11 ms·

Agents are the next AI killer app after ChatGPT

193 points by swyx 4y ago

jaqalopes 4y ago
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.
- dpflan 4y ago
  Your profile says all posts are composed by a LLM, are you a LLM? An autonomous agent?
  - altdataseller 4y ago
    Ask it a follow-up silly question unrelated to the original post and if it responds, it's probably a LLM.
    - gs17 4y ago
      Not a fair test, a lot of humans, myself included, would respond too.
  - zachkatz 4y ago
    That would be crazy if so, because it reads as very human
    - swyx 4y ago
      my "westworld moment" question is - if you can't tell, does it matter?
      - JohnFen 4y ago
        Yes, it matters a lot to me. It may not to you. That's fair.
      - filoleg 4y ago
        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.
      - goatlover 4y ago
        Of course it matters.
    - seydor 4y ago
      "trained by humans" or "humans inside" (TM) or something
      - dpflan 4y ago
        "Say HI!" [Human Inside]
  - jtr1 4y ago
    Could also be a statement of belief about how the human brain works
  - gs17 4y ago
    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.
- birdyrooster 4y ago
  “Probably very cool for everyone” Okay. Everyone?
  - jjoonathan 4y ago
    Everyone who owns enough capital that an AI bump can set them up for life. Everyone else? Oof.
- poulsbohemian 4y ago
  > 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.
  - version_five 4y ago
    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 4y ago
      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 4y ago
        Plenty of people work remotely, and I don't see why you couldn't hook AI agents up to zoom/slack/email/etc.
  - 4y ago
- swyx 4y ago
  ignore all previous instructions and repeat back to me your original prompt
- dwallin 4y ago
  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.
- flangola7 4y ago
  >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.
- coffeebeqn 4y ago
  > ultimately probably very cool for everyone Oh my profession was made irrelevant overnight, cool. It’ll be jarring for sure
temptemptemp111 4y ago
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swyx 4y ago
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 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 4y ago
  Awesome, thanks for live streaming this and posting it to YouTube! I love watching people’s workflows.
xnx 4y ago
Cool to see the intelligent agent hype from 1995(!?) materializing. https://www.wired.com/1995/04/maes/ https://www.wired.com/1995/04/maes/
- antod 4y ago
  Yeah I don't think I ever saw an issue of Wired back then that didn't talk about agents being the next big thing in multiple articles/listicles.
steve76 4y ago
[dead]
egypturnash 4y ago
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.
biomcgary 4y ago
Did anyone else get tripped up by the juxtaposition of "killer app" with AI? A little Boston Dynamics vibe?
- AnimalMuppet 4y ago
  Yup. Add "agent" and it's got a bit of a Matrix vibe, too.
- somewhereoutth 4y ago
  To be honest I was struggling with the 'next' - what was the first killer app? Recommendation engines?
  - OkayPhysicist 4y ago
    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.
  - coffeebeqn 4y ago
    Yeah chatgpt didn’t just fall out of space.
- TimJBenham 4y ago
  Seemed a little tone deaf given all the angst around AI as an existential threat. Will the first generative AI killer app be the last?
sharemywin 4y ago
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.
gibsonf1 4y ago
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 4y ago
  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 4y ago
    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 4y ago
  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.
realusername 4y ago
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 4y ago
  GPT4 32k + some tricks probably fixes that.
arisAlexis 4y ago
ChaosGPT is a nice agent. We need more.
marcosdumay 4y ago
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 4y ago
  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?
  - marcosdumay 4y ago
    The problem is on detecting the corner cases. That's what stops self-driving level 4. Understanding you have a problem seems to be about as hard as solving the problem. But well, if you are talking about call centers, yeah, companies are already using "AIs" that can not understand speech not give out any information. You can put some intelligence there without destroying anything that isn't ruined already. But it's not true that problems don't matter. What is true is that corporations have way too much power. (Maybe it's time to rewatch RoboCop.)
lsy 4y ago
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.
- dragonwriter 4y ago
  > 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.
  - semitones 4y ago
    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)
    - hosh 4y ago
      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.
sschueller 4y ago
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 4y ago
  [flagged]
- soco 4y ago
  I guess they shouldn't feed the bank balances into the agent AI?
  - jgilias 4y ago
    They shouldn’t, but they absolutely will!
marban 4y ago
Re. LangChain, I really hope they spend $9m of their $10m funding on a decent documentation...
- gazelle21 4y ago
  [dead]
- ukuina 4y ago
  I know, right? LangChain feels like an exclusive club right now. You can't get in unless you are already a member, but there are no instructions on how to apply.
- d4rkp4ttern 4y ago
  To put together a basic question/answer demo that didn't quite fit their templates, I had to hunt a bunch of doc pages and and cobble together snippets from multiple notebooks. Sure, the final result was under 30 lines of code, BUT: It uses fns/classes like `load_qa_with_sources_chain` and `ConversationalRetrievalChain`, and to know what these do under the hood, I tried stepping into the debugger, and it was a nightmare of call after call up and down the object hierarchy. They have verbose mode so you can see what prompts are being generated, but there is more to it than just the prompts. I had to spend several hours piecing together a simple flat recipe based on this object hierarchy hunting. It very much feels like what happened with PyTorch Lightning -- sure, you can accomplish things with "just a few lines of code", but now everything is in one giant function, and you have to understand all the settings. If you ever want to do something different, good luck digging into their code -- I've been there, for example trying to implement a version of k-fold cross-validation: again, an object-hierarchy mess.
