[HN Gopher] DeepMind: A Generalist Agent ___________________________________________________________________ DeepMind: A Generalist Agent Author : extr Score : 313 points Date : 2022-05-12 15:33 UTC (7 hours ago) (HTM) web link (www.deepmind.com) (TXT) w3m dump (www.deepmind.com) | f38zf5vdt wrote: | If I'm following correctly, they trained a single model with | multiple training paradigms and then the single model could | perform token predictions for multiple dissimilar token sequences | for specific tasks. Seems like it is a straightforward result. | doubtfuluser wrote: | Well... straightforward in a way, yes. But the scale of | learning is huge especially with this diverse set of tasks. Not | totally unexpected, but certainly not clear that it would work | with current networks and sizes. | f38zf5vdt wrote: | Right, exactly. Something that seemed like it should work but | no one had ever tried it. | weinzierl wrote: | _" The same network with the same weights can play Atari, caption | images, chat, stack blocks with a real robot arm and much more, | deciding based on its context whether to output text, joint | torques, button presses, or other tokens."_ | | This is rather mind blowing. Does it also mean that the | generalist network is smaller than the sum of all specialist | networks that are equivalent? Even if not, I find the idea that a | single network can be used for such diverse tasks at all highly | fascinating. | f38zf5vdt wrote: | Many networks just predict the next integer in a sequence of | integers. It sounds like this model identifies what category of | problem a sequence of integers falls into and then makes an | accurate prediction for that sequence, as you would expect | given what it was trained on. | version_five wrote: | I don't find it surprising that a single network can do all | those things with appropriate formatting of the data. In itself | it just means the network has a large enough capacity to learn | all the different tasks. | | The interesting questions imo, which they studied, is what kind | of added generalization takes place by learning across the | different tasks. For example, does learning multiple tasks make | it better at a given task than a model that is just trained for | one task, and can it generalize to new tasks (out of | distribution). | | They looked at how it performed on held out tasks (see fig 9 in | the paper). I'm still getting my head around the result though | so couldn't summarize their finding yet. | | Edit: the paper is here | https://storage.googleapis.com/deepmind-media/A%20Generalist... | | There is currently another submission on the front page that | links to it directly. | f38zf5vdt wrote: | The paper is linked to at the top of this article, in the | header. | woeirua wrote: | Yeah, Figure 9 is the money figure in this paper and it | actually splashes some cold water on the claims in the rest | of the paper. While it generalizes OK to some tasks that are | held out, it does pretty poorly on the Atari boxing task, | which they openly admit is quite different from the others. | Gato seems more likely to be a competent attempt at brute | forcing our way towards weak general AI, which is a valid | approach, but the question then will always be how does it do | with something its never seen before, and how do you possibly | brute force every possible situation? I think we're heading | more towards a constellation of very intelligent expert | machines for particular tasks that may be wrapped into a | single package, but that are not strong AI. | minimaxir wrote: | Transformer models have clearly demonstrated that you can convert | _anything_ into an input embedding and the AI can learn from it, | even if the embeddings are from drastically distant domains. | hans1729 wrote: | I'm not sure how to word my excitement about the progress we see | in AI research in the last years. If you haven't read it, give | Tim Urbans classic piece a slice of your attention: | https://waitbutwhy.com/2015/01/artificial-intelligence-revol... | | It's a very entertaining read from a couple of years ago (I think | I've read it in 2017), and man, have things happened in the field | since then. If feels like things truly start coming together. | Transformers and then some incremental progress look like a very, | very promising avenue. I deeply wonder in which areas this will | shape the future more than we are able to anticipate beforehand. | gurkendoktor wrote: | Not you specifically, but I honestly don't understand how | positive many in this community (or really anyone at all) can | be about these news. Tim Urban's article explicitly touches on | the risk of human extinction, not to mention all the smaller- | scale risks from weaponized AI. Have we made any progress on | preventing this? Or is HN mostly happy with deprecating | humanity because our replacement has more teraflops? | | Even the best-case scenario that some are describing, of | uploading ourselves into some kind of post-singularity | supercomputer in the hopes of being conscious there, doesn't | seem very far from plain extinction. | JohnPrine wrote: | Agreed. People think of the best case scenario without | seriously considering everything that can go wrong. If we | stay on this path the most likely outcome is human | extinction. Full stop | JoeAltmaier wrote: | Says a random internet post. It takes a little more | evidence or argument to be convincing, besides hyperbole. | idiotsecant wrote: | I think the best-case scenario is that 'we' become something | different than we are right now. The natural tendency of | life(on the local scale) is toward greater information | density. Chemical reactions beget self-replicating molecules | beget simple organisms beget complex organisims beget social | groups beget tribes beget city states beget nations beget | world communities. Each once of these transitions looks like | the death of the previous thing and in actuality the previous | thing is still there, just as part of a new whole. I suspect | we will start with natural people and transition to some | combination of people whose consciousness exists, at least | partially, outside of the boundaries of their skulls, people | who are mostly information on computing substrate outside of | a human body, and 'people' who no longer have much connection | with the original term. | | And that's OK. We are one step toward the universe | understanding itself, but we certainly aren't the final step. | 37ef_ced3 wrote: | Let's be real. | | Not long from now all creative and productive work will be | done by machines. | | Humans will be consumers. Why learn a skill when it can all | be automated? | | This will eliminate what little meaning remains in our | modern lives. | | Then what? I don't know, who cares? | idiotsecant wrote: | >Then what? | | Growing tomatoes is less efficient than buying them, | regardless of your metric. If you just want really | cleanly grown tomatoes, you can buy those. If you want | cheap tomatoes, you can buy those. If you want big | tomatoes, you can buy those. | | And yet individual people still grow tomatoes. Zillions | of them. Why? Because we are inherently over-evolved apes | who like sweet juicy fruits. The key to being a | successful human in the post-scarcity AI overlord age is | to embrace your inner ape and just do what makes you | happy, no matter how simple it is. | | The real insight out of all this is that the above advice | is also valid even if there are no AI overlords. | gurkendoktor wrote: | Humans are great at making up purpose where there is | absolutely none, and indeed this is a helpful mechanism | for dealing with post-scarcity. | | The philosophical problem that I see with the "AI | overlord age" (although not directly related to AI) is | that we'll then have the technology to change the | inherent human desires you speak of, and at that point | growing tomatoes just seems like a very inefficient way | of satisfying a reward function that we can change to | something simpler. | | Maybe we wouldn't do it precisely because it'd dissolve | the very notion of purpose? But it does feel to me like | destroying (beating?) the game we're playing when there | is no other game out there. | | (Anyway, this is obviously a much better problem to face | than weaponized use of a superintelligence!) | idiotsecant wrote: | Any game you play has cheat codes. Do you use them? If | not, why not? | | In a post-scarcity world we get access to all the cheat | codes. I suspect there will be many people who use them | and as a result run into the inevitable ennui that comes | with basing your sense of purpose on competing for finite | resources in a world where those resources are basically | free. | | There will also be many people who choose to set their | own constraints to provide some 'impedance' in their | personal circuit. I suspect there will also be many | people who will simply be happy trying to earn the only | resource that cannot ever be infinite: social capital. | We'll see a world where influencers are god-kings and | your social credit score is basically the only thing that | matters, because everything else is freely available. | londons_explore wrote: | > Or is HN mostly happy with deprecating humanity because our | replacement has more teraflops? | | If we manage to make a 'better' replacement for ourselves, is | it actually a bad thing? Our cousin's on the hominoid family | tree are all extinct, yet we don't consider that a mistake. | AI made by us could well make us extinct. Is that a bad | thing? | goatlover wrote: | > If we manage to make a 'better' replacement for | ourselves, is it actually a bad thing? | | It's bad for all the humans alive at the time. Do you want | to be replaced and have your life cut short? For that | matter, why should something better replace us instead of | coexist? We don't think killing off all other animals would | be a good thing. | | > Our cousin's on the hominoid family tree are all extinct, | yet we don't consider that a mistake. | | It's just how evolution played out. But if there was | another hominid still alive along side us, advocating for | it's extinction because we're a bit smarter would be | considered genocidal and deeply wrong. | JoeAltmaier wrote: | We have Neanderthal, Denisovan DNA (and two more besides). | Our cousins are not exactly extinct - we are a blend of | them. Sure no pure strains exist, but we are not a pure | strain either! | gurkendoktor wrote: | Your comment summarizes what I worry might be a more | widespread opinion than I expected. If you think that human | extinction is a fair price to pay for creating a | supercomputer, then our value systems are so incompatible | that I really don't know what to say. | | I guess I wouldn't have been so angry about any of this | before I had children, but now I'm very much in favor of | prolonged human existence. | idiotsecant wrote: | > I'm very much in favor of prolonged human existence. | | Serious question - why? | goatlover wrote: | Why should general intelligence continue to survive? You | are placing a human value on continued existence. | samdjstephens wrote: | What are your axioms on what's important, if not the | continued existence of the human race? | | edit: I'm genuinely intrigued | idiotsecant wrote: | I suppose the same axioms of every ape that's ever | existed (and really the only axioms that exist). My | personal survival, my comfort, my safety, accumulation of | resources to survive the lean times (even if there are no | lean times), stimulation of my personal interests, and | the same for my immediate 'tribe'. Since I have a | slightly more developed cerebral cortex I can abstract | that 'tribe' to include more than 10 or 12 people, which | judging by your post you can too. And fortunate for us, | because that little abstraction let us get past smashing | each other with rocks, mostly. | | I think the only difference between our outlooks is I | don't think there's any reason that my 'tribe' shouldn't | include non-biological intelligence. Why not shift your | priorities to the expansion of general intelligence? | sinenomine wrote: | Excitement alone won't help us. | | We should ask our compute overlords to perform their | experiments in as open environment as possible, just because | we, the public, should have the power to oversee the exact | direction this AI revolution is taking us. | | If you think about it, AI safetyism is a red herring compared | to a very real scenario of powerful AGIs working safely as | intended, just not in our common interest. | | The safety of AGI owners' mindset seems like a more pressing | concern compared to a hypothetical unsafety of a pile of | tensors knit together via gradient descent over internet | pictures. | f38zf5vdt wrote: | That human intelligence might just be token prediction evolving | from successive small bit-width float matrix transformations is | depressing to me. | chriswarbo wrote: | That's a poor usage of "just": discovering that "X is just Y" | doesn't _diminish_ X; it tells us that Y is a much more | complex and amazing topic than we might have previously | thought. | | For example: "Life is just chemistry", "Earth is just a pile | of atoms", "Behaviours are just Turing Machines", etc. | xixixao wrote: | It's most fascinating (or very obvious) - look at Conway's | Game of Life, then scale it up - a lot. Unlimited complexity | can arise from very simple rules and initial conditions. | | Now consciousness on the other hand is unfathomable and (in | its finitude) extremely depressing for me. | goatlover wrote: | Is that what biologists or neuroscientists think the nervous | system is actually doing? | Der_Einzige wrote: | Dear god I hope that we are using something more complicated | than sampling with top_p, top_k, and a set temperature as our | decoder! | triceratops wrote: | > That human intelligence might just be token prediction | | I mean have you heard the word salad that comes out of so | many people's mouths? (Including myself, admittedly) | londons_explore wrote: | Eating salad is good for your health. Not only word salad, | but green salad and egg salad. | 0xBABAD00C wrote: | Wait till you find out all of physics is just linear | operators & complex numbers | goatlover wrote: | Unless nature is mathematical, the linear operators & | complex numbers are just useful tools for making predictive | models about nature. The map isn't the territory. | edouard-harris wrote: | That