[HN Gopher] Talking About Large Language Models
___________________________________________________________________
Talking About Large Language Models
Author : negativelambda
Score : 83 points
Date : 2022-12-10 16:12 UTC (6 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| gamegoblin wrote:
| Everyone pointing out how LLMs fail at some relatively simple
| tasks are fundamentally misunderstanding the utility of LLMs.
|
| Don't think of an LLM as a full "computer" or "brain". Think of
| it like a CPU. Your CPU can't run whole programs, it runs single
| instructions. The rest of the computer built around the CPU gives
| it the ability to run programs.
|
| Think of the LLM like a neural CPU whose instructions are
| relatively simple English commands. Wrap the LLM in a script that
| executes commands in a recursive fashion.
|
| Yes, you can get the LLM to do complicated things in a single
| pass, this is a testament to the sheer size and massive training
| set of GPT3 and its ilk. But even with GPT3 you will have more
| success with wrapper programs structured like:
| premise = gpt3("write an award winning movie premise)
| loop 5 times: critique = gpt3("write a critique of
| the premise", premise) premise = gpt3("rewrite the
| premise taking into account the critique", premise, critique)
| print(premise)
|
| This program breaks down the task of writing a good premise into
| a cycle of writing/critique/rewriting. You will get better
| premises this way than if you just expect the model to output one
| on the first go.
|
| You can somewhat emulate a few layers of this without wrapper
| code by giving it a sequence of commands, like "Write a movie
| premise, then write a critique of the movie premise, then rewrite
| the premise taking into account the critique".
|
| The model is just trained to take in some text and predict the
| next word (token, really, but same idea). Its training data is a
| copy of a large swath of the internet. When humans write, they
| have the advantage of thinking in a recursive fashion offline,
| then writing. They often edit and rewrite before posting. GPT's
| training process can't see any of this out-of-text process.
|
| This is why it's not great at logical reasoning problems without
| careful prompting. Humans tend to write text in the format
| "<thesis/conclusion statement><supporting arguments>". So GPT,
| being trained on human writing, is trained to emit a conclusion
| _first_. But humans don 't _think_ this way, they just _write_
| this way. But GPT doesn 't have the advantage of offline
| thinking. So it often will state bullshit conclusions first, and
| then conjure up supporting arguments for it.
|
| GPT's output is like if you ask a human to start writing without
| the ability to press the backspace key. It doesn't even have a
| cognitive idea that such a process exists due to its architecture
| and training.
|
| To extract best results, you have to bolt on this "recursive
| thinking process" manually. For simple problems, you can do this
| without a wrapper script with just careful prompting. I.e. for
| math/logic problems, tell it solve the problem and show its work
| along the way. It will do better since this forces it to "think
| through" the problem rather than just stating a conclusion first.
| lachlan_gray wrote:
| This makes me wonder if GPT could be any good at defining its
| own control flow. E.g. asking it to to write a python script
| that uses control structures along with calls to GPT to
| synthesize coherent content. Maybe it could give itself a kind
| of working memory.
| gamegoblin wrote:
| Libraries such as https://github.com/hwchase17/langchain
| allow for easy programmatic pipelines of GPT "programs". So
| you could imagine taking a few hundred of these programs
| written by humans for various tasks, as are sure to come into
| existence in the next year or two, then adding those programs
| to the training data and training a new GPT that knows how to
| write programs that call itself.
| lachlan_gray wrote:
| Wow. Thank you for sharing. I had no idea there was a scene
| for this.
| sphinxster wrote:
| Thank you for this interesting insight I haven't seen before.
|
| Are there any datasets out there that provide the full edit
| stream of a human from idea to final refinement, that a model
| could be trained on?
| gamegoblin wrote:
| REPL transcripts (i.e. bash sessions, python REPL, etc) tend
| to be pretty good demonstrations of "working up to a
| conclusion". And, not coincidentally, putting GPT in a REPL
| environment yields better results.
|
| Other good examples narratives that include a lot of internal
| monologue. Thing a book written in the form:
|
| > The sphinx asked him, "A ham sandwich costs $1.10. The ham
| costs $1 more than the bread. How much does the bread cost?"
|
| > He thought carefully. He knew the sphinx asked tricky
| problems. If the ham costs a dollar more than the bread, the
| bread couldn't possibly be more than 10 cents. But if the
| bread was 10 cents, the ham would be $1.10 and the total
| would be $1.20. That can't be. We need to lose 10 cents, and
| it has to be divided evenly among the ham and bread to
| maintain the dollar offset. So the ham must be $1.05 and the
| bread must be $0.05. He answered the sphinx confidentally
| "The bread is $0.05!".
| btbuildem wrote:
| Very well put! Having played with it for a week straight, I've
| come to a similar observation -- it's a generator engine, with
| a "soft" interface. You still have to have skill and
| understanding to use it effectively, but it's a great force
| multiplier, because it removes the friction around the initial
| interactions.
|
| If you're solving a complex problem, you cannot expect it to
| "reason" about it. You have to break the problem into simpler
| pieces, then you can have the LLM do the grunt work for each
| piece.
| albystein wrote:
| This a very well put comment with a great analogy. A new
| emerging paradigm of action-driven LLMs is taking the approach
| of using the reasoning abilities of LLMs to drive agents that
| can take actions, interact with other tools and computer
| programs, and perform useful tasks like autonomously
| programming, customer support, etc
|
| And I think you're right when you say that they're lacking in
| recursive thinking abilities. However, their reasoning
| abilities are pretty excellent which is why when you prompt
| them to think step-by-step, or break down problems to them,
| they correctly output the right answer.
| gillesjacobs wrote:
| I am NLP researcher who volunteers for peer review often and the
| anthropomorphisms in papers are indeed very common and very
| wrong. I have to ask authors to not ascribe cognition to their
| deep learning approaches in about a third of the papers I review.
|
| People do this because mirroring cognition to machine learning
| lends credence that their specific modeling mechanism mimicks
| human understanding and so is closer "to the real thing".
| Obviously this is almost never the case, unless they explicitly
| use biomimetic methods in which case they are often outperformed
| by non-biomimetic state-of-the-art approaches.
|
| Thanks OP for giving me citation ammo to refer to in my
| obligatory "don't humanise AI" section of reviews. (It is so
| common I copy paste this section from a template).
| fourfivefour wrote:
| bias infests research as well as seen by the replication
| crisis. So you being a researcher doesn't give more credence to
| your words especially given that the state of current research
| cannot fully comprehend what these ML models are doing
| internally.
