[s1dechnl]

Having not defined what intelligence is and having not declared the nature of General Intelligence, you're sure an aspect of clearly defined weak AI is a significant contribution to 'AGI'... Interesting.

> It's true that AlphaZero's knowledge is unable to be generalized for other systems

Interesting admission.

> This is the first piece of the puzzle of more general AI.

The first piece is generalized intelligence. Architecturally, it looks nothing like Alpha Zero. However, you feel :

> Generalization, I would consider as the second piece of the puzzle to more general AI.

How is that the second piece? It's the piece.

> Generalization may be achieved with potential research in transfer learning, model based RL, symbolic network. But without a stable RL algorithm such as DQN as foundation, generalization has nothing to stand on.

So you're of the belief that current approaches are compatible with and are the underpinning of Artificial General Intelligence while Hinton is convinced one needs to scrap it and start over. Sound advice is being ignored and there is a clearly entrenched decision to continue pushing along w/ iterating weak AI. I came here to test the waters.. The commentary and the K-value feedback I've received so far informs me quite profitably.

[yonkshi]

Hinton's criticism is very valid, but it's not quite about AlphaGo and its branch of ML. his criticism revolves around supervised learning and back prop that cannot be used to achieve the so called AGI. Because ANN is nothing like our brain's real NN. When Hinton gave the speech back in 2014, NN had a huge explosion of hype, and NN was mostly for supervised learning, which is really only good at classification and regression problems, it cannot make decisions outside of its training.

The famous DeepMind DQN paper (the core of AlphaGo) were published after Hinton's talk. the DQN paper practically opened a new chapter in reinforcement learning field. I am not sure if you are familiar reinforcement learning. RL is learning by trial and error, model-less and largely non-bayesian, similar to how humans learn. Up until AlphaGo, RL field was stuck in a limbo because it was having a very hard time learning non-linear problems (which is the majority of problems in nature)

When I say generalization, I meant generalization of knowledge. Generalization is the second piece of the puzzle because, even as humans, we learn from experienc. After enough examples we began to generalize. Up until DQN came out, we couldn't even effectively learn. It's the equivalent of a human baby with severe memory problem. With deepmind's DQN, we can achieve much more stable learning on non-linear systems, and we can begin to add components such as generalization (such as transfer learning), intuitions (such as intuitive physics), symbolic network.

I am not too sure what you meant by weak AI and general AI. For me, an AI which can learn similar to how humans learn, able to use apply generalized knowledge when facing a brand new problem, independently think and make decisions without human assistance, that's general enough.

Yes, much work needs to be done, but I don't believe this is the wrong direction we are going. Though I'd be glad to be proven wrong and I am very fascinated by this debate, if you would like, we could continue discussing it over email/chat?