Regarding just 2 years old AlphaGo, we can easily predict AlphaGo will show more powerful results and ability in the near future.
What are the intelligent sources of AlphaGo and what are limitations?
There is no doubt that more than thousands of computers and stat-of-the-art algorithms like deep learning were attributed to AlphaGo. However, beyond hardware and software, if there were no accessible go game data, AlphaGo’s learning curve would be much more slow. The secret intelligent source of just 2 years old machine’s beating 33 years old human go game champion, Lee Sedol is accessible human game data.
Another condition I’d like to point out is the structure of accessible human game data. If we gave the go game data as speech or natural language format to AlphaGo, AlphaGo cannot learn from it or at least lots of difficulties to achieve current level of intelligence. Simply speaking, AlphaGo learns nothing from watching real human go game match.
Comparing to AlphaGo, humans are powerful at learning with various multimedia format including natural languages. However, AlphaGo is learning go game only by huge amount of data and digitalized format.
Here are basic co-operation strategy for humans and computers. Once human can generate their behavior and thoughts in digitalized form rather than multi-media or natural language forms, computers can have huge benefit from it.
We can call it human-computer interactive or human computation. Whatever we call it, I believe the future of artificial intelligence lies in human computer cooperation.
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