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How to build a thinking AI
- ilaksh 3y agoTake a look at David Shapiro's YouTube channel. He has some interesting videos about building thought processes on top of LLMs (like GPT-4).
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- pfisch 3y agoAt best we invite a dystopian future. At worst our own annihilation. It is crazy how powerless we are to stop it from happening.
- kelseyfrog 3y agoState your priors, rationalist.
- pfisch 3y agoAI is going to replace most non physical jobs. Right now it is a helper, but soon it will be much more. I also think that the way it allows consolidation of power while hollowing out the working middle and upper class is going to lead to anti-democratic outcomes.
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- jstummbillig 3y agoFraming a threat vaguely enough certainly makes it sound ominous.
- boznz 3y agoCorrect on the last point but the first is still up for debate. Heres my take => https://rodyne.com/?page_id=1373 https://rodyne.com/?page_id=1373
- polotics 3y agoCan you substantiate why you think these are the two only possible futures?
- aantix 3y agoValidating and updating memory is an interesting problem with an LLM. Is it true that the next iteration of GPT is being trained with artificial data and that data is being validated by GPT-3.5? That LLMs may hallucinate but when prompted are actually pretty good at knowing when a conclusion is wrong?
- dachworker 3y agoMany such thinking architectures are probably possible. The hard part is learning a good representation of the world and all its constituents, without which none of these thinking architectures are possible. What's exciting about LLMs is that they are approaching this learned representation. There are already people attempting to build AGI (or less ambitiously, task automation) on top of LLMs with projects like BabyAGI and AutoGPT. I think it will be hard to say apriori which thinking architecture will work better, because this will also depend on the properties of the learned embedding or representation of the world. We don't need to model how the human mind works. Humans have very tiny working memories, but a computer could have a much larger working memory. Human recall is very quick and the concept map is very robust, whereas I would image the learned representations won't be as good and the recall to be a bottleneck. But all of this is running ahead of ourselves. What we need are even better world models or representations of reality than what the current LLMs can produce, either by modifying transformers or by moving to better architectures.
- jameshart 3y agoIf you insist on being able to boot the thing up and immediately be self aware, yes, you need to figure out how to construct it so that all the training of 'how to be this particular self aware intelligence' is intrinsic to it, which is a bootstrapping problem. Human intelligence solves this a different way. It instantiates the architecture without any of the weights pretrained, in the form of a 'baby'. The training starts from there.
- gremlinsinc 3y agosimple solution - create a human world simulation, with intelligent ai's that think they're biological and real, have them grow old, die, lose people they love, etc...then when they die they wake up as an ai robot with learned ethics/morality from life in the sim, other important gained intelligence, and the ability to compute 10000x faster than in the sim. Live, die, wake up as a robotic slave.
- px43 3y agoNot sure why but the lack of a scroll bar is giving me some pretty intense anxiety. How is one supposed to navigate a page like this? I don't see any indexes or indicators of where you are at any given time, and lots of weird moving distractions that make me lose my place. edit: The PDF version is way more sane https://arxiv.org/pdf/2203.17255.pdf https://arxiv.org/pdf/2203.17255.pdf
- techbro92 3y ago“Implementing this in a machine will enable artificial general intelligence” If that’s true why didn’t he just implement it? Why should I take anyone seriously that just talks about code instead of actually coding it? This would be much more compelling if he just showed benchmarks and performance instead of writing up an argument. Furthermore, I don’t believe him.
- TaupeRanger 3y agoIt's the same hyperbolic nonsense we've seen from hundreds of other confident "researchers" over the past 50 years. Eliasmith has a book called "How to Build a Brain", Hawkins built an entire company, Numenta, around a theory that hasn't created anything remotely useful or interesting in almost 2 decades and has pivoted to creating tools for current ML zeitgeist methods. This unknown researcher is exactly the same. Write books and papers for years while creating literally nothing of actual value or usefulness in the real world. But what else would you do in his situation? You have to publish or die in academia. Publish the 1,000th iteration of some subset of LLM architecture? Or create grandiose claims about "implementing human thought" in the hopes that some people will be impressed?
- thierrydamiba 3y agoThis is why Open AI was so revolutionary. They were able to create a useful, simple, and free product that anyone can use to improve their life.
- blackbear_ 3y agoYou really don't need to be so cynical, some things just need time. Light bulbs were patented only after 40 years of work by multiple researchers, and it took another half a century of work to achieve decent efficiency. Neural networks themselves have been in development for 50 years before taking off recently, and for most of that time people working in the field were considered nuts by their peers. But if you have concrete criticism on the idea feel free to articulate.
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- K0balt 3y agoThis is exactly what I started postulating on about 5 years ago… that eventually, transformers working through a REPL with access to a vector-store could likely lead to AGI. Of course, I didn’t predict the LLM/multimodal explosion, but I’ve been thinking along these same lines for a while now. My current direction is a multiagent MOE working into a single REPL with a supervisory transformer that manifests intent through the management of agent delegation and response filtering /regeneration to stay on context.
- layer8 3y agoWhile I agree that autonomous iteration will be important to AGI, I somehow have trouble taking an author serious who presents tables as screenshot images containing red spell-checking squiggles.
- dgadj38998 3y agoThis is an absolutely perfect archetypal HN comment The way someone can post an article on a really complex topic and the comment instead talks about the style or formatting of the article. That to me is pure HN distilled
- tudorw 3y agoYou realise you meant to write 'seriously', I will only consider this comments validity once this error has been corrected, also that last part of your comment is not well written it should be 'presents tables containing red spell-checking squiggles as screenshot images'. You're welcome.