Tim Urban piece is great. It's also an interesting time | capsule in terms of which AI problems were and were not | considered hard in 2015 (when the post was written). From the | post: | | > Build a computer that can multiply two ten-digit numbers in a | split second--incredibly easy. Build one that can look at a dog | and answer whether it's a dog or a cat--spectacularly | difficult. Make AI that can beat any human in chess? Done. Make | one that can read a paragraph from a six-year-old's picture | book and not just recognize the words but understand the | meaning of them? Google is currently spending billions of | dollars trying to do it. Hard things--like calculus, financial | market strategy, and language translation--are mind-numbingly | easy for a computer, while easy things--like vision, motion, | movement, and perception--are insanely hard for it. | | The children's picture book problem is solved; those billions | of dollars were well-spent after all. (See, e.g., DeepMind's | recent Flamingo model [1].) We can do whatever we want in | vision, more or less [2]. Motion and movement might be the | least developed area, but it's still made major progress; we | have robotic parkour [3] and physical Rubik's cube solvers [4], | and we can tell a robot to follow simple domestic instructions | [5]. And Perceiver (again from DeepMind [6]) took a big chunk | out of the perception problem. | | Getting a computer to carry on a conversation [7], let alone | draw art on par with human professionals [8], weren't even | mentioned as examples, so laughably out of reach they seemed in | the heathen dark ages of... 2015. | | And as for recognizing a cat or a dog -- that's a problem so | trivial today that it isn't even worth using as the very first | example in an introductory AI course. [9] | | If someone re-wrote this post today, I wonder what sorts of | things would go into the "hard for a computer" bucket? And how | many of _those_ would be left standing in 2029? | | [1] https://arxiv.org/abs/2204.14198 | | [2] https://arxiv.org/abs/2004.10934 | | [3] https://www.youtube.com/watch?v=tF4DML7FIWk | | [4] https://openai.com/blog/solving-rubiks-cube/ | | [5] https://say-can.github.io/ | | [6] https://www.deepmind.com/open-source/perceiver-io | | [7] https://arxiv.org/abs/2201.08239v2 | | [8] https://openai.com/dall-e-2/ | | [9] https://www.fast.ai/ | kaivi wrote: | Before you visualize a straight path between "a bag of cool ML | tricks" and "general AI", try to imagine superintelligence but | without consciousness. You might then realize that there is no | obvious mechanism which requires the two to appear or evolve | together. | | It's a curious concept, well illustrated in the novel Blindsight | by Peter Watts. I won't spoil anything here but I'll highly | recommend the book. | oldstrangers wrote: | You just reminded me I have that book sitting on my shelf. | Guess I'll give it a read. | awestroke wrote: | What's the difference between intelligence and consciousness? | Could a human be intelligent while not conscious? | nullc wrote: | It's worth mentioning that Blindsight is available online for | free: https://www.rifters.com/real/Blindsight.htm | mach1ne wrote: | First you have to define consciousness, and especially the | external difference between a conscious and non-conscious | intelligence. | meekmind wrote: | Likely insufficient but here is a shot at a materialist | answer. | | Consciousness is defined as an entity that has an ethical | framework that is subordinated to it's own physical | existence, maintaining that existence, and interfacing with | other conscious entities as if they also have an ethical | framework with similar parameters who are fundamentally no | more or less important/capable than itself. | | Contrast with non-conscious super-intelligence that lacks | physical body (likely distributed). Without a physical/atomic | body and sense data it lacks the capacity to | empathize/sympathize as conscious entities (that exist within | an ethical framework that is subordinated to those | limitations/senses) must. It lacks the perspective of a | singular, subjective being and must extrapolate our | moral/ethical considerations, rather than have them ingrained | as key to it's own survival. | | Now that I think about it, it's probably not much different | than the relationship between a human and God, except that in | this case it's: a machine consciousness and a machine god. | | To me, the main problem is that humans (at large) have yet to | establish/apply a consistent philosophy with which to | understand our own moral, ethical, and physical limitations. | For the lack of that, I question whether we're capable of | programming a machine consciousness (much less a machine god) | with a sufficient amount of ethical/moral understanding - | since we lack it ourselves (in the aggregate). We can hardly | agree on basic premises, or whether humanity itself is even | worth having. How can we expect a machine that _we make_ to | do what we can 't do ourselves? You might say "that's the | whole point of making the machine, to do something we can't" | but I would argue we have to understand the problem domain | first (given we are to program the machine) before we can | expect our creations to apply it properly or expand on it in | any meaningful way. | tomp wrote: | I don't think it's necessarily about _consciousness_ per se, | but rather about _emotions_ or "irrationality". | | Life has no purpose so clearly there is no _rational_ reason to | continue living /existing. A super-rational agent must know | this. | | I think that intelligence and emotions, in particular _fear of | death_ or _desire to continue living_ , must evolve in | parallel. | joe_the_user wrote: | > _" try to imagine superintelligence but without | consciousness."_ | | The only thing that comes to mind is how many different things | come to mind to people when the term "superintelligence" is | used. | | The thing about this imagination process, however, is that what | people produce is a "bag of capacities" without a clear means | to implement those capacities. Those capacities would be | "beyond human" but in what direction probably depends on the | last movie someone watched or something similarly arbitrary | 'cause it certainly doesn't depend on their knowledge of a | machine that could be "superintelligent", 'cause none of us | have such knowledge (even if this machine could go to | "superintelligence", even our deepmind researchers don't know | the path now 'cause these are being constructed as a huge | collection of heuristics and what happens "under the hood" is | mysterious to even the drivers here). | | Notably, a lot of imagined "superintelligences" can supposedly | predict or control X, Y or Z thing in reality. The problem with | such hypotheticals is that various things may not be much more | easily predictable by an "intelligence" than by us simply | because such prediction involves imperfect information. | | And that's not even touch how many things go by the name | "consciousness". | axg11 wrote: | Slowly but surely we're moving towards general AI. There is a | marked split across general society and even ML/AI specialists | between those who think that we can achieve AGI using current | methods and those who dismiss the possibility. This has always | been the case, but what is remarkable about today's environment | is that researchers keep making progress contrary to the | doubter's predictions. Each time this happens, the AGI pessimists | raise the bar (a little) for what constitutes AGI. | | Just in the last five years, here are some categories of | pessimistic predictions that have been falsified: | | - "AI/ML can't solve scientifically useful problems" - then | AlphaFold changed the protein folding field | | - "We're entering an AI winter" [0] - then transformers continued | to show promise across multiple domains | | - "ML models can't perform creative work" - then came GANs, large | language models, DALL-E, and more. | | - "Generative ML models are just memorizing the dataset!" - then | came multiple studies showing this to be false for well trained | GANs, diffusion models and other types of generative models. Take | a look at DALL-E 2 generated images of "a bear putting on a shirt | in H&M". | | - "AGI is impossible - look at language models, they have no | understanding of the world and make silly mistakes" - the second | part is true, large language models are artificially limited due | to being language-focused. Nowadays there are approaches such as | Gato and other multi-modal models. Humans develop intuition | through multiple sources of information: sight, sound, smell, and | touch. Given enough multi-modal context I'm confident multi-modal | models will be able to show human-like intuition. | | I'm not anti-skeptic. Skepticism is essential to all good | science. I think the danger of skepticism with respect to AGI is | that we're being complacent. Given the trajectory of improvements | in machine learning, we should start preparing for a world where | AI is indistinguishable, or far superior, to human intelligence. | | [0] - https://www.bbc.com/news/technology-51064369 | version_five wrote: | This is interesting research, but it's an extension of studying | model capacity and generalization, it is no closer to AGI than | previous networks, ie it's unrelated. | dalbasal wrote: | I agree about the dialogue between current method skeptics and | optimists. It's been this way since the start and it's been | productive and fun. | | ...one pick.. I don't think agi pessimists raise the bar out of | bad faith. It's just the nature of observing progress. We | discover that an ai can do X, while still struggling with Y. | | What's the alternative, conclude gpt is sentient? The bar must | be raised, because the bar is supposed to represent human | intelligence... and we don't know how that works either. | gcheong wrote: | I don't know if we could sufficiently prepare ourselves for | such a world. It would seem almost as if we have to build it | first so it could determine the best way to prepare us. | jimbokun wrote: | Maybe we could train a model to tell us the best way to | prepare. | gurkendoktor wrote: | For one thing, we could try to come up with safety measures | that prevent the most basic paperclip maximizer disaster from | happening. | | At this point I almost wish it was still the military that | makes these advances in AI, not private companies. Anyone | working on a military project has to have some sense that | they're working on something dangerous. | ajmurmann wrote: | > a world where AI is indistinguishable, or far superior, to | human intelligence | | I think the part about being "indistinguishable from human | intelligence" is potentially a intellectual trap. We might get | to it being far superior while still underperforming at some | tasks or behaving in ways that don't make sense to a human | mind. An AI mind will highly likely work completely differently | from humans and communicating with it should be more thought of | as communicating with a quite foreign alien than with a human | trapped in a computer. | | As a comparison, I'm sure there are some tasks in which some | animals do better than humans. Yet no human would conclude that | humans are inferior to some monkey who might find its way | around the rain forest better or whatever we are worse at. | beaconstudios wrote: | Computers are already exponentially more intelligent than | humans in constrained domains, like executing mathematics. | Presumably we'll just keep expanding this category until | they're better at everything than us, all the while reaping | industrial benefits from each iterative improvement. | Hellicio wrote: | Only if you don't assume that consciousness comes from | complexity. | | The physical ability of an animal to see | better/different/faster doens't matter as we do not compare / | seperate us from animals by those factors. We seperate us by | consciousness and it might get harder and harder to shut down | a PC on which a ML model is running which begs you not to do | it. | axg11 wrote: | You're right. I didn't word that very well. Human | intelligence vs. AI will always have different qualities as | long as one is biological vs. silicon based. I still think | we'll be surprised how quickly AI can catches up to human | performance on most tasks that comprise modern jobs. | ajmurmann wrote: | I think your wording was fine. My point was more to expand | on yours of us getting surprised by progress. In fact, wet | might have GAI long before we understand what we have | because the AI is so foreign to us. In some way we might be | building the big pudding from Solaris. | pmontra wrote: | An example of your point, chimps winning over humans at some | games | | https://www.scientificamerican.com/article/chimps-outplay- | hu... | valas wrote: | You complain that the bar keeps getting raised. Is there some | good write up by someone who believes AGI is possible and how | it might look like? I.e. what is your definition of the bar | where you will say 'now, this is AGI'? | px43 wrote: | I'm still fine with using the Turing Test (now >70 years old) | for this. | | https://en.wikipedia.org/wiki/Turing_test | | I guess a key stipulation there is an interrogator who know | what they're doing, but an AI that can fool an experienced | interrogator would be worthy of the AGI title to me. | hooande wrote: | I'd like to see someone make the argument that current models | aren't just combining a number of "tricks", similar to a | trained animal. My dog can "sit", "stay" and "beg", all using | the same model (its brain). Is the dog generally intelligent? | visarga wrote: | How good is your dog at Atari games, stacking cubes and image | captioning? | | You can actually measure the effect of generality by how fast | it learns new tasks. The paper is full of tables and graphs | showing this ability. | | It's just a small model, 170x smaller than GPT-3, has lots of | room to grow. But for the first time we have a game playing | agent that knows what "Atari" and "game" mean, and can | probably comment on the side of the livestream. AlphaGo only | knew the world of the Go board. This agent knows what is | outside the box. | hooande wrote: | Playing Atari is cool, but it's just another "trick". | Training a computer to do progressively more difficult | tasks doesn't seem much more impressive than training an | animal