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Perhaps. But equally, even if they don’t know how to build one, they might be able to work through the steps, iterate on their ideas, and eventually figure it o
by urbandw311er 2y ago
Perhaps. But equally, even if they don’t know how to build one, they might be able to work through the steps, iterate on their ideas, and eventually figure it out. The hypothesis is that, by modelling language they have essentially modelled the underlying human abilities of logic and rationalism.
Let me put it another way. If you put a thousand human researchers in a room with all the existing data and came up with a process to assess their outputs and iterate on their best ideas, how long before they built an interstellar transport beam - fifty years? A hundred years?
Now do it with a thousand AI researchers, and feed back their test results to them and allow them to iterate. But this time, assume they can work maybe a million times faster than the humans, or maybe ten million times faster. They’re limited only by the time it takes to build and test their prototype hardware.
Now how long does it take?
- akdev1l 2y ago> iterate on their ideas I don’t think it’s clear that LLMs can have/express novel ideas. I would argue an LLM by definition cannot generate _novel_ ideas.
- kordlessagain 2y agoIn "Me" by Thomas T. Thomas, novel thinking came from introducing random retrieval of memories in the entity. With vector search, this might include singleton outliers added to the prompt.
- slowmovintarget 2y agoJust because the idea was expressed in science fiction, doesn't mean it's useful in the real world. Solving a non-trivial problem that hasn't been solved before is not something random walks on text token probabilities will get you. Text production algorithms have the problem of being unable to detect whether the text produced is internally consistent, let alone true in the logical sense. These two qualities are required for creating net new solutions to unsolved next-level problems.