LessWrong 2.0 Reader

But the Summoned Heroine doesn't know that until the end, and it's stated that she specifically set up the market to "help them anticipate and counter the Demon King’s next moves".

It's amazing how much better life could be if we could give humans the biological advances of birds - unidirectional breathing, hollow bones, feathers for insulation, more mass-efficient brains, and flight. The best shape would be some bird that could also swim and dive, living in three realms at once.

@Samuel Nellessen [LW · GW] 
Thanks for answering Gunnars question.

But also, I'm a bit nervous that posting their email here directly in the comments is too public, i.e. easy for spam-bots to find. 

If the research lead want to be contactable, their contact info is in their projekt document, under the "Team" section. Most (or all, I'm not sure) research leads have some contact info.

Thanks!

Is the following a fair paraphrasing of your main hypothesis? (I'm leaving out some subtleties with conjunctive successes, but please correct the model in that way if it's relevant.):

"""
Each deleterious mutation multiplies your probability of succeeding at a problem/thought by some constant. Let's for simplicity say it's 0.98 for all of them.

Then the expected number of successes per time for a person is proportional to 0.98^num_deleterious_mutations(person).

So the model would predict that when Person A had 10 less deleterious mutations than person B, they would on average accomplish 0.98^10 ~= 0.82 times as much in a given timeframe.
"""

I think this model makes a lot of sense, thanks!

In itself I think it's insufficient to explain how heavytailed human intelligence is -- there were multiple cases where Einstein seems to have been able to solve problems multiple times faster than the next runner ups. But I think if you use this model in a learning setting where success means "better thinking algorithms" then if you have 10 fewer deleterious mutations it's like having 1/0.82 longer training time, and there might also be compounding returns from having better thinking algorithms to getting more and richer updates to them.

Not sure whether this completely deconfuses me about how heavytailed human intelligence is, but it's a great start.

I guess at least the heavytail is much less significant evidence for my hypothesis than I initially thought (though so far I still think my hypothesis is plausible).

To push back a little:

In fact, I’d go further and argue that explainatory modeling is just a mistaken approach to predictive modeling. Why do we want to understand how things work? To make better decisions. But we don’t really need to understand how things work to make better decisions, we just need to know how things will react to our what we do.

The word "react" here is a causal term. To predict how things will "react" we need some sort of causal model.

What makes predictive modeling a better idea is that it also allows us to find factors that are not causal, but still useful.

Usefulness is also a causal notion. X is useful if it causes a good outcome. If X doesn't cause a good outcome, but is merely correlated with it, it isn't useful.

Mizumoto@jaist.ac.jp, most emails are linked in the associated google doc.

Suppose people's probability of solving a task is uniformly distributed between 0 and 1. That's a thin-tailed distribution.

Now consider their probability of correctly solving 2 tasks in a row. That will have a sort of triangular distribution, which has more positive skewness.

If you consider e.g. their probability of correctly solving 10 tasks in a row, then the bottom 93.3% of people will all have less than 50%, whereas e.g. the 99th percentile will have 90% chance of succeeding.

Conjunction is one of the two fundamental ways that tasks can combine, and it tends to make the tasks harder and rapidly make the upper tail do better than the lower tail, leading to an approximately-exponential element. Another fundamental way that tasks can combine is disjunction, which leads to an exponential in the opposite direction.

When you combine conjunctions and disjunctions, you get an approximately sigmoidal relationship. The location/x-axis-translation of this sigmoid depends on the task's difficulty. And in practice, the "easy" side of this sigmoid can be automated or done quickly or similar, so really what matters is the "hard" side, and the hard side of a sigmoid is approximately exponential.

That's what I mean (I'm talking about the input/output behavior of individual neurons).

Ah, I see. Nvm then. (I misunderstood the previous comment to apply to the entire brain -- idk why, it was pretty clear that you were talking about a single neuron. My bad.)

Yeah I know that's why I said that if a major effect was through few significantly deleterious mutations this would be more plausible. But i feel like human intelligence is even more heavitailed than what one would predict given this hypothesis.

If you have many mutations that matter, then via central limit theorem the overall distribution will be roughly gaussian even though the individual ones are exponential.

(If I made a mistake maybe crunch the numbers to show me?)

(initially misunderstood what you mean where i thought complete nonsense.)

I don't understand what you're trying to say. Can you maybe rephrase again in more detail?