14 ms·

Gato – A Generalist Agent

140 points by deltree7 4y ago

deltree7 4y ago
https://www.deepmind.com/publications/a-generalist-agent https://www.deepmind.com/publications/a-generalist-agent
zackees 4y ago
This is essentially the birth of AI. The lack of fanfare on this achievement is baffling.
- jjoonathan 4y ago
  Lack of fanfare? Every techie news outlet is plastered with it, and I'd expect it to diffuse from there.
- natly 4y ago
  You're hanging out in the wrong (or right) circles if that's your perception.
  - standardly 4y ago
    So.. Any circles?
- mrtranscendence 4y ago
  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.
- megaman821 4y ago
  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.
- joshcryer 4y ago
  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.
- mromanuk 4y ago
  Every other answer to your comment was a bit pessimistic. I’m intrigued, we need to see more!
- mdp2021 4y ago
  With "intelligence₂" we mean "the ability to produce a usable solution replacing the work of a professional in a specific task". It is a consolidated use with a pretty long history, and whether the task-generalist ANN offers a breakthrough for intelligence₂ has to be discussed.¹ With "intelligence₁" we remain referring to "the ability to reflect on world representations achieving (recursively) refined concepts and selectively producing founded conclusions". It seems that the research relevant to this submissions is still alien to the context of AGI. A claim that "this is the Dawn of It" requires clarifications and demonstrations (meaning, explicitness in clarifying why). -- ¹Some argument that it goes in that direction is in the Introduction: «Historically, generic models that are better at leveraging computation have also tended to overtake more specialized domain-specific approaches (Sutton, 2019), eventually».
tootyskooty 4y ago
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.
- deleted 4y ago
  [deleted]
inductive_magic 4y ago
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.
- Barrin92 4y ago
  >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 4y ago
    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.
gallerdude 4y ago
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 4y ago
  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 https://en.m.wikipedia.org/wiki/Automated_theorem_proving
- hans1729 4y ago
  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 4y ago
  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.
izzygonzalez 4y ago
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 https://twitter.com/izzyz/status/1525099159925116928
- moffkalast 4y ago
  Hmm 1.2B params... that's what, roughly 5 GB of VRAM? Surprisingly compact.
  - abel_ 4y ago
    This was done so that the IRL robot manipulation tasks could be done fast enough. In the future, we may always need small models mixed with large models for some tasks (e.g., for slow long term planning and fast short term planning), though compute does have a tendency to improve exponentially...
- aaaaaaaaaaab 4y ago
  Aha, ok
ruuda 4y ago
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-ai-system-is-devised/ https://www.metaculus.com/questions/3479/date-weakly-general... and https://www.metaculus.com/questions/5121/date-of-general-ai/ https://www.metaculus.com/questions/5121/date-of-general-ai/.
- joshcryer 4y ago
  What Gato shows is with the proper datasets and training we can achieve general purpose like results. It doesn't show any sort of learning behavior. So your personal home robot may be able to dice up an onion, but it is not going to sit down at your computer, look up its own schematics, and start learning about its existence. It doesn't have the active working memory to do so.
  - moffkalast 4y ago
    And frankly that's exactly what we need. When you really think about it the whole concept of self awareness is ultimately pointless for solving problems, and is mostly counterproductive. Diced onions > existential dread.
  - pmontra 4y ago
    Frankly I'd sleep much better with a great but dumb AI onion cutter in my kitchen than with one that does who knows what during the night. There are already billions of humans that can surprise me sometimes in unpleasant ways. No need to add artificial beings to the list. That said, true strong AI would be a wonderful achievement (/understatement) but I'm happy with useful partial results, like a 787 or a bird sized drone compare to an actual artificial pterodactyl or pigeon.
  - pyinstallwoes 4y ago
    Isn’t that simply a matter of computational resource? Learning is a limit of frequency and time taken between events.
- pyinstallwoes 4y ago
version_five 4y ago
Discussed a lot five days ago: https://news.ycombinator.com/item?id=31355657 https://news.ycombinator.com/item?id=31355657
efitz 4y ago
Roko’s Basilisk indicates that we all ought to support this project as much as possible.
abriosi 4y ago
Transformers managed to confuse me about what interpolation and extrapolation really mean in high dimensional space
- asdfman123 4y ago
  Yes, nice, I know some of these words
- moffkalast 4y ago
  Autobots, print out.
- qrian 4y ago
  Maybe what transformers actually transform are us..
- abel_ 4y ago
  There's a neat argument against these models doing interpolation: the manifold of the data is so sparse that it's infinitesimally unlikely for a good predictor to be doing interpolation between existing points on the manifold. https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485
sitkack 4y ago
Watching this video on it now https://www.youtube.com/watch?v=wSQJZHfAg18 https://www.youtube.com/watch?v=wSQJZHfAg18
mrfusion 4y ago
Can anyone help me understand the difference between this and saycan? Why is this approach better?
jerpint 4y ago
I was a little underwhelmed to see that the model was trained to imitate other SOTA RL agents, still very impressive results
zamadatix 4y ago
They call me Gato I have metal joints Beat me up And earn 15 Silver Points
- frazbin 4y ago
  thanks for this
r-w 4y ago
Chrono Trigger reference?
- warning26 4y ago
  Beat him up, and you’ll earn 15 silver points!