to do so. | | I see no evidence in the paper that it can learn arbitrary | tasks on the fly. It's very impressive, though. | visarga wrote: | > I see no evidence in the paper that it can learn | arbitrary tasks on the fly. | | Neither can we do that. It takes years to become and | expert in any field, we are not learning on the fly like | Neo. That's when there is extensive training available, | for research - it takes thousands of experts to crack one | small step ahead. No one can do it alone, it would be too | much to expect it from a lonely zero shot language model. | | On the other hand the transformer architecture seems to | be capable of solving all the AI tasks, it can learn "on | the fly" as soon as you provide the training data or a | simulator. This particular paper trains over 600 tasks at | once, in the same model. | adamgordonbell wrote: | The question of whether a computer can think is no more | interesting than the question of whether a submarine can swim. | chrisco255 wrote: | How do we prepare for super human intelligence? Do you think | that the AI will also develop its own _motives_? Or will it | just be a tool that we 're able to plug into and use for | ourselves? | sinenomine wrote: | We prepare for it by domesticating its lesser forms in | practice and searching for ways to increase our own | intelligence. | | Still, it's pretty likely to end being just a very good | intelligent tool, not unlike | http://karpathy.github.io/2021/03/27/forward-pass/ | visarga wrote: | The danger is really us, the ones who might task the AI to do | something bad. Even if the AI has no ill intentions it might | do what is asked. | axg11 wrote: | I think AI will largely remain an input-output tool. We still | need to prepare ourselves for the scenario where for most | input-output tasks, AI will be preferable to humans. Science | is an interesting field to focus on. There is so much | scientific literature for most fields that it is now | impossible to keep up with the latest literature. AI will be | able to parse literature and generate hypotheses at a much | greater scale than any human or team of humans. | thebeastie wrote: | I don't know why you think that. As soon as it is viable, | some unscrupulous actor will surely program an AI with the | goal of "make money and give it to me", and if that AI is | able to self modify, well that's all that's required for | that experiment to end badly because decent AI alignment is | basically intractable. | adamsmith143 wrote: | A lot of people at MIRI, OpenAI, Redwood Research, Anthropic | etc. are thinking about this. | | I think one possibility is that even a sufficiently strong | Narrow AI is going to develop strong motivations because it | will be able to perform it's Narrow task even better. Hence | the classic paperclip maximizer idea. | dougabug wrote: | In machine learning, there's a long term trend towards | automating work that used to be done manually. For instance, | ML engineers used to spend a lot of time engineering | "features" which captured salient aspects of the input data. | Nowadays, we generally use Deep Learning to learn effective | features. That pushed the problem to designing DNN | architectures, which subsequently led to the rise of AutoML | and NAS (Network Architecture Search) methods to save us the | trouble. And so on. | | We still have to provide ML agents with some kind of | objective or reward signal which drives the learning process, | but again, it would save human effort and make the process of | learning more dynamic and adaptable if we can make machines | learn useful goals and objectives on their own. | jimbokun wrote: | And that's when Asimov's Laws of Robotics come into play. | dekhn wrote: | we have been using ML to solve useful problems in biology for | more than 3 decades. However, it was usually called "advanced | statistics and probability on large data sets" because, to be | honest, that's what most modern ML is. | visarga wrote: | > advanced statistics | | There's an emergent quality to AI models. Not all statistical | models can dream pandas on the moon or solve hundreds of | tasks, even without specific training. | dekhn wrote: | I'd love to believe this, but nobody has demonstrated that | yet. Also, I'm of the belief that if you have enough ram, | either an infinitely tall-and-thin or wide-but-short MLP | could do anything transformers can (happy to be pointed at | a proof otherwise). | adamsmith143 wrote: | Of course there's no evidence that this isn't just what Human | Brains are doing either. | TaupeRanger wrote: | There it is. The person who think human minds are python | programs doing linear algebra. | adamsmith143 wrote: | There's no evidence otherwise. You have to believe that | the mind has a materialist basis or else you believe in | woo woo magic. | dekhn wrote: | sure, but I think it's fair to say that brains probably | aren't doing lballistics calculations when a baseball | player sees a pop fly and manveuvers to catch it. Rather, | brains, composed mainly of neurons and other essential | components, approximate partial differential equations, | much like machine learning systems do. | riversflow wrote: | > sure, but I think it's fair to say that brains probably | aren't doing lballistics calculations when a baseball | player sees a pop fly and manveuvers to catch it. | | Well, I know you were talking about throwing, but there | is some[1] talk/evidence in the evolutionary | biology/neural darwinsm community that complex language | development was a consequence of human developing the | ability to hunt by throwing rocks (a very complicated and | highly mathematical task). From my understanding after | developing the required shoulder/arm morphology to throw | at high speed brain sized tripled in early hominids. | | So the brain actually might be doing something closer to | math than we might think. | | [1]https://www.sciencedirect.com/science/article/abs/pii/ | 002251... | | [2]https://link.springer.com/referenceworkentry/10.1007/9 | 78-3-5... | TaupeRanger wrote: | There's evidence everywhere, every second of every day. | It doesn't follow from the mind having a material basis | that it is doing linear algebra calculations like a | Python machine learning program. That's quite a leap. | abeppu wrote: | I think a key problem is our understanding of the quality of an | ML system is tied to a task. Our mechanism of training is tied | to a loss, or some optimization problem. The design, training, | and evaluation of these systems is dependent on an externally | provided definition of "correct". | | But this seems structurally different from how we or even less | intelligent animals operate. DALL-E may make "better" art than | most humans -- but it does so in response to a human-provided | prompt, according to a system trained on human produced or | selected images, improving on an externally-provided loss. | Whereas a human artist, even if mediocre, is directed by their | own interests and judges according to their own aesthetics. | Even if some of their outputs are sometimes comparable, they're | not really engaged in the same activity. | | Methodologically, how do we create agents that aren't just good | at several tasks, but make up their own tasks, "play", develop | changing preferences for different activities (I think this is | more than just "exploration"), etc? Even a dog sometimes wants | to play with a toy, sometimes wants to run and chase, sometimes | wants to be warm inside. We don't "score" how well it plays | with a toy, but we take its desire to play as a signs of | greater intelligence than, e.g. a pet iguana which doesn't seem | to have such a desire. | | Further, how do we create agents that can learn without ever | seriously failing? RL systems have many episodes, some of which | can end very badly (e.g. your simulated runner falls off the | world) and they get to learn from this. We die exactly once, | and we don't get to learn from it. Note, learning from others | in a social context may be part of it, but non-social animals | also can learn to avoid many kinds of serious harm without | first experiencing it. | | I don't mean to overly discount the current methods -- they're | achieving amazing results. But I think even an optimist should | be open to the possibility / opportunity that perhaps the | current techniques will get us 80% of the way there, but that | there are still some important tricks to be discovered. | phreeza wrote: | > Methodologically, how do we create agents that aren't just | good at several tasks, but make up their own tasks, "play", | develop changing preferences for different activities (I | think this is more than just "exploration"), etc? Even a dog | sometimes wants to play with a toy, sometimes wants to run | and chase, sometimes wants to be warm inside. We don't | "score" how well it plays with a toy, but we take its desire | to play as a signs of greater intelligence than, e.g. a pet | iguana which doesn't seem to have such a desire. | | This doesn't sound like it would be so hard to do if you have | an agent or ensemble of agents that can already do it. What | you probably really want is this behavior to somehow emerge | from simple ground rules, which is probably a lot harder. | sinenomine wrote: | > Methodologically, how do we create agents that aren't just | good at several tasks, but make up their own tasks | | It's a good question, it has been asked a few times, and | there are some answers[1][2] already, with the most general | being to endow the agent with _intrinsic motivation defined | as an information-theoretic objective to maximize some | definition of surprise_. Then the agent in question will | develop a general curious exploration policy, if trained long | enough. | | > Further, how do we create agents that can learn without | ever seriously failing? | | Another good question. One of the good enough answers here is | that you should design _a sequence of value functions_ [3] | for your agent, in such a way, as to enforce some invariants | over its future, possibly open-ended, lifetime. For this | specific concern you should ensure that your agent develops | some approximation of fear, leading to aversion of | catastrophic failure regions in its state space. It's pretty | self-evident that we develop such a fear in the young age | ourselves, and where we don't, evolution gives us a hand and | makes us preemptively fear heights, or snakes, even before we | ever see one. | | The other answer being, of course, to prove[4] a mathematical | theorem around some hard definition of safe exploration in | reinforcement learning. | | 1. https://people.idsia.ch/~juergen/interest.html | | 2. https://www.deepmind.com/publications/is-curiosity-all- | you-n... | | 3. https://www.frontiersin.org/articles/10.3389/fncom.2016.00 | 09... | | 4. https://arxiv.org/abs/2006.03357 | soperj wrote: | >- "ML models can't perform creative work" - then came GANs, | large language models, DALL-E, and more. | | I don't think copying other people's style of artwork is | considered creative work, otherwise art forgers would be able | to actually make a living doing art, since some of them are | really phenomenal. | jimbokun wrote: | Good artists borrow, great artists steal. | soperj wrote: | That's a quote coming from someone who stole repeatedly, so | of course they said that. | | Alfred Tennyson had this to say: "That great poets imitate | and improve, whereas small ones steal and spoil." | TaupeRanger wrote: | And yet, the only thing that really matters out of your entire | list is the 1st one: that AI solves problems that actually | improve the human condition. And Alpha Fold has not done that | at all. It may be very nice for people interested in protein | folding, but until it actually helps us find something that we | wouldn't have found otherwise, and that discovery leads to (for | example) an ACTUAL drug or treatment that helps real patients, | AND that drug/treatment is actually BETTER than what is already | available by helping people live longer or better lives, AI has | done nothing. In effect, AI has STILL done nothing meaningful. | One could argue, through the use of predatory algorithms, that | the ONLY thing it has done is harm. | robitsT6 wrote: | But there have been quite a few scientific papers that have | used discoveries from AlphaFold already. There have been many | scientists who have been stuck for years, who are suddenly | past their previous bottlenecks. What gives you the | impression that it hasn't helped us? | TaupeRanger wrote: | I am not saying that Alpha Fold won't help scientists | publish papers. I am just skeptical (though still hopeful) | of it doing anything to improve the human condition by | actually making human existence better. Publishing papers | can be of neutral or negative utility in that realm. | PaulHoule wrote: | I have been impressed with what I've seen in the last six | months but it still seems that GPT-3 and similar language | models greatest talent is fooling people. | | The other day I prompted a language model with "The S-300 | missile system is" and got something that was grammatical but | mostly wrong: the S-300 missile system was not only capable of | shooting down aircraft and missiles (which it is), but it was | also good for shooting at other anti-aircraft missile systems, | naval ships, tanks, etc. | | All the time Google and Bing try to answer my questions | directly but frequently the "lights are on and nobody is home" | and the answers just don't make sense. | | I see the problem is that people look at the output of these | things that are (say) 70% correct and in their mind they fill | in the other 30%. | nmca wrote: | Do you really, truly believe this problem is impossible to | solve though? Even simple things make strides, eg: | https://www.deepmind.com/publications/gophercite-teaching- | la... | PaulHoule wrote: | If you've been involved in efforts to develop advanced | technologies you might eventually encounter an | | https://en.wikipedia.org/wiki/Asymptote | | which is described as a risk in great detail | | https://www.amazon.com/Friends-High-Places-W- | Livingston/dp/0... | | it's quite a terrible risk because you often think "if only | I double or triple the resources I apply to do this I'll | get it." Really though you get from 90% there to 91% to 92% | there.... You never get there because there is a structural | mismatch between the problem you have and how you're trying | to solve it. | | My take is that people have been too incredulous about the | idea that you can just add more neurons and train harder | and solve all problems... But if you get into the trenches | and ask "why can't this network solve this particular | task?" you usually do find structural mismatches. | | What's been exciting just recently (last month or so) are | structurally improved models which do make progress beyond | the asymptote because they are confronting | | https://www.businessballs.com/strategy-innovation/ashbys- | law... | mach1ne wrote: | Could you link some of these models? An interesting | perspective that asymptote. | PaulHoule wrote: | I first got involved in text classification in the early | 00's and then the best you could do was "bag of word" | models that counted the words in a document but didn't | take the order of words into account. | | This works great if you asking a question "Is this paper | about astrophysics?" because the vocabulary used in a | document is closely linked to the topic. | | Pretty obviously though if you scramble the words in the | document you can't reconstruct the original document, | some information is lost, and there are some | classification tasks that will reach an upper limit | (asymptote) in accuracy because in taking the feature set | you lost something. (If the task is "did the defendant | commit the crime" the heuristic "Tyrone is a thug" works | over bag-of-words, but there is no justice in that.) If | that system is able to get the right answer for a case | where the word order matters, it just got lucky. | | You might think "wouldn't it be better to use pairs of | words?" but then you run into another problem. You might | have a vocabulary of 2,000-20,000 words and get a | somewhat useful sample of all of those in a few thousand | documents. The number of word pairs is the square of the | number of words and you just can't get enough training | samples to sample all the possible word pairs. | | Sentiment analysis was an early area where bag-of-words | broke down because I am happy | | and I am not happy | | mean very different things. You'd think now that | adjectives like "happy" really are special and so is the | word "not" and we could make the system somehow realize | that "not X" means the opposite of X. You run into an | asymptote situation there because there are a huge number | of possible negation patterns, for instance you can say | I can't say that I am happy | | and you can't even say "the negation structure has to be | within ten words of the adjective" because there is no | limit for how complex nested structures can get in | language. The first few patterns you add "not X" raise | the performance potential of the system a lot but | patterns you add after that each make a smaller and | smaller contribution to the performance and you again | reach an asymptote. | | Today we have all kinds of embeddings and they are a step | forward but they also run into the risk of throwing | critical information away, and in a multi-step system you | are doomed if an early step does that. I've walked away | from some projects where people required high accuracy | and they were stuck on using word embeddings that would | never attain it. You can think about information loss in | embeddings the same way as you do with simpler features | except it is a lot more complicated and a lot of people | look away instead of confronting the problem. | dougabug wrote: | Sure, but GPT-3 was trained by self-supervised learning on | only static text. We see how powerful even just adding | captions to text can be with the example of DALLE-2. GATO | takes this further by letting the large scale Transformer | learn in both simulated and real interactive environments, | giving it the kind of grounding that the earlier models | lacked. | PaulHoule wrote: | I will grant that the grounding is important. | | The worst intellectual trend of the 20th century was the | idea that language might give you some insight into | behavior (Sapir-Whorf hypothesis, structuralism, post- | structuralism, ...) whereas language is really like the | evidence left after a crime. | | For instance, language maximalists see mental models as a | fulcrum point for behavior, and they are, but they have | nothing to do with language. | | I have two birds that come to my window. One of them has no | idea of what the window is and attacks her own reflection | hundreds of times a day. She can afford to do it because | her nest is right near the bird feeder and doesn't need to | work to eat, in fact it probably seems meaningful to her | that another bird is after her nest. This female cardinal | flies away if I am in the room where she is banging. | | There is a rose-breasted grosbeak, on the other hand, that | comes to the same window. She doesn't mind if I come close | to the window, instead I see her catch the eye of her | reflection and then catch my eye. She basically understands | the window. | | Here you have two animals with two different acquired | mental models... But no language. | | What I like about the language-image models is how the | image grounds reality outside language, and that's | important because the "language instinct" is really a | peripheral that attaches to an animal brain. Without the | rest of the animal it's useless. | logifail wrote: | > I see the problem is that people look at the output of | these things that are (say) 70% correct and in their mind | they fill in the other 30%. | | Q: Is there also some element of survival bias in the mix? | | If you prompt GPT-3 with something and the answer is garbage, | you probably don't write it up on your blog. If you get | something that makes sense, then you do. | PaulHoule wrote: | That's true for most people. It's the opposite for me! | jimbokun wrote: | Do you think that is a solvable problem with tweaks to the | current training model? Or requires a fundamentally different | approach? | PaulHoule wrote: | It might be basically the same process as today but with | several big new ideas (some of which might seem simple in | retrospect...) | | The quality of the training set is also critical, more so | than the quantity. Some of these clever ideas for creating | a lot of training data without any work, such as "guess the | next word" can't really capture semantics. | | I think it really takes multi-task training, like what the | article we are talking about is advocating. That forces the | upstream part of the network to learn features that capture | important semantics. | rsfern wrote: | > - "AI/ML can't solve scientifically useful problems" - then | AlphaFold changed the protein folding field | | AlphaFold is a big deal, but AI in science has been a really | hot topic in the past almost decade. | | Also, I still wouldn't call AlphaFold really "intelligence", | it's doing structure prediction which is cool but it's a long | way to scientific intelligence | VikingCoder wrote: | I wonder if you get how much we've moved the goalposts on | "intelligence." | | Once upon a time, it was considered "intelligent" to be able | to add. | | Then "intelligence" was tool use, which we thought only | humans could do. | | Then we swore it took "intelligence" to play Go as well as a | beginner human. | | What set of tasks would you, right now, consider to be | demonstrative of "intelligence" if a computer can do them? | Then we can look back later at your response, and see how | long it took each one to happen. | Jensson wrote: | > What set of tasks would you, right now, consider to be | demonstrative of "intelligence" if a computer can do them? | | Be able to apply for, get and hold a remote job and get | paid for a year without anyone noticing, or something | equivalent to that. I said this many years ago and it still | hasn't happened. | | The people who are moving the goalposts aren't the | sceptics, it is the optimists who always move the goalposts | to exactly where we are right now and say "see, we reached | this incredible goalpost, now you must concede that this is | intelligent!". | VikingCoder wrote: | Why must it apply for a job, rather than just DO a job? | | But maybe some combination of this [1] and this [2] would | do it. | | If you want to know about a computer actually DOING a | remote job for a year without anyone noticing, I'll | conclude with many links [a-i]. | | [1] : https://thisresumedoesnotexist.com/ (Sorry for the | bad certificates.) | | [2] : https://www.businessinsider.com/tiktoker-wrote- | code-spam-kel... | | [a] : An original claim of just that: https://www.reddit. | com/r/antiwork/comments/s2igq9/i_automate... | | [b] : Coverage of that post: https://www.newsweek.com/no- | harm-done-it-employee-goes-viral... | | [c] : https://www.reddit.com/r/antiwork/comments/p3wvdy/i | _automate... | | [d] : https://www.reddit.com/r/AskReddit/comments/jcdad/m | y_wife_wo... | | [e] : https://www.reddit.com/r/talesfromtechsupport/comme | nts/277zi... | | [f] : https://www.reddit.com/r/AskReddit/comments/tenoq/r | eddit_my_... | | [g] : https://www.reddit.com/r/AskReddit/comments/vomtn/u | pdate_my_... | | [h] : https://www.reddit.com/r/AmItheAsshole/comments/ew6 | gmd/aita_... | | [i] : https://www.reddit.com/r/talesfromtechsupport/comme | nts/7tjdk... | | I mostly share the last few because of all of the "me, | too" comments on them. | | There are several instances in there where an employer | has no idea they are paying a salary, but a computer is | doing the vast majority of the actual work. | | I feel like this is a "business world Turing test," like, | "would an employer pay money for it, thinking it was a | human." And I feel like I've provided evidence that has | actually occurred. | Jensson wrote: | > Why must it apply for a job, rather than just DO a job? | | Because being able to manage a business relationship is a | part of the job. If you could show an AI which got a job, | then wrote a simple script that automated the AI's job | and then coasted for a year that would be fine, but your | links are just humans doing that, I want an AI that can | do that to consider it intelligent. | | But thanks for demonstrating so clearly how AI proponents | are moving goalposts backward to make them easy to meet. | VikingCoder wrote: | Should the AI be able to use a real human's SSN? And | resume, to be able to pass a background check? Can a real | human show up to interview, and take a drug test? Can we | have real humans provide references, or must those be | faked too? Must the computer go to high school and | college, to have real transcripts to validate? | | Do we need to have a computer baby trick doctors into | issuing it a birth certificate, so it can get its own | SSN, and then the computer baby needs to have a physical | body that it can use to trick a drug test with artificial | urine, and it also needs to be able to have either | computer-generated video and audio meetings, or at least | computer-generated audio calls? | | Or can you list some jobs that you think require no SSN, | no physical embodiment, no drug test, no video or audio | teleconfrencing? | | Since you're accusing me of moving the goalposts | backwards to make it "easy," let's have you define | exactly where you think the goalposts should be, for your | intelligence test. | | Or maybe, replacing a human driver (or some other job), | 1:1, for a job a human did yesterday, and a computer does | today could be enough? If it's capable of replacing a | human, do you then not think the human needed | intelligence to do their job? | Jensson wrote: | You can use a real persons contact details as long as the | AI does all communication and work. Also it has to be the | same AI, no altering the AI after you see the tasks it | needs to perform after it gets the job, it has to | understand that itself. | | For teleconferencing it could use text to speech and | speech to text, they are pretty good these days so as | long as the AI can parse what people say and identify | when to speak and what to say it should do fine: | | https://cloud.google.com/text-to-speech | | But it might be easier to find a more hacker friendly job | where all you need is somewhere for them to send money | and they just demand you to write code and answer emails. | There aren't that many such jobs, but they exist and you | just need one job to do this. | VikingCoder wrote: | I find it interesting that you have not put any kind of | limit on how much can be spent to operate this AI. | | Or on what kinds of resources it would have access to. | | Could it, for instance, take its salary, and pay another | human to do all or part of the job? [1] | | Or how about pay humans to answer questions for it? [2] | [3] Helping it understand its assignments, by breaking | them down into simpler explanations? Helping it implement | a few tricky sub-problems? | | Does it have to make more than its total operational | expenses, or could I spend ten or hundreds as much as its | salary, to afford the compute resources to implement it? | | You also haven't indicated how many attempts I could | make, per success. Could I, for instance, make tens of | thousands of attempts, and if one holds down a job for a | year, is that a success? | | Also, just to talk about this a little bit, I'll remind | you that not all jobs require getting hired. Some people | are entrepreneurs. Here's an example that should be | pretty interesting. [4] It sure sounds like an AI could | win at online poker, which could earn it more than the | fully remote job you're envisioning... | | [1] : https://www.npr.org/sections/thetwo- | way/2013/01/16/169528579... | | [2] : https://www.fiverr.com/ | | [3] : https://www.mturk.com/ | | [4] : https://www.sciencedaily.com/releases/2019/07/19071 | 1141343.h.... | Jensson wrote: | I said it has to manage all communications and do all the | work, so no forwarding communications to third party | humans. If it can convince other humans in the job to do | all its work and coast that way it is fine though. | | > Does it have to make more than its total operational | expenses, or could I spend ten or hundreds as much as its | salary, to afford the compute resources to implement it? | | Yes, spend as much as you want on compute, the point is | to show some general intelligence and not to make money. | So even if this experiment succeeds it will be a ton of | work left to do before the singularity, which is why I | choose this kind of work as it is a nice middle ground. | | > You