But to many experts, it is still too early for us to worry about computers taking your jobs by outsmarting you in everything.
"No, absolutely not," said Hill." The game of Go is massively more constrained than the real world."
"There are many billions of combinations of things that can happen (in Go), but the possibilities are still finite, discrete and can be easily described. In real life, whether computing how things move through the air or trying to interpret or produce language, there are infinitely many possible actions at any one time, and an infinite number of times such a decision must be made," noted Hill.
Go is much more like learning how to multiply numbers than learning to master many real world problems including, say understanding language, said Hill.
Hill and his colleagues have been working on a web-based machine language system that can solve crossword puzzles, and it uses deep learning in some way. Deep learning and monte-carlo tree search are two key ingredients in AlphaGo. Both of these two methods individually have many successful applications. For example, deep learning is being applied to various areas, such as image recognition, text translation, audio/text processing, face recognition, robotics, etc.
AlphaGo learned to discover new strategies for itself, by playing thousands of games between its neural networks, and adjusting the connections using a trial-and-error process known as reinforcement learning, according to an article posted by Demis Hassabis on the Google official blog.
By learning and improving from its own matchplay experience, AlphaGo is now a much better Go player than when it beat the European champion late last year.
But Deisenroth told Xinhua that we "should be careful" when interpreting the results of these human-computer competitions.
"These competitions are interesting because they are milestones toward human-like AI... The game of Go was the most recent milestone -- Go is so much more complicated than chess that it was not expected to be solved before 2025," said Deisenroth. But "none of these milestones have so far led to a truly intelligent system that we would consider similar to human intelligence and behavior," he said.
In some sense, smart computer systems are already helping us do things every day -- Apple's Siri, Google Now, Facebook's friend suggestions, Amazon's purchase recommendations, Microsoft's ranking in video games. They all rely on AI technologies at their very core. But they are still far from the learned and talkative super AI program Jarvis we see in the movie Iron Man.
"If the success and funding of AI keeps accelerating at this rate, we may see something like 'Jarvis' already in 10 to 20 years," said Deisenroth.
As for Hill, he believes it will take much longer to reach that point. "Siri and Google Now are much harder challenges than something like Go. My intuition is that we will not get to the level of Jarvis for at least 50 years. I hope we do in my lifetime and I may be completely wrong," he said.
