Tag Archives: AlphaGo

South Korea commits almost a billion dollars to AI research

In reaction to the recent Go victory by a computer program over a human, the government of South Korea has quickly accelerated its plans to back research into the field of artificial intelligence with a commitment of $863 million and the establishment of public/private institute.

Scrambling to respond to the success of Google DeepMind’s world-beating Go program AlphaGo, South Korea announced on 17 March that it would invest $863 million (1 trillion won) in artificial-intelligence (AI) research over the next five years. It is not immediately clear whether the cash represents new funding, or had been previously allocated to AI efforts. But it does include the founding of a high-profile, public–private research centre with participation from several Korean conglomerates, including Samsung, LG Electronics and Hyundai Motor, as well as the technology firm Naver, based near Seoul.

The timing of the announcement indicates the impact in South Korea of AlphaGo, which two days earlier wrapped up a 4–1 victory over grandmaster Lee Sedol in an exhibition match in Seoul. The feat was hailed as a milestone for AI research. But it also shocked the Korean public, stoking widespread concern over the capabilities of AI, as well as a spate of newspaper headlines worrying that South Korea was falling behind in a crucial growth industry.

South Korean President Park Geun-hye has also announced the formation of a council that will provide recommendations to overhaul the nation’s research and development process to enhance productivity. In her 17 March speech, she emphasized that “artificial intelligence can be a blessing for human society” and called it “the fourth industrial revolution”. She added, “Above all, Korean society is ironically lucky, that thanks to the ‘AlphaGo shock’, we have learned the importance of AI before it is too late.”

Not surprisingly, some academics are complaining that the money is going to industry rather than the universities. For myself, I wonder if this crony capitalistic approach will produce any real development, or whether it will instead end up to be a pork-laden jobs program for South Korean politicians.

What next for the computer Go program?

Link here.

The software uses neural networks to learn from experience. For example, to train for its Go match the computer program studied 30 million Go board positions from human games, then played itself again and again to improve its skills.

DeepMind’s founder and chief executive Demis Hassabis mentioned the possibility of training a version of AlphaGo using self-play alone, omitting the knowledge from human-expert games, at a conference last month. The firm created a program that learned to play less complex arcade games in this manner in 2015. Without a head start, AlphaGo would probably take much longer to learn, says Bengio — and might never beat the best human. But it’s an important step, he says, because humans learn with such little guidance.

DeepMind, based in London, also plans to venture beyond games. In February the company founded DeepMind Health and launched a collaboration with the UK National Health Service: its algorithms could eventually be applied to clinical data to improve diagnoses or treatment plans. Such applications pose different challenges from games, says Oren Etzioni, chief executive of the non-profit Allen Institute for Artificial Intelligence in Seattle, Washington. “The universal thing about games is that you can collect an arbitrary amount of data,” he says — and that the program is constantly getting feedback on what’s a good or bad move by playing many games. But, in the messy real world, data — on rare diseases, say — might be scarcer, and even with common diseases, labelling the consequences of a decision as ‘good’ or ‘bad’ may not be straightforward.

Hassabis has said that DeepMind’s algorithms could give smartphone personal assistants a deeper understanding of users’ requests. And AI researchers see parallels between human dialogue and games: “Each person is making a play, and we have a sequence of turns, and each of us has an objective,” says Bengio. But they also caution that language and human interaction involve a lot more uncertainty.

It’s a sweep!

Google’s AlphaGo computer program today completed a three game sweep of Go professional champion Lee Sedol.

This might be the best quote from the article:

The algorithm seems to be holding back its power. Sometimes it plays moves that lose material because it is seeking simply to maximise its probability of reaching winning positions, rather than — as human players tend to do — maximise territorial gains. Jackson thinks that some of these odd-looking moves may have fooled Lee into underestimating the machine’s skills at the beginning of game 1 — which, I suppose, makes AlphaGo a kind of computerized hustler.