Chess and similar games have always been used to measure the “intelligence” of machines. Chess grandmasters have always seen an able sparring partner in a good chess engine running on a capable computer. The positional evaluation, which comes by intuition and is honed and sharpened by unforgiving hours of grueling practice, can be expressed as a set of mathematical models that fast computers can use to create gameplay.

So, when Vishwanathan Anand announced that he is publishing his autobiography, I relished at the prospect of learning how he thinks and what goes on in that massively knowledgeable and fast brain of his. Boy, was I in for a treat? Chess, team building, leadership, gameplay, psychology, memory, and work of the mind. I got much more than I expected. The book is named Mind Master: Winning Lessons From A Champion’s Life.

Technology, Computers and AI

I was not surprised but still delighted to find a chapter dedicated to technology, computers, and artificial intelligence. In today’s generation, a chess grandmaster cannot exist without access to technology. His anecdotal narrative of how computers, laptops, mobiles, and cloud computing follow the same cycle of sparse availability to maniacal proliferation is amazing, especially his initial struggle of importing a computer in the late 80s. It clearly shows how technology has quickly changed, and probably, bureaucracy has not.

Anand has been an early adopter of new technologies. Agility and the ability to adapt oneself to continually changing and evolving contexts are critical for surviving and winning. Having played chess across 4 decades with the best in the game, he not only had to learn new things quickly, but he also had to unlearn a few things quickly. His computer-aided contest with Anatoly Karpov served as a key reminder for him. He terms this game as “Anand with a computer against Karpov with a high-tech score sheet.” Stay updated to stay relevant. By his own admission, he was a late embracer of AI for chess. He was probably a year later than the early adopters like Carlsen and Nakamura and credited his trainer Gzregorz Gajewski for getting him started on it.

I quote verbatim from his book to ensure the transfer of value without loss of information (you will get this AI reference if you read his book).

“Inevitably, AI will approach a lot of problems differently and it is possible that this will revolutionize chess again. As it stands now, we’re being offered a whole lot of new information and a host of fresh conclusions – and we are left with the tiny question of dealing with them. One way to do this is to apply the lessons we took away from the computer revolution – wholeheartedly trying to understand the conclusions that don’t make sense to us in the beginning instead of rejecting them outright, or even accepting them without question. Computers are constantly churning out exceptions to every rule we knew. Lowering the resistance to change, removing bias from the picture, keeping an open mind and being willing to adapt is the best way to hit the ground running. It’s essentially the attitude I’ve come to adopt. Dogmas and, I’ve learnt, have to be shed in favor of facts. This allows me to become more open-minded and occasionally re-evaluate my views. It’s helped me evolve and stay relevant through every wave of change.”

The preceding extract is the single most valuable thing that a business manager finding his way in an AI world should read and adopt. I have seen my team putting in more work in convincing customers of the benefits of AI than in the building of the AI component. A lot of “white-box” algorithms provide transparency into how they operate and why an outcome is being deemed so. The advent of artificial neural networks and deep learning are very “black-box”; hence providing “reasoning” for a predictive outcome becomes difficult and near impossible.

AI, at its very core, is computing with the qualitative difference of self-learning (training) from examples (training set) without prior knowledge, which eliminates human biases and interventions. Anand refers to a very frequently cited example of AlphaZero (AI driven) taking down Stockfish (traditional chess engine) within 9 hours of learning chess rules from scratch.

Conclusion

For a person who has seen 4 cycles of the technology revolution, the following excerpt from the book carries much weight:

“Much of what people say about technology changing the world sound incredibly similar to the rhetoric on old-style computer programs when they burst upon the scene.”

It is important to understand that AI is in a stage of awakening, and its widespread adoption is still some distance away. The next wave is already beginning to form. It is called quantum computing (QuCo). QuCo is different computing, which is much more than “more computing” advancements, neatly and obediently biding to Moore’s law, the benefits of which we have gratefully exploited. Whether QuCo makes AI better or replaces it completely, is a question that time will answer.

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