I work in medical research. We are already seeing cool image based analysis, but it’s supervised machine learning that is only as good as the training set. This will apply to any machine learning algos. And that’s where we are going to run into issues. What I’d like to see in ML algos that can read 50 high impact papers in a field and put together a summary of the data. The problems arise when people have bad data. It might be fabricated, poorly designed expts or just bad statistics. The ML algos are going to assume that data is as real as the most well-performed experiments. The bad data will contaminate the good data and corrupt the conclusions drawn from the algos.

Will that problem get alleviated? Probably, but it’s going to take some time and it’s going to require a lot of bright people to curate the dataset to actually be able to draw better conclusions than we can arrive at alone. But in 15 years? God only knows. Maybe I’ll just submit whatever grant ChatGPT13 writes for me.