As someone who's background is biology and physics and who does ML work as well. This is an incredibly optimistic view of ML.
>Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else.
Ideally in science you would like to use literally anything else other than ML if possible, fitting models come with their own challenges and neural networks are even more of a nightmare. Understanding the world well enough to hard code a rule is always preferable to fitting to some data and hoping the model will come up with a rule. While there has been some attempts to use ML for feature detection it then takes a lot of experimenting to generally show if it detected signal or just some noise in your data.
Most of the things that would accelerate science would either require AI much more complex than we currently have (basically replacing lab assistants with AI) or are incredible research undertakings in their own right like Alpha Fold, Deep Potential Neural Networks, etc.
While AlphaFold 2 is a tremendous achievement, to me the major drawback is the blackbox approach. It means it is very difficult to know when the model is outputting garbage and it also doesn't directly lead to new insights.
A much more interesting approach: "Discovery of Physics From Data: Universal Laws and Discrepancies" [1]
If ML did that, then it would be much more interesting.
Actually, there might not be a good way to model or describe the difference between causal inference, correlation and causality.
Causality involves a deep understanding of a phenomenon in science.
For example, the standard model of physics is pretty good at describing the real world in a good enough manner because we understand a lot of it. The difference with correlation and causality, in my view, is human, scientific understanding of what things are. Formulas, data or drawing are not enough.
For example there might never be a way to prove natural selection, even if there is a lot of data available, but a lot of scientific consensus is enough to describe causality.
>Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else.
Ideally in science you would like to use literally anything else other than ML if possible, fitting models come with their own challenges and neural networks are even more of a nightmare. Understanding the world well enough to hard code a rule is always preferable to fitting to some data and hoping the model will come up with a rule. While there has been some attempts to use ML for feature detection it then takes a lot of experimenting to generally show if it detected signal or just some noise in your data.
Most of the things that would accelerate science would either require AI much more complex than we currently have (basically replacing lab assistants with AI) or are incredible research undertakings in their own right like Alpha Fold, Deep Potential Neural Networks, etc.