In the later sections of the book, the author explores work on training complex autonomous systems to learn appropriate behaviour (and potentially underlying human values) by observing human behaviour or working in cooperation with users. Does this mean that probabilistic reasoning needs to become part of computational thinking? This topic should be explored further in the ongoing discussions of AI literacy for schools. Computers indeed execute code written by humans, but with machine learning, it is not always possible to read the code and know what the output of the code will be, even for the person who wrote the program. We may need to update the intermediate-level explanations of computational thinking to include explanations of the capabilities of machine learning. This is interesting because it has long been a goal of computational thinking education to explain to learners that “computers can only do what programmers tell them to”. Christian observes that “We are now… well beyond the point where our machines can do only that which we program into them in the explicit language of math and code” (p261). Reading the book gave me pause to consider whether the concept of computational thinking – which rose to ascendency in 2006 – needs to be updated. It covers the problems with bias in datasets and the perils of using predictive algorithms for policing in some detail, along with the difficulties which arise when ML algorithms don’t generate human-understandable explanations of their reasoning. The book gives a good non-technical grounding in supervised and unsupervised machine learning (ML) and reinforcement learning early in the book and uses this as a foundation for forays into very new research into AI safety. I’ve just finished The Alignment Problem which has hugely extended my thinking about AI and its societal implications, through a fascinating presentation of recent breakthroughs in machine learning. I studied AI at university, and I try to keep up with the field through occasional reading.
Through an impressive mastery of cutting-edge papers in AI, a deep understanding of the history of cognitive science, and four hundred interviews with leading scientists, Brian Christian presents an in-depth account of AI and how we can engineer it to align with human values.
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