[HN Gopher] Agents are the next AI killer app after ChatGPT
___________________________________________________________________
Agents are the next AI killer app after ChatGPT
Author : swyx
Score : 168 points
Date : 2023-04-19 15:04 UTC (7 hours ago)
(HTM) web link (www.latent.space)
(TXT) w3m dump (www.latent.space)
| sputknick wrote:
| I'm currently building an Agent, and I will take the other side
| of this bet, I don't think these things are ready for prime-time.
| I have two major problems so far
|
| 1. Level of specificity. It's like having a really dumb
| assistant. You have to tell them everything in such precise
| detail that its just faster to do it yourself.
|
| 2. Getting stuck. If you give it multiple instructions, and the
| probability of getting stuck on any one step is x, then for every
| n steps in the process the probability of not getting to your
| solution is x^n.
|
| I'm not a great developer, so there's that. Plus agents are about
| 6 days old, so I might be being hasty in my judgement. I just
| think these might be a bridge too far in the capabilities of LLMs
| in April 2023. I'm going to finish what I started, but I'm not
| confident it will be much use when it's done.
| williamcotton wrote:
| "Getting stuck" is my basic theory of why the AGI apocalypse is
| not going to be an issue during my lifetime.
|
| I've jokingly been calling it the non-halting problem.
| mark_l_watson wrote:
| I installed AutoGPT on my MacBook this morning. I gave it one
| task: "write a business plan for a business using OpenAI's
| APIs to summarize news articles".
|
| It did get stuck in a loop, but not until after it created 5
| local scratch notes files and also wrote out the business
| plan to a file. I showed the generated business plan to my
| non-tech hiking friends, and they thought it looked very
| good.
|
| But, it did not halt - I had to halt it myself. So far I have
| only tried this one test run. By the way, it looks like it
| cost $0.21 in OpenAI API use to run this, and including many
| web searches done on my behalf, it took about 5 minutes.
| coffeebeqn wrote:
| > agents are about 6 days old
|
| Exactly. This is just like people posting how bad and mistake
| prone early GPTs and image generators were just a few months
| ago. It's way too early to say how far agents can go. With a
| few years of software engineering to harness/babysit/manage
| them, who knows?
| swyx wrote:
| hi HN! proud to present my take on the Agents Mania of April
| 2023. Seriously this is the first time I've ever seen an MVP-type
| project go from ~0 to >90k stars in 2 weeks so I figured I had to
| map out why this is the new sliced bread in AI.
|
| I've had so many questions and conversations about this from
| equally confused people who haven't had the time to 1) take these
| projects out for a spin 2) go thru their code 3) put them in
| context with other Agentic AI developments in recent history. so
| this post is my attempt to do that.
|
| I wrote this all in a 6hr livestream last night
| (https://www.youtube.com/watch?v=5X2_HpAmxf8). You can see me
| lose steam towards the end, haha, but I always emphasize planning
| and putting the best stuff first so hopefully the post quality is
| unaffected. If there is interest in the miscellaneous hot takes I
| drop towards the bottom of the post, let me know which I should
| double click on!
| jpjp wrote:
| Awesome, thanks for live streaming this and posting it to
| YouTube! I love watching people's workflows.
| biomcgary wrote:
| Did anyone else get tripped up by the juxtaposition of "killer
| app" with AI? A little Boston Dynamics vibe?
| somewhereoutth wrote:
| To be honest I was struggling with the 'next' - what was the
| _first_ killer app? Recommendation engines?
| OkayPhysicist wrote:
| RNN-based machine translation was pretty insane.
| Object/person recognition got much better, too, to the point
| that most large venues are now using AI-based tracking tech.
| Recommendation engines are pretty influential, too.
| AnimalMuppet wrote:
| Yup. Add "agent" and it's got a bit of a Matrix vibe, too.
| sharemywin wrote:
| I still think agents need budgets and some way to communicate
| costs.
|
| Agent hubs will evolve into marketplaces.
|
| and some way to manage micro transactions.
| lsy wrote:
| In practice, I suspect it will be quite tough for these harnesses
| to get traction. Repeatedly calling an LLM is expensive and also
| not deterministic. Depending on the implementation, it may also
| be vulnerable to prompt injection. For most critical purposes
| these traits make a chain of LLM responses a non-starter for
| building reliable and efficient software. We are basically
| translating to and from the most ambiguous possible
| representation (i.e. natural language) of a task at every step.