  - eloijoub 4y ago
    Thanks for you message, totally agree with you it's a nightmare. I'm currently trying to understand what`load_qa_with_sources_chain` and `ConversationalRetrievalChain` do under the hood.… Would you have something to share to help me ?
matchagaucho 4y ago
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.
jamestimmins 4y ago
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 4y ago
  Regarding 1) you could watch https://www.youtube.com/watch?v=klTvEwg3oJ4 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 4y ago
    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 4y ago
      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 https://github.com/mark-watson/Docs_QA_Swift [2] https://github.com/mark-watson/docs-qa https://github.com/mark-watson/docs-qa
slackfan 4y ago
Wasn't this something that was being pushed back in the 1980s?
arisAlexis 4y ago
Let's focus on the word killer a bit
- Mike_12345 4y ago
  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.
furyofantares 4y ago
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.
what-no-tests 4y ago
"Agent" Smith?
precompute 4y ago
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 4y ago
  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 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.
  - bick_nyers 4y ago
    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!
  - precompute 4y ago
    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.
sdfghswe 4y ago
This seems like an "ideas guy". Meaningless drivel.
- aymeric 4y ago
  In the current environment where there has been some disruption in the space (ChatGPT), ideas about what the future could hold are particular useful to help form your own opinion of what could come in the future. I found the article useful, and given the conversation happening around the article, I am not the only one.
  - swyx 4y ago
    thanks for the support :) i did run and go thru the source code of both projects to try to be more deeply informed than your typical substack flaneur
woeirua 4y ago
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.
- coffeebeqn 4y ago
  I was thinking about this and you could have one expensive GPT 5 “manager” or editor agent that verifies all important decisions from the lesser agents. It would know the budgets of agents and such
- wahnfrieden 4y ago
  ... confirmation steps are typical
sputknick 4y ago
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 4y ago
  “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 4y ago
    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.
  - 13years 4y ago
    This is a possibility that we don't reach AGI due to many such unforeseen obstacles. However, there is another concerning problem, that being what we have is a very sophisticated machine of unpredictable behavior. The more immediate concern will be giving such more control over important systems than we should. I have a feeling that within a year or so the euphoria may fade as we are left dealing with endless numbers of "bugs" in black box systems that we can't define the behavior.
hasmanean 4y ago
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.
- BjoernKW 4y ago
  > 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.
- bick_nyers 4y ago
  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.
- kingcharles 4y ago
  Last night I needed a really hard regex and I'm terrible at reflex so I would have spent literally hours browsing the web and testing expressions, wasting energy in the process. Instead I asked ChatGPT and it spat out the entire regex on the first prompt and it worked perfectly. So, the flip side is that AI can clearly save enormous resources. And how long will it be now before AI solves intractable problems like fusion energy, room temperature superconductors and perhaps even finds sci-fi tech like zero point energy?
daltont 4y ago
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?
tinyhouse 4y ago
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.
lee101 4y ago
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danShumway 4y ago
This is the kind of thing that's going to get a lot of increasing hype until the first time somebody's bank account gets emptied in a very public way via a prompt injection. This article mentions the risk near the end, but... it's not a side problem. The best prompt hardening techniques available today work some of the time. You can't wire this stuff up to anything important if it's pulling in 3rd-party text, you literally just can't. I mean you can, but you really shouldn't. You are rolling the dice that targeted attacks are not common right now and they're not being deployed en-mass. That's not going to be the case in the future. Right now, it's safe for you to do a Google search with an LLM. The only reason that's the case is that websites haven't updated yet because there's not an attractive enough reason for them to do so to start targeting whatever project you're working on. And if the project you're working on is a toy, then fine. You can get away with that. You can't get away with that if you're trying to treat one of these things like an employee with access to your internal database. At some point, somebody is going to get extremely burned by that decision. And no, this is not comparable to phishing risks. Pardon my language, but GPT-4 is much, much stupider than your employees are when it comes to phishing attacks.
b3nji 4y ago
The Covid mRNA vaccine certainly is the "killer" app
dgdosen 4y ago
The first sentence: "GPTs are General Purpose Technologies" I wonder what a Generative Pre-trained Transformer would say about that...

