In Machines of Loving Grace, Dario Amodei writes, “...[biological] data is often lacking—not so much in quantity, but quality: there is always a dearth of clear, unambiguous data that isolates a biological effect of interest from the other 10,000 confounding things that are going on, or that intervenes causally in a given process, or that directly measures some effect (as opposed to inferring its consequences in some indirect or noisy way)... Given all this, many biologists have long been skeptical of the value of AI and “big data” more generally in biology. … there’s still a perception that AI is (and will continue to be) useful in only a limited set of circumstances. A common formulation is “AI can do a better job analyzing your data, but it can’t produce more data or improve the quality of the data. Garbage in, garbage out”. But I think that pessimistic perspective is thinking about AI in the wrong way. If our core hypothesis about AI progress is correct, then the right way to think of AI is not as a method of data analysis, but as a virtual biologist who performs all the tasks biologists do…” For example, neuroscientists are critical of studies analyzing large amounts of brain imaging data using machine learning pipelines, although they are superficially attractive: https://www.reddit.com/r/datascience/s/eGS8hZrRTn

Thank you for sharing this with me! I agree that these criticisms are more about AI analyses of biological and neuroscientific data like some by MedARC, as antichain on reddit and Amodei in the quote above describe them.