I've been uncomfortable for a while with statements like Eliezer's remark that:
When heavier-than-air flight or atomic energy was a hundred years off, it looked fifty years off or impossible; when it was five years off, it still looked fifty years off or impossible.
This really is picking and choosing specific technological examples rather than looking at the overall pattern. In 1964, five years before the first moon landing, it looked a few years off but certainly not a hundred years off.
Perhaps the best online tool for calibration training is PredictionBook.com
I strongly agree with this. I've used it to make a variety of predictions, including tech predictions. One issue it does have is that there's no easy categorization so one can't use it for example to see at a glance whether one's tech predictions are more or less accurate than one's predictions about politics or other subjects.
Mathematicians seldom try and predict when major problems will be solved, because they recognise that insight is very hard to predict.
Noteworthy counterexample: Soon after the Feit-Thompson theorem, people started talking about classifying all finite simple groups, but this was because Gorenstein had a specific blueprint that was thought might be able to get the full result. But even then, the time period was shorter.
In cases like the Riemann hypothesis we have a few ideas of things that might work, but none look that promising, and results one would expect to fall first, like the Lindelof hypothesis remain apparently unassailable. So one major sign of a problem being genuinely far off is that even to our eyes, much simpler problems look far off. I'm not sure how to apply that to AI. Do modern practical successes like machine learning count plausibly as successes of related minor aspects? It will be a lot easier to tell after there's some form of general AI and we have more of an idea about its structure. Similar issues apply to almost any future tech.
This really is picking and choosing specific technological examples rather than looking at the overall pattern. In 1964, five years before the first moon landing, it looked a few years off but certainly not a hundred years off.
I don't think Eliezer meant to say that breakthrough technologies always seem 50 years off or impossible until they are invented. Those who were paying attention to computer chess could predict it passing the human level before the end of the millenium, and we've seen self-driving cars coming for a while now. Anyway, I've added a clarifying note below the Eliezer quote, now.
George Polya, 34 years before Pearl (1988) launched the probabilistic revolution in AI
Ernest Rutherford in 1933, 18 years before the first nuclear reactor went online
Wilbur Wright, in a 1908 speech
Startling insights are hard to predict.1 Polya and Rutherford couldn't have predicted when computational probabilistic reasoning and nuclear power would arrive. Their training in scientific skepticism probably prevented them from making confident predictions about what would be developed in the next few decades.
What's odd, then, is that their scientific skepticism didn't prevent them from making confident predictions about what wouldn't be developed in the next few decades.
I am blessed to occasionally chat with some of the smartest scientists in the world, especially in computer science. They generally don't make confident predictions that certain specific, difficult, insight-based technologies will be developed soon. And yet, immediately after agreeing with me that "the future is very hard to predict," they will confidently state that a specific, difficult technology is more than 50 years away!
Error. Does not compute.
What's going on, here?
I don't think it's always a case of motivated skepticism. I don't think think Wilbur Wright was motivated to think flight was a long way off. I think he was "zoomed in" on the difficulty of the problem, didn't see a way to solve it, and misinterpreted his lack of knowledge about the difficulty of flight as positive information that flight was extremely difficult and far away.
As Eliezer wrote:
(Of course, we can predict some technological advances better than others: "Five years before the first moon landing, it looked a few years off but certainly not a hundred years off.")
There may also be a psychological double standard for "positive" and "negative" predictions. Skepticism about confident positive predictions — say, that AI will be invented soon — feels like the virtuous doubt of standard scientific training. But oddly enough, making confident negative predictions — say, that AI will not be invented soon — also feels like virtuous doubt, merely because the first prediction was phrased positively and the second was phrase negatively.
There's probably some Near-Far stuff going on, too. Nuclear fusion and AI feel abstract and unknown, and thus they also feel distant. But when you're ignorant about a phenomenon, the correct response is to broaden your confidence intervals in both directions, not push them in one direction like the Near-Far effect wants you to.
The scientists I speak to are right to say that it's very hard to predict the development of specific technologies. But one cannot "simultaneously claim to know little about the future and to be able to set strong lower bounds on technology development times," on pain of contradiction.
Depending on the other predictions these scientists have made, they might be3 manifesting a form of overconfidence I'll call "overconfident pessimism." It's well-known that humans are overconfident, but since overconfident pessimism seems to be less-discussed than overconfident optimism, I think it's worth giving it its own name.
What can we do to combat overconfident pessimism in ourselves?
The most broadly useful debiasing technique is to "consider the opposite" (Larrick 2004):
Or, consider this variant of "consider the opposite":
Another standard method for reducing overconfidence and improving one's accuracy in general is calibration training (Lichtenstein et al. 1982; Hubbard 2007).
The calibration training process is pretty straightforward: Write down your predictions, then check whether they came true. Be sure to also state your confidence in each prediction. If you're perfectly calibrated, then predictions you made with 60% confidence should be correct 60% of the time, while predictions you made with 90% confidence should be correct 90% of the time.
You will not be perfectly calibrated. But you can become better-calibrated over time with many rounds of feedback. That's why weather forecasters are so much more accurate than most other kinds of experts (Murphy & Winkler 1984): every week, they learn whether their predictions were correct. It's harder to improve your calibration when you have to wait 5 or 30 years to see whether your predictions (say, about technological development) were correct, but calibration training in any domain seems to reduce overconfidence in general, since you get to viscerally experience how often you are wrong — even on phenomena that should be easier to predict than long-term technological development.
Perhaps the best online tool for calibration training is PredictionBook.com. For a story of one person becoming better calibrated using PredictionBook.com, see 1001 PredictionBook Nights. Another tool is the Calibration Game, available for Mac, Windows, iOS, and Android.
To counteract overconfident pessimism in particular, be sure to record lots of negative predictions, not just positive predictions.
Finally, it may help to read lists of failed negative predictions. Here you go: one, two, three, four, five, six.
Notes
1 Armstrong & Sotala (2012) helpfully distinguish "insight" and "grind":
2 See also Speirs-Bridge et al. (2009).
3 The original version of this post incorrectly accused the scientists I've spoken with of overconfidence, but I can't rightly draw that conclusion without knowing the outcomes of their other predictions.