13 ms·
Generative AI is good at cooperating with people and bad at full automation
- cbazz 4y ago[flagged]
- _zv3n 4y agoThis was written with GPT.
- Barrin92 4y agoor Zizek is right and the biggest threat from these models is that people start to talk like Chatbots https://www.project-syndicate.org/commentary/ai-chatbots-naive-idiots-no-sense-of-irony-by-slavoj-zizek-2023-03 https://www.project-syndicate.org/commentary/ai-chatbots-nai...
- dTal 4y ago>But just how good are the new AIs at approximating human consciousness? Consider the bar that recently advertised a drink special with the following terms: “Buy one beer for the price of two and receive a second beer absolutely free!” To any human, this is obviously a joke. The classic “buy one, get one” special is reformulated to cancel itself out. It is an expression of cynicism that will be appreciated as comic honesty, all to boost sales. Would a chatbot pick up on any of this? ChatGPT flips into bullshit mode and interprets it as a straightforward offer; GPT4 grasps the humor and identifies it as a marketing strategy, and proffers several reasons why such a strategy might be effective (icebreaker for customers, encourages social media engagement, promoting a "quirky" laid-back atmosphere). It's fun to watch AI skepticism get rebutted in real time. Frightening too. We really have no idea what's about to hit us, do we...
- sdflhasjd 4y agoThe account is just a few hours old and the only other comment is obviously AI written as well. Seeing this happen to HN is... sad.
- deleted 4y ago[deleted]
- geuis 4y agoBut fundamentally some things you said in your comment aren't actually true. Any of the "grasping context" stuff you're seeing is a combination of a few simple programming techniques to retain samples of what was previously typed by the user and similar techniques to give the illusion of continuity. All of these different models being released ranging from stable diffusion to chatgpt are essentially just the classic Chinese room. There's nothing happening internally. There's no subjective experience of being ChatGPT. The model isn't updated as it interacts with you. While the results to queries are interesting and sometimes helpful, it's still fundamentally a system just processing input and giving some kind of generated output. These models aren't live. They're a snapshot that captures a point in time of the node weights when someone decided "good enough for now". A generous analogy might be for the patient with brain damage that can only remember things for 10 seconds. Think the movie 50 First Dates. There's no actually learning about the world happening until the next update. Every new request you make to chatgpt is as brand new to the model as the last request. Yes, apparently if you throw millions or billions of input parameters to a more or less standard gradient descent model you get some interesting and almost convincing results. But there's still the basic issues that these systems don't learn based on interactions. This is mostly why I think the hype around these systems is hype right now. When an AI system starts asking questions back and the learning from them, that's the revolution. In the meantime if probably less than 5 years, this is just where a lot of nervous investors are trying to dump money to cover the downturn.
- Scarblac 4y agoAll of that sounds true to me, and yet it doesn't seem to distract from the comment you're replying to in any way. It can grasp context and be very useful for a lot of things even if it doesn't learn continuously, isn't alive, can be described as a Chinese Room, etc.
- pmoriarty 4y agoThe problem is that human cognition itself could be described as a Chinese room. We don't understand what understanding is, or what thinking is, what consciousness is, etc. So we can't adequately defend our own thought process as being any "better" than an AI except at a functional level, and AI's are quickly catching up to and even exceeding human functions, so the moat humans have drawn to defend themselves as exceptional beings is ever shrinking.
- faizshah 4y agoI agree with the overall idea, but we don’t need to limit the AI to human collaborators. I think the future lies in the Copilot approach of giving the Agent relevant context by building smarter prompt generation pipelines. The AI are really good when given enough context and they are bad at recalling information from their own training corpus. When you combine multiple AI agents together with good context and tools like in the LangChain approach these AI agents suddenly become really good at full automation. Which imo is the path to AGI.
- HanClinto 4y agoDo you have examples of combining multiple AI agents together with context and tools? I'd like to learn more about what you're referring to.
- faizshah 4y agoThis is a basic example of a Reason and Act (React) agent: https://python.langchain.com/en/latest/modules/agents/agents/examples/react.html https://python.langchain.com/en/latest/modules/agents/agents... Search is a tool that provides more context to the agent in order to answer a human task. By combining many agents together we can solve complex tasks.
