The future of deep learning
> In DeepMind's AlphaGo, for example, most of the "intelligence" on display is designed and hard-coded by expert programmers (e.g. Monte-Carlo tree search);<p>Not true. This paraphrases the original paper:
<a href="https://www.tastehit.com/blog/google-deepmind-alphago-how-it-works/" rel="nofollow">https://www.tastehit.com/blog/google-deepmind-alphago-how-it...</a><p>> They tested their best-performing policy network against Pachi, the strongest open-source Go program, and which relies on 100,000 simulations of MCTS at each turn. AlphaGo's policy network won 85% of the games against Pachi! I find this result truly remarkable. A fast feed-forward architecture (a convolutional network) was able to outperform a system that relies extensively on search.<p>Also, this article reeked of AGI ideas. Deep learning isn't trying to solve AGI. Reasoning and abstraction and high level AGI concepts that I don't think apply to deep learning. I don't know the path to AGI but I don't think it'll be deep learning. I think it would have to be fundamentally different.