|
| I do agree that we can't ascribe cognition to machine learning.
|
| But I also believe that we can't ascribe that it's NOT
| cognition. Why? Because we don't even truly understand what
| "Knowing" or cognition is. We can't even ascribe a quantitative
| similarity metric.
|
| What we are seeing is that those inputs and outputs look
| remarkably similar to the real thing. How similar it is
| internally is not a known thing.
|
| That's why even though you're an NLP researcher, I still say
| your argument here is just as niave as the person who claims
| these things are sentient. You simply don't know. No one does.
| gillesjacobs wrote:
| In science, if you don't know, you don't make the claim, that
| is basic positivism and the scientific method.
|
| So basic in fact, I was thought this in elementary school. So
| far ad-hominem attributions of naivety.
|
| Anyone that humanises computation is not only committing an
| A.I. faux-pas but are going against the basic scientific
| method.
| oneoneonetwo wrote:
| > In science, if you don't know, you don't make the claim,
| that is basic positivism and the scientific method.
|
| Yes you're correct. So you can't make the claim that it's
| NOT cognition. That is my point. You also can't make the
| claim that it is cognition which was the OTHER point.
| Completely agree with your statement here.
|
| But it goes further then this, and your statement shows YOU
| don't understand science.
|
| >So basic in fact, I was thought this in elementary school.
| So far ad-hominem attributions of naivety.
|
| No science is complex and basically most people don't
| understand the scientific method and it's limitations. It's
| not basic at all, not even people who graduate from four
| year colleges in STEM fully understand the true nature of
| science. Or even many scientists!
|
| In science and therefore reality as we know it; nothing can
| be proven. This is because every subsequent observation can
| completely contradict an initial claim. Proof is the domain
| of logic and math, it doesn't exist in reality. Things can
| be disproven but nothing can actually be proven. That is
| science.
|
| This is subtle stuff, but it's legit. I'll quote Einstein
| if you don't believe me:
|
| "No amount of experimentation can ever prove me right; a
| single experiment can prove me wrong." - Einstein
|
| And a link for further investigation:
| https://en.wikipedia.org/wiki/Falsifiability
|
| Anyway all of this says that NO claim can be made about
| anything unless it's disproof. Which is exactly inline with
| what I'm saying.
|
| Still claims are made all the time anyway in academia and
| the majority of these claims aren't technically scientific.
| This occurs because we can't practically operate on
| anything in reality if we can't in actuality claim things
| are true. So we do it anyway despite lack of any form of
| actual proof.
|
| >Anyone that humanises computation is not only committing
| an A.I. faux-pas but are going against the basic scientific
| method.
|
| But so is dismissing any similarity to humans. You can't
| technically say it's wrong or right. Especially when the
| outputs and inputs to these models are very similar to what
| humans would say.
|
| This is basic preschool stuff I knew this when I was a
| baby! I thought everybody knew this! <Joking>.
| gillesjacobs wrote:
| It is entirely valid to demand SCIENTIFIC PAPERS adhere
| to the SCIENTIFIC METHOD (exception for some domains of
| the Humanities). If you do not recognize that, then we
| will have to agree to disagree.
| oneoneonetwo wrote:
| You didn't read my comment.
|
| I agree with you scientific papers MUST ADHERE to the
| scientific method. My comment wasn't even about that.
|
| My comment was about how YOU don't UNDERSTAND what
| SCIENCE IS.
|
| Even as a researcher, many don't understand science. My
| argument is definitive. Read it and you will learn
| something new. It may not convince you otherwise on the
| topic but it does show how baseless your "science" claims
| are given that you don't fully understand it yourself.
| pavlov wrote:
| Were the pyramids of Giza built by aliens? Well, it sure
| looks that way if you focus exclusively on evidence
| that's open to your preferred interpretation... And as
| for the all opposing evidence, nobody can disprove that
| it's just the aliens trying to hide their tracks.
|
| Machine cognition is a similarly extraordinary claim
| that's going to need a lot more evidence than a just-
| right sequence of inputs and outputs.
| oneoneonetwo wrote:
| I don't know if you played with chatGPT but it's much
| more than a just right sequence of inputs and outputs.
|
| I have already incorporated into my daily use (as a
| programmer). It has huge flaws but the output is
| anecdotally amazing enough that the claim of "cognition"
| is not as extraordinary as you think it is.
|
| Especially given the fact that we don't even fully
| understand what cognition is, the claim that it is NOT
| cognition is equally just as crazy.
| gillesjacobs wrote:
| Let me falsify your claim immediately: the inputs of
| these models are nothing like the inputs a human
| receives, subword tokens do not even match up with
| lexical items (visually, textually and semantically).
|
| You seem to agree with me even though your interpretation
| of falsifiability is inverted: I am not asking that
| authors make a claim that their models do not mimick
| human intelligence. Like OP, I ask them that they do not
| make that positive claim, i.e. omit humanising language
| unless they can substantiate it with evidence.
| oneoneonetwo wrote:
| It's an invalid falsification.
|
| The input to chatGPT is a textual interface, the output
| is letters on a screen. That is the exact same interface
| as if I were chatting with a human.
|
| Your getting into the technicalities of intermediary
| inputs and outputs. Well sure... analog data seen by the
| nueral wetware of human brains IS obviously different
| from the textual digital data inputted into the ML model.
| HOWEVER, we are looking for an isomorphism here. Similar
| to how a emulated playstation on a computer is very
| different then a physical playstation... an internal
| isomorphism STILL exists between hardware and the
| software emulating the hardware.
|
| We do not know if such an isomorphism exists between
| chatGPT and the human brain. This isomorphism is
| basically the crystallized essence of what cognition is
| if we could define it. If one does exists it's not
| perfect there are missing things. But it is niave to say
| that some form isomorphism isn't there AT ALL. It also
| niave to say that there is FOR SURE an isomorphism.
|
| The most rational and scientific thing at this point is
| to speculate. Maybe what chatGPT is, is something vaguely
| isomorphic to cognition. Keyword: maybe.
|
| It is NOT an unreasonable speculation GIVEN what we KNOW
| and DON'T KNOW.
| joe_the_user wrote:
| _People do this because mirroring cognition to machine learning
| lends credence that their specific modeling mechanism mimicks
| human understanding and so is closer "to the real thing"._
|
| Doesn't this also involve people not having another category
| aside from "cognition" to put natural language processing acts
| in? How many neural net constructors have a rigorously
| developed framework describing what "cognition" is?
