Hacker News new | past | comments | ask | show | jobs | submit login

I don't think I agree with the conclusions here.

""neural algorithms that generalize well, the way that the human cortical algorithm generalizes well"."

I think we have already been seeing this for years with image recognition, speech recognition, and other pattern recognition problems. As with those problems, playing Go is one of those things you can easily get heaps of data for and formulate it as a nice supervised learning task. The task is still spotting patterns on raw data with learned features.

However, the current deep learning methods don't (seem to) generalize well to all that our brains do - most of all learning to do many different things with online small input of data. I have not seen any research into large scale heterogenous unsupervised or semi-supervised learning with small batches of input - these big neural nets are still used within larger engineered systems to accomplish single specific tasks that require tons of data and computing power. Plus, the approach here still uses Monte Carlo Search in a way that is fairly specific to game playing - not general reasoning.

Clearly this is another demonstration Deep Learning can be used to accomplish some very hard AI tasks. But I don't think this result merits thinking the current approaches will scale to 'real' AI (though perhaps a simple variation or extension will).




It seems to me that images and sounds are 'alike' in a way that doesn't (on its obvious face) expand to include Atari game strategies and evaluating Go positions. In which case generalizing across the latter gap is more impressive than a single algorithm working well for both images and sounds.

The difference isn't easy to describe, but one such difference would be that a single extra stone can change a Go position value much more than a single pixel changes an image classification.


I think his point is that it's very easy to create a lossless input representation of the Go board, and the ultimate loss function is obvious. We're then left with a large sequential prediction task. Previous learning algorithms were stumped by the non-linearities, but this is exactly the situation where deep learning shines.

The problem changes dramatically when the AI is supposed to take arbitrary input from the world. Then the AI needs to determine what input to collect, and the path length connecting its decisions to its reward grows enormously.

I still agree with your take though: there's an important milestone here.


> The difference isn't easy to describe, but one such difference would be that a single extra stone can change a Go position value much more than a single pixel changes an image classification.

A CNN can still distinguish extremely subtle differences of various animal breeds, exceeding human performance in such tasks. Why was that advance not a warning sign? The rotational-translational invariance prior of the convolutional neural network probably helps because, by default, local changes of the patterns can massively change the output value without the need to train that subtle change for all translations. Also, AlphaGo does a tree search all the way to the games end, which can probably easily detect such dramatic changes of single extra stones. Reality is likely much too unconstrained to to able to efficiently simulate such things.




Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: