In my 2013 article on strong AI forecasting, I made several suggestions for how to do better at forecasting strong AI, including this suggestion quoted from Phil Tetlock, arguably the leading forecasting researcher in the world:
Signposting the future: Thinking through specific scenarios can be useful if those scenarios “come with clear diagnostic signposts that policymakers can use to gauge whether they are moving toward or away from one scenario or another… Falsifiable hypotheses bring high-flying scenario abstractions back to Earth.”
Tetlock hadn’t mentioned strong AI at the time, but now it turns out he wants suggestions for strong AI signposts that could be forecast on GJOpen, the forecasting tournament platform.
@PTetlock Any thoughts on the AlphaGo victory?
— Brandon Wilson (@brandonwilson) March 20, 2016
important: it was one of our signpost indicators. It nudges probability we are on a strong AI scenario trajectory https://t.co/XuAcpjlWtm
— Philip E. Tetlock (@PTetlock) March 20, 2016
@PTetlock What other signpost indicators are you watching?
— Alexander Berger (@albrgr) March 21, 2016
— Philip E. Tetlock (@PTetlock) March 21, 2016
Specifying crisply formulated signpost questions is not easy. If you come up with some candidates, consider posting them in the comments below. After a while, I will collect them all together and send them to Tetlock. (I figure that’s probably better than a bunch of different people sending Tetlock individual emails with overlapping suggestions.)
Tetlock’s framework for thinking about such signposts, which he calls “Bayesian question clustering,” is described in Superforecasting:
In the spring of 2013 I met with Paul Saffo, a Silicon Valley futurist and scenario consultant. Another unnerving crisis was brewing on the Korean peninsula, so when I sketched the forecasting tournament for Saffo, I mentioned a question IARPA had asked: Will North Korea “attempt to launch a multistage rocket between 7 January 2013 and 1 September 2013?” Saffo thought it was trivial. A few colonels in the Pentagon might be interested, he said, but it’s not the question most people would ask. “The more fundamental question is ‘How does this all turn out?’ ” he said. “That’s a much more challenging question.”
So we confront a dilemma. What matters is the big question, but the big question can’t be scored. The little question doesn’t matter but it can be scored, so the IARPA tournament went with it. You could say we were so hell-bent on looking scientific that we counted what doesn’t count.
That is unfair. The questions in the tournament had been screened by experts to be both difficult and relevant to active problems on the desks of intelligence analysts. But it is fair to say these questions are more narrowly focused than the big questions we would all love to answer, like “How does this all turn out?” Do we really have to choose between posing big and important questions that can’t be scored or small and less important questions that can be? That’s unsatisfying. But there is a way out of the box.
Implicit within Paul Saffo’s “How does this all turn out?” question were the recent events that had worsened the conflict on the Korean peninsula. North Korea launched a rocket, in violation of a UN Security Council resolution. It conducted a new nuclear test. It renounced the 1953 armistice with South Korea. It launched a cyber attack on South Korea, severed the hotline between the two governments, and threatened a nuclear attack on the United States. Seen that way, it’s obvious that the big question is composed of many small questions. One is “Will North Korea test a rocket?” If it does, it will escalate the conflict a little. If it doesn’t, it could cool things down a little. That one tiny question doesn’t nail down the big question, but it does contribute a little insight. And if we ask many tiny-but-pertinent questions, we can close in on an answer for the big question. Will North Korea conduct another nuclear test? Will it rebuff diplomatic talks on its nuclear program? Will it fire artillery at South Korea? Will a North Korean ship fire on a South Korean ship? The answers are cumulative. The more yeses, the likelier the answer to the big question is “This is going to end badly.”
I call this Bayesian question clustering because of its family resemblance to the Bayesian updating discussed in chapter 7. Another way to think of it is to imagine a painter using the technique called pointillism. It consists of dabbing tiny dots on the canvas, nothing more. Each dot alone adds little. But as the dots collect, patterns emerge. With enough dots, an artist can produce anything from a vivid portrait to a sweeping landscape.
There were question clusters in the IARPA tournament, but they arose more as a consequence of events than a diagnostic strategy. In future research, I want to develop the concept and see how effectively we can answer unscorable “big questions” with clusters of little ones.
(Note that although I work as a GiveWell research analyst, my focus at GiveWell is not AI risks, and my views on this topic are not necessarily GiveWell’s views.)