[HN Gopher] Gato - A Generalist Agent
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Gato - A Generalist Agent
Author : deltree7
Score : 61 points
Date : 2022-05-17 19:45 UTC (3 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| izzygonzalez wrote:
| I made some concept maps of the first parts of the paper. It
| might help with clarifying some of it.
|
| https://twitter.com/izzyz/status/1525099159925116928
| efitz wrote:
| Roko's Basilisk indicates that we all ought to support this
| project as much as possible.
| tootyskooty wrote:
| One important thing to note here is that this model was trained
| purely in a supervised fashion. It would be interesting to see a
| paper at a similar scale that's based on reinforcement learning.
| The reinforcement learning context (specifically the exploring
| part) gives a lot more opportunities to see the effects of
| positive/negative transfer. That approach would of course be much
| more expensive, though.
| ruuda wrote:
| This paper caused quite a big shift in the Metaculus predictions
| on when "AGI" will be achieved,
| https://www.metaculus.com/questions/3479/date-weakly-general...
| and https://www.metaculus.com/questions/5121/date-of-general-ai/.
| hans1729 wrote:
| This, again, sparks the "is this general ai?" question, which
| often results in low quality, borderline-flaming content... My
| take:
|
| the point of this paper isn't "here, we solved general
| intelligence". It's "look, multi modal token prediction is a
| sound iteration". Look at the scale of the model in comparison
| to, say, gpt-3: this is a PoC, they didn't bother scaling it,
| because we've already seen where scaling these mechanisms leads.
|
| What _I_ would love to know is what kind of architectures
| deepmind et al are playing with in-house. Token prediction is a
| promising avenue, but it 's more of a language that an
| intelligent agent may operate in, opposed to the self-sufficient
| structure of the intelligent agent itself -- the _symbolic
| system_ that implements algos like gato. If that symbolic system
| will be the result of a generator-function, that generator
| function won 't be token prediction by trade. I mean, maybe
| somewhere in the deep depths of a multi modal model, intelligent
| structure may emerge, but that would be a very weird byproduct.
| sva_ wrote:
| > because we've already seen where scaling these mechanisms
| leads.
|
| In the case of GPT-3, scaling seemed to continuously improve
| results, they just kinda ran out of data. Are you implying this
| must be the same for this model? Or were you intending to say
| something different that I didn't see?
| Barrin92 wrote:
| >but it's more of a language that an intelligent agent may
| operate in, opposed to the self-sufficient structure
|
| yes, this kind of functional intelligence seems distinct from
| an actual living entity, which is the thing that uses
| subordinate functions to pursue goals and has some interior
| state, motivations and some sort of architecture. To reduce
| intelligence to tokens predicting more tokens is kind of like
| saying f(x), just solve for intelligence. When prediction
| itself is only partially what intelligent systems are about.
|
| Agent is a very important word because it's accurate ( _" a
| means or instrument by which a guiding intelligence achieves a
| result_") And it's the latter I think we ought to be after when
| talking about 'general ai'.
| jawarner wrote:
| It's possible that in serving the function of prediction, the
| model forms a complex internal representation akin even to
| goals, motivations, etc. It is true that DL architectures are
| not explicitly designed to do this, not yet anyway. But my
| point is that the task of prediction can give rise to such
| architectural patterns. According to Karl Friston's Free
| Energy Principle, biological brains serve the purpose of
| predicting the value of different actions available.
| version_five wrote:
| Discussed a lot five days ago:
| https://news.ycombinator.com/item?id=31355657
| zackees wrote:
| This is essentially the birth of AI.
|
| The lack of fanfare on this achievement is baffling.
| natly wrote:
| You're hanging out in the wrong (or right) circles if that's
| your perception.
| standardly wrote:
| So.. Any circles?
| mrtranscendence wrote:
| I disagree. It's not even clear from the paper exactly how much
| learning transfer is actually happening. I think it's fair not
| to be rolling out the red carpet and showering the authors with
| awards.
| joshcryer wrote:
| This result is unsurprising. "Give a model a bunch of unique
| datasets and it can do a bunch of unique things." There's
| nothing showing any sort of generalized learning or capability
| here.
| megaman821 wrote:
| What is the achievement? It seems that the author has shown
| that this path is fruitful, but transfer learning is no where
| near being solved.
| jjoonathan wrote:
| Lack of fanfare? Every techie news outlet is plastered with it,
| and I'd expect it to diffuse from there.
| deltree7 wrote:
| https://www.deepmind.com/publications/a-generalist-agent
| gallerdude wrote:
| There's a breakthrough that I've been waiting for that I haven't
| heard anything about: when will an AI agent (probably a language
| model) discover something scientific that humans had not at the
| time it was trained. What if there was a math proof, physics
| interaction, ... that emerged from the model's approximation of
| our world?
|
| Right now, the state of the art AlphaZero models can destroy
| humans at Go. But what if the machine learning models could teach
| us things about how Go works that humans have not yet discovered.
| SemanticStrengh wrote:
| Narrow deep learning ai is generally not suited for this.
| However automated theorem provers are a thing and have proven
| major conjectures/theorems that weren't solved by humans
| before. E.g. The four color problem IIRC. Although the best
| results are generally obtained with semi-automated theorems
| provers
|
| But still, this is not cleverness, this just show that raw
| bruteforce + a few tricks can solve a few problems, by
| generating proofs of multiple terabytes(yes this is absurd
| scaling). The asymmetry between compute power and computer lack
| of intelligence is remarkable.
|
| https://en.m.wikipedia.org/wiki/Automated_theorem_proving
| hans1729 wrote:
| It very likely already did, specifically in Go. The problem is
| that humans would still be required to comprehend what they are
| seeing :-) letting agents develop strategies in an unsupervised
| manner has already yielded strategies we haven't figured out
| ourselves. Other examples that come to mind are video
| compression (see twominutepapers) and proteine folding!
|
| Think about it like this: if the domain of a problem we want AI
| to solve is so complex that we can barely formulate the
| question, how could we be confident that we can understand 100%
| of the answer we get? "Here, gpu, make sense of this
| 20-dimensional problem my brain can't even approximately
| visualize!"
| axg11 wrote:
| You are describing most successful machine learning models.
| Take AlphaFold, it has surely discovered relationships that
| govern protein folding better than any human has ever
| previously understood.
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