Charlie Sanders

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Strong Evidence is Common

One implication of the Efficient Market Hypothesis (EMH) is that is it difficult to make money on the stock market. Generously, maybe only the top 1% of traders will be profitable. 

Nitpick: it's incredibly easy to make money on the stock market: just put your money in it, ideally in an index fund. It goes up by an average of 8% a year. Almost all traders will be profitable, although many won't beat that 8% average. 

The entire FIRE movement is predicated on it being incredibly simple to make money on the stock market. It takes absolutely zero skill to be a sufficiently profitable trader, given a sizeable enough initial investment. 

I get that you're trying to convey above-market-rate returns here, but your wording is imprecise. 

The case for aligning narrowly superhuman models

Right, but I'm not sure how you'd "test" for success in that scenario. Usefulness to humanity, as demonstrated by effective product use, seems to me like the only way to get a rigorous result. If you can't measure the success or failure of an idea objectively, then the idea probably isn't going to matter much. 

The case for aligning narrowly superhuman models

On fuzzy tasks: I think the appropriate frame of comparison is neither an average subset (Mechanical Turk) or the ideal human (Go), but instead the median resource that someone would be reasonably likely to seek out. To use healthcare as an example, you'd want your AI to beat the average family doctor that most people would reach out to, as opposed to either a layman's opinion or the preeminent doctor in the field. 

In Addition to Ragebait and Doomscrolling

https://www.youtube.com/watch?v=vRBsaJPkt2Q

If you’re interested in this topic more and have an hour and a half to burn, there’s worse ways to spend it.

When Money Is Abundant, Knowledge Is The Real Wealth

The world would undoubtedly be better if more Data Scientists became monks.