Comment by sdenton4 | Hacker News Reader

Comment by sdenton4 | Hacker News Reader

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Hardware is expensive to iterate on. ML research is already expensive, without worrying about hardware. I expect we'll see plenty of additional attempts in robotics, regardless of what makes economic sense for OpenAI in the short run.

"No statistical model can escape this trap"

Your claim here is that intelligence requires innovation?

AlphaGo certainly went beyond the bounds of the existing training data. Likewise, zero-shot learning (as we see in Dall-e 2) demonstrates the ability to combine concepts combinatorially, rather than drawing from raw prior observation.

I still wouldn't call this intelligence, but it's yet another indication of how the goalposts move in the conversation. (Never mind that we typically at this point ask to satisfy indicators which most humans could not satisfy...)

For just about any simple indicator of intelligence there's been a concerted effort to make a neutral network with that property. And most of them have had a degree of success, moreso over time. The 'confusion' comes because these simple indicators have repeatedly been set and overcome.

To me, these arguments are vaguely reminiscent of the philosophical arguments from the 80s and 90s. I also remember some people using Go as an example of a problem to which ML approaches won't work. We've gone from people giving up on computers solving Go, to human Go masters retiring because AlphaGo is impossible to beat.

I mean, AlphaZero is trained solely on self-play. It is not even given the rules of the game, it exists in the world where it is rewarded or punished by the 'laws of physics' of the Go board the way we exist in a environment with physical rules that constrain and reward or punish our biology.

To say that AlphaZero is just data compression of the inputs seems hand wavy. It is data compression only in the sense that phenomena from the world is stream of data, and humans developing representations of that data (eg laws of physics) around that data are a compression of it.

But AlphaZero wasn't given a huge feed of pre-played world data. Rather, it interacted with, and poked around in a simulated environment, until it was able to make good predictions on how its interactions would turn out. I learn that dropping a ball falls to the ground, and so I can make a prediction of what happens if I drop a ball. How is AlphaZero predicting the outcome of moves purely from self-play just another kNN? If so, why isn't our brain's learning just a kNN then?

My claim is that intelligence is more than just statistical associations and abstract symbol shuffling. It's impressive what large statistical models can do but they still can not solve sudoku so something is clearly missing here because neural networks do not have feedback loops and backtracking. It's like saying all we need to do is continue building bigger and bigger abaci and stacking them in just the right way as to emulate the statistical properties of the real world. Dall-e is dazzling but it is still a statistical model with no symbolic understanding (it's still a giant abacus). It's obvious that people have symbolic understanding (e.g. written language, mathematics, solving sudoku, writing code/software, etc.). So if people are the benchmark of intelligence (dubious but let's assume for the sake of argument) then at what point do you suppose there will be statistical models with symbolic understanding? Furthermore, what reason is there to believe that larger and larger statistical models are going to get us closer to non-human intelligent systems that do more than generate stimuli adapted to our senses?

There is also a meta-problem that no one seems to address when discussing AI. All the systems we have built rely on compositional symbolic systems (mathematics) for expressing statistical associations and human interpretation of their inputs/outputs. Clearly there is something people can do that no existing AI system can which is to generate a symbolic description of statistical models that can be adapted to various data sets.

I could say more here but none of what I'm saying is anything new. Others much more capable of describing the issues and shortcoming of the existing approaches to AI have written books exploring the issues in much more detail, e.g. Gary Marcus, Melanie Mitchell, Douglas Hofstadter, Gian-Carlo Rota, etc.

"It's impressive what large statistical models can do but they still can not solve sudoku so something is clearly missing here because neural networks do not have feedback loops and backtracking."

Again, the things you ask for exist. Recurrent networks and reinforcement learning both have feedback loops. (And there's a reasonable argument that residual networks can be interpreted as 'unrolled' recurrent networks.)

Here's a completely random paper on reinforcement learning for Sudoku with non-zero win rates (and a few other games): https://arxiv.org/abs/2102.06019

I'm not sure anyone's bothered to take a real crack at Sudoku specifically. It's another example of a weak indicator, though: someone will happily solve it if you're willing to call it the bar for intelligence. Given where we're at on game-playing generally, it seems very doable with current technology.

"at what point do you suppose there will be statistical models with symbolic understanding?"

Understanding again has no real definition, so this is open to endless argument. I think it's fair to say that DALL-E understands what an astronaut looks like, though.

Ok, thanks for the references.

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