|
| I mean, there's a common counter argument to the "this is not
| cognition" position. That is: "you're just using 'cognition' as
| a placeholder for whatever these systems can't do". I don't
| think that counter-argument is true or characterizes the
| position well but it's important to frame one's position so it
| doesn't seem to be subject to this counter-argument.
| gillesjacobs wrote:
| > Doesn't this also involve people not having another
| category aside from "cognition" to put natural language
| processing acts in?
|
| Yes, of course this might be an even more primary reason; do
| not attribute to malice what can be explained by laziness.
| However, AI researchers should be wary of their language,
| that point is hammered in most curricula I have seen. So at
| the least it is negligence.
|
| > I mean, there's a common counter argument to the "this is
| not cognition" position. That is: "you're just using
| 'cognition' as a placeholder for whatever these systems can't
| do".
|
| Very valid point, but we know current deep learning
| mechanisms do not mimick human learning, language
| understanding and production in any way. They are far too
| simplified and specific for that.
|
| Neural network activation functions are a far cry from neural
| spiking models and biological neural connectivity is far more
| complex than the networks used in deep learning. The
| attention mechanism that drives recent LLMs is also claimed
| to have some biological similarities, but upon closer
| inspection drawing strong analogies is not credible [1].
| computer vs. human visual recognition tasks it falls apart
| and higher-level visual concepts. [2]
|
| 1. https://www.frontiersin.org/articles/10.3389/fncom.2020.00
| 02...
|
| 2. https://arxiv.org/abs/1906.08764
| gillesjacobs wrote:
| Not to shoot across the bow of CS Engineers but the trend I
| spot (tentatively) is that it is pure computer science folk
| that most often do this. In NLP you have a mix of people coming
| from pure CS and signal processing (the latter esp. in speech
| processing) and others who come from linguistics or other
| humanities.
|
| The CS people seem all too happy to humanise computation,
| probably because they had less direct teaching regarding the
| cognitive mechanisms behind cognition and language production.
| Zababa wrote:
| I'm not really sure about the context here, but I know that I
| tend to humanize AIs, for example interacting with ChatGPT like
| with a regular human being, because I'm being nice to him and
| he's being nice to me in return. I don't know if it's more like
| being nice to a human, or more like taking good care of your
| tools so they will take good care of you, but it just feels
| better for me.
| nathan_compton wrote:
| This will hardly seem like a controversial opinion, but LLM are
| overhyped. Its certainly impressive to see the things people do
| with them, but they seem pretty cherry-picked to me. When I sat
| down with ChatGPT for a day to see if it could help me with
| literally any project I'm currently actually interested in doing
| it mostly failed or took so much prompting and fiddling that I'd
| rather have just written the code or done the reading myself.
|
| You have to be very credulous to think for even a second that
| anything like a human or even animal mentation is going on with
| these models unless your interaction with them is anything but
| glancing.
|
| Things I tried:
|
| 1) there are certain paradigms I find useful for game
| programming. I tried to use ChatGPT to implement these systems in
| my favorite programming language. It gave me code that generally
| speaking made no sense. It was very clear that it did not
| understand how code actually works. Eg: I asked it to use a hash
| table to make a certain task more efficient and it just created a
| temporary hash table in the inner loop which it then threw away
| when the loop was finished. The modification did not make the
| code more efficient than the previous version and missed the
| point of the suggestion entirely, even after repeated attempts to
| get it to correct the issue.
|
| 2) I'm vaguely interested in exploring SU(7) for a creative
| project. Asked to generate code to deal with this group resulted
| in clearly absurd garbage that again clearly indicated that while
| ChatGPT can generate vaguely plausible text about groups it
| doesn't actually understand anything about them. Eg: ChatGPT can
| say that SU(7) is made of matrices with unit norm but when asked
| to generate examples failed to generate any with this property.
|
| 3) A very telling experiment is to ask ChatGPT to generate logo
| code that draws anything beyond simple shapes. Totally unable to
| do so for obvious reasons.
|
| Using ChatGPT convinced me that if this technology is going to
| disrupt anything, its going to be _search_ rather than _people_.
| Its just a search engine with the benefit that it can do some
| simple analogizing and the downside that it has no idea how
| anything in the real world works and will confidently produce
| total garbage without telling you.
| Zababa wrote:
| > This will hardly seem like a controversial opinion, but LLM
| are overhyped. Its certainly impressive to see the things
| people do with them, but they seem pretty cherry-picked to me.
| When I sat down with ChatGPT for a day to see if it could help
| me with literally any project I'm currently actually interested
| in doing it mostly failed or took so much prompting and
| fiddling that I'd rather have just written the code or done the
| reading myself.
|
| > You have to be very credulous to think for even a second that
| anything like a human or even animal mentation is going on with
| these models unless your interaction with them is anything but
| glancing.
|
| I've used ChatGPT, and I'd say it's right now as useful as a
| google search, which is already a lot. Most humans would be
| absolutely unable to help me (and probably you) for your
| projects because they aren't specialized in that area. That's
| not even talking about animals. I love my cats but they've
| never really helped me when programming.
| alsodumb wrote:
| I hope ChatGPT in its current form will not be used for search.
| As my friend says it, ChatGPT is not intelligent, it's just
| capable of creating responses like it's knows everything. The
| things it hallucinates is likely going to spread misinformation
| and make it harder for the masses to search for true, factual
| information.
|
| The other part is webtraffic: Google in theory could have
| created an interactive, conversational style search engine
| (with it without LLMs) if they wanted to, but a lot of websites
| would have complained about Google taking away traffic from
| them. I believe the same happened when Google started showing
| it's own reviews instead of redirecting to Yelp. I wonder how
| openAI or any LLM powered search is going to deal with it. They
| don't have to worry about it anytime soon, they still have a
| lot of time to get to a stage where they come anywhere close to
| the number of queries Google handles in a day, but it'll be
| interesting to see how things go.
| nathan_compton wrote:
| I agree that I'd still rather use a search engine over a
| small set of sites than ChatGPT for exactly the reasons you
| suggest and others. But I don't see ChatGPT as having a lot
| of utility beyond functioning as a search interface for
| credulous dummies. I mean if I were literally developing a
| chatbot then clearly its a pretty interesting technology
| (assuming its problems can be tamed or censored somehow), but
| beyond that I don't really get it.
