Skip to main content

Common Sense, The Turing Test and the Quest for Real AI - Hector Levesque *****

It was fascinating to read this book immediately after Ed Finn's What Algorithms Want. They are both by academics on aspects of artificial intelligence (AI) - but where reading Finn's book is like wading through intellectual treacle, this is a delight. It is short, to the point, beautifully clear and provides just as much in the way of insights without any of the mental anguish.

The topic here is the nature of artificial intelligence, why the current dominant approach of adaptive machine learning can never deliver true AI and what the potential consequences are of thinking that learning from big data is sufficient to truly act in a smart fashion.

As Hector Levesque points out, machine learning is great at handling everyday non-exceptional circumstances - but falls down horribly when having to deal with the 'long tail', where there won't be much past data to learn from. For example (my examples, not his), a self-driving car might cope wonderfully with typical traffic and roads, but get into a serious mess if a deer tries to cross the motorway in front of it, or should the car encounter Swindon's Magic Roundabout.

There is so much here to love. Although the book is compact (and rather expensive for its size), each chapter delivers excellent considerations. Apart from the different kinds of AI (I love that knowledge-based AI has the acronym of GOFAI for 'good old-fashioned AI'), this takes us into considerations of how the brain works, the difference between real and fake intelligence, learning and experience, symbols and symbol processing and far more. Just to give one small example of something that intrigued me, Levesque gives the example of a very simple computer program that generates quite a complex outcome. He then envisages taking the kind of approaches we use to try to understand human intelligence - both psychological and physiological - showing how doing the same thing with this far simpler computer equivalent would fail to uncover what was happening behind the outputs.

For too long, those of us who take an interest in AI have been told that the 'old-fashioned' knowledge-based approach was a dead end, while the modern adaptive machine learning approach, which is the way that, for instance, programs like Siri and Alexa appear to understand English, is the way forward. But as the self-driving car example showed above, anything providing true AI has to be reliable and predictable to be able to cope with odd and relatively unlikely circumstances - because while any individual unlikely occurrence will probably never happen, the chances are that something unlikely will come along. And when it does, it takes knowledge to select the most appropriate action.

Highly recommended.

Hardback:  

Kindle 
Review by Brian Clegg

Comments

Popular posts from this blog

Beyond Weird - Philip Ball *****

It would be easy to think 'Surely we don't need another book on quantum physics.' There are loads of them. Anyone should be happy with The Quantum Age on applications and the basics, Cracking Quantum Physics for an illustrated introduction or In Search of Schrödinger's Cat for classic history of science coverage. Don't be fooled, though - because in Beyond Weird, Philip Ball has done something rare in my experience until Quantum Sense and Nonsense came along. It makes an attempt not to describe quantum physics, but to explain why it is the way it is.

Historically this has rarely happened. It's true that physicists have come up with various interpretations of quantum physics, but these are designed as technical mechanisms to bridge the gap between theory and the world as we see it, rather than explanations that would make sense to the ordinary reader.

Ball does not ignore the interpretations, though he clearly isn't happy with any of them. He seems to come clo…

Conjuring the Universe - Peter Atkins *****

It's rare that I'd use the term 'tour de force' when describing a popular science book, but it sprang to mind when I read Conjuring the Universe. It's not that the book's without flaws, but it does something truly original in a delightful way. What's more, the very British Peter Atkins hasn't fallen into the trap that particularly seems to influence US scientists when writing science books for the public of assuming that more is better. Instead of being an unwieldy brick of a book, this is a compact 168 pages that delivers splendidly on the question of where the natural laws came from.

The most obvious comparison is Richard Feynman's (equally compact) The Character of Physical Law - but despite being a great fan of Feynman's, this is the better book. Atkins begins by envisaging a universe emerging from absolutely nothing. While admitting he can't explain how that happened, his newly created universe still bears many resemblances to  nothing a…

Big Bang (Ladybird Expert) - Marcus Chown ****

As a starting point in assessing this book it's essential to know the cultural background of Ladybird books in the UK. These were a series of cheap, highly illustrated, very thin hardbacks for children, ranging from storybooks to educational non-fiction. They had become very old-fashioned, until new owners Penguin brought back the format with a series of ironic humorous books for adults, inspired by the idea created by the artist Miriam Elia. Now, the 'Ladybird Expert' series are taking on serious non-fiction topics for an adult audience.

Marcus Chown does a remarkable job at packing in information on the big bang, given only around 25 sides of small format paper to work with. He gives us the concepts, plenty about the cosmic microwave background, plus the likes of dark energy, dark matter, inflation and the multiverse. To be honest, the illustrations were largely pointless, apart from maintaining the format, and it might have been better to have had more text - but I felt …