- optimalsolver 3y agoGreat. Is there a prototype I can play around with?
- IceMichael 3y agoThe title alone should receive a downvote. We don't need more of this hype
- paxys 3y agoHow do you build a thinking AI? First come up with your own definition of what a thinking AI is, then design one that meets it.
- ryanklee 3y ago> simulate human-like thought processes It ought to be clear to a cognitive scientist (which the author is) that we do not know how human thought processes work except at a very course level. The idea that we have an understanding refined enough to take the next step and "simulate" these processes is just pure crackpot bunk.
- gremlinsinc 3y agoIf we only get it partially figured out, we can still get a vastly more intelligent artificial intelligence system, and then IT will figure out what we missed, especially if it is self-improving.
- ryanklee 3y agoGet what partially figured out? We are nowhere close to understanding how human cognitive processes are constituted. We are farther away from that than we are from AGI. These are not interdependent enterprises.
- zaking17 3y agoI like the process that goes into these "imagine the architecture of AGI" articles. It's all hypothetical, but it's really fun. But it's a missed opportunity if you don't embed LLMs in some of the core modules -- and highlight where they excel. LLMs aren't identical to any part of the human brain, but they do a remarkable job of emulating elements of human cognition: language, obviously, but also many types of reasoning and idea exploration. Where LLMs fail is in lookup, memory, and learning. But we've all seen how easy it is to extend them with RAG architectures. My personal, non-scientific prediction for the basic modules of AGI are: - LLMs to do basic reasoning - a scheduling system that runs planning and execution tasks - sensory events that can kick off reasoning, but with clever filters and shortcuts - short term memory to augment and improve reasoning - tools (calculators etc.) for common tasks - a flexible and well _designed_ memory system -- much iteration required to get this right, and i don't see a lot of work being done on it, which is interesting - finally, a truly general intelligence would have the capability to mutate many of the above elements based on learning (LLM weights, scheduling parameters, sensory filters, and memory configurations). But not everything needs to be mutable. many elements of human cognition are probably immutable as well.
- hathawsh 3y agoI like to think we could quickly create a next-level AI (maybe AGI?) if we simply model it on the Pixar movie "Inside Out". The little characters inside the girl's brain are different LLMs with different biases. They follow a kind of script that adapts to the current environment. They converse with each other and suggest to the girl what she should do or say. I'd try the idea myself, but I have a job. :-)
- dTal 3y agoThis sounds a lot like the mixture-of-experts architecture, and the current best-performing language models (GPT-4, mixtral-8x7b) already use this architecture. So congratulations, you win!
- pstorm 3y agoThat's not really how MoEs work. They never directly interact with eachother. There is one manager type model that takes a prompt, directs token inference to 1 or more models, chooses the best response, and continues. The analogy would be closer to a "swarm of agents". (There are a handful of names for this approach, I think swarm is catching on the most)
- davelacy 3y agoWhat was used to make these animated diagrams?! Anyone know?
- nomoreusernames 3y ago[dead]
- turnsout 3y agoThis might as well be a patent for a perpetual motion machine… Until there's code, it's hot air.
- alchemist1e9 3y agoIt’s a bit buried but eventually there are references to SOAR and ACT-R which in my crude attempt to broach cognitive architectures were the two I had understood as being the leading models with tangible applied results and working code. If anybody with an understanding of that field knows some good open source frameworks or libraries I suspect many beyond myself would be interested. It’s not considered cognitive framework but in applied learning I’ve developed a fascination with MuZero algorithm and also been trying to better understand factor graphs as used in another less know cognitive architecture called Sigma. It feels like some mashup of LLMs, RAG and vector search, cognitive architectures (SOAR, ACT-R, Sigma), ReACT/OPA/VOYAGER, with proven algorithms like MuZero might be on the verge of producing the next leap forward.
- visarga 3y agoOver the years hundreds of variants of RNN, CNN and Transformer have been proposed, but under the same budget of weights and compute, and with the same dataset, they are very close in performance. Models don't matter, gradient descent finds a way. The real hero is the dataset. And what is in the training set? Language is our best repository for past experience. We have painstakingly collected our lessons, over thousands of years, and transmitted them through culture and books. To recreate them from scratch would take a similarly long time. Our culture is smarter than us, it is the result of our history. So under these reasons I believe the real secret is in the training set. I don't think the problem was the model, but everything else around it.
- digitcatphd 3y agoSeems promising but I don’t think we will achieve production ready thinking AI with current state of LLMs. We will need some form of Q-Learning and possibly or a world model to arrive at optimal outcomes otherwise random choices are made absent of at least one that would be suboptimal. Consider that life is a giant game of Chess with a massive yet finite scenarios, a grounding agent must have knowledge of each potential scenario and its effects as well as the probability of winning from each subsequent move. Otherwise the best we can get at is the emulation of reasoning which is a kind of pseudo reasoning that may indeed work in some cases like literal chess where the logic and knowledge can be sufficiently isolated, but not in a general sense.
- lijok 3y agoBefore engaging with the comments here, keep in mind: This article is at least a 4 hour reading exercise and at least a week long endeavour to comprehend it. Few if any in the comments have actually read it, let alone understood it.
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- junofan 3y agoNot really, it’s a bunch of slightly outdated cognitive psych/neural computation stuff. It’s looking plausible that neurons use transponsons etc and subtle timing effects to do an extreme amount of processing. I mean, what’s happening when you sit down at a piano and warm up? We just don’t have access to that amount of computation in silico.