navigating physical
| spaces.
|
| Maybe I'm wrong and we are well on our way to AI tools for
| this, but right now if I tell any of the current generation
| of image models to do something like "rotate object 70
| degrees, tilt camera down 20 degrees and re-render" then
| what comes out is never even approximately close.
| littlestymaar wrote:
| > All in a few seconds
|
| That part is unrealistic: even just loading in RAM and decoding
| all movies Nicolas Cage appears in would take much more than a
| few seconds unless you thrown an insane amount of compute at
| the job.
|
| That being said, the current LLM tech is probably enough to
| help you implement a program that parses IMDB to get the list
| of all Nicolas Cage movie, then download it on thepiratebay and
| then implement the blink count you're looking for. And you'd
| likely get the result in just a couple hours.
| 015a wrote:
| So what you're saying is, LLMs are good enough to do
| something that humans are already capable of doing, in a
| timeframe that a human would be reasonably capable of doing
| it in, and its unrealistic to believe that LLMs will ever be
| able to do something truly superhuman. Got it :+1:
| littlestymaar wrote:
| Being able to do "stuff a human is capable of doing" used
| to be the definition of "artificial intelligence" and until
| very recently it was seen as a dream that may never happen.
| And it hasn't completely happened yet BTW, there are still
| plenty of trivial stuff LLM can't do just because there's
| no available training data for that. Also their ability to
| do "reasoning" or few-shot-learning is overhyped (even if
| impressive).
|
| If your definition of AI has become "superhuman
| intelligence" then it's definitely moving goalposts. And
| regaarding my initial remark, AI isn't going to do "faster
| than the speed of light" MPEG decoding ever, all physical
| limits apply to it.
| 015a wrote:
| > AI isn't going to do "faster than the speed of light"
| MPEG decoding ever, all physical limits apply to it.
|
| This simply isn't a good faith take, because you're
| straw-manning the implementation of the query that the
| original poster put forward. They aren't asserting that
| the AI would need to do supernatural super-real time
| decoding of MPEG encoded files. What if the AI had
| already seen them? And was able to encode in the
| typically-compressed way LLMs do the information it needs
| to answer questions like that _without_ re-decoding the
| original movies?
|
| This raises many valid questions on topics like the
| structuring of data within an LLM, how large LLMs may
| eventually become, what systems should orbit around the
| LLM (does it make more sense for LLMs to watch YouTube
| videos, or have _already_ watched YouTube videos?).
|
| My definition of AI is the same definition that Nick
| Bostrom talks about in his 2014 book Superintelligence.
| There's no moving goalposts. Goal posts have been set in
| cement since 2014. Achieving human-level parity has
| obviously only been a "goal" insomuch as its a 10
| millisecond stop on the gradient toward
| superintelligence. OpenAI is not worth $150 billion
| dollars because it purports to be building a human-and-
| nothing-more in a box.
| littlestymaar wrote:
| > This simply isn't a good faith take, because you're
| straw-manning the implementation of the query that the
| original poster put forward. They aren't asserting that
| the AI would need to do supernatural super-real time
| decoding of MPEG encoded files.
|
| No, they literally said the AI would watch every frame on
| demand:
|
| > I expect an "AI" to be capable pf obtaining every
| movie, watching them frame-by-frame, and getting an
| accurate count.
|
| Talk about bad faith.
|
| > What if the AI had already seen them? And was able to
| encode in the typically-compressed way LLMs do the
| information it needs to answer questions like that
|
| LLM are encoding (in a very lossy way) "important"
| details, that's what allow them to compress their
| knowledge in little amount of space with respect to the
| input. But if you're asking completely random questions
| like this there's no way an LLM will contain such an
| info, because storing all the random trivia like that is
| going to be wasting an enormous amount of space.
|
| > There's no moving goalposts. Goal posts have been set
| in cement since 2014.
|
| Wait until you realize that AI is something much older
| than 2014... Also, note how the book you're quoting isn't
| called "artificial intelligence".
|
| > OpenAI is not worth $150 billion dollars because it
| purports to be building a human-and-nothing-more in a
| box.
|
| And yet there are many companies with much higher
| valuation with goals much more mundane than this. OpenAI
| has a hundred billion dollar valuation _because investors
| believe it can make money_ , not matter what it
| technologically achieves in order to do so.
| dev1ycan wrote:
| There's no actual improvement for real world tasks, just in-lab
| word prediction... it's disappointing to see so much money poured
| into obvious vaporware, Every 10-5 years we have a new generation
| of clueless VCs pouring money into something they don't
| understand based on lies by grifters, no different than the
| esports scene.
| briandw wrote:
| My 12 YO and I just built a fishing game using o1 preview.
| Prompt: "make a top down game in pyxel. the play has to pay off a
| debt to a cat by catching fish. the goal is for the player to
| catch the giant king fish. To catch the king fish the player
| needs to sell the fish to the cat and get money to buy better
| rods, 3 levels of rod, last one can catch the king fish."
|
| It nailed the execution. Amazing.
| ikety wrote:
| mind sharing the code? I asked o1 preview this verbatim and it
| produced errors. Just wanted to make sure its not me
| causal wrote:
| My first few attempts at getting it to work with an existing
| codebase have not been impressive. Perhaps 1o is best suited to
| difficult problems that can be stated in only a few sentences.
| rm_-rf_slash wrote:
| I've had the opposite experience: terrific use with modifying
| existing codebases. But then again I've been using GPT4 to
| code for over a year now and so I'm used to writing out
| prompts with my eyes closed.
| B1FF_PSUVM wrote:
| AFAICT, we got the ELIZA 60th anniversary edition, and are now
| headed for some Prolog/production systems iteration.
|
| One of these days those contraptions will work well enough, not
| because they're perfect, but because human intelligence isn't
| really that good either.
|
| (And looking in this mirror isn't flattering us any.)
| resters wrote:
| I pasted in some markdown content with symbolic logic expressions
| and proof examples and the model said I was violating the terms
| of service.
| throwuxiytayq wrote:
| Welcome to The Future, where computation has terms of service
| and each workload you submit for execution is automatically
| verified to be in compliance.
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