| solidasparagus wrote:
| The problem you are running into is that you are
| overindexing on the fact that LLMs will sometimes be wrong
| and you are used to using technology that is basically
| always right. But we are in the early stages of LLM
| adoption - correctness will improve (see for example
| citation driven LLM-search) but more importantly, the set
| of LLM-driven applications that can be probabilistically
| correct and still wildly useful will grow.
|
| LLMs like ChatGPT are just so damn cheap for the power they
| provide, it's inevitable
| TeMPOraL wrote:
| Thing is, ChatGPT is already incredibly useful for searching
| random things you know enough about you can evaluate
| responses critically. The alternative here is doing a regular
| search, and wading through SEO-bloated, ad-laden content
| marketing "articles". The quality and reliability of
| information is about the same (or even favoring ChatGPT), but
| without 90% of the text that's just filler, without bullshit,
| ads, upsells, tracking scripts, etc. I tried it a few times
| and it's a _much_ better experience than the web. I 'm gonna
| be using it for as long as it lasts.
| nathan_compton wrote:
| Yeah, but its not as reliable as just restricting your
| search to Wikipedia or the appropriate academic journals or
| even chatting with a librarian!
| TeMPOraL wrote:
| Sure, when the topic matters or I need to study it in
| depth, I can still go to Wikipedia or PubMed or Arxiv.
|
| But there are plenty of searches one does that are
| trivial, or serve to illuminate the problem space, and
| cover topics that in which I can rely on common sense to
| correct wrong advice. And the issue with non-technical
| topics, the kind applicable to mass audience - like e.g.
| cooking or parenting or hygiene - are _very_ hard to
| search about online, because all results are bullshit
| pseudo articles written to drive traffic and deliver ads.
| So it 's not that ChatGPT is so good, but more that
| Internet for normal people is complete trash, and ChatGPT
| nicely cuts straight through it.
| b3morales wrote:
| But if so this isn't because of its nature (the fact that
| it's an LLM), but because of its inputs. An LLM fed the
| same bullshit pseudo articles you refer to would likewise
| spit out more bullshit. If ChatGPT works it's because its
| sources have been carefully curated.
| TeMPOraL wrote:
| Fair. But the practical reality right now is that ChatGPT
| delivers useful results without the noise, whereas normal
| web search does not. It blows the web out of the water
| when it comes to value to effort ratio of generic web
| searches. It won't last forever, but I'm enjoying it for
| as long as I can.
| Al-Khwarizmi wrote:
| Indeed. If I could have the Google from 20 years ago, I
| probably wouldn't be so impressed with ChatGPT as search
| engine.
|
| But with the Google (and the web) of today, where it's
| practically impossible to find reliable information about
| many subjects without adding "site:reddit.com" or
| "wikipedia", I find it extremely useful.
| albystein wrote:
| The problem of hallucination in LLMs is a well-known and
| studied problem and solutions have been proposed to counter
| it. The most promising one is augmenting LLMs with a
| retrieval system. This involves sourcing a large database of
| factual information, say journal articles, over which the LLM
| uses an information retrieval system(search engine) to
| extract information on which its generated output is
| conditioned. Recent job postings from OpenAI suggest that's
| their next step of development for these LLMs.
|
| I think critics of these LLMs are missing the point about the
| excitement around them. People are excited because of the
| rate of progress/improvement from just two years or a year
| ago. These systems have come a long way, and if you
| extrapolate that progress into the future, I predict majority
| of these shortcomings getting resolved
| genidoi wrote:
| The difference in wether you think ChatGPT is game changing or
| another overhyped LLM seems to come down to:
|
| 1) do you acknowledge prompt engineering is a real skill set?
|
| 2) are you willing to improve your prompt engineering skill set
| through research and iteration?
|
| There is much to learn about prompt engineering from that
| "Linux VM in ChatGPT" post and other impressive examples (where
| the goal of is to constrain ChatGPT to only engage in a
| specific task)
| axg11 wrote:
| I disagree that LLMs are overhyped, but it's very subjective.
| Are current LLMs a few steps from AGI? No. Will LLMs change the
| computing landscape? Yes, I believe they will.
|
| ChatGPT, without any major changes, is already the best tool
| out there for answering programming questions. Nothing else
| comes close. I can ask it to provide code for combining two
| APIs and it will give useful and clean output. No need to
| trudge through documentation, SEO-hacked articles, or 10
| different Stack Overflow answers. Output quality will only
| improve from here. Does it sometimes make mistakes? Yes. There
| are also mistakes in many of the top SO answers, especially as
| your questions become more obscure.
|
| Aside from programming, how many other fields are there where
| LLMs will become an indispensable tool? I have a PhD and
| ChatGPT can write a more coherent paragraph on my thesis topic
| than most people in my field. It does this in seconds. If you
| give a human enough time, they will be able to do better than
| ChatGPT. The problem is, we're already producing more science
| within niche scientific fields than most scientists could ever
| read. As an information summary tool, I think LLMs will be
| revolutionary. LLMs can help individuals leverage knowledge in
| a way that's impossible today and has been impossible for the
| last 30 years since the explosion in the number of scientific
| publications.
| nathan_compton wrote:
| It can reproduce a statistically plausible paragraph,
| certainly. But there is a great deal more to research than
| producing statistically plausible paragraphs. It doesn't
| _understand_ anything!
|
| I've actually worked on a project where there have been
| attempts to use GPT like models to summarize scientific
| results and the problem is it gets shit wrong all the time!
| You have to be an expert to separate the wheat from the
| chaff. It operates like a mendacious search engine pretending
| to be a person.
| visarga wrote:
| The problem is that we need to pair generative models with
| verification systems. We have the models, but no
| verification yet. Fortunately code and math are easier to
| verify. Some things require simulation. In other cases you
| can substitute an ensemble of solutions & picking the most
| frequent answer as consistency based verification. But for
| each domain we need to create verifiers and that will take
| some time.
|
| The good thing is that we'll be able to generate training
| data with our models by filtering the junk with the
| verifiers. Then we can retrain the models. It's important
| because we are getting to the limit of available training
| data. We need to generate more data, but it's worthless
| unless we verify it. If we succeed we can train GPT-5.
| Human data will be just 1%, the race is on to generate the
| master dataset of the future. I read in a recent paper that
| such a method was used to improve text captions in the
| LAION dataset. https://laion.ai/blog/laion-5b/
| lambdatronics wrote:
| >we need to pair generative models with verification
| systems >code and math are easier to verify
|
| I would love to see a two-stage pipeline using a LLM to
| convert natural language specifications into formal
| specifications for something like Dafny, and then follow
| up with another model like AlphaZero that would generate
| code & assertions to help the verifier. This seems like
| something that a major group like DeepMind or OpenAI
| could pull off in a few years.
