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> But when sufficient data can be generated from acting in the environment, then a model can surpass human level, like AlphaZero. Yes, in a very very very very

> But when sufficient data can be generated from acting in the environment, then a model can surpass human level, like AlphaZero. Yes, in a very very very very very limited environment like a board game. Let's up the ante just a tiny bit and say the AI has to be able to learn how to play Go. But it also has to learn how to get and setup the board, and the cups holding the Go-Stones. It has to place them on the table, or find the table where someone else placed them. It has to figure out how to place the stones. And it has to do so regardless of what else is on the table, what table it is, where the table is, what the light conditions are, or if its playing with stones, chips, painted pebbles or peanuts vs. roasted almonds (yes, I played Go that way once :D ) Now, how much more difficult does this make the task to an AI? Bear in mind that all these changes to the task are absolutely trivial to the only (G)eneral(I)ntelligence that we know (humans). We effortlessly combine so much prior knowledge about movement, our limbs, the way physical objects work, how to gather and integrate information into our model of the world, the ability to formulate and adapt goals _and their similarity to prior goals_ and the solutions to those, that all these tasks I just added to the problem are absolutely trivial, and the only hard exercise is where to place stones on the board. That's how far our current systems are from an AGI.

mannykannot 4y ago
> Now, how much more difficult does this make the task to an AI? Given that I can do all these things, but I can't beat Lee Sedol, I suspect they don't add an insurmountable level of difficulty to the problem. The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible.
- usrbinbash 4y ago
  > I suspect they don't add an insurmountable level of difficulty to the problem. Not for you, because to humans, moving, finding things, gathering information, formulating new goals, etc. are common tasks. And we generalized them to a point where we can integrate them with other tasks, even such which are completely abstract to our daily lives like a game where little stones are put on a grid. But to a machine? Is it easier to make a machine play Go or make it find arbitrary objects while walking around? Is gathering information on its own difficult? Is goal formulation difficult? Is moving through arbitrary spaces difficult? And how difficult are all these tasks relative to one another? > The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible. As I understood it, impossible in the current research paradigms. And given the difference in complexity between the task of playing a board game, and playing a board game as I described above, this seems very likely.
  - mannykannot 4y ago
    >> The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible. > As I understood it, impossible in the current research paradigms. You can't get around the fact that they are making a claim that something is impossible. If they merely said it was difficult, or the 'something' was something of no interest, there would be nothing here worth discussing.
  - naasking 4y ago
    > As I understood it, impossible in the current research paradigms. No, they literally say that biological "affordances" cannot be captured by algorithms that run on Turing machines.
Retric 4y ago
These systems are also learning how to operate in the real world, just look at self driving cars. The famous LiDAR image of a woman chasing ducks on a wheelchair with a broom comes to mind. That’s a true WTF moment but the system dealt with it just fine. They don’t work everywhere, but you can get picked up by an empty self driving car in a few cities and driven to your destination. Meanwhile these systems are currently being tested on snowy mountain roads etc. When your camera picks out faces in an image you can literally point it at anything and it generally works quite well. That’s the future of AI not constrained games like chess.
- usrbinbash 4y ago
  Again, in a very limited setting, under a limited set of rules. And even in that setting: Have we achieved a 100% completely autonomous car yet, that can drive a vehicle safely, no matter the conditions, without ever requiring any human intervention? My point isnt't that the systems we can build today aren't impressive. They are, beyond belief sometimes. But they are not AGIs, nor are they close to, and making systems of limited scope better at their limited tasks, doesn't equate to getting closer to a generally intelligent system. One thing that would make an AGI an actually general intelligence, is the ability to apply knowledge of one task to an arbitrary number of tasks. For example, in terms of drawing the beautiful volcano-landscapes that my GPU tower running stable diffusion is currently making, even the best self-driving AI is useless. Likewise, as impressive as stable diffusion is, I doubt it could even get my car out of the driveway.
  - Retric 4y ago
    I disagree it’s a very limited setting. We are constantly moving to less constrained problems. What used to be a very limited setting is now perhaps a somewhat limited one. The options aren’t binary chose of using A* for pathing in a video game or AGI, it’s finding ever more generalizable solutions to ever wider range of problems. The thing is as processing power keeps improving we need ever fewer limits to active human levels of performance from these systems. I don’t think AGI is just waiting for sufficient flops, but I think that’s closer to the truth than we want to admit.
visarga 4y ago
> Yes, in a very very very very very limited environment like a board game. They work in math and coding, and these two are quite general. > CodeRL is a new framework for program synthesis through holistic integration of pretrained language models and deep reinforcement learning. By utilizing unit test feedback as part of model training and inference, and integrating with an improved CodeT5 model, CodeRL achieves state-of-the-art results on competition-level programming tasks. https://blog.salesforceairesearch.com/coderl/ https://blog.salesforceairesearch.com/coderl/
- tsimionescu 4y ago
  Programming competitions are still extremely narrow games, nowhere similar to the vast majority of real-life programming tasks. They have narrowly defined inputs and outputs, and even these are described in formal language. It's nice that CodeRL can solve them, but to say that they work in "coding" and "math" is a huge stretch.

