It seems a long time ago (1997), that IBM’s Deep Blue computer beat Garry Kasparov — then world champion — in chess, and we started talking about artificial intelligence seriously. However, we all thought AI would be limited to logical, rational, linear models of “thinking” that a machine can be programmed to do. Computers can be taught to play chess, but would never be able to beat a human at the game Go, said many futurists, even as recently as two years ago.

Now that we have AlphaGo, Google’s computer program designed to beat humans at Go, do we need to rethink the “technology doesn’t always beat labor” proposition? Advisers who want to take their relationship with clients to the next level certainly must.

Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent. It is much more complex than chess. To play a decent game of Go requires the ability to recognize subtle, complex patterns and to draw on intuitive knowledge that is the hallmark of human intelligence. In March this year, a frontier in artificial intelligence was reached, with humanity cheering Korean Go grandmaster Lee Sedol when he won his first game against AlphaGo, Google DeepMind’s artificially intelligent computing system, after losing three straight games. AlphaGo had already claimed victory in the best-of-five contest, but as Wired reported, “Lee Sedol clawed back a degree of pride for himself and the millions of people who watched the match online.”

And here are other advances: Facebook is now using AI to automatically generate captions for photos in the news feed of people who can’t see them. The tool is called automatic alternative text, and it dovetails with text-to-speech engines that allow blind people to use Facebook in other ways. Using deep neural networks, the system can identify particular objects in a photo, from cars and boats to ice cream and pizza. It can pick out particular characteristics of the people in the photo, including smiles and beards and eyeglasses. And it can analyze a photo in a more general sense, determining that a photo depicts sun or ocean waves or snow. The text-to-speech engine will then “read” these things aloud.

What is happening here? Machines gaining the ability to seek and respond to meaningful patterns — closer to human intuition, closer to what it means to think. It is getting into practical applications such as speech recognition, face recognition and pattern recognition to identify objects and scenes.

Behind the machine

But wait — there’s hope for humanity. As we understand how the machine intelligence was achieved, we uncover the human brains working behind it.

AlphaGo learning algorithms were trained using a database of millions of moves made in the past by human players. This amounts to many human lifetimes. Combine computing speed with ”big data,” and you get fascinating results from the computer.

But how does that compare with the “squishy” neural nets in our heads, shaped by half a billion years of evolution and a training set as big as the world? And how does a computer match the fluidity of the human mind? AlphaGo only recognizes patterns related to Go boards and has no ability to generalize beyond that — even to games similar to Go, but with different rules. Computers are programmed for a single purpose. Computers can never replace humans that are good at coming up with new ideas. It is not only about solving problems, but also about finding problems and opportunities.

In the new age of advisers providing service, we are becoming “brokers of capability” (capability = skill + potential). Potential in this case is the ability to understand context and apply technologies to reimagine or re-shape business models and business processes for new results — making the pieces work together. Key is the ability to tap into the capabilities internal and external to an organization and facilitate collaboration to define and solve a problem. We need to do so with critical thinking and problem solving, creativity, data modeling, scenario building and industry and functional expertise. Can we be transformational service partners and stay ahead of the new competition, that of AI?

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