Google DeepMind is getting serious about making money

Demis Hassabis DeepMindGoogle DeepMindDeepMind cofounder and CEO Demis Hassabis.

DeepMind — an artificial intelligence (AI) lab acquired by Google for £400 million in January 2014 — is growing up.

The research company announced over the weekend that one of its most celebrated research teams will stop focusing on board games and instead start putting their efforts into solving real world problems.

These include:

  • finding new cures for diseases
  • dramatically reducing energy consumption
  • or inventing revolutionary new materials

These new algorithms will likely provide Google parent company Alphabet with a new revenue source. DeepMind has mostly not been making any money up to this point.

DeepMind CEO Demis Hassabis and lead AlphaGo programmer David Silver wrote in a coauthored blog post: “If AI systems prove they are able to unearth significant new knowledge and strategies in these domains too, the breakthroughs could be truly remarkable. We can’t wait to see what comes next.”

The AlphaGo team is home to some of DeepMind’s most impressive computer scientists and several of them have worked on papers that have made the front cover of Nature, an academic journal held in high regard by scientists worldwide.

DeepMind already has teams working on healthcare and energy so it’s likely that the AlphaGo researchers will simply start working more closely with the existing units.

The AlphaGo AI became famous last year after beating world champion Lee Sedol 4-1 in a five game match. AlphaGo beat its second world champion in China last week at the Future of Go Summit in Wuzhen, thrashing Ke Jie 3-0.

“This week’s series of thrilling games with the world’s best players, in the country where Go originated, has been the highest possible pinnacle for AlphaGo as a competitive program,” wrote Hassabis and Silver. “For that reason, the Future of Go Summit is our final match event with AlphaGo.”

DeepMind plans to publish publish a final academic paper later this year that will detail the improvements it made to the algorithms’ efficiency and potential to be generalised across a broader set of problems.

Hassabis and Silver added: “We have been humbled by the Go community’s reaction to AlphaGo, and the way professional and amateur players have embraced its insights about this ancient game. We plan to bring that same excitement and insight to a range of new fields, and try to address some of the most important and urgent scientific challenges of our time. We hope that the story of AlphaGo is just the beginning.”

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