So has two heuristics to generally describe . One is Mediocristan, which basically means things that are on a Gaussian distribution such as height and/or weight of people.

The other is called Extremistan, which describes a more Pareto like or fat-tailed distribution. An example is wealth distribution, 1% of people own 50% of the wealth or something close to that and so predictability from limited data sets is much harder or even impossible. This is because you can add a single sample to your data set and the consequences are so large that it breaks the model, or has an effect so large that it cancels out any of the benefits from prior accurate predictions. In fact this is how he claims to have made money in the stock market(which is in extremistan), because everyone else was using bad, Gaussian distribution models to the market, which actually would work for a short period of time but when things went wrong, they went really wrong which would cause you to have net losses in the market despite years of consistent .

I found this video of Taleb being asked about . His claim is that A.I. doesn’t work (as well) for things that fall into extremistan.

Is he right? Will some things just be inherently unpredictable even with A.I.? Such as the stock market?

Here is the video I am referring to

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