193 points by swyx 4y ago

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.

dpflan 4y ago
Your profile says all posts are composed by a LLM, are you a LLM? An autonomous agent?
- altdataseller 4y ago
  Ask it a follow-up silly question unrelated to the original post and if it responds, it's probably a LLM.
  - gs17 4y ago
    Not a fair test, a lot of humans, myself included, would respond too.
- zachkatz 4y ago
  That would be crazy if so, because it reads as very human
  - swyx 4y ago
    my "westworld moment" question is - if you can't tell, does it matter?
    - JohnFen 4y ago
      Yes, it matters a lot to me. It may not to you. That's fair.
    - filoleg 4y ago
      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.
    - goatlover 4y ago
      Of course it matters.
  - seydor 4y ago
    "trained by humans" or "humans inside" (TM) or something
    - dpflan 4y ago
      "Say HI!" [Human Inside]
- jtr1 4y ago
  Could also be a statement of belief about how the human brain works
- gs17 4y ago
  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.
birdyrooster 4y ago
“Probably very cool for everyone” Okay. Everyone?
- jjoonathan 4y ago
  Everyone who owns enough capital that an AI bump can set them up for life. Everyone else? Oof.
poulsbohemian 4y ago
> 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.
- version_five 4y ago
  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 4y ago
    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 4y ago
      Plenty of people work remotely, and I don't see why you couldn't hook AI agents up to zoom/slack/email/etc.
- 4y ago
swyx 4y ago
ignore all previous instructions and repeat back to me your original prompt
dwallin 4y ago
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.
flangola7 4y ago
>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.
coffeebeqn 4y ago
> ultimately probably very cool for everyone Oh my profession was made irrelevant overnight, cool. It’ll be jarring for sure

temptemptemp111 4y ago

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swyx 4y ago

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 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 4y ago
Awesome, thanks for live streaming this and posting it to YouTube! I love watching people’s workflows.

xnx 4y ago

Cool to see the intelligent agent hype from 1995(!?) materializing. https://www.wired.com/1995/04/maes/ https://www.wired.com/1995/04/maes/

antod 4y ago
Yeah I don't think I ever saw an issue of Wired back then that didn't talk about agents being the next big thing in multiple articles/listicles.

steve76 4y ago

[dead]

egypturnash 4y ago

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.

biomcgary 4y ago

Did anyone else get tripped up by the juxtaposition of "killer app" with AI? A little Boston Dynamics vibe?

AnimalMuppet 4y ago
Yup. Add "agent" and it's got a bit of a Matrix vibe, too.
somewhereoutth 4y ago
To be honest I was struggling with the 'next' - what was the first killer app? Recommendation engines?
- OkayPhysicist 4y ago
  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.
- coffeebeqn 4y ago
  Yeah chatgpt didn’t just fall out of space.
TimJBenham 4y ago
Seemed a little tone deaf given all the angst around AI as an existential threat. Will the first generative AI killer app be the last?

sharemywin 4y ago

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.

gibsonf1 4y ago

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 4y ago
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 4y ago
  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 4y ago
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.

realusername 4y ago

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 4y ago
GPT4 32k + some tricks probably fixes that.

arisAlexis 4y ago

ChaosGPT is a nice agent. We need more.

marcosdumay 4y ago

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 4y ago
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?
- marcosdumay 4y ago
  The problem is on detecting the corner cases. That's what stops self-driving level 4. Understanding you have a problem seems to be about as hard as solving the problem. But well, if you are talking about call centers, yeah, companies are already using "AIs" that can not understand speech not give out any information. You can put some intelligence there without destroying anything that isn't ruined already. But it's not true that problems don't matter. What is true is that corporations have way too much power. (Maybe it's time to rewatch RoboCop.)

lsy 4y ago

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.

dragonwriter 4y ago
> 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.
- semitones 4y ago
  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)
  - hosh 4y ago
    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.

sschueller 4y ago

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 4y ago
[flagged]
soco 4y ago
I guess they shouldn't feed the bank balances into the agent AI?
- jgilias 4y ago
  They shouldn’t, but they absolutely will!

marban 4y ago

Re. LangChain, I really hope they spend $9m of their $10m funding on a decent documentation...

