AI’s Guttenberg Moment – Chess and Go
Beating world champion Chess and Go Masters became “Guttenberg” moments in the history of AI. In both cases, AI computers demonstrated near super-human reasoning and cognitive intelligence capabilities.
In the Chess example, IBM’s Deep Blue, using a rule based AI software system, beat world champion Garry Kasparov in 1997. It did so by brute computing power. It could respond to every Kasparovian move by analyzing future potential moves and assign values at super-human speeds.
In the case of Go, the ancient Chinese board game which has infinitely more possible moves than chess, a different type of AI program was employed to beat the reigning world champion in 2016. That type of AI program is based on Artificial Neural Networks (ANNs).
Artificial neural networks (ANNs) learn more (or self-teach) by analyzing and comparing new data input. For example, an ANN can learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the analytic results to identify cats in other images.
Go, the ancient Chinese board game has so many possible moves that it puts rule-based AI algorithms into computer hell. There are 361 possible first moves in GO. And each subsequent move also has 361 permutations. Winners don’t know if they’ve won until the game is over. In short, Go requires players to play at an abstract and intuitive level—as opposed to a rule-based approach.
The only way to be a consistent winner at Go is to be very, very good at pattern recognition.
An ANN designed software program called AlphaGo used a “policy neural network” AI algorithm to establish values for each move and then it calculated the value of each pattern of Go pieces in terms of a winning outcome. AlphaGo then played against itself millions of times in order to create patterns of winning at Go.
As it generated these patterns of Go, it recalibrated the values for each pattern. In short, it learned as it played. It used its neural network to become better. Until, literally, it became super-human.
AlphaGo played 60 professional games on a Go website under the name “Master” and won every single game, against dozens of world champions. In 2016 it crushed the best Go player in the world.
That event was China’s “Sputnik” moment and is largely credited with fueling a massive wave of funding for AI companies and research in China.
Applying AI to Orthopedics
Accepting, first, that AI is already in orthopedics in the form of diagnostic programs, robot assist programs and patient treatment algorithms, the question is where (and when) will AI deliver its most impact effect on orthopedic physicians and patients.
Based on the presentations at the Boston conference, one application stood out above all others—applying AI to electronic health records.
Electronic health records (EHR) are mixed blessing for physicians in the U.S. While the data is nice, the cost is rising levels of burn-out among physicians. This not a trivial problem.
One speaker, who runs the data department at a major hospital network, described her hospital’s interest in using AI to “read and hear” interactions between the physician and patient and “write” the electronic health record based on the recording of those interaction. Essentially, she described using an advanced rule-based AI algorithm to compare tens of thousands of recorded interactions to, in classic analytical AI form, create an autonomous and highly accurate EHR writing program.
That, is seems to me, would be a very welcome tool for all physicians.
The other start-up company that created serious buzz at the Boston Medical Innovation Forum was Yidu Cloud, a Chinese electronic health records aggregator.
