Learn a task by Reading About it

Chess, analyzed from a description?  Research yes, but mastery takes experience.

Artificial Intelligence / Machine Learning inTechnology Review.
Instead of practicing, this AI mastered chess by reading about it
Machines that appreciate “brilliant” and “dumb” chess moves could learn to play the game—and do other things—more efficiently.
by Will Knight

Chess fans love nothing more than discussing a masterful sacrifice by Bobby Fischer or an ingenious line of attack from current world champion Magnus Carlsen. It turns out that this chatter could help AI programs learn to play the game in a new way. One day, the same technique could allow machines to use the emotional content of our language to master various practical tasks.

The chess algorithm, called SentiMATE, was developed by a team of researchers at University College London. It evaluates the quality of chess moves by analyzing the reaction of expert commentators.

The team analyzed the text of 2,700 chess game commentaries available online. They pruned out commentary that didn’t relate to high-quality moves, and examples that were too ambiguous. Then they used a special type of recurrent neural network and word embeddings (a mathematical technique that connects words on the basis their meanings), trained on another state-of-the-art model for analyzing language.

AI has recently made significant progress in parsing language. For example, an algorithm developed by researchers at OpenAI, a research company in San Francisco, proved capable of generating whole news stories from a prompt of a few words.

“The next step in the advancement of natural language processing is to convert this learnt information into tangible actions to help solve real-world tasks,” the researchers said in an email to MIT Technology Review. “We felt that learning strategy from text-based data could be a very important research avenue to explore.”   ..... '