From Pentland's Social Physics:
what can a single individual do to increase rate of idea flow in their part of their social network? Fortunately, there are many ways. In 1985, Bob Kelly of Carnegie Mellon University launched the now famous Bell Stars study. Bell Laboratories, a premier research laboratory, wanted to know more about what separates a star performer from the average performer. Is it something innate or can star performance be learned? Bell Labs already hired the best and the brightest from the world’s most prestigious universities, but only a few lived up to their apparent potential for brilliance. Instead, most hires developed into solid performers but did not contribute substantially to AT&T’s competitive advantage in the marketplace.
What Kelly found was that star producers engage in “preparatory exploration”; that is, they develop dependable two-way streets to experts ahead of time, setting up a relationship that will later help the star producer complete critical tasks. Moreover, the stars’ networks differed from typical workers’ networks in two important respects. First, they maintained stronger engagement with the people in their networks, so that these people responded more quickly and helpfully. As a result, the stars rarely spent time spinning their wheels or going down blind alleys.
Second, star performers’ networks were also more diverse. Average performers saw the world only from the viewpoint of their job, and kept pushing the same points. Stars, on the other hand, had people in their networks with a more diverse set of work roles, so they could adopt the perspectives of customers, competitors, and managers. Because they could see the situation from a variety of viewpoints, they could develop better solutions to problems.
More (#2) from Social Physics:
...For the entire community, we measured activity levels by using the accelerometer sensors embedded in their mobile phones. Unlike typical social science experiments, FunFit was conducted out in the real world, with all the complications of daily life. In addition, we collected hundreds of thousands of hours and hundreds of gigabytes of contextual data, so that we could later go back and see which factors had the greatest effect.
On average, it turned out that the social network incentive scheme worked almost four times more ef
One open question in AI risk strategy is: Can we trust the world's elite decision-makers (hereafter "elites") to navigate the creation of human-level AI (and beyond) just fine, without the kinds of special efforts that e.g. Bostrom and Yudkowsky think are needed?
Some reasons for concern include:
But if you were trying to argue for hope, you might argue along these lines (presented for the sake of argument; I don't actually endorse this argument):
The basic structure of this 'argument for hope' is due to Carl Shulman, though he doesn't necessarily endorse the details. (Also, it's just a rough argument, and as stated is not deductively valid.)
Personally, I am not very comforted by this argument because:
Obviously, there's a lot more for me to spell out here, and some of it may be unclear. The reason I'm posting these thoughts in such a rough state is so that MIRI can get some help on our research into this question.
In particular, I'd like to know: