AlphaGo vs The World - Thoughts From Your Humble Curators
Back 10 years ago, no one would believe computer Go program can ever beat humans. Many experts estimate it would take 25-50 years to make Go to compete in 9-dan, not become the world champion.
This is exactly what happened yesterday - AlphaGo defeated Ke Jie, the strongest human Go player according to Go Ratings. Go joins the pantheon of games like Chess, where computers have proven to be better than humans. As with Chess, research funding will move from Go to some other more complex A.I.-vs-human research projects.
The big question is - what's the next big game for A.I. to challenge humans? Our guess is the next target is Starcraft/Warcraft. No doubt it would require another sets of technical breakthrough to defeat such complex game with so many game-states.
But before that, deep learning made some real history today.
Other than coverage of AlphaGo (4 items), we also cover SoftBank and statistics of NIPS. As always, if you like our letter, feel free to subscribe/forward it to your colleagues!
It's happening! The first game between AlphaGo and the strongest Go player in the planet, Ke Jie. According to DeepMind's coverage, Ke Jie took an active stance and use the most favorite opening (3-3 corner invasion) of AlphaGo to start with. The game was very tight until move 50.
The game seems to be very close, AlphaGo only won by half a point. The Twitterverse shows a different view though. For example, Andrey Karpathy (@karpathy) and Denny Britz (@dennybritz) both believe this is perhaps more of a feature of optimization - AlphaGo is optimized to win more, rather than to win more stones. Still, a win is a win. It doesn't matter how many stones we are talking about.
Remark of the game:
Ahead of today, I studied and prepared quite a lot. At the very beginning, I made fierce, targeted moves, two 3-3 moves. I copied some moves that AlphaGo liked to use in past games. AlphaGo also made some unexpected moves as well. I was deeply impressed. Also, there was a cut that quite shocked me, because it was a move that would never happen in a human-to-human Go match. But, afterwards I analyzed the move and I found that it was very good. It is one move with two or even more purposes. We call it one stone, two birds. I am quite convinced by this loss that AlphaGo is really strong. From AlphaGo there are lots of things that are worthwhile learning and exploring. The influence of AlphaGo has been widespread. We should explore our minds and expand our thinking. - Ke Jie, 9 Dan Professional, in the post-match press conference
AlphaGo's way is not to claim territory here or there, but to place every stone in a position where it will be most useful. This is the true theory of Go: not 'what do I want to build?' - Fan Hui, The European Champion
Just like computer chess continues to advanced after Kasparov is beaten so is computer Go. While the main event is still AlphaGo vs the top human, more or more the focus turns into whether there are other Go machines which can compete with AlphaGo. Then the name of Tencent comes up, because as a computer Go machine goes, its career is no less impressive than AlphaGo.
Tencent Jueyi, or in Chinese 絕藝 (literally means "extraordinary skills", translated as FineArt), won the championship title (link in Chinese), and it has 11 consecutive wins against other opponents, and 13 consecutive wins against Ke Jie. The amazing thing, is it is only being developed for around a year.
Perhaps AlphaGo should go against Jueyi, it would be a truly interesting competition to showcase which company has the best hardware plus software stacks.
This adds to its ARM portfolio, which is rumored to be working on their own low-powered A.I. chip for the edge. Our view is that in 10-20 years, Masa will be known as the Warren Buffet of tech. You heard it hear first.
The second game. Once again, AlphaGo is undefeated. And officially AlphaGo becomes the strongest Go player in the world.
Several notable quotes:
AlphaGo made some moves which were opposite from my vision of how to maximize the possibility of winning. I also thought I was very close to winning the game in the middle but maybe that's not what AlphaGo was thinking. I'm a little bit sad, it's a bit of a regret because I think I played pretty well. - Ke Jie
AlphaGo wins game 2. What an amazing and complex game! Ke Jie pushed AlphaGo right to the limit.
Demis Hassabis, at Twitter
A piece by Mariya Yao, summarized achievements by women in the field of A.I. Again, a topic that we here at AIDL always feel strongly about. Women's contribution to CS has historically been minimized (intentionally or unintentionally). Keep these comin'...
Bay Area-based startup Dishcraft looking for a machine learning engineer. Well-funded by tier-1 brand-name investors (led by First Round Capital) and are doing extremely well. For the right candidate, willing to relocate the person.
Looking for basic traditional ML (SVM and boosting). Kaggle experience is a plus, Deep Learning for 2D images and 3D volumetric data (CNN focused), Tensorflow + Keras. Desirable computer vision skills: point cloud processing, signal and image processing, computational photography (familiarity with multi-view geometry and stereo vision, and color processing)
This time we have Sarah Faye from Glasswing Ventures and Drew Volpe from Procyon Ventures, both of whom have generously taken time to give us some insight into how VCs think about funding when it comes to A.I.-related companies. Both are thoughtful early-stage investors that we have had the pleasure of knowing for a while. In this office hour, we discussed what is fundable by VCs, what are the key verticals that will be disrupted by A.I. and the like.
Question by Nishanth Gandhidoss: Is the following three is what AI is all about to learn for a Data Scientist?
Natural language processing
Reinforcement learningA: (By Arthur) On the terms - "reinforcement learning" (RL) is usually used as one sub-branch of machine learning, usually goes parallel with "supervised learning" (SL) and "unsupervised learning" (UL). Briefly, RL usually means that you don't have correct output in your training (unlike SL), what you have is just a reward and the reward could be delayed. That makes RL very different from SL, and usually it has its own class of techniques."computer vision" (CV) and "natural language processing" (NLP), on the hand, is more applications for machine learnings. You can use techniques from SL, UL and RL in either one of these fields. So that's why the terms CV, NLP and RL seldom compare with each others.
On data scientist's learning of all these subjects - it depends on the type of jobs. More conventionally (say 5 years ago), being data scientist usually means processing data in table forms (R dataframe, SQL etc). But nowadays due to deep learning, data scientist can also be asked to work on high-dimensional data such as computer vision and NLP (through word vector). So I am not surprised that some job description include CV and NLP. If you have some knowledge about them, you do have an edge.
Artificial Intelligence and Deep Learning Weekly
Speech Recognition, Machine Learning, and Random Musing of Arthur Chan