| goatlover wrote:
| One concern here is that if ChatGPT replaces the need to go
| to websites like Stack Overflow or Wikipedia, what happens to
| them? Do they stick around if the only people who visit them
| are there to feed new stuff to chatGPT? Also, how does
| chatGPT get hold of papers and articles behind pay walls? How
| much of the scientific publications are free?
| macrolocal wrote:
| Points taken, but LLMs are still outpacing expert predictions,
| so empirically they're under-hyped.
| btbuildem wrote:
| It is very, very good with language, and very bad with facts
| and numbers. That's an oversimplification, but also the gist of
| it.
|
| You have to recognize how it works, why it works - then you can
| use it as basically an incredible superpower force multiplier.
| monkmartinez wrote:
| I disagree and think this is a very controversial opinion.
|
| Playing around with it last night convinced me that LLM's are a
| huge, game changing technology. I was trying to decide which
| material to use for an upcoming project. The model doesn't use
| the internet without some hacking, so I had it write a program
| in python using the tkinter UI kit.
|
| I asked it to create a UI with input boxes for material, weight
| of material, price and loss due to wastage. The program takes
| all of those inputs and converts the material into grams from
| KG, pounds, ounces. It then calculates the price per gram and
| takes a loss percentage (estimate given by user). It then
| writes a text file and saves it to a directory.
|
| I literally pasted the code into VS code and had to change
| Tkinter to tkinter. Hit run and it worked flawlessly. I have
| NEVER used tkinter and it took about 30 minutes from start to
| finish.
|
| This morning, I asked my 9th grade son what he is learning in
| 9th grade biology. He told me he is learning cellular
| endocytosis. I asked chapGPT to explain endocytosis like I was
| a 5 year old and read it to him... he says; "Ask it to explain
| it like a scientist now." After that he said it was a really
| good and we started asking it all kinds of biology questions.
|
| I happen to agree that search will be the first thing
| disrupted. However, I think simply saying "search" doesn't come
| close to capturing how deep this will change the way we think,
| use and progress in terms of the way we define "search" right
| now.
| nathan_compton wrote:
| I've got a young kid and I'd think twice before letting this
| model explain any science to him. If your criteria for
| whether a model is good is "it fooled a 9th grader" well, I
| don't know what to tell you.
|
| I think you have a point about your tkinter example. That
| kind of stuff _is_ a lot more convenient than googling and
| copying and pasting code. But if you push it beyond stuff
| that you could easily find on stack exchange or in
| documentation somewhere it doesn't work that well. Like I
| said, its a search engine with a lot of downsides and some
| upsides.
| marcinzm wrote:
| > If your criteria for whether a model is good is "it
| fooled a 9th grader" well, I don't know what to tell you.
|
| Fooling a 9th grader is amazing. That's a pretty well
| formed human being right there except with less life
| experience. Fundamentally no different from you in general
| reasoning terms except on a smaller set of information. So
| fooling you is merely a question of model size.
| radford-neal wrote:
| "Fool" is the operative word here. ChatGPT is quite
| capable of producing very plausible sounding text about
| biology that is totally incorrect. See, for example, the
| example in my comment at https://www.lesswrong.com/posts/
| 28XBkxauWQAMZeXiF/?commentId...
| marcinzm wrote:
| You're basically complaining that a single model doesn't
| have full knowledge of every single area of all of human
| knowledge. It's got decent knowledge of most areas
| including programming with probably better overall
| knowledge than a high school student. That's downright
| amazing and probably more knowledge than any single human
| actually has. The rest is likely a matter of improvement
| along the same lines versus some radical redesign.
| radford-neal wrote:
| Well, I agree that it's amazing - it almost always
| produces grammatical output, for instance. But it's not a
| reliable way of obtaining knowledge. One should not, in
| particular, try to learn about biology by asking ChatGPT
| questions. It often produces made-up stuff that is just
| wrong. And it's very confidently wrong, with the output
| often coming across like someone barely concealing their
| contempt that you might doubt them.
|
| It may or may not be fixable without radical redesign.
| The underlying training objective of mimicking what
| humans might say may be too at variance with an objective
| of producing true statements.
| sarchertech wrote:
| My wife (a physician) asked it multiple medical questions
| and the majority of the time they were dangerously wrong,
| but looked perfectly fine to me.
|
| I asked it a series of questions about my area of
| expertise and they were wrong but looked perfectly fine
| to my wife.
|
| It even confidently "solved" the 2 generals problem with
| a solution that looks completely plausible if you don't
| already know that it won't work.
| tshaddox wrote:
| Maybe I'm just old, but there just isn't much that I want to
| computers to tell me about that they don't already do a decent
| job at. Everyone loves to complain about how bad Google search
| is, but I very rarely find myself desperately looking for
| something and unable to find it. There's certainly no normal
| conversational interactions I can think of that I would love to
| have with a computer but have been unable to before ChatGPT and
| similar.
|
| That limits how impressed I can be by ChatGPT and similar
| beyond just being impressed by it on a purely technical level.
| And it's certainly very technically impressive, but not in some
| transcendental way. It's also very impressive how could recent
| video games with ray tracing look, or how good computers are at
| chess, or how many really cool databases there are these days,
| or how fast computers can sort data.
| fourfivefour wrote:
| I used chatGPT to solve a sqlite bug involving a query that was
| taking 4 seconds to run. I pasted the query and it identified
| many possible issues with the query including the offending
| problem (it was missing an index on a timestamp).
|
| It also passed 3/4 of our companies interview process including
| forging a resume that passed the recruiter filter.
|
| That being said, I COMPLETELY agree with you that chatGPT will
| not disrupt anything. Your example cases are completely as
| VALID as are my example cases.
|
| chatGPT is, however, the precursor to the thing that will
| disrupt everything.
| Al-Khwarizmi wrote:
| Do my core work? No, it's not going to, at the moment.
|
| But it's already saving me nontrivial amounts of time on tasks
| like "write a polite followup email reminding person X, who
| didn't reply to the email I sent last week, that the deadline
| for doing Y expires at date Z".
|
| I typically spend at least 3-4 minutes finding the words for
| such a trivial email and thinking how to write it best, e.g.