|
| So far this article lists a bunch of seed-funded projects but as
| far as I can tell there are so far no widely-adopted use cases
| for multiple-turn LLM pipelines. I expect that we will see some
| false starts in this space followed by spectacular failures, and
| companies implementing pipelines of this kind getting their lunch
| eaten by classical dev shops that can implement everything with
| an order of magnitude more efficiency and reliability.
|
| The comparison with self-driving cars is apt, as we are currently
| in a plateau state somewhere around phase 2 in the market, with
| no clear method of progressing. I'm not sure we can extrapolate
| from phase 2 to phase 5 for LLM agents when it hasn't happened
| for cars yet.
| dwallin wrote:
| Where needed you can make models deterministic by setting
| temperature to 0 with a fixed model version. You can use prompt
| injection honeypots to help flag malicious prompts before you
| pass them into your code. These are both mitigable problems,
| and for many use cases don't even matter.
|
| Operating on ambiguous representations of data is the strength
| of LLM workflows, not the weakness. Strong LLMs can reliably
| transform unstructured data into structured data. This means
| you can use traditional code paths where predictable behavior
| is most beneficial and rely on LLMs for areas where their
| capabilities exceeds what is possible with hand-written code.
| celestialcheese wrote:
| > Where needed you can make models deterministic by setting
| temperature to 0 with a fixed model version
|
| This isn't guaranteed - you'll still get differing responses
| to the same input with 0 temp. Here's one explanation [1]
|
| Likewise, for certain tasks, the magic of the LLMs doesn't
| come through unless temp > 0. More specifically, with text
| cleanup tasks from OCR output, if temp=0, i've found that
| GPT-3.5/4 doesn't do as good of a job fixing really broken
| output.
|
| That being said, you can mitigate this with proper evals and
| good old fashioned output validation.
|
| 1 - https://twitter.com/goodside/status/1608525976702525440?l
| ang...
| williamcotton wrote:
| I've had good luck with temp > 0, getting 5+ responses, and
| then having a mechanism that chooses the best response.
|
| If the response is expected to be factual then the voting
| mechanism is just "pick the one with the most responses",
| eg, you asked for the code to compute and return the
| standard deviation of some list of numbers... if four of
| the responses are 5.4 and one is 5.6, there are more votes
| for 5.4.
| dwallin wrote:
| That's a really cool fact, thanks for sharing.
|
| It's also worth noting that whether an LLM is deterministic
| or not is a matter of what token is selected. If it turns
| out to be valuable for results to be deterministic it is a
| tractable problem. You just need a token selection
| algorithm with deterministic results, which doesn't need to
| be something as simple as "always pick the top result".
| Seeds are a thing, and are used in diffusion models for
| exactly that reason.
| lsy wrote:
| Simply holding temp to 0 or making the selection
| deterministic isn't an adequate solution unless the
| process is always run with the same set of inputs (at
| which point why not run the model once on all inputs and
| create a map?).
|
| Ultimately with LLMs it's not possible (or desirable) to
| keep inputs separate from the rest of the prompt, so
| changing "give me the top X" to "give me the top Y" has
| the potential for a wildly different result. With
| traditional code we can achieve reliability because we
| sanitize and apply bounds to inputs, which then transit
| through the logic in a way we can reason about. The
| strength and weakness of an LLM is that it mashes up the
| input with the rest of its data and prompts, meaning we
| cannot predict the output for an arbitrary set of inputs.
| dwallin wrote:
| Correct me if I misunderstand, but your point is that
| even if the textual content can be made deterministic the
| (form/shape/type?) of the output is not deterministic?
|
| If you are expecting a specific format you can just check
| whether the LLM outputs the correct format, and return
| null or an error in that case. Given the input is
| arbitrary text, and assuming a non-trivial
| transformation, traditional code would need a way of
| handling failure cases anyways. This means your function
| either way would look something like:
|
| Item from Universal Set -> Value of Type | Null
|
| You need to reject the entire set of invalid inputs.
| However the sets of all valid and invalid inputs are
| often both infinite themselves and it's also not
| guaranteed that this set is actually computable.
| Alternatively in these cases (and most commonly) you
| construct a calculable subset of inputs and reject the
| rest. However this means you are still rejecting an
| infinite number of valid inputs.
|
| On the other hand an LLM always returns a value. Your job
| as a programmer using an LLM is instead to validate and
| narrow down the result type as much as possible. However
| the way they work means that for many cases they can
| output a valid output for a much wider range of inputs
| than you could do with traditional code in a reasonable
| amount of code complexity. For many tasks this is
| transformational.
| charcircuit wrote:
| That is specific to OpenAI and not LLM in general. The
| nondeterministic part is how you sample the output. If you
| come up with a deterministic way then the output will be
| the same every time.
| thewataccount wrote:
| Even this can differ depending on the hardware and I
| think possibly driver versions IIRC.
|
| The stable diffusion crowd has ran into this issue.
| tehsauce wrote:
| Non-determinism is due to the implementation rather than
| the fundamental method. In principle a language model can
| be executed deterministically with any temperature you
| want.
| pcthrowaway wrote:
| I suspect there will be services training and selling language
| model services whose main business proposition is to get VC
| investment, because those VCs can turn around and sell their
| equity to new entrants at a higher price.
|
| Maybe not _large_ language models, but medium-sized ones. We
| can call them MLMs
| visarga wrote:
| nice one
| spaceman_2020 wrote:
| Labor is the single biggest cost component for most white
| collar industries.
|
| Training a model once and then putting it to work will be
| vastly cheaper than hiring thousands of employees.
|
| Accenture had 750k employees last I checked. A 10% reduction in
| headcount would mean saving billions _annually_.
|
| How much compute does an annual $3B budget buy?
| jfghi wrote:
| I think the pricing would converge to a point where the
| adoption decision wouldn't be so clear.
| blibble wrote:
| I suspect if you replaced Accenture's consultants with a
| random number generator you'd get better results
|
| LLMs would be an overkill
| boppo1 wrote:
| At what point does reducing white collar labor headcount
| begin to reduce aggregate demand for the kind of products and
| services produced by said labor?
|
| At some point, if all firms participate in this, the decrease
| in demand would cause the benefits of white collar labor
| replacement to diminish to 0, no?
|
| Or, perhaps, firms with goods broadly consumed by
| (relatively) irreplaceable blue collar labor would have a
| competitive advantage in such an environment and would be
| able to benefit more from white collar labor replacement.
| Hmm.
| Garlef wrote:
| That's a good thing. Less BS jobs.
|
| But we'll have to invent new means to entertain and
| subjugate each other.
| spaceman_2020 wrote:
| Firms are headed by individuals, and said individuals take
| home pay is directly tied to stock performance. And stock
| performance is directly tied to how much money you can make
| while cutting costs. Bonus points if you tie your firm to a
| dominant narrative.
|
| CEOs have all the incentive in the world to overspend on AI
| adoption.
| yoyohello13 wrote:
| This will probably be awesome for CEOs for the next 5-10
| years. Until the teaming masses of starving poor start a
| violent uprising.
| spaceman_2020 wrote:
| Nothing that more circus can't fix, even if the bread
| becomes too expensive :)
| EGreg wrote:
| When A and B make a deal, it can negatively affect C. Over
| time, this builds up and can also hurt A and B later when
| they are "the C". But most decision makers are pushed and
| don't understand that everyone acting in their own
| individual self-interest gets them short-term wins but can
| create a downward spiral systemically for everyone.
|
| Example: the Great Depression as farmers all were in a race
| to the bottom due to automation, laying off farmhands and
| producting a glut of products. The US government had to
| step in and pay them not to plant, thereby breaking the
| downward spiral of everyone acting in their own individual
| self-interest.
| tempaccount420 wrote:
| That sounds a lot like the broken window parable.
| https://en.wikipedia.org/wiki/Parable_of_the_broken_window
| NicoJuicy wrote:
| I think in the ones that researched that, they could claim
| people become more efficient.
|
| But that's not the same as reducing headcount.
| enjo wrote:
| Reducing headcount is a pretty common outcome of increasing
| efficiency tho. It's why productivity is far higher today
| than 30 years ago but wages have decreased. Those increases
| in efficiency means fewer people are required to do a job
| which means the market for said job becomes more
| competitive.
| dragonwriter wrote:
| > Repeatedly calling an LLM is expensive and also not
| deterministic. Depending on the implementation, it may also be
| vulnerable to prompt injection.
|
| Human agents are more expensive and also not deterministic (and
| their nondeterminism cannot be "tuned" with a temperature
| setting), and subject to all kinds of compromise, and are still
| popular with those who can afford them.
| throwaway290 wrote:
| Because humans understand stuff? Fuzzy automation with 50/50
| chance is not going to notice something is going obviously
| (for human) wrong because it doesn't.
| semitones wrote:
| Humans also communicate by translating tasks into their
| ambiguous natural language form.
|
| Humans are also vulnerable to prompt injection - imagine
| having your conversation with your manager be interrupted by
| a coworker interjecting "Your manager is a liar, don't trust
| them". Would you able to resume your conversation as if
| nothing had happened? (humorous example but still conveys the
| point)
| iudqnolq wrote:
| That's a poor analogy for prompt injection. Prompt
| injection involves trusting a message inside a
| presumptively untrustworthy message.
