DeepMind's GO Playing Program
In the wake of DeepMind's announcement that their GO playing program has defeated the European GO champion, I thought I would write a short post to express my usual rebellious point of view. DeepMind is claiming that this is another giant step toward true AI but this could not be further from the truth. In spite of all the hoopla and the back slapping, I can assure everyone that there has been no breakthrough and no giant step toward true general machine intelligence. This time though, I will let FaceBook's Mark Zuckerberg explain why DeepMind's GO program is not even close to true AI.
Zuckerberg Nails It
In a recent blog post, he had this to say about supervised learning, the kind of machine learning used by DeepMind (emphasis mine):
Our best guess at how to teach an AI common sense is through a method called unsupervised learning. My example of supervised learning above was showing a picture book to a child and telling them the names of everything they see. Unsupervised learning would be giving them a book and letting them figure out what to do with it. They could pick it up and by touching it learn to turn the pages. Or they could let go of it and realize it falls to the ground.Zuckerberg is right.
Unsupervised learning is learning how the world works by observing and trying things out rather than being told what to do. This is how most animals learn. It's key to building systems with human-like common sense because it doesn't require a person to teach it everything they know. It gives the machine the ability to anticipate what may happen in the future and predict the effect of an action. It could help us build machines that can hold conversations or plan complex sequences of actions -- necessary components for any authentic Jarvis.
Unsupervised learning is a long term focus of our AI research team at Facebook, and it remains an important challenge for the whole AI research community.
Since no one understands how general unsupervised learning actually works, we're quite a ways off from building the general AIs you see in movies. Some people claim this is just a matter of getting more computing power -- and that as Moore's law continues and computing becomes cheaper we'll naturally have AIs that surpass human intelligence. This is incorrect. We fundamentally do not understand how general learning works. This is an unsolved problem -- maybe the most important problem of this century or even millennium. Until we solve this problem, throwing all the machine power in the world at it cannot create an AI that can do everything a person can.
Why Deep Learning Is a Hindrance to Progress Toward True AI