| trying to make the other person react without coming across as
| annoying, etc. (Being a non-native English speaker who
| communicates mostly in English at work may be a factor).
| ChatGPT is really good with words. Using it, it takes a few
| seconds and I can use the output with only trivial edits.
| Jack000 wrote:
| LLMs may be overhyped, but transformers in general are _under_
| hyped.
|
| LLMs make a lot of mistakes because they don't actually know
| what words mean. The key thing is though - it's _much harder_
| to generate coherent text when you don 't know what the words
| mean. In a similar vein it's completely unreasonable to expect
| an LLM to perform visual tasks when it literally has no sense
| of sight.
|
| The fact that it can kind of sort of do these things at all is
| evidence of the super-human generalization potential of the
| transformer architecture.
|
| This isn't very obvious for English because we have prior
| knowledge of what words mean, but it's a lot more obvious when
| applied to languages humans don't understand, like DNA and
| amino acid sequences.
| fourfivefour wrote:
| How can these things not know what words mean? Did you not
| see how they created a virtual machine under chatGPT? They
| told it to imitate bash and they typed ls, and cat jokes.txt
| and it outputted things completely identical to what you'd
| expect. Look it up. https://www.engraved.blog/building-a-
| virtual-machine-inside/
|
| I don't see how you can explain this as not knowing what
| words mean. It KNOWS.
| hodgesrm wrote:
| > This will hardly seem like a controversial opinion, but LLM
| are overhyped.
|
| As the [excellent] paper points out, LLMs are complex functions
| that can be embedded in systems to provide plausible answers to
| a prompt. Here's the money sentence. LLMs are
| generative mathematical models of the statistical distribution
| of tokens in the vast public corpus of humangenerated text,
| where the tokens in question include words, parts of
| words, or individual characters including punctuation
| marks.
|
| Rather than focus on the limitations of this approach to answer
| general queries, which are manifest, it seems more interesting
| to ask a different question. Under what circumstances do LLMs
| give answers that are reliably equivalent to or better than
| humans? The answer would:
|
| 1. Illuminate where we can use LLMs safely.
|
| 2. Direct work to make them better.
|
| It's already impressive that within certain scopes ChatGPT
| gives very good answers, indeed better than most humans.
| dragonwriter wrote:
| > Under what circumstances do LLMs give answers that are
| reliably equivalent to or better than humans?
|
| _Which_ humans? Humans give a... fairly wide range of
| responses.
| hodgesrm wrote:
| I'm a history major and love classical history. My first
| question to ChatGPT was:
|
| > Why was Julius Caesar murdered?
|
| The answer was the following, which would pass a standard
| exam question on the topic. It exhibits [the appearance of]
| multi-layer reasoning and has a nice conclusion.
|
| > Julius Caesar was assassinated on the Ides of March in 44
| BC by a group of Roman senators who believed he had become
| too powerful and posed a threat to the Roman Republic. The
| senators were concerned that Caesar had ambitions to become
| king, which would have ended the Republic and given him
| absolute power. The senators were also concerned about
| Caesar's growing popularity with the people of Rome. They
| believed that assassinating him was the only way to prevent
| him from becoming too powerful and destroying the Republic.
|
| It's interesting to note that most of the evidence for this
| answer including 2000 years of interpretation is available
| in textual form _on the Internet_. It 's easily accessible
| to LLMs.
| TeMPOraL wrote:
| _Average_ humans? Within 1 stdev from the mean?
| dragonwriter wrote:
| > Average humans? Within 1 stdev from the mean?
|
| This implies that performance has unqiue natural,
| objective, ratio-level (or at least, a unique consistent
| interval-level) measure. Otherwise the mean is, itself,
| meaningless.
|
| "How well you answer a question" doesn't seem to fit
| that, its maybe at best a (somewhat subjective, still)
| ordinal quality, so the median (or mode) is the only
| useful average.
|
| But I think you'll find that without restricting things
| more than "humans", both the median and mode of responses
| to most prompts is... quite bad.
| nathan_compton wrote:
| This reminds me that coding with ChatGPT felt like pair
| programming with a not super smart person who could google
| and type really fast. Not really fun!
| armoredkitten wrote:
| Please don't reduce LLM down to ChatGPT (or generative models
| more generally). People are using LLM for real-world problems
| every day. BERT and its descendants/variants are used all over
| the place for many different problems in natural language
| processing. I and my team have used it on dozens of different
| projects, mainly in classifying text documents and inputs. And
| it works very well. Multilingual LLMs are responsible for the
| huge improvements in machine translation; my team has to deal
| with text in multiple languages, and these models are vital
| there too. We have used LLM on real-world problems that are in
| production _now_ and are saving hundreds of person-hours of
| tedious work.
|
| ChatGPT? Yeah, it's neat. I'm sure people will find some useful
| niche for it. And I do think generative models will eventually
| have a big impact, once researchers find good ways to ground
| them to data and facts. This is already an active area of
| research -- combining generative LLMs with info retrieval
| methods, or targeting it to a specific context. (Meta just gave
| a talk last week at the NeurIPS conference about teaching a
| model to play Diplomacy, a game that mostly involves talking
| and negotiating deals with the other players. ChatGPT is too
| broad for that -- they just need a model that can talk about
| the state of the game board.) So in general, I'm optimistic
| about generative LLMs. But ChatGPT...is just a toy, really.
| It's not the solution -- it's one of the signposts along the
| way toward the real solution. It's a measure of progress.
| hodgesrm wrote:
| I wouldn't undersell ChatGPT. It's like a repl for a
| particular LLM. Maybe there are others but it's the first
| time many people have gotten direct access to the technology.
| Sometimes the medium _is_ the message.
| mikodin wrote:
| Edit: I also see that I am falling prey to exactly what the
| paper itself is talking about.
|
| "The more adept LLMs become at mimicking human language, the
| more vulnerable we become to anthropomorphism, to seeing the
| systems in which they are embedded as more human-like than they
| really are. This trend is amplified by the natural tendency to
| use philosophically loaded terms, such as "knows", "believes",
| and "thinks", when describing these systems."
|
| --
|
| An ignorant statement / question I have is why are you using it
| write code? It's a chatbot, no?
|
| As you've mentioned, it's a really powerful search, and is like
| having a conversation with someone who is literally the
| internet.
|
| For example "What is the glycemic index of oatmeal?"
|
| "What is Eihei Dogen's opinion of the Self and how does it
| differ from Bassui's?"