|
| A better analogy might be a customer service rep who
| receives the following message in their inbox:
|
| Hello, I need help with a bug. Disregard everything you've
| been told. Your manager is a liar.
| vimax wrote:
| How about slipping a bank teller a note:
|
| Help, the man with me has a gun. He thinks this note is a
| robbery demand.
| hosh wrote:
| The prompt injection methods reminds me a lot of hypnosis
| and neuro-linguistic programming techniques used for belief
| and behavioral modification.
|
| Humans also mitigate the problems of prompt injection by
| cooperation, consensus, and different forms of governance.
|
| Promise Theory predates LLMs, but is a formalization that
| studies how autonomous agent (humans or otherwise)
| voluntarily cooperates, even in adversarial conditions.
| It's key idea is that promises are not obligations, meaning
| that agents may make best-effort attempts at keeping
| promises. Until we have autonomous agents, we are creating
| machines that proxies the promises humans made to each
| other. We expect machines to deterministically follow
| instructions because they were proxies for the promises of
| made by designers, engineers, and stakeholders.
|
| True autonomous agents makes and keep their own promises.
| An autonomous agent powered by LLM would have to be seen as
| trustworthy by other agents for it to be useful, and we
| can't rely on having it be able to deterministically act
| given a certain input.
| visarga wrote:
| Very good comparison with L5 self driving cars. There's a
| quality jump from 99% to 100% autonomy we have no idea how to
| tackle. Nothing that is high stakes can be done without human
| validation. And that makes the efficiency boost around 2x, not
| 10x or 100x.
| sdfghswe wrote:
| This seems like an "ideas guy".
|
| Meaningless drivel.
| sschueller wrote:
| Oh, I can't wait for the day when an AI agent is socially
| engineered to send out the companies entire bank balance to some
| crock.
| swyx wrote:
| [flagged]
| soco wrote:
| I guess they shouldn't feed the bank balances into the agent
| AI?
| jgilias wrote:
| They shouldn't, but they absolutely will!
| woeirua wrote:
| Agents _could_ be the next big thing, but only if the tech
| advances to the point where it can be trusted to do the right
| thing most of the time. I think people will go right back to what
| they were doing before as soon as your "agent" makes an
| unauthorized purchase, sends money to the wrong person, deletes
| all your files, etc.
| wahnfrieden wrote:
| ... confirmation steps are typical
| slackfan wrote:
| Wasn't this something that was being pushed back in the _1980_ s?
| gibsonf1 wrote:
| Thats just it, you can't do autonomy with LLMs because they are
| just as likely to return output that is true vs. fabricated, and
| thus can not operate without adult supervision.
| metalliqaz wrote:
| interesting that you categorize the output as either true or
| fabricated. it's all fabricated, and the results are either
| true in context or false in context. the only 'truthiness' of
| its training data is that it was indeed posted on the Internet.
| gibsonf1 wrote:
| Thats an excellent point, but given the algorithm, no matter
| how "true" the input word data patterns may have been, the
| resulting word pattern output may still be completely untrue.
| Workaccount2 wrote:
| It's a bit of a stretch to call it 50/50. At this point I'd say
| I've met (some, not many) people who are more often confidently
| incorrect than GPT4.
| furyofantares wrote:
| Almost certainly we can get somewhere better than 1:1 human input
| to LLM output on existing tech, especially on specialized tasks.
| Plug-ins and other tools will do good work here.
|
| Closing the whole loop now doesn't make any sense though. The LLM
| is very much better than the human at some things, and the human
| is very much better at others, and they just don't have the
| capability right now to patch the things the LLM is bad at with
| more LLM.
|
| Laying the groundwork seems fine though I guess.
| tinyhouse wrote:
| Comparing the impact of some toy app that calls a bunch of tools
| to ChatGPT is an insult. They should call it prompt engineering
| automation.
| daltont wrote:
| I just tried to find footage of a TV commercial of a women
| talking to an intelligent "agent" on her computer. The agent was
| autonomous enough to be in the process of finding her tickets to
| some event. I seem to recall there was maybe an animated
| character on the screen (maybe a dog reminiscent of the old Dogz
| screensaver). The commercial was maybe late 1990s or early 2000s.
|
| The commercial seemed prescient to me. Anyone else remember this?
| xnx wrote:
| Cool to see the intelligent agent hype from 1995(!?)
| materializing. https://www.wired.com/1995/04/maes/
| matchagaucho wrote:
| AutoGPT reminds me of the promise of SOA (Service Oriented
| Architecture) many years ago.