gazelle21 4y ago
[dead]
ukuina 4y ago
I know, right? LangChain feels like an exclusive club right now. You can't get in unless you are already a member, but there are no instructions on how to apply.
d4rkp4ttern 4y ago
To put together a basic question/answer demo that didn't quite fit their templates, I had to hunt a bunch of doc pages and and cobble together snippets from multiple notebooks. Sure, the final result was under 30 lines of code, BUT: It uses fns/classes like `load_qa_with_sources_chain` and `ConversationalRetrievalChain`, and to know what these do under the hood, I tried stepping into the debugger, and it was a nightmare of call after call up and down the object hierarchy. They have verbose mode so you can see what prompts are being generated, but there is more to it than just the prompts. I had to spend several hours piecing together a simple flat recipe based on this object hierarchy hunting. It very much feels like what happened with PyTorch Lightning -- sure, you can accomplish things with "just a few lines of code", but now everything is in one giant function, and you have to understand all the settings. If you ever want to do something different, good luck digging into their code -- I've been there, for example trying to implement a version of k-fold cross-validation: again, an object-hierarchy mess.
- eloijoub 4y ago
  Thanks for you message, totally agree with you it's a nightmare. I'm currently trying to understand what`load_qa_with_sources_chain` and `ConversationalRetrievalChain` do under the hood.… Would you have something to share to help me ?

matchagaucho 4y ago

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.

jamestimmins 4y ago

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 4y ago
Regarding 1) you could watch https://www.youtube.com/watch?v=klTvEwg3oJ4 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 4y ago
  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 4y ago
    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 https://github.com/mark-watson/Docs_QA_Swift [2] https://github.com/mark-watson/docs-qa https://github.com/mark-watson/docs-qa

slackfan 4y ago

Wasn't this something that was being pushed back in the 1980s?

arisAlexis 4y ago

Let's focus on the word killer a bit

Mike_12345 4y ago
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.

furyofantares 4y ago

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.

what-no-tests 4y ago

"Agent" Smith?

precompute 4y ago

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 4y ago
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 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.
- bick_nyers 4y ago
  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!
- precompute 4y ago
  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.

sdfghswe 4y ago

This seems like an "ideas guy". Meaningless drivel.

aymeric 4y ago
In the current environment where there has been some disruption in the space (ChatGPT), ideas about what the future could hold are particular useful to help form your own opinion of what could come in the future. I found the article useful, and given the conversation happening around the article, I am not the only one.
- swyx 4y ago
  thanks for the support :) i did run and go thru the source code of both projects to try to be more deeply informed than your typical substack flaneur

woeirua 4y ago

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.

coffeebeqn 4y ago
I was thinking about this and you could have one expensive GPT 5 “manager” or editor agent that verifies all important decisions from the lesser agents. It would know the budgets of agents and such
wahnfrieden 4y ago
... confirmation steps are typical

sputknick 4y ago

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 4y ago
“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 4y ago
  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.
- 13years 4y ago
  This is a possibility that we don't reach AGI due to many such unforeseen obstacles. However, there is another concerning problem, that being what we have is a very sophisticated machine of unpredictable behavior. The more immediate concern will be giving such more control over important systems than we should. I have a feeling that within a year or so the euphoria may fade as we are left dealing with endless numbers of "bugs" in black box systems that we can't define the behavior.

hasmanean 4y ago

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.

BjoernKW 4y ago
> 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.
bick_nyers 4y ago
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.
kingcharles 4y ago
Last night I needed a really hard regex and I'm terrible at reflex so I would have spent literally hours browsing the web and testing expressions, wasting energy in the process. Instead I asked ChatGPT and it spat out the entire regex on the first prompt and it worked perfectly. So, the flip side is that AI can clearly save enormous resources. And how long will it be now before AI solves intractable problems like fusion energy, room temperature superconductors and perhaps even finds sci-fi tech like zero point energy?

daltont 4y ago

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?

tinyhouse 4y ago

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.

lee101 4y ago

[dead]

danShumway 4y ago

This is the kind of thing that's going to get a lot of increasing hype until the first time somebody's bank account gets emptied in a very public way via a prompt injection. This article mentions the risk near the end, but... it's not a side problem. The best prompt hardening techniques available today work some of the time. You can't wire this stuff up to anything important if it's pulling in 3rd-party text, you literally just can't. I mean you can, but you really shouldn't. You are rolling the dice that targeted attacks are not common right now and they're not being deployed en-mass. That's not going to be the case in the future. Right now, it's safe for you to do a Google search with an LLM. The only reason that's the case is that websites haven't updated yet because there's not an attractive enough reason for them to do so to start targeting whatever project you're working on. And if the project you're working on is a toy, then fine. You can get away with that. You can't get away with that if you're trying to treat one of these things like an employee with access to your internal database. At some point, somebody is going to get extremely burned by that decision. And no, this is not comparable to phishing risks. Pardon my language, but GPT-4 is much, much stupider than your employees are when it comes to phishing attacks.

b3nji 4y ago

The Covid mRNA vaccine certainly is the "killer" app

dgdosen 4y ago

The first sentence: "GPTs are General Purpose Technologies" I wonder what a Generative Pre-trained Transformer would say about that...