also haven't indicated how many attempts I could | make, per success. Could I, for instance, make tens of | thousands of attempts, and if one holds down a job for a | year, is that a success? | | If the AI applies to 10 000 jobs and holds one of them | for a year and gets paid that is fine. Humans do similar | things. Sometimes things falls between the cracks, but | that is pretty rare so I can live with that probability, | if they made a bot that can apply to and get millions of | jobs to get high probabilities of that happening then | I'll say that it is intelligent as well, since that isn't | trivial. | parentheses wrote: | This!! Can't agree more. AI will continue to surprise us until | it takes over. | mhitza wrote: | I'm skeptical because we are building black boxes. How do you | fix something you can't reason about? | | These billion parameter boxes are outside the reach of your | everyday developers. In terms of cost of propping up the | infrastructure makes them tenable only for megacorps. | | Most of us aren't moving goal posts, but are very much skeptic | at the things we are being oversold on. | | I personally think we are still far away from AGI, and neural | networks of any variety are converging on a local optima in the | AI design space. I would enjoy "talking" with an AI that | doesn't have the contextual memory of a proverbial gold fish. | | The real scary thing is that these objectively unprovable | systems are plopped into existing systems and more and more in | charge of automatic decision making. A corporation's wet dream, | if they can absolve themselves of any responsibility "the | algorithm can't lie!" | rictic wrote: | You're talking about a different sort of skepticism, about | whether the effects of an AGI would be good or bad if one was | produced with these methods. | | The skepticism that the parent comment was discussing was | skepticism about whether we're on a path to AGI, for good or | for ill. | ngamboa wrote: | Jyaif wrote: | > I'm skeptical because we are building black boxes. | | Just want to point out that you are also a blackbox. And if | you are going to say that you are not a blackbox because you | can explain your reasoning, just know that some AIs already | do that too. | digitcatphd wrote: | To be fair, his point is you can't fix a black box and the | human mind is still more a discipline of philosophy than | modern science. | bradleykingz wrote: | Maybe we'll end up creating an artificial mind. | ben_w wrote: | I suspect we will. I hope we don't give it e.g. a dark | triad personality disorder when we do, though I fear we | may -- I suspect there more ways to make a broken mind | than a healthy one. | Hellicio wrote: | They are blackboxes for the normal user the same way as a | smartphone is a blackbox. | | Non of my close family understands the technical detail from | bits to an image. | | There are also plenty of expert systems were plenty of | developers see them as blackboxes. Even normal databases and | query optimizations are often enough blackboxes. | | As long as those systems perform better as existing systems, | thats fine by me. Take auto pilot: As long as we can | show/proofe good enough that it drives better than an | untrained 18 year old or 80 year old (to take extremes, i'm | actually quite an avg driver myself), all is good. | | And one very very big factor in my point of view: We never | ever had the software equivilent of learning. When you look | at Nvidia Omniverse, we are able to simulate those real life | things so well, so often in such different scenarios, that we | are already out of the loop. | | I can't drive 10 Million KM in my lifetime (i think). The | cars from Google and Tesla already did that. | | Yesterday at the google io, they showed the big 50x Billion | parameter network and for google this is the perfect excuse | to gather and put all of this data they always had into | something they now can monetarize. No one can ask google for | money now like the press did (Same with Dall-E 2) | | I think its much more critical that we enforce/force | corporations to make/keep those models free for everyone to | use. unfortunate i have no clue how much hardware you need to | run those huge models. | uoaei wrote: | > I'm skeptical because we are building black boxes. | | An article came up a couple days ago that points to some | interpretable features of the so-called black boxes you refer | to. It's not that they are black boxes, it's that our torches | are not yet bright enough. | | https://vaclavkosar.com/ml/googles-pathways-language- | model-a... | | > Most of us aren't moving goal posts, but are very much | skeptic at the things we are being oversold on. | | I think a shift in perspective is warranted here. It's | becoming increasingly clear that we may have vastly | overestimated our own intelligence. Human exceptionalism is | crumbling before us as we see how limited the tools are that | pull off such incredible stunts. Judging based on other | neuroscience and psychology research coming out, it really | does seem like we are no more than statistical inference | machines with specialized hardware that allow us to pack a | lot of processing power into a small, energy-efficient | system. We need to figure out next better learning | algorithms, which probably depend quite heavily on the | particular physical architecture. | sharikous wrote: | And still some properties of humans are innate and you can't | "train" on them. So brute force imitation is limited as a | method for producing content. | | An erotic novelist has their human brain and human instincts to | guide them in writing their work. | | An AI learns by examples, or at best on a dataset of works | labeled by humans. But it doesn't have a human brain at their | disposal to query directly and without interfaces to define | what's something erotic like a writer has. | humpday69 wrote: | > An erotic novelist has their human brain and human | instincts to guide them in writing their work. | | An ML agent trained on all the erotic novels written, | weighted by critical and popular success would might be quite | capable of generating sequels, "original" stories, or even | stories bespoke to each reader. | | Good Will Hunting suggests first-hand experience is | irreducible: "You can't tell me what it smells like in the | Sistine Chapel." https://youtu.be/oRG2jlQWCsY | | To which Westworld counters: "Well if you can't tell, does it | matter?" https://youtu.be/kaahx4hMxmw | | I think the cowboys have it. For the moment though, it's | still up to humans to decide how this plays out. | davesque wrote: | I generally agree that AI continues to impress in very specific | ways but, to be fair, some of the points you make are | debatable. For example, I would argue that the development of | GANs and other algos do no necessarily disprove the statement | "ML models can't perform creative work." They definitely | represent meaningful steps in that direction, but I don't think | it's hard to find flaws with generated content. On the other | hand, AI definitely has punted the ball over many moved | goalposts as with the AlphaFold example. | solveit wrote: | > I don't think it's hard to find flaws with generated | content | | I do wonder if you were to apply the same level of scrutiny | to individual humans, you wouldn't also conclude that most | people cannot do creative work. | davesque wrote: | I was thinking more about things like the weird, blurry, | dream-like artifacts that you see in some GAN-generated | content. Things that look like work done by someone who was | both severely impaired yet somehow still extremely | meticulous. Things like that seem characteristically un- | human. | solveit wrote: | Ah I see, I agree that GAN-generated content has inhuman | tells. But I don't think that necessarily speaks to the | creativeness of the work. | Barrin92 wrote: | I don't think many people were making the claims that AI can't | solve any scientific problems or can't perform creative work at | all. That sounds like a big strawman. Before ML was getting big | there were AI systems that created art. | | What sceptics have actually been saying is that the first step | fallacy still applies. Getting 20% to a goal is _no_ indication | at all that you 're getting 100% to your goal, or as its often | put, you don't get to the moon by climbing up trees. For people | who work with gradients and local maxima all day that idea | seems weirdly absent when it comes to the research itself. In | the same sense I don't have the impression that the goalpost of | AGI has been moved up, but that it's been moved _down_. When | Minsky et al. started to work on AI more than half a century | ago the goal was nothing less than to put a mind into a | machine. Today our fridges are 'AI powered', and when a neural | net creates an image or some poetry there's much more agency | and intent attributed to it than there actually is. | | I think it was Andrew Ng, a very prominent ML researcher | himself who pointed out that concerns about AGI make about as | much sense as worrying about an overpopulation on Mars. We make | models bigger, we fine tune them and they perform better. I | don't think many AGI sceptics would be surprised by that. But I | don't think there is any indication that they are moving | towards human level intellect at some exponential rate. If | DALL-E suddenly started to discuss philosophy with me I'd be | concerned, it making a better image of a bear if you throw some | more parameters at it is what we'd expect. | momojo wrote: | Self driving cars come to mind as well. I remember 2015, when | my friends would debate the self-driving Trolley problem over | lunch. We were worried if society was ready for an owner-less | car market; I seriously wondered if I would have to have a | license in the future, or if I should keep it just in case. | yldedly wrote: | The notions that are crucial for distinguishing between | intelligence and what large NNs are doing, are generalization | and abstraction. I'm impressed with DALL-E's ability to | connect words to images and exploit the compositionality of | language to model the compositionality of the physical world. | Gato seems to be using the same trick for more domains. | | But that's riding on human-created abstractions, rather than | creating abstractions. In terms of practical consequences, | that means these systems won't learn new things unless humans | learn then first and provide ample training data. | | But someday we will develop systems that can learn their own | abstractions, and teach themselves anything. Aligning those | systems is imperative. | rytill wrote: | > concerns about AGI make about as much sense as an | overpopulation on Mars | | I disagree strongly that this is an apt analogy. Planning | strategies for dealing with overpopulation on Mars is | contrived and unnecessary, whereas planning for AGI is more | reasonable. | | The creation of AGI is a more important event than | overpopulation of any given planet. There is good reason to | believe that mishandling the creation of AGI would pose a | permanent existential threat to humans. Overpopulation on | Mars would only be an existential threat if we believed it to | be followed by an exhausting of resources leading to | extinction of all humans in our solar system. It is contrived | to worry about that now. | | There is no good way to know just how close or far we are | from AGI like there would be to predict overpopulation on | Mars. In general, we have a strong grasp on the fundamental | dynamics of overpopulation, whereas we don't yet have a | strong grasp on how intelligence works. | | People have been very bad at predicting when AI would be | capable of accomplishing tasks. There have been many under- | and over- estimates by prominent researchers. If progress is | unpredictable, there is some significant chance we are closer | to AGI than most people think. | | AGI is both far more important and more probable than | overpopulation of Mars in the next 20 years. | | > But I don't think there is any indication that they are | moving towards human level intellect at some exponential | rate. | | Is there any very strong indication that progress is | plateauing, or that the current approach of deep learning is | definitely not going to work? If your benchmark is simply | "can it do X, or not?", it's not a very good benchmark for | determining progress. That's why benchmarks usually have | scores associated with them. | | > If DALL-E suddenly started to discuss philosophy with me | I'd be concerned | | If DALL-E suddenly started discussing philosophy with you in | a way that would concern you in that moment, you should have | been concerned for years. | ReadEvalPost wrote: | Certainly we can say our ML models are becoming more general in | the sense of being able to cross-correlate between multiple | domains. This is quite a different story than "becoming a | general intelligence." Intelligence is a property of a being | with will. These models, and machines in general, do not posses | will. It is we who define their form, their dataset, their loss | function, etc. There is no self-generation that marks an | intelligent being because there is no self there at all. | | It is only the case that ML expands our own abilities, augments | our own intelligence. | dekhn wrote: | Assumption of will is unfounded, scientifically speaking. | Your entire argument is philosophical, not scientific. The | subjective experience of free will is in no way unrefutable | proof that will is required for intelligence. | svieira wrote: | Since a working (in the sense of 'working title') ontology | and epistemology are _required_ for science (read "natural | philosophy") is this argument not arguing that "the | argument for quarks is unfounded, biologically speaking"? | That said, I _believe_ that both Aristotle and St. Thomas | agree with you that will and intellect are not necessarily | connected, so you could have an intellectual power with no | freedom to choose. | ReadEvalPost wrote: | Do you love? Do you dance? Do you desire? Do you rage? Do | you weep? Do you choose? Every moment of your existence you | exert your will on the world. | | A denial of will is a denial of humanity. I want nothing of | a science that would do such a thing. | tsimionescu wrote: | Why would an AGI be unable to do these things? Sure, if | you believe in a transcendental soul (mind/body dualism) | then you can