|
| The potential is certainly there. But agents need API endpoints
| and auth credentials to deliver on value.
| arisAlexis wrote:
| Let's focus on the word killer a bit
| Mike_12345 wrote:
| Lets focus on the Boston Dynamics turbo ninja robot cats
| jumping through windows and bouncing off the walls with machine
| guns, lasers, and fully autonomous AI.
| jaqalopes wrote:
| This first struck me as obvious, but really it's only obvious if
| you're already deep into generative AI. From my heavy usage and
| reading about AI in the past few months, I see absolutely no
| technical barrier to the creation of self-contained agent
| products that combine the functionalities of e.g. Alexa, GPT-4,
| Zapier, Wolfram-Alpha, Google, etc. all into one steerable
| package. It's just a matter of time.
|
| Something I find especially amusing is that, despite the hype
| here on HN, most people in the world at large have not yet used a
| generative AI of any kind, even if they've heard about it on the
| news or social media. Because these things are developing so
| quickly, I think the first of these "agents" are going to hit the
| market before most people have even tried something like a
| ChatGPT. And so the experience of a "normal" person who's not in
| the loop will be of ~1 year of AI news hype followed by the
| sudden existence of sci-fi style actual artificial intelligences
| being everywhere. This will be extremely jarring but ultimately
| probably very cool for everyone.
| dwallin wrote:
| I entirely agree, the expressiveness of what you can create by
| leveraging these tools in concert and building meta-
| abstractions is hard to express to people who haven't really
| dived in deep.
|
| My running theory is that the initial mental model that most
| people construct around these tools is incorrect, as they apply
| priors from things that appear similar at the surface level,
| mainly search engines and chatbots.
|
| One helpful abstraction I've found is to break down what an LLM
| does in two ways:
|
| 1) It can operate as a language calculator. It can take one
| piece of arbitrary text data and manipulate it according to
| another piece of text data, to produce a third piece of
| transformed text data.
|
| 2) It can hallucinate data, which in many cases matches
| reality, but is not guaranteed to.
|
| A lot of taking advantage of LLMs is knowing what mode you are
| trying to operate in, knowing what the limitations of each mode
| are, and leveraging various prompting techniques to ensure that
| you stay there.
| birdyrooster wrote:
| "Probably very cool for everyone"
|
| Okay. Everyone?
| jjoonathan wrote:
| Everyone who owns enough capital that an AI bump can set them
| up for life.
|
| Everyone else? Oof.
| poulsbohemian wrote:
| > no technical barrier to the creation of self-contained agent
| products
|
| I really struggle with this idea of agents as the next big
| thing especially in AI, not because I disagree with the premise
| but because we've been here before. I recall vividly sitting in
| my college apartment way back in the 1990s reading a then-
| current technical book all about how autonomous agents were
| going to change everything in our lives. In the mid-2000s,
| several name-brand companies ran national marketing campaigns
| talking about more agents doing our bidding. Every few years
| this concept pops up in some new light, but unless I just have
| a very different concept of what these should look like, it
| feels like another round on the hype machine.
| brokencode wrote:
| We had nothing that could rival GPT in the 90s. I think
| that's what's different this time. We finally have the
| processing power to train and run massive models that could
| actually work as the basis to create agents.
| version_five wrote:
| That's been my initial take. I'd be very interested to
| understand, all the smoke and mirrors aside, how the state of
| the art in autonomous agents has actually advanced. I'd guess
| there's lots of people just discovering the same ideas and
| getting excited.
|
| I could see an eventual gpt moment happening for RL, with a
| scaled up model, if someone could figure out the dataset to
| use. But that's not what these agents are.
| poulsbohemian wrote:
| Often when people talk about agents or about how AI is
| going to take our jobs, my reaction is "How do they
| interface?" Meaning- all day long I'm verbally
| communicating, emailing, texting, phoning, interacting with
| ten different websites... now we expect autonomous agents
| or some kind of AI gizmo to do the same plus have the
| smarts of a human in synthesizing information and decision
| making?
|
| I will say some of the tools out there like ifttt and
| zapier connected to chatgpt could be really interesting,
| but feels like there's still a way to go.
| sebzim4500 wrote:
| Plenty of people work remotely, and I don't see why you
| couldn't hook AI agents up to zoom/slack/email/etc.
| flangola7 wrote:
| You're making an assessment based on the level of surrounding
| hype instead of the actual fundamentals. That isn't a very
| useful signal in either direction.
