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” →

New Scientist Artificial Intelligence day – Session One; the Mainstream – Irina Higgins

Irina Higgins is a senior research scientist at DeepMind, and has a background in neuroscience.

The second presentation at this event largely focused on telling a story about DeepMind’s development of AlphaGo – using this as a vehicle to explain DeepMind’s approach and give insights into its culture.

She told us that DeepMind now has 300 scientists, and was keen to emphasise the high-minded aspirations of the organisation – from its mission statement;

Solve intelligence. Use it to make the world a better place.

to its ‘intentionally designed culture’, which aims to mesh the best aspects of industry and academia; the intense focus and resources of the former with the curiosity driven open-ended approach of the latter.

DeepMind’s operating definition of general intelligence is apparently; Continue reading “New Scientist Artificial Intelligence day – Session One; the Mainstream – Irina Higgins” →

DeepMind takes baby steps, but this is significant

When, a year ago, Google bought, for a reported $400M, a small and previously little known British artificial intelligence startup called DeepMind, the acquisition was widely reported, the general press essentially saying; “Gosh golly, these tech type geniuses are, like .. wow! And they get rich too!”(1,2).

Continue reading “DeepMind takes baby steps, but this is significant” →