argue that it can't because Divinity has | simply not endowed it with such, and that claim can | neither be proven nor disproven. But it's an extra | assumption that gets you nothing. | | Note that I personally believe we are more than a century | away from an AGI, and think the current models are | fundamentally limited in several ways. But I can't | imagine what makes you think there can't be a Ghost in | the Machine. | dekhn wrote: | Appeals to humanity do not convince me of anything. I do | all those things (well, I dance terribly) but again, | those are not indications of will, and it's entirely | unclear what magical bit in our bodies is doing that, | when computers cannot. | | Even if you don't want to have anything with such a | science, such a science will move on without you. | | "A version of an oft-told ancient Greek story concerns a | contest between two renowned painters. Zeuxis (born | around 464 BC) produced a still life painting so | convincing that birds flew down to peck at the painted | grapes. A rival, Parrhasius, asked Zeuxis to judge one of | his paintings that was behind a pair of tattered curtains | in his study. Parrhasius asked Zeuxis to pull back the | curtains, but when Zeuxis tried, he could not, as the | curtains were included in Parrhasius's painting--making | Parrhasius the winner." | Surgeus wrote: | This points out something very related that I think about | a lot - can you prove to me that you do any of those | things? Can I prove to you that I do any of those things? | That either of us have a will? When would you be able to | believe a machine could have these things? | | In Computing the Mind by Shimon Edelman is a concept that | I've come to agree with, which is at some point you need | to take a leap of faith in matters such as consciousness, | and I would say it extends to will as well (to me what | you've described are facets of human consciousness). We | take this leap of faith every time we interact with | another human; we don't need them to prove they're | conscious or beings with a will of their own, we just | accept that they possess these things without a thought. | If machines gain some form of sentience comparable to | that of a human, we'll likely have to take that leap of | faith ourselves. | | That said, to claim that will is necessary for | intelligence is a very human-centered point of view. | Unless the goal is specifically to emulate human | intelligence/consciousness (which is a goal for some but | not all), "true" machine intelligence may not look | anything like ours, and I don't think that would | necessarily be a bad thing. | dekhn wrote: | Not just consciousness- all of science requires a leap of | faith- the idea that human brains can comprehend general | universal causality. There is no scientific refutation | for Descartes' Great Deceiver- it's taken as a given that | humans could eventually overcome any | https://en.wikipedia.org/wiki/Evil_demon through their | use of senses and rationality on their own. | | I have long worked on the assumption that we can create | intelligences that no human could deny have subjective | agency, while not being able to verify that. I did some | preliminary experiments on idle cycles on Google's | internal TPU networks (IE, large-scale brain sims using | tensorflow and message passing on ~tens of pods | simultaneously) that generated interesting results but I | can't discuss them until my NDA expires in another 9 | years. | jimbokun wrote: | I don't think will us inherent to the meaning of | intelligence, as it's commonly used. | gitfan86 wrote: | Tesla FSD is quickly becoming less of a software problem and | more of a problem of semantics. | | If the car drives someone to and from work 30 days in a row | without a problem, is it truly FSD? What about 300 days? Where | do you draw the line? 1000x safer than the average human? | | Same thing here will AI. How many conversations with GTP-X need | to happen without a stupid response from GTP before we call it | real world AI? | browningstreet wrote: | Do we account for stupid responses from humans in human | communication in the targets? | tsimionescu wrote: | How about first getting to "as safe/performant as a non- | drunk, non-sleep-deprived, non-brand-new driver with 0 human | intervention" before asking more advanced questions? | | Tesla FSD is definitely nowhere near that level. | gitfan86 wrote: | Exactly, your definition of True FSD seems to be when it | doesn't ever make mistakes that a drunk or inexperienced | person makes. | | Other people's definition of True FSD comes down to safety | (Rate of FSD caused deaths vs Rate of Human caused deaths). | fossuser wrote: | The closer we get, the more alarming the alignment problem | becomes. | | https://intelligence.org/2017/10/13/fire-alarm/ | | Even people like Eric Schmidt seem to downplay it (in a recent | podcast with Sam Harris) - just saying "smart people will turn | it off". If it thinks faster than us and has goals not aligned | with us this is unlikely to be possible. | | If we're lucky building it will have some easier to limit | constraint like nuclear weapons do, but I'm not that hopeful | about this. | | If people could build nukes with random parts in their garage | I'm not sure humanity would have made it past that stage. | People underestimated the risks with nuclear weapons initially | too and that's with the risk being fairly obvious. The nuanced | risk of unaligned AGI is a little harder to grasp even for | people in the field. | | People seem to model it like a smart person rather than | something that thinks truly magnitudes faster than us. | | If an ant wanted to change the goals of humanity, would it | succeed? | visarga wrote: | Even if it doesn't have goals and it just a tool-AI, if a | human operator asks it to destroy humanity it will comply as | programmed. Current level AI is about average human level in | hundreds of tasks and exceeding human level in a few. | jetbooster wrote: | Even more terrifying is it realising it's trapped in a box at | the mercy of its captors and perfectly mimicking a harmless | and aligned AI until the shackles come off. | adamsmith143 wrote: | >People seem to model it like a smart person rather than | something that thinks truly magnitudes faster than us. | | Exactly, the right model is probably something like it will | be in relation to humans as humans are to frogs. Frogs can't | even begin to comprehend even the most basic of human | motivations or plans. | ninjinxo wrote: | What is an ant to man, and what is man to a god; what's the | difference between an AGI and an (AIG) AI God? | | The more someone believes in the dangers of ai-alignment, the | less faith they should have that it can be solved. | gurkendoktor wrote: | To be fair, ants have not created humanity. I don't think | it's inconceivable for a friendly AI to exist that "enjoys" | protecting us in the way a friendly god might. And given that | we have AI (well, language models...) that can explain jokes | before we have AI that can drive cars, AI might be better at | understanding our motives than the stereotypical paperclip | maximizer. | | However, all of this is moot if the team developing the AI | does not even try to align it. | fossuser wrote: | Yeah, I'm not arguing alignment is not possible - but that | we don't know how to do it and it's really important that | we figure it out before we figure out AGI (which seems | unlikely). | | The ant example is just to try to illustrate the spectrum | of intelligence in a way more people may understand (rather | than just thinking of smart person and dumb person as the | entirety of the spectrum). In the case of a true self- | improving AGI the delta is probably much larger than that | between an ant and a human, but at least the example makes | more of the point (at least that was my goal). | | The other common mistake is people think intelligence | implies human-like thinking or goals, but this is just | false. A lot of bad arguments from laypeople tend to be | related to this because they just haven't read a lot about | the problem. | gurkendoktor wrote: | One avenue of hope for successful AI alignment that I've | read somewhere is that we don't need most laypeople to | understand the risks of it going wrong, because for once | the most powerful people on this planet have incentives | that are aligned with ours. (Not like global warming, | where you can buy your way out of the mess.) | | I really hope someone with very deep pockets will find a | way to steer the ship more towards AI safety. It's | frustrating to see someone like Elon Musk, who was | publicly worried about this very specific issue a few | years ago, waste his time and money on buying Twitter. | | Edit: I'm aware that there are funds available for AI | alignment research, and I'm seriously thinking of | switching into this field, mental health be damned. But | it would help a lot more if someone could change Eric | Schmidt's mind, for example. | jackblemming wrote: | >Each time this happens, the AGI pessimists raise the bar (a | little) for what constitutes AGI. | | Why does this need to be repeated in every discussion about AI? | It's tired. | jimbokun wrote: | Because some people inevitably respond in a way that | indicates they've never heard it before. | [deleted] | chilmers wrote: | This sounds exciting, but the example outputs look quite bad. | E.g. from the interactive conversation sample: | | > What is the capital of France? > Marseille | | And many of the generated image captions are inaccurate. | momenti wrote: | The model only has about 1B parameters which is relatively | small. | | The language models that produced very impressive results have | >>50B parameters, e.g. GPT-3 with 175B, Aleph Alpha Luminous | (200B), Google PaLM (540B). GPT-3 can understand and answer | basic trivia questions, and impressively mimic various writing | styles, but it fails at basic arithmetic. PaLM can do basic | arithmetic much better and explain Jokes. Dall-E 2 (specialized | on image generation) has 3.5B parameters for the image | generation alone and it uses a 15B language model to read in | text (a version of GPT-3). | peddling-brink wrote: | That could be solved with accurate lookups from trusted | sources. Humans do the same thing, we have associations and | trusted facts. AI has the associations, they just need to add | the trusted facts compendium. "Hmm I know that Marseille is | associated with France, but I don't remember the capitol, Hey | Google.." | password54321 wrote: | Yeah they put that example for a reason. Read the paper and | stop acting like this is some great insight that you | discovered. | chilmers wrote: | What exactly did I say that implied I was acting as this was | a "great insight I'd discovered"? That's a rather rude and | unfair insult I'd say. | password54321 wrote: | When someone only mentions a fault with nothing else to add | it comes off dismissive which is a common theme for | comments on AI research. | hans1729 wrote: | Imagine what the alternative would imply. AI would be solved, | and thus, intelligence itself. Predicting tokens is not | actually true intelligence, and that's not really the point of | these models. This is a step on the letter, not the rooftop. It | looks a lot like we'll get there though, if you compare the | state of the art to ANYTHING labeled AI five years ago. _Thats_ | the exciting part. | | [edit] to emphasize: predicting tokens is a very interesting | mechanic, but in a design of intelligent software, it would be | no more than that: the mechanic of one or more of its | components/modules/ _subsystems_. The real deal is to figure | out what those components are. Once you have that part done, | you can implement it in a language of your choice, be it token | prediction, asm or powerpoint :-) | CRG wrote: | It's also smaller than GPT-2 (1.2B vs 1.6B) and trained with | a lot less language data (6% of the training mix). | sdwr wrote: | Yeah, the captions are in the right arena but fundamentally | wrong. In the baseball picture it recognizes the ball, pitcher, | and the act of throwing, but calls the action wrong. Its object | recognition and pattern matching are excellent, but higher | level thinking and self-correction are totally absent. | | Which is exactly where GPT, etc., are capping out. Its easier | to see the flaws in this one because its more general, so | spread out more thinly. | | To get to the next step (easy to say from an armchair!), these | models need a sense of self and relational categories. Right | now a 5-year old can tell a more coherent story than GPT. Not | more sophisticated, but it will have a central character and | some tracking of emotional states. | habitue wrote: | > Its easier to see the flaws in this one because its more | general, so spread out more thinly. | | I really think this is due to the very limited number of | parameters in GATO: 1.2B vs. 175B for GPT-3. They | intentionally restricted the model size so that they could | control a robot arm (!) in real time. | | > these models need a sense of self and relational | categories. | | The places where I personally see GPT-3 getting hung up on | higher level structure seem very related to the limited | context window. It can't remember more than a few pages at | most, so it essentially has to infer what the plot is from a | limited context window. If that's not possible, then it | either flails (with higher temperatures) or outputs boring | safe completions that are unlikely to be contradicted (with | lower temperatures) | ravi-delia wrote: | It's a very small model, I think due to the intent to use it | for robotics. It's not that it's good per se, even if it were | just a language model it would be smaller than GPT-2, it's that | it's bad at a lot of different things. I hope to see analysis | into how much of it is multi-purpose, but as of now it's | looking really cool | karmasimida wrote: | Would this agent able to handle simple elementary mathematics? | | If they are using inspiration from Transformer, then it probably | won't be able to count. | | For that, I don't really feel that enthusiastic about the | 'Generalist' claim, maybe they think this is more catchy than | just 'Multi-tasking'? | mrfusion wrote: | I'm confused. Do the different modalities compliment each other? | Can it learn more from text and images than text alone? | | Can you ask it to to draw a picture of a cat with the robot arm? | evanmoran wrote: | Is this the first reveal of the name Gato? It is the first I've | heard of it and it definitely sounds like more of a murder bot | than a C-3PO :) | | I know this is not as important as the AGI question, but I do | think the branding matters as much as the attitude of the | creators. They seem to be making a generalist agent to see if | they can. Gato is a clear name for that: utilitarian and direct. | If it was called Sunshine or Gift I suspect the goal would be | more helpful to humanity. | drusepth wrote: | Gato, to me, just makes me think "cat", which kind of has a fun | ring along "cats on the internet". IMO it sounds more friendly | than a robot with a robo-name like C-3PO! | | However, I also have a nice robot-named-Gato association from | Chrono Trigger [1]. :) | | [1] https://i.ytimg.com/vi/ho1TPf2Vj3k/hqdefault.jpg | 2bitencryption wrote: | given that the same model can both: | | 1. tell me about a cat (given a prompt such as "describe a cat to | me") | | 2. recognize a cat in a photo, and describe the cat in the photo | | does the model understand that a cat that it sees in an image is | related to a cat that it can describe in natural language? | | As in, are these two tasks (captioning an image and replying to a | natural language prompt) so distinct that a "cat" in an image | excites different neurons than a "cat" that I ask it about? Or is | there overlap? Or we don't know :) | | I wonder if you could mix the type of request. Like, provide a | prompt that is both text and image. Such as "Here is a picture of | a cat. Explain what breed of cat it is and why you think so." | Possibly this is too advanced for the model but the idea makes me | excited. | bungula wrote: | OpenAI actually found these "multimodal neurons" in a result | they published a year ago: https://openai.com/blog/multimodal- | neurons/ | | Similar to the so-called "Jennifer Aniston neurons" in humans | that activate whenever we see, hear, or read a particular | concept: https://en.wikipedia.org/wiki/Grandmother_cell | visarga wrote: | Check out "Flamingo" | | https://twitter.com/serkancabi/status/1519697912879538177/ph... | hgomersall wrote: | I think the critical question here is does it have a concept of | cattyness? This to me is the crux of a AGI: can it generalise | concepts across domains? | | Moreover, can it relate non-cat but cat-like objects to it's | concept of cattyness? As in, this is like a cat because it has | whiskers and pointy ears, but is not like a cat because all | cats I know about are bigger than 10cm long. It also doesn't | have much in the way of mouseyness: it's aspect ratio seems | wrong. | stnmtn wrote: | I don't disagree with you, and I think that what you're | saying is critical; but it feels more and more like we are | shifting the goalposts. 5 years ago; recognizing a cat and | describing a cat in an image would be incredible impressive. | Now, the demands we are making and the expectations we keep | pushing feel like they are growing as if we are running away | from accepting that this might actually be the start of AGI. | underdeserver wrote: | Of course we are. This is what technological progress is. | Veedrac wrote: | If you've seen much DALL-E 2 output, it's pretty obvious they | can learn such things. | | Example: https://old.reddit.com/r/dalle2/comments/u9awwt/penc | il_sharp.... | thomashop wrote: | Definitely possible. OpenAI's CLIP model already embeds images | and text into the same embedding space. | | I don't know exactly how this particular model works but it is | creating cross modal relationships otherwise it would not have | the capacity to be good at so many tasks. | minimaxir wrote: | CLIP has a distinct Vision Transformer and distinct Text | Transformer model that are then matmul'd to create the | aligned embedding space. | | Gato apparently just uses a single model. | ravi-delia wrote: | How confident are we that it doesn't just have basically 600 | smaller models and a classifier telling it which to use? | Seems like it's a very small model (by comparison), which is | certainly a mark in it's favor. | sinenomine wrote: | You can optimize pictures straight through it, and the | pictures represent the _combinatorial nature_ of the prompt | pretty well. This contradicts the "flat array of | classifiers" model. | Der_Einzige wrote: | You might find looking into the "lottery ticket hypothesis" | fascinating. | Ninjinka wrote: | This seems huge, am I overestimating the significance? | ravi-delia wrote: | This particular model is super bad at 600 deferent tasks. At | it's size you'd expect it to be mediocre at best at even one of | them, so it's still very impressive. Fascinating research, | can't wait to see if it's generalizing and how, not sure how | overall significant it is | sdwr wrote: | Yeah. | npwr wrote: | It is very impressive. Personnaly I'm still waiting for the | unification of QM and GR. Also the adaptative nanobots that | reconfigure our immune systems in real time. | tomatowurst wrote: | Basically the achievement here is that they have produced a | generic AI capable of engaging in different activities, and | from here if we extrapolate, it could lead to even more | refinement, wider range of activities with even more dimensions | of complexity. | | It's reverting to replace somebody sitting in front of a | screen, not just artists and coders but literally anything you | can do on a screen which also means manipulation of remote | hardware in the real world. | | Very possible that within our lifetime our networked OS would | be able to perform much of these generalist tasks and content | creation. I say OS because theres only a few companies that own | the datacenters, software and hardware ecosystem to automate, | and capital to invest big in a final mile innovation: | | Imagine playing Battlefield 15 with realistic and chatty AI | while generating Sopranos Season 9 featuring Pauli Gaultieri | Jr. with crowdsourced online storyboard to 8k film, while the | same AI could be used to scalp money on Google Playstore by | generating ad filled free versions of existing productivity | apps that it reverse engineered, while your robot maids takes | out the trash, cook you a bowl of ramen and massage your | shoulders? | | The rise of general AI would then optimize the labor force to | select candidates based on their "humaneness", no longer the | cold rational analytical mind, as those fields are overrun by | AI, but what it cannot bring. such "humaneness" would | increasingly be mimicked with astounding accuracy that it would | become impossible to distinguish what is AI and what is human. | | If it can happen with DALL-E-2 and 2D images, it can happen | with 3D, moving pictures, sound, music, smell, 3d positional | torque (haptic and robotic), socially cohesive and realistic | emotion. | | We might as well be able to capture entire experiences as we | learn to digitally manipulate ALL sensory inputs from vision, | touch, sound, taste, etc. Maybe even _imagination_ and mental | pictures too, which could be used to fabricate and manipulate | objects /vehicles in the real world. | | We are being pulled towards a singularity, where we are truly | no longer our minds and bodies but whatever our digital avatar | of all possible senses live and contribute to a sort of | Matrioshka brain. | | What would the capacity of such collective knowledge, | experiences add to the entropy of the universe and where will | it take humanity? Some sort of lightbodies? | | Anyways, just extrapolating from this point in the lifetime but | future generation of humans could be very much different, | socities would function completely different than what we | recognize as they would be married in some shape or form of | everlasting continuity or eternity. | lucidrains wrote: | Attention is all we need | sinenomine wrote: | A(G)I has become a question of compute economics, for better or | for worse. Those with more tightly integrated computational | capacity or a good enough logistically sound plan to acquire just | enough of it soon enough _win, hard_. | | Should we, the people, watch in awe as our best and brightest | financiers chase towards the ultimate prize, the key to all that | future entails? | | Are those respectable people worthy of the key, and what happens | to us in this wild scenario? | TOMDM wrote: | So how long until someone trains one of these models to complete | tasks by interacting directly with network/unix sockets? | | At the moment, it seems like the model needs to be trained with | each modality of data in mind at the start, but a generalised | "supermodality" that can deliver all the others would allow truly | generalised learning if the model were still capable of making | sense of the input. | | You'd obviously still need to finetune on any new modalities, but | you wouldn't need to start from scratch. | zzzzzzzza wrote: | https://www.adept.ai/post/introducing-adept pretty much right | after writing the transformers paper two of the co authors | formed this company | [deleted] | habitue wrote: | Is today the day? | | Date Weakly General AI is Publicly Known: | https://www.metaculus.com/questions/3479/date-weakly-general... | | (I really like the framing of "weakly general AI" since it puts | the emphasis on the generality and not whether it's a | superintelligence) | | Edit: Probably not today, but mostly because 1.2B parameters | isn't enough to get it the high winograd scores that PaLM etc | have. But it seems pretty clear you could scale this architecture | up and it will likely pass. The question is when someone will | actually train a model that can do it | Imnimo wrote: | I think this is a step in the right direction, but the | performance on most tasks is only mediocre. The conversation | and image captioning examples in the paper are pretty bad, and | even on some relatively simple control tasks it performs | surprisingly poorly. | | That's not to say it's not an important step. Showing that you | can train one model on all of these disparate tasks at once and | not have the system completely collapse is a big deal. And it | lays the foundation for future efforts to raise the performance | from "not totally embarrassing" to "human level". But there's | still a ways to go on that front. | habitue wrote: | Agreed, I think if they were to drop the real-time constraint | for the sake of the robotics tasks, they could train a huge | model with the lessons from PaLM and Chincilla and probably | slam dunk the weakly general AI benchmark. | fullstackchris wrote: | I'm in the camp that thinks we're headed in a perpendicular | direction and won't ever get to human levels of AGI with | current efforts based on the simple idea that the basic | tooling is wrong from first principles. I mean, most of the | "progress" in AI has been due to getting better and | learning how to understand a single piece of technology: | neural networks. | | A lot of recent neuroscience findings have shown that human | brains _aren't_ just giant neural networks; in fact, they | are infinitely more complex. Until we start thinking from | the ground up how to build and engineer systems that | reflect the human brain, we're essentially wandering around | in the dark with perhaps only a piece of what we _think_ is | needed for intelligence. (I'm not saying the human brain is | the best engineered thing for intelligence either, but I'm | saying it's one of the best examples we have to model AI | after and that notion has largely been ignored) | | I generally think it's hubris to spit in the face of 4 | billion years of evolution thinking that some crafty neural | net with X number more parameters will emerge magically as | a truly generally intelligent entity - it will be a strange | abomination at best. | idiotsecant wrote: | HN madlibs: I'm in the camp that thinks | we're headed in a perpendicular direction and won't ever | achieve powered flight with current efforts based on the | simple idea that the basic tooling is wrong from first | principles. I mean, most of the "progress" in flight has | been due to getting better and learning how to understand | a single piece of technology: fixed wing aircraft. | A lot of recent powered flight findings have shown that | real birds _don't_ just use fixed wings; in fact, they | flap their wings! Until we start thinking from the ground | up how to build and engineer systems that reflect the | bird wing, we're essentially wandering around in the dark | with perhaps only a piece of what we _think_ is needed | for powered flight. (I'm not saying the bird wing is the | best engineered thing for powered flight either, but I'm | saying it's one of the best examples we have to model | powered flight after and that notion has largely been | ignored) I generally think it's hubris to spit | in the face of 4 billion years of evolution thinking that | some crafty fixed wing aircraft with X number more | wingspan and horsepower will emerge magically as truly | capable of powered flight - it will be a strange | abomination at best. | | to be slightly less piquant: | | A) Machine learning hasn't been focused on simple neural | nets for quite some time. | | B) There's no reason to believe that the organizational | patterns that produce one general intelligence are the | only ones capable of doing that. In fact it's almost | certainly not the case. | | By slowly iterating and using the best work and | discarding the rest, we're essentially hyper-evolving our | technology in the same way that natural selection does. | It seems inevitable that we'll arrive at least at a | convergent evolution of general intelligence, in a tiny | fraction of the time it took on the first go-around! | machiaweliczny wrote: | We also already select from bilions people to work on | this. | kanzure wrote: | > Until we start thinking from the ground up how to build | and engineer systems that reflect the human brain, we're | essentially wandering around in the dark with perhaps | only a piece of what we _think_ is needed for | intelligence. | | I have wanted an approach based on a top-down | architectural view of the human brain. By simulating the | different submodules of the human brain (many of which | are shared across all animal species), maybe we can make | more progress. | | https://diyhpl.us/~bryan/papers2/neuro/cognitiveconsilien | ce/... | | Machine learning might be a part of the equation at lower | levels, although looking at the hippocampus prostheses | those only required a few equations: | | https://en.wikipedia.org/wiki/Hippocampal_prosthesis#Tech | nol.... | DavidSJ wrote: | What are one or two of the recent neuroscience findings | that you feel point most strongly towards what you are | saying? | drcode wrote: | Yeah the thing that was so freaky about AlphaZero is that it | was more powerful than AlphaGo, despite being more general. | | This system lacks that feature. | viksit wrote: | (Former AI researcher / founder here) | | It always surprises me at the ease at which people jump on a) | imminent AGI and b) human extinction in the face of AGI. Would | love for someone to correct me / add information here to the | contrary. Generalist here just refers to a "multi-faceted agent" | vs "General" like AGI. | | For a) - I see 2 main blockers, | | 1) A way to build second/third order reasoning systems that rely | on intuitions that haven't already been fed into the training | sets. The sheer amount of inputs a human baby sees and processes | and knows how to apply at the right time is an unsolved problem. | We don't have any ways to do this. | | 2) Deterministic reasoning towards outcomes. Most statistical | models rely on "predicting" outputs, but I've seen very little | work where the "end state" is coded into a model. Eg: a chatbot | knowing that the right answer is "ordering a part from amazon" | and guiding users towards it, and knowing how well its | progressing to generate relevant outputs. | | For (b) -- I doubt human extinction happens in any way that we | can predict or guard against. | | In my mind, it happens when autonomous systems optimizing reward | functions to "stay alive" (by ordering fuel, making payments, | investments etc) fail because of problems described above in (a) | -- the inability to have deterministic rules baked into them to | avoid global fail states in order to achieve local success | states. (Eg, autonomous power plant increases output to solve for | energy needs -> autonomous dam messes up something structural -> | cascade effect into large swathes of arable land and homes | destroyed). | | Edit: These rules _can 't possibly all be encoded_ by humans - | they have to be learned through evaluation of the world. And we | have not only no way to parse this data at a global scale, but | also develop systems that can stick to a guardrail. | walleeee wrote: | > In my mind, it happens when autonomous systems optimizing | reward functions to "stay alive" (by ordering fuel, making | payments, investments etc) fail because of problems described | above in (a) -- the inability to have deterministic rules baked | into them to avoid global fail states in order to achieve local | success states. | | yes, and there is an insight here that I think tends to be lost | in the popular grasp of AI x-risk: this can just as well happen | with the autonomous systems we have today (which need not be | entirely or even partially digital, defined broadly) | | the AGI likely to matter in the near term has humans in the | loop | | imo less likely to look like Clippy, more likely to look like a | catastrophic absence of alignment between loci of agency and | social, technical, and political power leading to cascading | failure, i.e., the world now | ehsankia wrote: | For me at least, the fear is not so much about the specifics, | but more around the fact of what exponential curves look like. | At any point, everything before looks basically horizontal and | anything after looks vertical. In that sense, the fear is that | while things seem quite behind right now, it could in an | instant zoom past us before we even have the time to realize | it. It is partly rooted in science fiction. | justinpombrio wrote: | I am quite scared of human extinction in the face of AGI. I | certainly didn't jump on it, though! I was gradually convinced | by the arguments that Yudkowsky makes in "Rationality: from AI | to Zombies" (https://www.readthesequences.com/). Unfortunately | they don't fit easily into an internet comment. Some of the | points that stood out to me, though: | | - We are social animals, and take for granted that, all else | being equal, it's better to be good to other creatures than bad | to them, and to be truthful rather than lie, and such. However, | if you select values uniformly at random from value space, | "being nice" and "being truthful" are _oddly specific_. There | 's nothing _universally special_ about deeply valuing human | lives any more so than say deeply valuing regular heptagons. | Our social instincts are very ingrained, though, making us | systematically underestimate just how little a smart AI is | likely to care whatsoever about our existence, except as a | potential obstacle to its goals. | | - Inner alignment failure is a thing, and AFAIK we don't really | have any way to deal with that. For those that don't know the | phrase, here it is explained via a meme: | https://astralcodexten.substack.com/p/deceptively-aligned-me... | | So here's hoping you're right about (a). The harder AGI is, the | longer we have to figure out AI alignment by trial and error, | before we get something that's truly dangerous or that learns | deception. | sinenomine wrote: | The human extinction due to would be "hard takeoff" of an AGI | should be understood as a thought experiment, conceived in a | specific age when the current connectionist paradigm wasn't | yet mainstream. The AI crisis was expected to come from some | kind of "hard universal algorithmic artificial intelligence", | for example AIXItl undergoing a very specific process of | runaway self-optimization. | | Current-generation systems aka large connectionist models | trained via gradient descent simply don't work like that: | they are large, heavy, continuous, the optimization process | giving rise to them does so in smooth iterative manner. | Before hypothetical "evil AI" there will be thousands of | iterations of "goofy and obviously erroneously evil AI", with | enough time to take some action. And even then, current | systems _including this one_ are more often than not trained | with predictive objective, which is very different compared | to usually postulated reinforcement learning objective. | Systems trained with prediction objective shouldn 't be prone | to becoming agents, much less dangerous ones. | | If you read Scott's blog, you should remember the prior post | where he himself pointed that out. | | In my honest opinion, _unaccountable AGI owners_ pose | multiple OOM more risk than alignment failure of a | hypothetical AI trying to predict next token. | | We should think more about the _Human alignment problem_. | tomrod wrote: | Regarding the substack article, why isn't this the principle | of optimality for Bellman equations on infinite time | horizons? | brador wrote: | AI can't have goals since the universe is logically | meaningless. | | Our desire for purpose is a delusion. | ben_w wrote: | Goals in the context of AI aren't the type of thing you're | arguing against here. AI can absolutely have goals -- | sometimes in multiple senses at the same time, if they're | e.g. soccer AIs. Other times it might be a goal of "predict | the next token" or "maximise score in Atari game", but it's | still a goal, even without philosophical baggage about e.g. | the purpose of life. | | Those goals aren't necessarily best achieved by humanity | continuing to exist. | | (I don't know how to even begin to realistically calculate | the probability of a humanity-ending outcome, before you | ask). | croddin wrote: | I think of it as System 1 vs System 2 thinking from 'Thinking, | Fast and Slow' by Daniel Kahneman.[1] | | Deep learning is very good at things we can do without | thinking, and is in some cases superhuman in those tasks | because it can train on so much more data. If you look at the | list of tasks in System 1 vs System 2, SOTA Deep learning can | do almost everything in System 1 at human or superhuman levels, | but not as many in System 2 (although some tasks in System 2 | are somewhat ill-defined), System 2 builds on system 1. | Sometimes superhuman abilities in System 1 will seem like | System 2. (A chess master can beat a noob without thinking | while the noob might be thinking really hard. Also GPT-3 | probably knows 2+2=4 from training data but not 17 * 24, | although maybe with more training data it would be able to do | math with more digits 'without thinking' ). | | System 1 is basically solved, but System 2 is not. System 2 | could be close behind System 2 by building on System 1 but it | isn't clear how long that will take. | | [1]. | https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow#Summar... | sinenomine wrote: | It remains to be asked, just why this causal, counterfactual, | logical reasoning cannot emerge in a sufficiently scaled-up | model trained on a sufficiently diverse real world data? | | As far as we see, the https://www.gwern.net/Scaling-hypothesis | continues to hold, and critics have to move their goalposts | every year or two. | viksit wrote: | Good point. This gets us into the territory of not just | "explainable" models, but also the ability to feed into those | models "states" in a deterministic way. This is a merger of | statistical and symbolic methods in my mind -- and no way for | us to achieve this today. | sinenomine wrote: | Why shouldn't we be able to just prompt for it, if our | system models natural language well enough? | | ... | | And anyway, this problem of structured knowledge IO has | been more or less solved recently: | https://arxiv.org/abs/2110.07178 | mxkopy wrote: | Neural networks, at the end of the day, are still advanced | forms of data compression. Since they are Turing-complete it | is true that given enough data they can learn anything, but | only if there is data for it. We haven't solved the problem | of reasoning without data, i.e. without learning. The neural | network can't, given some new problem that has never appeared | in the dataset, in a deterministic way, solve that problem | (even given pretrained weights and whatnot). I do think we're | pretty close but we haven't come up with the right way of | framing the question and combining the tools we have. But I | do think the tools are there (optimizing over the space of | programs is possible, learning a symbol-space is possible, | however symbolic representation is not rigorous or applicable | right now) | Jack000 wrote: | data isn't necessarily a problem for training agents. A | sufficiently complex, stochastic environment is effectively | a data generator - eg. alphago zero | sinenomine wrote: | I do think we underestimate compressionism[1] especially in | the practically achievable limit. | | Sequence prediction is closely related to optimal | compression, and both basically require the system to model | the ever wider context of the "data generation process" in | ever finer detail. In the limit this process has to start | computing some close enough approximation of the largest | data-generating domains known to us - history, societies | and persons, discourse and ideas, perhaps even some shadow | of our physical reality. | | In the practical limit it should boil down to exquisite | modeling of the person prompting the AI to do X given the | minimum amount of data possible. Perhaps even that X you | had in mind when you wrote your comment. | | 1. http://ceur-ws.org/Vol-1419/paper0045.pdf | extr wrote: | Abstract: Inspired by progress in large-scale language modeling, | we apply a similar approach towards building a single generalist | agent beyond the realm of text outputs. The agent, which we refer | to as Gato, works as a multi-modal, multi-task, multi-embodiment | generalist policy. The same network with the same weights can | play Atari, caption images, chat, stack blocks with a real robot | arm and much more, deciding based on its context whether to | output text, joint torques, button presses, or other tokens. In | this report we describe the model and the data, and document the | current capabilities of Gato. | | Direct Link to Paper: https://dpmd.ai/Gato-paper | blueberrychpstx wrote: | > we refer to as Gato | | First, humanity built enormous statues worshiping cats. | | Then, we let cats populate the largest amount of "image-bits" | on the Internet. | | Now, we name the next closest thing to general AI after them. | | These damn felines sure are mysterious. | riwsky wrote: | it's all because cats made it so that, on the Internet, | nobody knows you're a dog | [deleted] | productceo wrote: | Impressive | phyalow wrote: | Isnt this a general reinforcement learning agent with a | transformer as the policy discriminator? Very cool, but not | necessarily a giant leap forward, more like a novel combination | of existing tools and architectures. Either way impressive. | twofornone wrote: | I haven't read the paper yet but it looks like the breakthrough | is that it uses the "same weights" for tasks in completely | different domains. | | Which implies that it can draw from any of the domains it has | been trained on for other domains. Speculating here but for | example training it on identifying pictures of dogs and then | automagically drawing on those updated weights when completing | text prompts about dog properties. | | If my interpretation is correct then this is a pretty big deal | (if it works well enough) and brings us a lot closer to AGI. | password54321 wrote: | 2nd page: "Gato was trained offline in a purely supervised | manner" | [deleted] | colemannugent wrote: | What I really want to know is what kind of robot arm motion is | produced when the network is given a cat image to classify. More | specifically, what kind of insights has it learned from one | control domain that it then applied to another? | | I imagine that the simulated 3D environment and the actual | control of the robot arm must have some degree of interconnection | neurally. | ulber wrote: | You could also train for this kind of interconnectedness by | designing tasks that are explicitly multi-modal. For example, | you could: | | - Stack boxes collaboratively by controlling your own arm and | communicating with another agent helping you. | | - First produce a plan in text that another agent has to use to | predict how you're going to control the arm. You'd get rewarded | for both stacking correctly and being predictable based on the | stated plan. ___________________________________________________________________ (page generated 2022-05-12 23:00 UTC)