[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.
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