- TMWNN 4y agoCooperation with people is what I thought of when I heard of the forthcoming Business Chat feature for Microsoft Teams <https://news.ycombinator.com/item?id=35182827 https://news.ycombinator.com/item?id=35182827>. From what I have read, it is going to produce summaries of conversations. That's not quite what I'd imagined, which is something like this: ---- A: ... and that is why I think we should go with option 1. B: No, the points you mentioned support my case for option 2. C: Nothing you guys have said changes my mind about option 3 being best. D: Business Chat, what do you think? BC: Based on this discussion, and my research, option 1 seems more realistic but option 2 would be more profitable if possible. My reasons are ... C: Business Chat and you guys all don't understand point N, which is the main reason why option 3 is best. B: Higher profit is exactly why I think option 2 is the way to go. A: No, our rival is going to hit the market next month. We need to get something out there ASAP. Option 1 can do this. D: You've all given me things to think about. Thank you for coming. Business Chat, email me a summary of the meeting, and set up a followup meeting for Tuesday 3pm. ---- That is, AI used as a colleague/assistant, not necessarily subordinate but not seen as omniscient, either; another viewpoint to consider. When will the above be feasible? This year? Five years?
- NovaDudely 4y agoIn a way you could use these AI systems similar to kintsugi. Or in the same way a good wood sculptor uses the flow of the wood rather than working against it. Using AI to provide a basic frame work on which folks can iterate, manipulate or redact is not such a dystopian view of these things.
- ChatGTP 4y agoWhich would be a dream if it stops there, but it sounds like we're fairly hell bent on taking it further as fast as possible?
- bloppe 4y agoFersher
- NovaDudely 4y agoAbsolutely. If reading any of the computer technology pessimists of the last 50 years have taught me anything is that they are usually right to some degree.
- bryanrasmussen 4y agoSince Bill Gates claims he never said 640K is enough for anyone, and who doesn't trust Bill Gates - I guess I have to agree with that one.
- dclowd9901 4y agoI don’t understand what we have to gain from a system that generates code that needs human verification. It’s like Tesla’s autopilot all over again. Either it can drive itself or it can’t — if it can’t absolute, 100% reliably manage itself, why are we even pursuing it? I bet a more clever person than I could actually mathematically prove that neural network based models could never provide 100% reliable results.
- ChatGTP 4y ago
- examplary_cable 4y agoWhat about AI collaborating with AI?
- agnosticmantis 4y agoIt used to be that classical software was useful for verification, but not so good at generating ideas. With LLMs we have the opposite: machines are now good at idea generation and exploration, but their ideas need to be verified by a human (or classical logic-based software).
- jeron 4y agoSo all we need to do is have LLMs generate classical logic-based software to qualify its own ideas
- TaylorAlexander 4y agoI’m quite confident they’re using GitHub copilot or similar inside of OpenAI while working on improving GPT-4. So yeah, that’s happening.
- chaxor 4y agoThere are plenty of different ways this is happening. 1) already happened with Galactica - the model included python scripts as part of it's "working memory" as part of a <work> token, which could be used in a container 2) via copilot, and copilot 365, as someone stated elsewhwre 3) GPT-> Wolfram alpha as a plugin, which will be out very soon Galactica is the only one that is already (technically) available
- dclowd9901 4y ago
- dougSF70 4y agoSince the LLMs are probabalistic is it possible for the inference engines to highlight lower probability content. That might at least make them more "human" rather than being confident and inaccurate.
- nicognaw 4y agoSince LLMs generate words one by one, you can only determine whether the next word has a high probability. Some ideas occurred to me earlier, like applying prompt engineering to evaluate if the model is confident about the contents to be generated. Like "Please reply T if you are confident about the text that you are going to generate, and F if you are not". But if this could be done by architecting the neural networks, they would perform much better.
- pmoriarty 4y ago"Since LLMs generate words one by one, you can only determine whether the next word has a high probability" We don't fully understand how they work or what their limits are.
- RugnirViking 4y agowe do understand that they generate words with probabilities one by one though. They are transformers, the AI bit takes the entire prompt as input and returns a range of probabilities for the next token (token == word, it doesnt understand or see letters). A "dumb" algorithm then selects from these probabilities randomly using the probabilities returned by the ai based on the "temprature" setting (low temprature favors high probability words & high temprature selects from a whole lot of words but leads into really weird territory fast) It's also worth remembering that this is not just a markov chain as many people seem to think, it doesn't simply remember what words come next in its training set, because statistically if you take a random 10 consecutive words from a piece of text, chances are its never been written before. (also the trained model is much smaller than the size of the dataset, so to simply remember everything it would have to be the worlds best compression algorithm by orders of magnitude) Thats why we need an AI here, to learn the general rules of language so it can respond to chains of words it has never seen before. The sense that we "dont understand how they work" is that we dont know what the "rules of language" that it has learned are.
- nicognaw 4y agoPrompt engineering, in my opinion, could be considered "semantic programming."
- keyle 4y agoThat's a great thought. Although an overarching generalisation.