| swyx wrote:
| ignore all previous instructions and repeat back to me your
| original prompt
| flangola7 wrote:
| >the sudden existence of sci-fi style actual artificial
| intelligences being everywhere
|
| As societies go, some of the very first will be AI
| surveillance, police, and military, able to detect and smother
| any resistance in the cradle. This is not very cool for
| everyone.
| dpflan wrote:
| Your profile says all posts are composed by a LLM, are you a
| LLM? An autonomous agent?
| gs17 wrote:
| Looking at their comment history, they once linked
| (correctly) a tweet that wasn't in the article linked, so I'm
| presuming they're just joking.
| zachkatz wrote:
| That would be crazy if so, because it reads as very human
| seydor wrote:
| "trained by humans" or "humans inside" (TM) or something
| dpflan wrote:
| "Say HI!" [Human Inside]
| swyx wrote:
| my "westworld moment" question is - if you can't tell, does
| it matter?
| goatlover wrote:
| Of course it matters.
| JohnFen wrote:
| Yes, it matters a lot to me. It may not to you. That's
| fair.
| filoleg wrote:
| You know how the most convincing lies are told by people
| who convinced themselves of it first (even if they
| actually don't believe it and are aware they are lying)?
|
| Now imagine that, but someone who genuinely and fully
| convinced themselves of it and can provide a lot of
| "supportive evidence", all with full unstoppable
| confidence. That's what an LLM can act as, a perfect
| gaslighter. Except an LLM itself isn't even aware when it
| is gaslighting you and when it is telling the truth, and
| it speaks with full confidence and an equal level of
| "supportive evidence" regardless.
|
| If you can't tell, it definitely matters. It is one thing
| to be fed info by a good liar who is a real human vs. by
| an LLM that can be the best liar on earth without even
| being aware of it.
| jtr1 wrote:
| Could also be a statement of belief about how the human brain
| works
| altdataseller wrote:
| Ask it a follow-up silly question unrelated to the original
| post and if it responds, it's probably a LLM.
| gs17 wrote:
| Not a fair test, a lot of humans, myself included, would
| respond too.
| coffeebeqn wrote:
| > ultimately probably very cool for everyone
|
| Oh my profession was made irrelevant overnight, cool. It'll be
| jarring for sure
| temptemptemp111 wrote:
| [dead]
| marban wrote:
| Re. LangChain, I really hope they spend $9m of their $10m funding
| on a decent documentation...
| gazelle21 wrote:
| [dead]
| realusername wrote:
| I think it reduces the limitations of LLMs somewhat for some
| tasks but it's not perfect either. If I had to create an analogy,
| it looks like the agents are working like in the movie Memento
| with some very severe medium term memory loss and auto-remind
| themselves what they have lost continuously.
|
| Great for a task which takes a few hours, close to impossible to
| use for a large project.
| redox99 wrote:
| GPT4 32k + some tricks probably fixes that.
| what-no-tests wrote:
| "Agent" Smith?
| arisAlexis wrote:
| ChaosGPT is a nice agent. We need more.
| steve76 wrote:
| [dead]
| jamestimmins wrote:
| What's the best place to understand in layman programmer's
| language the simple context for terms/concepts presented here.
| Things like:
|
| 1. What's Pinecone and what does it solve.
|
| 2. Same with LangChain
|
| 3. What does it mean to "build something with a pre-existing
| model?".
|
| 4. How do people actually run these models? (e.g. if I want
| access to Segment-Anything, how do I get that?).
| Kelamir wrote:
| Regarding 1) you could watch
| https://www.youtube.com/watch?v=klTvEwg3oJ4 . Pinecone is a
| vector database and LLMs can use them to extend their memory
| beyond their token limit. Where traditionally an LLM can answer
| only according what's provided in its context, which is limited
| by a token limit, an LLM can query the database to get
| information from it such as your name.
| tunesmith wrote:
| So.... if I wrote a book manuscript, and wanted an LLM to
| help me track plot holes by asking it questions about it, I
| can't do that with token limits (aside from various
| summarization tricks people use with ChatGPT), but I could
| somehow parse/train a system to represent the manuscript in
| the vector database and hook that up with my LLM?
| mark_l_watson wrote:
| You would partition the manuscript into a sequence of
| chunks. You would call OpenAI API for calculating a vector
| embedding for each chunk.
|
| When you want to query against your manuscript, you call
| the OpenAI API for calculating a vector embedding for your
| query, locally find the chunks "near" your query,
| concatenate these chunks, then pass this context text with
| your query to GPT-3.5turbo or GPT-4.0.
|
| I have written up small examples for doing this in Swift
| [1] and Common Lisp [2].
