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I have a suspicion that greenfield science will be the last thing automated, at least the non-brute-force kind. AI assistants to do the drugery (smart search ag
by ethbr1 3y ago
I have a suspicion that greenfield science will be the last thing automated, at least the non-brute-force kind. AI assistants to do the drugery (smart search agents), but not pick the directions to proceed in.
Too little relevant training data in niche, state of the art topics.
But to the broader point, isn't this progress in a nutshell?
(1) Figure out a thing can be done, (2) figure out how to manufacture with humans, (3) maximize productivity of human effort, (4) automate select portions of the optimized and standardized process, (5) find the last 5% isn't worth automating, because it's too branchy.
From that perspective, software development isn't proceeding differently than any other field historically, with the added benefit that all its inputs and outputs are inherently digital.
- SubiculumCode 3y agoI think that picking a direction is not that hard, and I don't know that AI couldn't do it better. I'm not sure mid-tier CEO's won't be on their way out, just like middle management.
- ethbr1 3y agoI was talking more about science. On the people-direction side, I expect the span of control will substantially broaden, which will probably lead to fewer manager/leader jobs (that pay more). You'll always need someone to do the last 5% that it doesn't make sense to data engineer inputs/outputs into/from AI.
- SubiculumCode 3y agoYeah. Right now, its been helping me be more productive in my science by writing code quicker...mainly on the data management side of things. I do however wonder, at what point do I just describe the hypothesis, point to the data files, and have it design an analysis pipeline, produce the results, interpret the results, then suggest potential follow-up hypotheses, do a literature search on that, then have it write up the grant for it.
- ethbr1 3y agoIt'll probably be like automating most other tasks: the effort is dominated by finding the right data, transforming it into a standardized input stream, then transforming the output back into actions. Programming became high-level programming (of compilers) became library-glueing/templating/declarative programming... becomes data engineering.