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by mnky9800n
251 days ago
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I thnk of it like this. I am a computational scientist. I have a new idea, I have claude code churn on this idea for some days, making plots of data, doing random things with it, plotting it, modeling it, etc. This leads to some thoughts about the data as to whether it is worth pursuing or not. This reduces that initial idea exploration from weeks to days. Then when I come upon an idea about the data, I start over. This time I build the analysis myself, the analysis has less overall to do because of the claude exploration. Also, because I have had claude interacting with the data, I can ask it some questions like, which column out of 1000+ columns and tables is the one with X variable? Or I can say to claude, go download the PDFs of a bunch of papers I want to read on the topic, and it might even suggest others. I do think that if people think they will orchestrate AI to do complex scientific endeavors for them, it is not going to work unless it is really a specific thing to do. But the truth is, this is the point of the last iteration of new software, instead of writing software, you write neural networks to solve the problem. And so, if you want to detect some event in some data, the old way is to write a custom algorithm, the new way is to pull a trained neural network off the shelf to find it for you (or train one yourself), and the new new way is to convince an AI agent to do it for you. And so I think that ultimately, there are likely lots of tasks AI agents are going to be happy to do for you, but there are a great deal many, at least from a science point of view, that will be sort of meaningless if the AI does it, because the act of doing it is what builds the scientific meaning. It's a mixed bag. I am both very bullish on AI in the future and rather pessimistic about AI today. |
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