In the previous post of this series, I wrote about how “AI” has been unable to provide usable solutions for very lucrative problems which have been shown to be partially amenable to computational techniques. The fact that we still do not have “AI” which can drive a car better than the median human driver under all road conditions and at normal speeds tells you a lot about its systemic limitations. Similarly the inability of “AI” to identify molecules capable of binding to sites within proteins at a rate better, or with more ease, than a human is another glaring display of the true limitations of what is being promoted as “AI”. It is also noteworthy that computational techniques which form basis of ML, DL etc have been used in these areas for over two decades, not to mention that both are grounded in engineering and hard science with much less space for subjective ambiguity than other fields.
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