Archive for August, 2010

Drive

Tuesday, August 31st, 2010

Book review: Drive: The Surprising Truth About What Motivates Us, by Daniel H. Pink.

This book explores some of the complexities of what motivates humans. It attacks a stereotype that says only financial rewards matter, and exaggerates the extent to which people adopt that fallacy. His style is similar to Malcolm Gladwell’s, but with more substance than Gladwell.

The book’s advice is likely to cause some improvement in how businesses are run and in how people choose careers. But I wonder how many bosses will ignore it because their desire to exert control over people outweighs their desire to create successful companies.

I’m not satisfied with the way he and others classify motivations as intrinsic and extrinsic. While feelings of flow may be almost entirely internally generated, other motivations that he classifies as intrinsic seem to involve an important component of feeling that others are rewarding you with higher status/reputation.

Shirking may have been a been an important problem a century ago for which financial rewards were appropriate solutions, but the nature of work has changed so that it’s much less common for workers to want to put less effort into a job. The author implies that this means standard financial rewards have become fairly unimportant factors in determining productivity. I think he underestimates the importance they play in determining how goals are prioritized.

He believes the changes in work that reduced the importance of financial incentives was the replacement of rule-following routine work with work that requires creativity. I suggest that another factor was that in 1900, work often required muscle-power that consumed almost as much energy as a worker could afford to feed himself.

He states his claims vaguely enough that they could be interpreted as implying that broad categories of financial incentives (including stock options and equity) work poorly. I checked one of the references that sounded like it might address that (”When performance-related pay backfires”), and found it only dealt with payments for completing specific tasks.

His complaints about excessive focus on quarterly earnings probably have some value, but it’s important to remember that it’s easy to err in the other direction as well (the dot-com bubble seemed to coincide with an unusual amount of effort at focusing on earnings 5 to 10 years away).

I’m disappointed that he advises not to encourage workers to compete against each other without offering evidence about its effects.

One interesting story is the bonus system at Kimley-Horn and Associates, where any employee can award another employee $50 for doing something exceptional. I’d be interested in more tests of this – is there something special about Kimley-Horn that prevents abuse, or would it work in most companies?

Memory and the Flynn Effect

Thursday, August 26th, 2010

Memory tests appear to exhibit a Flynn Effect.

Quantifying Artificial Intelligence

Tuesday, August 17th, 2010

The most interesting talk at the Singularity Summit 2010 was Shane Legg’s description of an Algorithmic Intelligence Quotient (AIQ) test that measures something intelligence-like automatically in a way that can test AI programs (or at least the Monte-Carlo AIXI that he uses) on 1000+ environments.

He had a mathematical formula which he thinks rigorously defines intelligence. But he didn’t specify what he meant by the set of possible environments, saying that would be a 50 page paper (he said a good deal of the work on the test had been done last week, so presumably he’s still working on the project). He also included a term that applies Occam’s razor which I didn’t completely understand, but it seems likely that that should have a fairly non-controversial effect.

The environments sound like they imitate individual questions on an IQ test, but with a much wider range of difficulties. We need a more complete description of the set of environments he uses in order to evaluate whether they’re heavily biased toward what Monte-Carlo AIXI does well or whether they closely resemble the environments an AI will find in the real world. He described two reasons for having some confidence in his set of environments: different subsets provided roughly similar results, and a human taking a small subset of the test found some environments easy, some very challenging, and some too hard to understand.

It sounds like with a few more months worth of effort, he could generate a series of results that show a trend in the AIQ of the best AI program in any given year, and also the AIQ of some smart humans (although he implied it would take a long time for a human to complete a test). That would give us some idea of whether AI workers have been making steady progress, and if so when the trend is likely to cross human AIQ levels. An educated guess about when AI will have a major impact on the world should help a bit in preparing for it.

A more disturbing possibility is that this test will be used as a fitness function for genetic programming. Given sufficient computing power, that looks likely to generate superhuman intelligence that is almost certainly unfriendly to humans. I’m confident that sufficient computing power is not available yet, but my confidence will decline over time.

Brian Wang has a few more notes on this talk

Disadvantages of Stories

Thursday, August 12th, 2010

Tyler Cowen has a good video describing why we shouldn’t be too influenced by stories. He exaggerates a bit when he says

There are only a few basic stories. If you think in stories, that means you are telling yourself the same thing over and over

but his point that stories allow storytellers to manipulate our minds deserves more emphasis. For me, one of the hardest parts of learning how to beat the stock market was to admit that I did poorly when I was influenced by stories, and did well mainly when I relied on numbers that are available and standardized for most companies, and on mechanical rules which varied little between companies (I sometimes use different rules for different industries, but beyond that I try to avoid adapting my approach to different circumstances).

For example, The stories I heard about Enron’s innovative management style gave me a gut feeling that it was a promising investment. But its numbers showed an uninteresting company, and persuaded me to postpone any investment.

But I’ve only told you a story here (it’s so much easier to do than provide rigorous evidence). If you really want good reasons, try testing for yourself story versus non-story approaches to something like the stock market.

(HT Patri).