This week on my podcast, I read Swapping money for expertise, a recent essay from my Pluralistic blog about the political economy of “AI” based on theory-free inference.
The need to understand and describe a thing before you could apply “AI” to it is a bottleneck, because it requires programmers to understand how a thing works before they can incorporate it into an “AI” system. Lots of programmers know how to play chess, but far fewer understand the human pancreas, planetary weather systems, or patterns of mineral deposition in the Earth’s crust.
For programmers to apply machine learning to these domains, they need to collaborate with experts who do understand them, who furthermore expect to be paid for this work. The need for expert input into this kind of “AI” represents a significant increase in the wage-bill paid by the programmer’s employer: it means they have to pay for programmers and experts.
Even worse: this kind of “AI” can’t be applied to systems we don’t understand. We can observe far more causal relationships – instances in which A reliably causes B – in the universe than we can explain. There are lots of examples of us operationalizing these observations without understanding them. If you get sick today, your doctor might well prescribe one of the many medicines whose method of action is either incompletely understood or not understood at all. We know that molecule A reliably treats pathology B, but not why, and while that why is the subject of ongoing research, it’s not necessary that the why be known before the molecule can be given to ailing patients.
