Yann LeCun's comment on AlphaGo and true AI

It seems that AI does well when the problem and the performance metrics are well defined: chess, Go, various scheduling problems, pattern recognition, etc. At the very least we can track, quantitatively, how far off we are from a satisfactory solution, and we know we can only ever get closer.

"True", or general-purpose AI, is harder to pin down, and thus harder to define well. I'd argue that the moment we have define it formally (and thus provided the relevant performance metrics) is the moment we have reduced it to a specialized AI problem.

Strong AI means a different thing.

Preface: AlphaGo is an amazing achievement and does show an interesting advancement in the field.

Yet ... it really doesn't mean almost anything that people are predicting it to mean. Slashdot went so far as to say that "We know now that we don't need any big new breakthroughs to get to true AI". The field of ML/AI is in a fight where people want more science fiction than scientific reality. Science fiction is sexy, sells well, and doesn't require the specifics.

Some of the limitations preventing AlphaGo from being general:

+ Monte Carlo tree search (MCTS) is really effective at Go but not applicable to many other domains we care about. If your problem is in terms of {state, action} pairs and you're able to run simulations to predict outcomes, great, but otherwise, not so much. Go also has the advantage of perfect information (you know the full state of the board) and deterministic simulation (you know with certainty what the state is after action A).

+ The neural networks (NN) were bootstrapped by predicting the next moves in more matches than any individual human has ever seen, let alone played. It then played more against itself (cool!) to improve - but it didn't learn that from scratch. They're aiming to learn this step without the human database but it'll still be very different (read: inefficient) compared to the type of learning a human does.

+ The hardware requirements were stunning (280 GPUs and 1920 CPUs for the largest variant) and were an integral part to how well AlphaGo performed - yet adding hardware won't "solve" most other ML tasks. The computational power primarily helped improve MCTS which roughly equates to "more simulations gets a better solution" (though with NNs to guesstimate an end state instead of having to simulate all the way to an end state themselves)

Again, amazing, interesting, stunning, but not an indication we've reached a key AI milestone.

For a brilliant overview: http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr...

John Langford also put his opinion up at: http://hunch.net/?p=3692542

(note: copied from my Facebook mini-rant inspired by Langford, LeCun, and discussions with ML colleagues in recent days)

We can describe Solomonoff-based agents like AIXI. None of them are fully sufficient for true general AI, but you could probably accomplish quite a bit with an AIXI-like agent.

The physical world is so slow to give you feedback, I think DeepMind has the right approach with video games, you can move to 3d games, then games with physics where the agent directly controls artificial muscles, while iterating much faster than you can in the real world.

https://www.youtube.com/watch?v=nMR5mjCFZCw&feature=youtu.be