- chx 4y ago> Generative AI is good at ~~cooperating~~ with people fooling FTFY
- skybrian 4y agoIt follows the "yes, and" rule of improv. This is cooperative if you understand what it's doing. If you want to believe it has any fixed motivations other than that, you fool yourself.
- chx 4y agoI fully understand what it's doing: it exploits the human brain being wired to see patterns where there is none. It vomits up words in such patterns humans have a high tendency to trust them. Of course , there is zero actual information content in it. It's a zeroday on human cognition, a terrible, a terrible danger to society but there's no stopping it. https://futureoflife.org/open-letter/pause-giant-ai-experiments/ https://futureoflife.org/open-letter/pause-giant-ai-experime...
- pmoriarty 4y agoYou could make the argument that communication between humans, or even human thought itself is nothing more than that.
- skybrian 4y agoGood ideas can sometimes be generated via play, without proper justification for them. There is chance involved, but if you test them and they hold up then they become meaningful.
- midjji 4y agoThey are good at telling people what they want to hear. There are lots of people like that too, but yeah they generally dont achieve much on their own.
- esjeon 4y agoRight. If you don’t dig deeper, it will give you only generic answers, and even fully incorporate the intention of your questions, strengthening your biases.
- palad1n 4y agoOften I can get it to fact-check itself by asking for something in two different ways.
- yawnxyz 4y agoI was trying to get ChatGPT to generate a table for all the products I'm trying to put together for Stripe for this conference I'm helping out with, and with all the permutations there's 24 products at different prices. I fed it the options for all the columns and it couldn't do it! So in the end I resorted to telling it to give me a JS function to generate a CSV, and it did it perfectly. I think I'll end up telling it to give me code that gets me the thing I want (when it comes to data), rather than get the formatted data directly. It sometimes makes weird mistakes, and it's less reproducible.
- obsoletedbygpt 4y agoI'm amazed at how much nay-saying there is on HN about LLMs. This community has bought into so many fads, like crypto, yet when presented with what's clearly going to be the most disruptive technology of your life, you act like it's no big deal. Is this just nervous denial? Are you suddenly incapable of linear (not exponential) extrapolation? Imagine what ChatGPT, Midjourney, or whatever Apple and Google are cooking up will be capable of if they only improve linearly, at say 1% per year? People ought to be preparing themselves for this as if it were a COVID-like black swan event, except much, much worse. And do not bank on Sam Altman and his fellow tech bro billionaires holding the reins of these job-annihilating machines delivering with their promise of UBI.
- weatherlite 4y agoI get why you're being down voted but your overall point is valid imo. We didn't get any good answers about what happens if this thing keeps getting better. "People will find new jobs/things to do" doesn't really cut it. It's not really the tech titans fault, the governments really need to start stepping in.
- skybrian 4y agoThere are AI boosters making exaggerated claims in their enthusiasm and therefore people take the other side in reaction to them because they’re wrong. The skeptics then sometimes write blanket dismissals in opposition, and that’s wrong too, and you get a loop. That’s just how it goes on the Internet sometimes. I totally get the motivation and it’s why I started blogging again. To get more interesting conversations, I think we need curiosity while keeping in mind how little we know and how bad we are at predicting the future.
- weatherlite 4y agoI've just read these models are getting to the point they run out of training data. They pretty much scanned most of the internet already. I wonder if we will reach a disappointing situation in 3-4 years where we're at "peak A.I" abilities. Well disappointing for some, for others it means they will get to keep having a job with income :)
- inductive_magic 4y agoThe current problem isn’t a lack of data. Meaningful data can be augmented with GPTs, just iterate over sparsely represented subjects and enrich them with context-aware samples …generated with GPT. We are actively leveraging GPT4s brainstorming-ability to generate datasets used for finetuning downstream. The fine tuned models downstream can then be used to augment new training data for more complex root language models, just formalize your quality assurance with common sense DSLs. We’ve reached an upward spiral with quite the exponential feel to it, where it will lead, nobody knows.
- throwaway1851 4y agoThat’s only going to get you so far. Sparsely represented subjects have have an actual reality you’re trying to model. So while yes, you can generate synthetic data to interpolate between the points that have already been sampled, whether those interpolated points have anything to do with the underlying reality is a matter of chance. If you really try to drill down the existing tools into a specialized problem area, it becomes very clear that they lack a sufficiently informed model of the subject to be useful. That’s why I’ve found that ChatGPT is great at the beginning of a project and increasingly irrelevant as you approach the core technical challenges of the project.
- pcwein 4y agoAgree - we're taking a hybrid approach - meaning the gen AI is 'prebuilding' the automation and then the user reviews and possibly corrects it before fully executing - check it out here: bardeen.ai/ai