As with any new area of technology, business understanding of AI lags the work being done by AI practitioners.  With the massive amounts of investments being made on AI, it is important, especially for executive decision makers, to look behind the curtain and get a better understanding of the constraints and applicability of AI applications to their business.

Three applications that have got a lot of press in the last year are AlphaGo Zero, Amazon Go, and autonomous cars. This post looks at the relative complexity, constraints, and cost to implement each solution, as well as their potential for disruption. It’s no surprise that AlphaGo Zero, which has shown the sharpest of results and is the least expensive to implement, has the narrowest applicability of the three. On the other end of the spectrum, autonomous cars have the potential to be fundamentally disruptive. They also represent the most complexity (what is the definition of “safe” in bits and bytes?) and are the most expensive of the three applications. While the technology is already having an impact on our lives, fully autonomous cars are still many years away.

A deeper examination of each, nonetheless, can provide insights on how to evaluate the constraints by which AI operates, and how it can have an impact on achieving business goals.

AphaGo, a computer program developed by DeepMind, beat Lee Sedol, a leading exponent of Go, by four games to one in 2016. It has been improving rapidly. In May Alpha Go beat Ke Jie, the number one ranked player, by a score of 3 – 0. DeepMind has since unveiled a new program called AlphaGo Zero. It took just two days of training for AlphaGo Zero to beat the version of Alpha Go used against Lee Sedol.

The difference in programming is that the first program began its training on thousands of actual games played by human experts. The resulting potential winning strategies were then refined using millions of simulated matches played against itself. AlphaGo Zero, however, skipped the initial training phase and just started by randomly playing against itself, before establishing chosen strategies.

The latter method can be of significant advantage in a situation with a lot of structure and an enormous level of possibilities. It avoids the potential inefficiency of having to supply the initial set of “training” data. It also avoids potential human biases in solving problems.

That’s the good news. On the other hand, the real world sometimes thumbs its nose at orderliness and structure. A leading area of research in machine learning is image recognition (cat pictures …). The applications are vast, including driverless cars. Recently, researchers from Kyushu University showed they could consistently get incorrect results by just a one-pixel change in test images. This vulnerability was true of all the state-of-the-art systems the researchers tested.

The timing was unfortunate. Shortly after, the city of Las Vegas debuted a self-driving shuttle bus in November this year. The shuttle had an accident on its first day out when it was unable to avoid a delivery truck that backed into it. No one was hurt. The issue was probably not one of image recognition, since the shuttle stopped when the delivery truck started backing up. However, the shuttle was unable to successfully address a common traffic event.

The future remains bright. There has been more than $20B of M&A activity in AI related fields this year. But, some caution is in order. Richard Branson committed to taking his family on the first flight of Virgin Galactic. Any similar takers on driverless cars?

Sources: The Economist; BBC