Having been long interested in artificial intelligence, it was one of my focus areas in university, I looked for theoretical background beyond the hype. If it is also summer where you are, you may have some extra reading time, too. So, I present you: an AI reading list.
This list is mostly about the philosophy of AI: what intelligence means, how it can be represented, what the ethical implications are of teaching machines, those kinds of questions. Expect to read about psychology, mathematics, linguistics, philosophy and computer science. It’s all of those things together that the field is made up of.
A common theme in these books is that AI application aren’t that intelligent yet or anywhere near ready to replace human intelligence. Phew. I discovered this myself looking for recent books on philosophy and AI. Online bookstore search engines (or category pages) were not that helpful for finding what I was looking for. Look in the ‘philosophy’ department and a book by Plato is listed first. Who reads Plato just by themselves? Book review pages in newspapers, blogs and humans in book stores helped more. Yes, humans!
The New Dark Age
In The Guardian’s review of this book they said enjoyment of it depends on whether you are a glass half full or half empty kind of person. Very much true, I think. The New Dark Age describes a lot of dark consequences of what Bridle calls ‘computational thinking’, the idea of throwing tech at every problem. This thinking seems prominent in Silicon Valley and it is dangerous, Bridle explains, because our problems are less about what we know (data) and much more about what we do and think. This book is not just about AI, it is also a lot about the impact of technology on society. We should think twice if we want to outsource decision making to systems, Bridle warns. Buy from the publisher to get a free e-book with your hardback.
Plato and the Nerd
Is AI ‘our biggest existential threat’, as Elon Musk once claimed? Edward Ashford Lee, writer of Plato and the Nerd doesn’t think so. What is more likely to happen and what we should want to happen, he explains in this book, is that humans and machines complement each other. We are creative, they can crunch lots of data at mesmerising speeds. We may not be more than just neurons (Lee isn’t a dualist), but it is unlikely we’ll ever be able to reconstruct human brains and minds in machines. If we’re making abstractions, like Plato did with his theory of Ideas, we should be careful not to confuse the map with the territory. This book brilliantly explains machines from semiconductors to programming languages to mathematical possibilities. It gets very technical and mathy at points. Lee shows how engineers are creative rather than technical: the most technical layers are abstracted away from them. He also talks about the relationship between tech and society: ‘I do not see how a true humanist today can understand society without understanding technology’, he says and I could not agree more.
From Bacteria to Bach: The Evolution of Mind
From Bacteria to Bach by philosopher Daniel Dennett is about evolution, what it means to (not) understand something (explained with the interesting notion of ‘competence without comprehension’) and how that changes our view on artificial intelligence, language, culture, consciousness and much more. The book is full of anecdotes and side steps, which for me at some point started to prevent Dennett from clearly getting his point across, it was a bit overwhelming. But then again, the book is full of interesting analysis of where the fields of philosophy, psychology and computer science have overlap. See also The Guardian’s review, who said this about the book:
This is an infuriating book – too long and self-referential – but underlying it all is an interesting argument
Common sense, the Turing test and the quest for real AI
In 2018 many of us think of adaptive machine learning (AML) if we think about AI. In Common sense, the Turing test and the quest for real AI, Hector J. Levesque takes us back to what it all started with: good old-fashioned artificial intelligence (GOFAI). It goes into detail about what can’t really be learned by machines: common sense. He explains Winograd Schemas, which is his modern equivalent of the Turing test: they can be used to figure out if a machine is ‘making it or faking it’. I liked how concise and lucid this book is.
Turing’s Vision, which I raved about before, is about one of Alan Turing’s most interesting papers, in which he tries to prove the mathematician Hilbert wrong. That paper shines new light on something called the ‘decision problem’ (‘whether we can write algorithms that can decide if certain mathematical statements are true or false’). This book is fairly technical, I had to skip parts because I had not enough intelligence. Your mileage may vary.
That’s all for now, happy reading! I’d love to hear what others are reading in comments or e-mail.