This seems huge, am I overestimating the significance?
DeepMind: A Generalist Agent
11–20 of 345 posts
Re: DeepMind: A Generalist Agent
#12This 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.
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 :-)
Re: DeepMind: A Generalist Agent
#13"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…
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…
The paper is linked to at the top of this article, in the header.
Re: DeepMind: A Generalist Agent
#14I’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…
That human intelligence might just be token prediction evolving from successive small bit-width float matrix transformations is depressing to me.
Re: DeepMind: A Generalist Agent
#15This 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.
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 to…
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).
Re: DeepMind: A Generalist Agent
#16I’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…
That human intelligence might just be token prediction evolving from successive small bit-width float matrix transformations is depressing to me.
> 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)
Re: DeepMind: A Generalist Agent
#17If 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.
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.
Re: DeepMind: A Generalist Agent
#18Abstract: 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 base…
> 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.
it's all because cats made it so that, on the Internet, nobody knows you're a dog
Re: DeepMind: A Generalist Agent
#19Abstract: 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 base…
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Re: DeepMind: A Generalist Agent
#20This seems huge, am I overestimating the significance?
Yeah.