Yeah, that’s kind of an issue here. What if you’ve got to work on a hard problem? I’d rather know what influences expert ability on a given domain, in case I want to be a good expert but don’t have a free choice of which problems not to work on.
The lower entries on the table seem to be susceptible for moving from right to left. As for the top ones—well they proclaim that you should widen your error bars.
In practice, it’s often been found that simple algorithms can perform better than experts on the right handed problems. We can’t really have an algorithm for designing AI, but maybe for timeline work it could be good?
I strongly suspect that the primary result of such an algorithm would be very wide error bars on the timeline, and that it would indeed outperform most experts for this reason. You can’t get water from a stone, nor narrow estimates out of ignorance and difficult problems, no matter what simple algorithm you use. Though I would be quite intrigued to be proven wrong about this, and I have seen Fermi estimates for quantities like e.g. the mass of the Earth apparently extract narrow and correct estimations out of the sums of multiple widely erroneous steps.
and I have seen Fermi estimates for quantities like e.g. the mass of the Earth apparently extract narrow and correct estimations out of the sums of multiple widely erroneous steps.
Some of the examples given with good performance—eg firefighting, chess—are not easy problems, they can be phenomenally hard to master. It’s just that they can be mastered.
The headers could be changed to “easy problem”, “hard problem”. I’d make the same breakdown for machine learning.
Yeah, that’s kind of an issue here. What if you’ve got to work on a hard problem? I’d rather know what influences expert ability on a given domain, in case I want to be a good expert but don’t have a free choice of which problems not to work on.
The lower entries on the table seem to be susceptible for moving from right to left. As for the top ones—well they proclaim that you should widen your error bars.
In practice, it’s often been found that simple algorithms can perform better than experts on the right handed problems. We can’t really have an algorithm for designing AI, but maybe for timeline work it could be good?
I strongly suspect that the primary result of such an algorithm would be very wide error bars on the timeline, and that it would indeed outperform most experts for this reason. You can’t get water from a stone, nor narrow estimates out of ignorance and difficult problems, no matter what simple algorithm you use. Though I would be quite intrigued to be proven wrong about this, and I have seen Fermi estimates for quantities like e.g. the mass of the Earth apparently extract narrow and correct estimations out of the sums of multiple widely erroneous steps.
out of how many wrong/wide estimates using the same method?
You still might be able to get some use out of knowing which claims of expertise are at least plausible.
The chart gives on and only one hint—do something to move your problem from the hard column to the easy column.
Some of the examples given with good performance—eg firefighting, chess—are not easy problems, they can be phenomenally hard to master. It’s just that they can be mastered.