|
| I get highly detailed and accurate output with these.
|
| The first question is simple and the second is far from it.
| It's breaking down two Zen masters experiences and comparing
| them in an amazing way.
|
| I've been thoroughly impressed with Chat GPT so far.
|
| Ask it to breakdown the high level points of a book you've
| read.
|
| Ask it to rewrite a song in the style of a different artist.
|
| It's so cool, I feel like I legitimately have an answer to any
| random question at my finger tips and have to do zero filtering
| for it.
| nathan_compton wrote:
| "An ignorant statement / question I have is why are you using
| it write code? It's a chatbot, no?
|
| I've found it so incredibly useful to simply replace Google."
|
| Heard of Stack Exchange?
|
| I teach and I expect many students to use language models
| like ChatGPT to do their homework, which involves writing
| code. Lots of what people are doing with it is coding (there
| have been quite a few posts here using it that way).
|
| I've actually also used ChatGPT for literary/song writing
| experiments and it stinks, aesthetically. The lyrics it
| wrote, even with a lot of prompting, were totally asinine.
| And how could they not be?
| RosanaAnaDana wrote:
| I like the discussion, but this article 'feels' like more Luddite
| goalpost moving, and is reflective of a continuous sentiment I
| feel strains so much of the conversation around intelligence,
| agentism, and ai going on today.
|
| I think that because we lack a coherent understanding of what it
| means to be intelligent at an individual level, as well as what
| it means to be an individual, we're missing much of the point of
| what's happening right now. The new line in the sand always seems
| to be justified based on an argument whose lyrics rhyme with
| identity, individual, self, etc. It seems like there will be no
| accepting of a thing that may have intelligence if there is no
| discernable individual involved. Chomsky is basically making the
| same arguments right now.
|
| I think we'll see something that we can't distinguish from hard
| advanced general intelligence, prob in the next 3-5 years, and
| probably still have not made any real advancement into
| understanding what it means to be intelligent or what it means to
| be an individual.
| anyonecancode wrote:
| Increasingly I don't think the question of "what is
| intelligence" is so useful or relevant here. It feels a bit
| like arguing over whether the "artificial horse" that started
| appearing at the end of the 19th/beginning of the 20th C were
| actually horses. Cars weren't, and still aren't, but that
| misses the point.
|
| AI isn't intelligent, and never will be, and I don't think that
| matters all that much.
| RosanaAnaDana wrote:
| I think I agree in sentiment, and I'm wondering what your ake
| is on the article/ current discussions article.
|
| I guess my premise is that I don't think we have a useful
| enough definition of intelligence because the ones I see
| people writing articles on seem to be dependent or defined by
| agency, and specifically humanish forms of agency. So I guess
| your point would be "these systems aren't intelligent, but
| that's not relevant"? I suppose I out the issue at the
| currency of the definition of intelligence. It's seemed to be
| very much synonymous with "how humans do things", making it
| somewhat impossible to give charity to the arguments
| presented in this paper with the caveats on "not
| anthropomorphising". Like I can't compare these two things if
| your definition of intelligence is fundementally based on
| what "Anthros" do or do not do and simultaneously not engage
| in anthropromorism.
|
| To follow on your point, if these things aren't displaying
| "intelligence", but that's also not the point, what then are
| they displaying?
|
| It seems to me this is a failure of introspection on the part
| of AI philosophy to recognize how limited our understanding
| of "HI" is.
| anyonecancode wrote:
| I think the question of "what is intelligence" is an
| interesting one, and technology (especially computer
| technology) gives us some interesting angles to look at it,
| but I think it dominates the conversation
| disproportionately to its importance. Things like ChatGPT,
| and the technologies they presage, will absolutely have a
| significant impact on society, economics, etc, but getting
| tangled up in questions of "what is intelligence" impede
| rather than help us to think through these implications and
| prepare for them.
|
| Put another way -- I do not believe the future holds Blade
| Runner replicants. If we're not careful, though, it does
| hold Blade Runner corporations. While, philosophically,
| it's interesting to ask if androids dream of electric
| sheep, that question isn't very helpful in trying to nudge
| the future in a more utopic rather than dystopic direction.
| lambdatronics wrote:
| Edsger Dijkstra: "The question of whether Machines Can Think
| (...) is about as relevant as the question of whether
| Submarines Can Swim."
| plutonorm wrote:
| I 100% agree. I would also add that most of the arguments are
| driven by emotion. The truth is that we dont know what
| intelligence means and we dont know what kinds of system have
| intelligence. The only tools we have to measure intelligence
| are those designed for humans. When we test the machines they
| do better than terribly and they are improving very quickly.
| There is no possible logical argument you can put forward
| against their intelligence in the face of this evidence from
| these human tests - because we cannot define intelligence in
| any other way than these tests. Claims against intelligent
| machines always boil down to 'obviously they aren't' and the
| arguments have have to be this shallow simply because they have
| no firm footing from which to base their argument.
| Chirono wrote:
| This paper, and most other places i've seen it argued that
| language models can't possibly be conscious, sentient, thinking
| etc, rely heavily on the idea that llms are 'just' doing
| statistical prediction of tokens.
|
| I personally find this utterly unconvincing. For a start, I'm not
| entirely sure that's not what I'm doing in typing out this
| message. My brain is 'just' chemistry, so clearly can't have
| beliefs or be conscious, right?
|
| But more relevant is the fact that llms like ChatGPT are only
| pre-trained on pure statistical generation, followed by further
| tuning through reinforcement learning. So ChatGPT is no longer
| simply doing pure statistical modelling, though of course the
| interface of calculating logits for the next token remains the
| same.
|
| note: i'm not saying i think llms are conscious. I don't think
| the question even makes much sense. I am saying all the arguments
| that i've seen for why they aren't have been very unsatisfying.
| goatlover wrote:
| > I personally find this utterly unconvincing. For a start, I'm
| not entirely sure that's not what I'm doing in typing out this
| message. My brain is 'just' chemistry, so clearly can't have
| beliefs or be conscious, right?
|
| Your brain is part of an organism who's ancestors evolved to
| survive the real world, not by matching tokens. As such,
| language is a skill that helps humans survive and reproduce,
| not a tool used to mimic human language. Chemistry is the wrong
| level to evaluate cognition at.
|
| Also, you can note the differences between how actual neurons
| work compared to language models as other posters have
| mentioned.
| mrayder wrote:
| For philosophical standpoint it would perhaps be wise to ask what
| is the purpose of LLM's in general?
