Out of Time

Some realistic questions about prospective machine intelligence regulation:

… we still don’t have a concrete answer about how to effectively regulate the use of algorithms. AI is just another very complex layer added to this already complex discussion, sometimes directly related to “big data” (in the case of deep learning, for example) and other times addressing far bigger questions (in the case of sentient machines, for example).

The UF (accelerationist) response is probably predictable: There isn’t time to reach answers. Acceleration means only (and exactly) that the problem is receding, or escaping. If it would only slow down, everything would be okay. It won’t.

Kant around the back

Schmidhuber exemplifies the path, while talking about robots:

One important thing about consciousness is that the agent, as it is interacting with the world, will notice that there is one thing that is always present as it is interacting with the world — which is the agent itself.

(Some room for quibbling, but it doesn’t get serious. This is where transcendental subjectivity comes from.)

Quotable (#208)

From an engrossing discussion of AI threats by Yampolskiy and ‘Spellchecker’ (?):

An AI researcher studying Malevolent AI is like a medical doctor studying how different diseases are transmitted, how new diseases arise and how they impact the patients organism.

If the diseases concerned could read medical papers, that analogy would be perfect.

Quotable (#195)

James Thompson on the lessons of AlphaGo for intelligence research:

Very interestingly, getting more computer power does not help AlphaGo all that much. Between the first match against the professional European Champion Fan Hui and then the test match against World Champion Lee Sedol, AlphaGo improved to a 99% win rate against the 6 month earlier version. Against the world champion Lee Sedol, AlphaGo played a divine move: a move with a human probability of only 1 in 1000, but a value move revealed 50 moves later to have been key to influencing power and territory in the centre of the board. (The team do not yet have techniques to show exactly why it made that move). Originally seen by commentators as a fat finger miss-click, it was the first indication of real creativity. Not a boring machine. […] The creative capabilities of the deep knowledge system is only one aspect of this incredible achievement. More impressive is the rate at which it learnt the game, going up the playing hierarchy from nothing, 1 rank a month, to world champion in 18 months, and is nowhere near asymptote yet. It does not require the computer power to compute 200 million positions a second that IBMs Deep Blue required to beat Kasparov. Talk about a mechanical Turk! AlphaGo needed to look at only 100,000 positions a second for a game that was one order of magnitude more complicated than chess. It becomes more human, comparatively, the more you find out about it, yet what it does now is not rigid and handcrafted, but flexible, creative, deep and real. …

And on an optimistic note:

What about us poor humans, of the squishy sort? Fan Hui found his defeat liberating, and it lifted his game. He has risen from 600th position to 300th position as a consequence of thinking about Go in a different way. Lee Sedol, at the very top of the mountain till he met AlphaGo, rated it the best experience of his life. The one game he won was based on a divine move of his own, another “less than 1 in 1000” moves. He will help overturn convention, and take the game to new heights. […] All the commentary on the Singularity is that when machines become brighter than us they will take over, reducing us to irrelevant stupidity. I doubt it. They will drive us to new heights.