The Value of Deep Learning

Let's take a break from DeepMind's AlphaZero, seen last week in 'Game Changer' PGN, and consider AlphaZero's underlying technology. The motivation is an article that appeared this week, DeepMind's Losses and the Future of Artificial Intelligence (wired.com) by Gary Marcus. It starts,

Alphabet’s DeepMind lost $572 million last year. What does it mean? DeepMind, likely the world’s largest research-focused artificial intelligence operation, is losing a lot of money fast, more than $1 billion in the past three years. DeepMind also has more than $1 billion in debt due in the next 12 months. Does this mean that AI is falling apart?

Author Marcus asks several important questions, of which one touches on chess. Here's the question:-

Is DeepMind on the right track scientifically?

It's a good question, although I suspect it's one of those questions that seven wise men couldn't answer. Here's the chess connection:-

DeepMind has been putting most of its eggs in one basket, a technique known as deep reinforcement learning. That technique combines deep learning, primarily used for recognizing patterns, with reinforcement learning, geared around learning based on reward signals, such as a score in a game or victory or defeat in a game like chess. [...] The trouble is, the technique is very specific to narrow circumstances

While working on posts for this blog, I frequently rely on services like OCR and language translation that have improved considerably over the last five years, mostly thanks to the same technology that was used to develop AlphaZero. How do we put a value on those services?