AI and the Zone of Hubris

AI progress and a landscape of problem conditions

I’ve mentioned this ‘Zone of Hubris’ idea in a couple of earlier posts, and it’s time I made it clear what I mean by this slightly over-blown phrase.

The basic idea is that the sort of AI we are making at the moment is being developed against a range of problems with very clear success metrics, and relatively high levels of available information. Recent rapid progress is giving rise to significant confidence in our ability to begin to address really useful problems with the aid of AI (nothing in this post relates to currently imaginary super-intelligent Artificial General Intelligence).

This is likely to lead us to seek to apply our shiny new successes more ambitiously – as well we should. But we need to be aware that we have been sharpening these tools in a particular arena, and that it is not at all certain that they will work well in different circumstances.

“Well, of course..” you might say; “we’re quite aware of that – that’s exactly how we’ve been proceeding – moving into new problem domains, realising that our existing tools don’t work, and building new ones”. Well yes, but I would suggest that it hasn’t so much been a case of building new tools, as it is has been about refining old ones. As is made clear in some earlier posts, most of the building blocks of today’s AI were formulated decades ago, and on top of that, there appears to have been fairly strong selection for problem spaces that are amenable to game/game-theoretic approaches.

‘Hubris’ is defined as ‘excessive or foolish pride or self-confidence‘. Continue reading “AI and the Zone of Hubris”

Games and Game-Theory – the trouble with paradigms…

First of a few posts with my own thoughts arising from the recent New Scientist ‘Instant Expert’ event.


Games and Game Theory appear to be the ruling paradigm for the current AI top dogs. Both Irina Higgins and Simon Lucas made clear cases for the choice of gaming environments as AI training grounds, and referenced Game Theory, too.

Don’t worry, I’m not going to try to argue with them – but I do think it is worth examining the assumptions that underlie gaming approaches and Game Theory, and considering these as they relate to the problem spaces which we dearly wish that AI could help us with. As you might guess, I am not sanguine… Continue reading “Games and Game-Theory – the trouble with paradigms…”

New Scientist Artificial Intelligence day – Session One; the Mainstream – Simon Lucas


Simon Lucas is Professor of computer science at the University of Essex. His research is focused on the application of artificial intelligence and machine learning to games, evolutionary computation and pattern recognition.

This was the foundation-laying talk of this event, and it was excellent – a rapid-fire but followable overview of the history and principal themes of AI research and development, and more detail on the approach currently producing the results that have been making headlines – neural networks. There was nothing here that some general reading wouldn’t get you, but it was engagingly and thoroughly presented at speed.

Continue reading “New Scientist Artificial Intelligence day – Session One; the Mainstream – Simon Lucas”