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

Rockets and Rayguns - Andrew May ****

The Cold War period saw dramatic developments in science and technology, coinciding with the flourishing of the science fiction genre. In Rockets and Rayguns, Andrew May draws on the parallels between reality and fiction, each influencing the other.

Inevitably a major Cold War theme was the threat of nuclear war, and May opens with the bomb. It's fascinating that fiction got there first - nuclear weapons were featured in science fiction when many physicists were still doubting the practicality of using nuclear energy. Of course, it's a lot easier to simply take a concept and dream up a weapon than it is to make it for real - for example, H. G. Wells' prophetic nuclear bombs from his 1914 The World Set Free were nothing like the real thing. And some science fiction devices concepts - notably ray guns and force fields - came to very little in reality. However this doesn't prevent the parallels being of interest.

May gives us a mix of the science - describing how nuclear we…

Galileo Galilei, the Tuscan Artist – Pietro Greco ****

Near the beginning of John Milton’s epic poem Paradise Lost, he refers to a ‘Tuscan artist’ viewing the Moon through an optic glass. He’s talking about Galileo – one of history’s greatest scientists, but not the most obvious person to slap an ‘artist’ label on. Yet Galileo lived at a time – the Renaissance – when it was fashionable to dabble impartially in both the arts and sciences. Look up ‘Renaissance man’ on Wikipedia and you’ll see Galileo’s picture right there underneath Leonardo da Vinci’s. It’s a less well-known side to his life, but it crops up again and again – interspersed among his many scientific achievements – in this excellent new biography by Pietro Greco.

If you’re looking for interesting trivia, you’ll find plenty in this book. Galileo’s father was a musician with scientific leanings, who carried out some of the first experiments on musical acoustics – which Galileo may have assisted with. As a young professor of mathematics, Galileo delivered a couple of lectures on …

Enjoy Our Universe - Alvaro de Rújula ***

I’m going to start this review with a longish quote from the author’s preface, for several reasons. It explains De Rújula’s purpose in writing the book, as well as the audience he’s trying to reach, while giving a taste of his idiosyncratic writing style (which he keeps up throughout the book). It’s also a good starting point for discussing the book’s strengths and weaknesses. Here’s the quote:

'This book is not intended for (very) young kids nor for physicists. It is intended for anyone – independently of the education (s)he suffered – who is interested in our basic current scientific understanding of the universe. By "universe" I mean everything observable from the largest object, the universe itself, to the smallest ones, the elementary particles that "function" as if they had no smaller parts. This is one more of many books on the subject. Why write yet another one? Because the attempts to understand our universe are indeed fun and I cannot resist the tempta…