Machine learning, a subset of AI, will almost certainly transform the world. Advances in the last 12 months, demonstrate the potential. In early 2017, Libratus beat the best pro poker players in the world. DeepMind created AlphaGo Zero, which taught itself to become the best Go player on the planet. Then last November, AlphaGo Zero became a superhuman Chess and Shogi player in less than 24 hours. Not only are the algorithms getting more powerful, they’re also becoming more efficient.
It is clearly an exciting period for technology, but for analytics, does this mean firing your team and going all in with AI? I don’t think so, and in fact, advise against it. Tim Harford argues that AlphaGo is actually an outlier, and corporations are getting less and less involved in groundbreaking scientific research. Not only that, finding the talent to successfully run machine learning projects is very hard; the big tech firms swallow up the brightest talent before they’ve graduated, as reported by The Economist (“Battle of the Brains”).
Attend any business technology conference, and the agenda will be full of AI and machine learning sessions. At those events, the message is much more mundane than the bleeding-edge technology described above. The current recommendation is to begin with small, well-defined questions that can be tested against large datasets. This is not because the experts aren’t highly talented, it’s because the real-world business applications aren’t yet as capable as the media hype.
If you can’t make it to conferences such as Hubb in Germany or Strata in the United States, I recommend reading two fascinating books on artificial intelligence, which paint a useful picture of the state of play. Deep Thinking by Garry Kasparov is about his history of playing against computers in chess, with a deep focus on his matches against Deep Blue in 1997. Kasparov interlaces his optimistic opinions about AI and its implications on society and business throughout the book.
For an excellent counterpoint to Kasparov, read Cathy O’Neil’s “Weapons of Math Destruction." There is growing concern that the rampant spread of algorithms into our daily life is happening before we’ve asked ourselves if they actually help society (see, for example, teacher assessments and YouTube’s Kids Channel). Her book is sobering and a wake-up call for individuals and politicians to examine if can we change regulation fast enough to keep up with technology.