FT article: "How I used AI to become a chess grandmaster at 16"

Indian teenage GM Aarav Dengla just had a high-profile opinion article on chess preparation and AI for education published in the Financial Times - see the FT.com article here. Stripping away the opinion part of it, there is a succinct and useful description of how he has employed chess engines and databases to prepare for particular games and in his general chess education. In particular, comparing different engine types and their recommendations for strategic plans is an example of typical high-level use.

Unfortunately, as in much of the abundant but shallow "AI" public discourse, different types of "AI" are mixed and (mis)matched in comparing and making assertions about the benefits of different types of use cases, as well as the user population. In the chess world, modern engines have incorporated AIML (machine learning) since the debut of AlphaZero and LC0 and of course have surpassed human players in strength for significantly longer than that, once sufficient computing speed and memory became available. No serious competitive player does without engine and database tools for reference.

Of course, now everyone from GMs to amateurs can (and probably should) use a (free) engine and a large database of games to objectively check their analysis and help prepare for their next tournament. Links to computer resources are in this blog's sidebar for a reason, as I do the same - while keeping in mind the pitfalls of computer analysis. 

Even with excellent resources available to everyone with an internet connection, the overall mix of tool usage for chess training still varies widely in practice, with old school players extolling the value of studying classic games in books and using a physical board when possible, while some of the recent generation of professional players boast of having a 100% digital use profile. Having a spread of different study tools and techniques is very typical for any sufficiently large population, as different people will naturally absorb, integrate and (effectively) use material in different ways.

The cited article, however, takes a big leap from there as the author argues that his (and his similarly highly-educated friends') experience using "AI" with chess and language learning is a valid example of why generalizing usage of "AI" tools, to the broader population and for general educational assistance, should be supported as obviously having a positive impact on outcomes.

It is interesting that language learning was also included as an example that supposedly uniquely supports the case for general AI adoption, since - like chess - excellent computer tools have been available in that area since before the so-called AI boom. Major real-world issues involving the general population, such as widespread cheating on exams and AI-generated homework, are also ignored. This continues to be a major problem in the online chess world as well (and not just in the general population) - see the latest Chess.com "Fair Play update - while nowadays participating in OTB tournaments with large prizes sometimes feels like going through airport security (repeatedly).

Personally, I think people should "choose their own adventure" in life and bring to bear whatever tools are best suited for themselves, as long as others are not cheated in the process. Thankfully chess offers this possibility, with longer-format OTB tournaments still validated as the "gold standard" for competitive play. And, as pointed out by the author, AI cannot help you while at the board.