Google’s, a computer algorithm, beat world champion Lee Se-do in the ancient Chinese board game of Go on March 9, 2016. This was a milestone as this entailed artificial intelligence (AI) beating the intuitive thinking of a human champion player. As Demis Hassabis, co-founder of DeepMind , says, “it’s (Go’s) a very intuitive game”.Unlike IBM’s Deep Blue and Watson , which have defeated humans in the past, it is not possible to use brute force method to calculate the next move in Go, a game that has more possible positions than atoms in the universe. AlphaGo mastered the game just like humans by learning on its own from raw inputs, it is not pre-programmed which means there are no how-to instructions to achieve an objective.In an email interview with Economictimes.com,, research scientist at, explains the significance of the AlphaGo’s victory in the Go game and the efforts involved in building it. David Silver has published several papers on AI and he is the main programmer of AlphaGo. He says AlphaGo like ​program can be applied in several areas like medical diagnosis, household robotics, or smartphone assistants, but adds that it’s very early days and we are decades away from human level artificial general intelligence (AGI). Edited excerpts:Go has long been considered a grand challenge for artificial intelligence (AI) due to the enormous complexity of the game. Humans play the game intuitively, and so long this intuition had proven hard for machines to emulate. However, the most exciting thing about AlphaGo is not the achievement itself, but the manner in which it was achieved. We did not tell AlphaGo what was the strategy to use — it learnt that for itself from hundreds of thousands of human expert games, and from millions more games of self-play. This means that in the future, the same methods can potentially be applied to many other applications with positive impact for society, for example medical diagnosis, household robotics, or smartphone assistants.The AlphaGo project lasted around two years and 100 man-months of effort. But it started small. The first year of AlphaGo was really just a pilot research project to see whether a neural network could understand and play the game of Go. When we discovered how successful this was, we scaled up to try and beat the world’s strongest players. Last October, we were able to beat the European champion Fan Hui, but there was still a large gap to world champions such as Lee Sedol. Because AlphaGo uses principled machine learning methods, rather than handcrafted strategies, we were able to significantly improve our performance in just a few months.Perhaps the biggest surprise in AlphaGo is that a single approach can learn about every part of the game. Previous Go programmes have used special knowledge for all the different aspects of the game: opening, endgame, life-and-death, ko fights, etc. But AlphaGo’s neural networks figure all of these different things out for themselves, without any special casing.DeepMind has around 250 employees. AlphaGo scaled up incrementally from an initial pilot project with Aja Huang and myself, through to a larger effort with around 10 researchers.We’re still at the very early stages and there is much more research needed before we can begin to apply it to real world problems.Our goal has always been to solve intelligence and use that to help society solve some of its toughest problems. Joining Google has helped us turbocharge that mission.We see AI as a tool to help humans make progress. In the future, our hope is that some of these techniques could help scientists make faster progress and breakthroughs for example.Technology itself is neutral but we think very carefully about ethics to ensure it is used in the right way. We have an ethics board, we publish openly and we arrange and attend conferences to encourage debate on this topic. Keep in mind it’s very early days yet and we are decades away from human level AGI.I’ve been trying to solve the game of Go for over ten years, and AlphaGo has certainly been a dream project in many ways. Now it’s complete, there are so many possibilities. I’ll take my time before committing to the next challenge.