140 points by deltree7 4y ago

deltree7 4y ago

https://www.deepmind.com/publications/a-generalist-agent https://www.deepmind.com/publications/a-generalist-agent

zackees 4y ago

This is essentially the birth of AI. The lack of fanfare on this achievement is baffling.

jjoonathan 4y ago
Lack of fanfare? Every techie news outlet is plastered with it, and I'd expect it to diffuse from there.
natly 4y ago
You're hanging out in the wrong (or right) circles if that's your perception.
- standardly 4y ago
  So.. Any circles?
mrtranscendence 4y ago
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.
megaman821 4y ago
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.
joshcryer 4y ago
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.
mromanuk 4y ago
Every other answer to your comment was a bit pessimistic. I’m intrigued, we need to see more!
mdp2021 4y ago
With "intelligence₂" we mean "the ability to produce a usable solution replacing the work of a professional in a specific task". It is a consolidated use with a pretty long history, and whether the task-generalist ANN offers a breakthrough for intelligence₂ has to be discussed.¹ With "intelligence₁" we remain referring to "the ability to reflect on world representations achieving (recursively) refined concepts and selectively producing founded conclusions". It seems that the research relevant to this submissions is still alien to the context of AGI. A claim that "this is the Dawn of It" requires clarifications and demonstrations (meaning, explicitness in clarifying why). -- ¹Some argument that it goes in that direction is in the Introduction: «Historically, generic models that are better at leveraging computation have also tended to overtake more specialized domain-specific approaches (Sutton, 2019), eventually».

tootyskooty 4y ago

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.

deleted 4y ago
[deleted]

inductive_magic 4y ago

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.

Barrin92 4y ago
>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 4y ago
  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.

gallerdude 4y ago

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 4y ago
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 https://en.m.wikipedia.org/wiki/Automated_theorem_proving
hans1729 4y ago
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 4y ago
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.

izzygonzalez 4y ago

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 https://twitter.com/izzyz/status/1525099159925116928

moffkalast 4y ago
Hmm 1.2B params... that's what, roughly 5 GB of VRAM? Surprisingly compact.
- abel_ 4y ago
  This was done so that the IRL robot manipulation tasks could be done fast enough. In the future, we may always need small models mixed with large models for some tasks (e.g., for slow long term planning and fast short term planning), though compute does have a tendency to improve exponentially...
aaaaaaaaaaab 4y ago
Aha, ok

ruuda 4y ago

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-ai-system-is-devised/ https://www.metaculus.com/questions/3479/date-weakly-general... and https://www.metaculus.com/questions/5121/date-of-general-ai/ https://www.metaculus.com/questions/5121/date-of-general-ai/.

joshcryer 4y ago
What Gato shows is with the proper datasets and training we can achieve general purpose like results. It doesn't show any sort of learning behavior. So your personal home robot may be able to dice up an onion, but it is not going to sit down at your computer, look up its own schematics, and start learning about its existence. It doesn't have the active working memory to do so.
- moffkalast 4y ago
  And frankly that's exactly what we need. When you really think about it the whole concept of self awareness is ultimately pointless for solving problems, and is mostly counterproductive. Diced onions > existential dread.
- pmontra 4y ago
  Frankly I'd sleep much better with a great but dumb AI onion cutter in my kitchen than with one that does who knows what during the night. There are already billions of humans that can surprise me sometimes in unpleasant ways. No need to add artificial beings to the list. That said, true strong AI would be a wonderful achievement (/understatement) but I'm happy with useful partial results, like a 787 or a bird sized drone compare to an actual artificial pterodactyl or pigeon.
- pyinstallwoes 4y ago
  Isn’t that simply a matter of computational resource? Learning is a limit of frequency and time taken between events.
pyinstallwoes 4y ago

version_five 4y ago

Discussed a lot five days ago: https://news.ycombinator.com/item?id=31355657 https://news.ycombinator.com/item?id=31355657

efitz 4y ago

Roko’s Basilisk indicates that we all ought to support this project as much as possible.

abriosi 4y ago

Transformers managed to confuse me about what interpolation and extrapolation really mean in high dimensional space

asdfman123 4y ago
Yes, nice, I know some of these words
moffkalast 4y ago
Autobots, print out.
qrian 4y ago
Maybe what transformers actually transform are us..
abel_ 4y ago
There's a neat argument against these models doing interpolation: the manifold of the data is so sparse that it's infinitesimally unlikely for a good predictor to be doing interpolation between existing points on the manifold. https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485

sitkack 4y ago

Watching this video on it now https://www.youtube.com/watch?v=wSQJZHfAg18 https://www.youtube.com/watch?v=wSQJZHfAg18

mrfusion 4y ago

Can anyone help me understand the difference between this and saycan? Why is this approach better?

jerpint 4y ago

I was a little underwhelmed to see that the model was trained to imitate other SOTA RL agents, still very impressive results

zamadatix 4y ago

They call me Gato I have metal joints Beat me up And earn 15 Silver Points

frazbin 4y ago
thanks for this

r-w 4y ago

Chrono Trigger reference?

warning26 4y ago
Beat him up, and you’ll earn 15 silver points!