What can and can't AI do, and what should and shouldn't AI do?
that can let you run the numbers yourself. If you only draw 20 marbles and get one yellow, you can have 95 percent confidence that the yellows would be between 0.13 percent and 24.9 percent of the total—not very exact. If you draw 1,000 marbles and get 50 yellows, you can have 95 percent confidence that yellows would be between 3.7 percent and 6.5 percent of the total; closer, but perhaps not something you’d sign your name to in a quarterly filing.
But more important, in order to have any basis for overseeing the creation of a machine-learning product, or to have a rationale for investing in a machine-learning company, it’s hard to see how one could be successful without a decent grounding in the rudiments of machine learning, and where and how it is best applied to solve a problem. And yet, team Musk here is suggesting they do lack that knowledge.
This is a basic reality in the world of tech. In my own experience talking with investors who make investments in “AI” technology, it’s often quite clear that they know almost nothing about these basic aspects of how machine learning works. I’ve even spoken to CEOs of rather large companies that rely at their core on novel machine learning efforts to drive their product, who also clearly have no understanding of how the work actually gets done.
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