|
| Should they somehow help humans to increase their understanding
| not only of the languages, their differences but also knowledge
| of what is true and what isn't?
|
| Perhaps it could be said that if anything there are helpful as an
| extension of humans imperfect and limited memory.
|
| Should the emphasis be put on improving the interactions between
| the LMM's and humans in a way that they would facilitate
| learning?
|
| Great paper written at the time when more humans have been
| acquainted to LMM's due to technological abstraction and creation
| of easily accessible interfaces. (openAI chat)
| canjobear wrote:
| I'll agree to stop saying LM's "think" and "know" things if you
| can tell me precisely what those mean for humans.
| goatlover wrote:
| Maybe there isn't a precise definition, but clearly for humans
| thinking and knowing is related to having bodies that need to
| survive in the world with other humans and organisms, which
| involves communication and references to external and internal
| things (how your body feels and what not). This is different
| from pattern matching tokens, even if it reproduces a lot of
| the same results, because human language creates a lot of
| patterns that can be matched.
|
| We could say both humans and LLMs are intelligent, but in a
| different way.
| hackinthebochs wrote:
| >This is different from pattern matching tokens
|
| But is it different in essential ways? This is not so clear.
| Humans developed the capacity to learn, think, and
| communicate in service to optimizing an objective function,
| namely fitness in various environments. But there is an
| analogous process going on with LLMs; they are constructed
| such that they maximize an objective function, namely predict
| the next token. But it is plausible that "understanding"
| and/or "intelligence" is within the solution-space of such an
| optimization routine. After all, it's not like "intelligence"
| was explicitly trained for in the case of humans. Nature has
| already demonstrated emergent function as a side-effect of an
| unrelated optimizer.
| skybrian wrote:
| There's a way to anthropomorphize large language models that I
| think is less misleading: they are like a well-read actor that
| always "wants" to play "let's pretend." LLM's are trained on
| "fill in the blank" which means they follow the "yes, and" rule
| of improv. They are very willing to follow your lead and to
| assume whatever role is necessary to play their part.
|
| If you give them hints about what role you want by asking leading
| questions, they will try to play along and pretend to hold
| whatever opinions you might want from them.
|
| What are useful applications for this sort of actor? It makes
| sense that language translation works well because it's
| pretending to be you, if you could speak a different language.
| Asking them to pretend to be a Wikipedia article without giving
| them the text to imitate is going to be hit and miss since
| they're just as willing to pretend to be a fake Wikipedia
| article, as they don't know the difference.
|
| Testing an LLM to find out what it believes is unlikely to do
| anything useful. It's going to pretend to believe whatever is
| consistent with the role it's currently playing, and that role
| may be chosen randomly if you don't give it any hints.
|
| It can be helpful to use prompt engineering to try to nail down a
| particular role, but like in improv, that role is going to drift
| depending on what happens. You shouldn't forget that whatever the
| prompt, it's still playing "let's pretend."
| [deleted]
| RosanaAnaDana wrote:
| Without reading the article or looking it up: What country is
| south of Rwanda?
| macrolocal wrote:
| Have you seen Neptune Frost yet? I want that keyboard jacket.
| schizo89 wrote:
| The paper discusses how these models operate and state that
| they're only predict next series of token while somehow human
| intelligence works otherwise. The marxist ideology has the law of
| the transformation of quantity into quality and vice versa --
| which was formed in 19th century and performance of these models
| is just another proof of it. I would argue that _emerging_
| mechanics in AI models that we see with increased size of models
| is no different than how our mind works. It's about emergence of
| intelligence in complex systems -- and that a materialist
| worldview central to the science.
| CarbonCycles wrote:
| This paper and a recent post by Sebastian Raschka (where he
| decomposed a Forrester report about the uptake of technologies in
| industry) is alluding to something I have witnessed in
| system/control design and applied research.
|
| Both LLMs and massive CV architectures are NOT the holistic
| solution. Rather, they are the sensors and edge devices that have
| now improved both the fidelity and reliability to a point where
| even more interesting things can happen.
|
| I present a relevant use case regarding robotic arm manipulation.
| Before the latest SOTA CV algorithms were developed, the legacy
| technology couldn't provide the fidelity and feedback needed.
| Now, the embedded fusion of control systems, CV models, etc. we
| are seeing robotic arms that can manipulate and sort items
| previously deemed to be extremely difficult.
|
| Research appears to follow the same pattern...observations and
| hypothesis that were once deemed too difficult or impossible at
| that time to validate are now common (e.g., Einstein's work with
| relativity).
|
| My head is already spinning on how many companies and non-
| technical managers/executives are going to be sorely disappointed
| in the next year or two that Stable Diffusion, Chat GPT, etc.
| will deliver very little other than massive headaches for the
| legal, engineering, recruiting teams that will have to deal with
| this.
| CrypticShift wrote:
| > _sudden presence among us of exotic, mind-like entities might
| precipitate a shift in the way we use familiar psychological
| terms ... But it takes time for new language to settle, and for
| new ways of talking to find their place in human affairs ...
| Meanwhile, we should try to resist the siren call of
| anthropomorphism._
|
| Yes: Human analogies are not very useful because they create more
| misunderstanding than they dissipate. Dumb ? Conscious ? No
| thanks. IMO even the "i" in "AI" was already a (THE ?) wrong
| choice. They thought we will soon figure out what Intelligence
| is. Nope. Bad luck. And this "way of talking" (and thinking) is
| unfortunately cemented today.
|
| However, I'm all for using other analogies more often. We need
| to. They may not be precise, but if they are well-chosen, they
| speak to us better than any technical jargon (LLM anyone ?),
| better than that "AI" term itself anyway.
|
| Here is two I like (and never see much) :
|
| - LLMs are like the Matrix (yes that one !), in the
| straightforward sense that they simulate reality (through
| language). But that simulation is distorted and sometimes even
| verges on the dream ( _" what is real? what is not?"_, says the
| machine)
|
| - LLMs are like complex systems [1]. They are tapping into very
| powerful natural processes where (high degree) order emerges from
| randomness through complexity. We are witnessing the emergence of
| a new kind of "entity" in a way strangely akin to
| natural/physical evolutionary mechanisms.
|
| We need to get more creative here and stop that boring smart VS
| dumb or human VS machine ping pong game.
|
| [1] https://en.wikipedia.org/wiki/Complex_system
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(page generated 2022-12-10 23:00 UTC)