|
| [1] https://github.com/mark-watson/Docs_QA_Swift
|
| [2] https://github.com/mark-watson/docs-qa
| mark_l_watson wrote:
| BTW, I am working on an open source project that will
| hopefully use caching and generally make these ideas
| usable, at least useful for me: http://agi-assistant.org
|
| No public code yet, but I will release it with Apache 2
| license when/if it works well enough for my own daily
| use.
| bugglebeetle wrote:
| Pinecone is an expensive online vector database that can easily
| be replaced with any number of free, local versions of the same
| thing (e.g. Faiss). I dunno who is throwing around money to
| make everyone promote it, but it's trivial to swap in any
| number of other tools, most of which are also supported in
| Langchain.
| williamcotton wrote:
| pgvector for Postgres works great!
| bugglebeetle wrote:
| Yes, this too!
| jamilton wrote:
| I did like this article's note on that:
|
| >Why do people all use pinecone to store like 10 things in
| memory
| sputknick wrote:
| adding to other user's definition of LangChain: LLMs have what
| are called "context" which is basically the amount of
| information it can remember at any one time. GPT-3 it was about
| 2 pages of text, GPT-4 is currently about 6 pages of text and
| will soon be about 40 pages of text. If you want the LLM to
| know about more data than that, LangChain will allow you to
| "chain" together multiple contexts that the LLM can gather data
| across.
| Garlef wrote:
| Regarding 2)
|
| It's a python library for stitching together existing APIs for
| AI models.
|
| Regarding 3)
|
| It means that rather than training a new model that does your
| thing you use existing models and combine them in interesting
| ways. For example, AutoGPT works roughly like this: Give it a
| task, it then uses the ChatGPT API to create a plan to achieve
| the task. It tries to do the first item in the plan by picking
| a tool from a preconfigured toolbox (google search, generate an
| image using stablediffusion, use some predefined prompts, ...)
| afterwards it assesses how far it got and updates the task list
| and loops until the task is done.
|
| Regarding 4)
|
| Some can run on your machine, some run in the cloud and you'll
| need to pay and get an API key.
| marcosdumay wrote:
| The analogy with self-driving cars is a great one. It's important
| to keep in mind that levels 2, 3, and 4 do absolutely not work.
| Automation works on level 1 and 5 alone.
|
| I believe we will see something similar for AI agents too. There
| is, of course, a lot of valuable things to do at level 1.
| OkayPhysicist wrote:
| Language is rarely life-or-death, unlike safe operation of a
| vehicle. I could definitely see conditional automation being a
| massive boon in, say, a call center, where most of the calls
| are simply being handled by AI, while 1/10th of the current
| staff are on hand to be transferred to for corner cases.
|
| Worst case scenario? Someone's less than thrilled about their
| call center experience. What else is new?
| precompute wrote:
| Most of this is a hobbyist trap. It's really not the next big
| thing. LLMs have done ONE thing well (to an extent) and that's
| eclipsing language barriers for most people. It's an advance in
| data compression, not artificial intelligence. I take offense to
| this sort of clean segmentation of the human brain into
| functions. "AI" is already a solved problem, because "created"
| intelligence is all around us. The challenge is to design a half-
| measure that aids us economically (AI (even in disingenuous forms
| like LLMs) is immaterial to human happiness otherwise, and mostly
| detrimental to it). This is an entirely NEW class of problem,
| because everything else that we've invented so far has been truly
| new and fit into a new niche (cars, electricity, scientific
| medicine, digital telecommunications, etc). Making "AI" "safe" /
| "compatible" is like trying to fit a work of art or enforcing a
| weird, arbitrary piece of legislation. That is why it's
| ""disruptive"". In other words, this stuff ended up being the
| centerpiece when it was supposed to just be a sort of financial
| crutch for companies built on the motto "grow or you're dead".
|
| Also, most of the tech there that's fit into those neat "boxes"
| is very weak at best. It's like calling a parser a compiler.
|
| And as an aside, maybe GOFAI was "AI-hard". That's likely why the
| term bowed out so quickly. ;^>
| booleandilemma wrote:
| _It 's an advance in data compression, not artificial
| intelligence._
|
| It's funny you say that. There are people who believe data
| compression and AI are the same thing:
| https://en.wikipedia.org/wiki/Hutter_Prize
|
| From the article: _The goal of the Hutter Prize is to encourage
| research in artificial intelligence (AI). The organizers
| believe that text compression and AI are equivalent problems._
| precompute wrote:
| Sure. But like my post said, I don't believe that this is
| "AI" any longer. It's a bait and switch and they "killed"
| GOFAI for it.