mannykannot 4y ago

> Now, how much more difficult does this make the task to an AI? Given that I can do all these things, but I can't beat Lee Sedol, I suspect they don't add an insurmountable level of difficulty to the problem. The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible.

usrbinbash 4y ago
> I suspect they don't add an insurmountable level of difficulty to the problem. Not for you, because to humans, moving, finding things, gathering information, formulating new goals, etc. are common tasks. And we generalized them to a point where we can integrate them with other tasks, even such which are completely abstract to our daily lives like a game where little stones are put on a grid. But to a machine? Is it easier to make a machine play Go or make it find arbitrary objects while walking around? Is gathering information on its own difficult? Is goal formulation difficult? Is moving through arbitrary spaces difficult? And how difficult are all these tasks relative to one another? > The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible. As I understood it, impossible in the current research paradigms. And given the difference in complexity between the task of playing a board game, and playing a board game as I described above, this seems very likely.
- mannykannot 4y ago
  >> The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible. > As I understood it, impossible in the current research paradigms. You can't get around the fact that they are making a claim that something is impossible. If they merely said it was difficult, or the 'something' was something of no interest, there would be nothing here worth discussing.
- naasking 4y ago
  > As I understood it, impossible in the current research paradigms. No, they literally say that biological "affordances" cannot be captured by algorithms that run on Turing machines.

Retric 4y ago

These systems are also learning how to operate in the real world, just look at self driving cars. The famous LiDAR image of a woman chasing ducks on a wheelchair with a broom comes to mind. That’s a true WTF moment but the system dealt with it just fine. They don’t work everywhere, but you can get picked up by an empty self driving car in a few cities and driven to your destination. Meanwhile these systems are currently being tested on snowy mountain roads etc. When your camera picks out faces in an image you can literally point it at anything and it generally works quite well. That’s the future of AI not constrained games like chess.

usrbinbash 4y ago
Again, in a very limited setting, under a limited set of rules. And even in that setting: Have we achieved a 100% completely autonomous car yet, that can drive a vehicle safely, no matter the conditions, without ever requiring any human intervention? My point isnt't that the systems we can build today aren't impressive. They are, beyond belief sometimes. But they are not AGIs, nor are they close to, and making systems of limited scope better at their limited tasks, doesn't equate to getting closer to a generally intelligent system. One thing that would make an AGI an actually general intelligence, is the ability to apply knowledge of one task to an arbitrary number of tasks. For example, in terms of drawing the beautiful volcano-landscapes that my GPU tower running stable diffusion is currently making, even the best self-driving AI is useless. Likewise, as impressive as stable diffusion is, I doubt it could even get my car out of the driveway.
- Retric 4y ago
  I disagree it’s a very limited setting. We are constantly moving to less constrained problems. What used to be a very limited setting is now perhaps a somewhat limited one. The options aren’t binary chose of using A* for pathing in a video game or AGI, it’s finding ever more generalizable solutions to ever wider range of problems. The thing is as processing power keeps improving we need ever fewer limits to active human levels of performance from these systems. I don’t think AGI is just waiting for sufficient flops, but I think that’s closer to the truth than we want to admit.

visarga 4y ago

> Yes, in a very very very very very limited environment like a board game. They work in math and coding, and these two are quite general. > CodeRL is a new framework for program synthesis through holistic integration of pretrained language models and deep reinforcement learning. By utilizing unit test feedback as part of model training and inference, and integrating with an improved CodeT5 model, CodeRL achieves state-of-the-art results on competition-level programming tasks. https://blog.salesforceairesearch.com/coderl/ https://blog.salesforceairesearch.com/coderl/

tsimionescu 4y ago
Programming competitions are still extremely narrow games, nowhere similar to the vast majority of real-life programming tasks. They have narrowly defined inputs and outputs, and even these are described in formal language. It's nice that CodeRL can solve them, but to say that they work in "coding" and "math" is a huge stretch.