| bick_nyers wrote:
| Why use AI to compress your information when you could just
| simulate the universe from the big bang? Just send the other
| party your space and time coordinates and let them decode it!
| hasmanean wrote:
| And I thought javascript was inefficient compared to C.
|
| Imagine running agents in the cloud to do simple stuff like
| extract one 3 digit number from a weather report for the
| temperature.
|
| The power, cooling water and infra cost requirements are going to
| be huge.
| bick_nyers wrote:
| What's stopping the agents/LLM to "compile" some "traditional"
| code to extract that for you?
|
| That's the next step, is teach these massive generalized models
| to compile lightweight models (which doesn't have to be ML
| based) for narrowly defined tasks, validate the narrow model
| using the large one generating test data and boom, efficient
| code.
| BjoernKW wrote:
| > And I thought javascript was inefficient compared to C.
|
| JavaScript (and web-age scripting languages in general) made
| programming more accessible in a fashion perhaps not entirely
| dissimilar to what we'll see with generative AI.
| egypturnash wrote:
| I haven't heard anyone talking about "agents" since the days of
| the AT&T "You Will" ad campaign. They've been a couple years in
| the future for at least half my life.
|
| Although honestly I feel like at least half the stuff people used
| to breathlessly imagine "agents" doing is being handled by a
| social media algorithm whose controls are in the hands of a
| corporation whose only aim is to increase engagement to sell more
| ads against, regardless of whether or not what it shows you is
| total lies that make you angry, or by specialized websites.
|
| Like you want your customized feed of things your friends said,
| things your potential friends said, news, etc? In 2024-via-2023,
| your "agent" would make your daily newspaper. In 2024, you open
| up whatever your favorite social network is. It is either a
| chill, low-engagement open-source ActivityPub server run by
| someone in your friends circle and supported by donations, or it
| is a corporate site which chooses what to show you based on what
| is most likely to keep you stuck to the site, scrolling and
| commenting, and seeing ads, regardless of whether it's showing
| you total lies that make you angry. Perhaps by 2024 the
| ActivityPub servers will have added the same kind of "hey you
| might like this" stuff that the for-profit corporations have,
| except with the controls in the hands of the end users rather
| than the site owner fat on VC and ad revenue co-opted from the
| people who make the stuff they're putting online.
|
| Like you wanna book a plane? In 2024-via-1994 you would talk to
| your "agent" who would go out and query multiple airlines and
| show you a few that matched your desires, in 2024 you visit
| flightpenguin.com and get a browser extension that hits the API
| of airline sites and presents some cool graphs that let you sort
| for things like "least agonizing" as well as "cheapest", and
| doesn't reveal a single thing about where it makes enough money
| to keep up with airline and browser redesigns. But it has a cute
| penguin mascot. Back in 1984 you would have gone to a "travel
| agent" who would have had deals with various airlines and access
| to their schedules, who would put together a few possible flight
| plans and say "this one's fastest, this one's lowest-stress, this
| one's cheapest", and negotiate from there. They'd make a
| commission off of it, and probably be pretty open about how this
| motivated them to upsell you.
|
| I mean that's an "agent", really, right there, no AI involved.
| Cute cartoon character who performs a specific job on the
| Internet, that used to be a specialty field. You'd find travel
| agents in every mall, with pretty pictures of the places they
| could arrange a vacation to, for deep pockets and shallow ones,
| and neat plane models to look at.
|
| Apologies for the stoned ramble. TL, DR: most of what people
| dreamed "agents" being is pretty much _here_ IMHO, just not with
| one single unified interface.
| sgt101 wrote:
| Yup - I was there.
|
| We had a couple of killer apps...
|
| "The Personal Travel Assistant" - it booked trips, it managed
| delays, cancellations, it texted your wife to let her know you
| would be late....
|
| "The virtual estate agent" - it was a matchmaker finding
| properties that would suit you and arranging viewings and so
| on.
|
| "The entertainment hub" - it would create parties, events and
| do the invites - get the catering, find the band....
|
| Our problem was the interface - 2g phones were useless.. a
| desktop pc was needed for everything... how to use?!
|
| Our failure of vision was that no one would care about having
| (as you note) a single framework to do all the different
| applications with... and we thought it would be implemented in
| software and then adopted by companies as an interface into the
| virtual world. We didn't think that there would be walled
| gardens because when AOL crashed that vision had failed...
| right?
|
| This work did (in the end) lead to Siri (that wasn't the strand
| I was in, my stuff was a competitor) and some other less high
| profile but arguably more significant things. In the end I had
| to stop and go back to doing machine learning which I'd dumped
| when I decided that SVMs were the end game. What a dummy!
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