I like your comment regarding the (usually) thoughtful effort that goes into creative endeavours. I know that there are those who claim that deliberate effort is antithetical to the creative process, but even serendipitous results have to be deliberately examined and refined. Until a system can say “oh, that’s interesting enough to investigate further” I’m not convinced that it can be called creative. In the context of LLMs, I think that means giving them access to their own outputs in some way.
As for the dangers, I’m pretty sure that most of us, even those of us looking for danger, will not recognize it until we see it. That doesn’t mean we should just barrel ahead, though. Just the opposite. That’s why we need to move slowly. Our reflexes and analytical capabilities are pretty slow in comparison to the potential rate of development.
In the context of LLMs, I think that means giving them access to their own outputs in some way.
That’s what the AUTOGPTs do (as well as others, there’s so many now) they pick apart the task into smaller things and feed the results back in, building up a final result, and that works a lot better than just a one time mass input. The biggest advantage and main reason for these being developed was to keep the LLM on course without deviation.
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I like your comment regarding the (usually) thoughtful effort that goes into creative endeavours. I know that there are those who claim that deliberate effort is antithetical to the creative process, but even serendipitous results have to be deliberately examined and refined. Until a system can say “oh, that’s interesting enough to investigate further” I’m not convinced that it can be called creative. In the context of LLMs, I think that means giving them access to their own outputs in some way.
As for the dangers, I’m pretty sure that most of us, even those of us looking for danger, will not recognize it until we see it. That doesn’t mean we should just barrel ahead, though. Just the opposite. That’s why we need to move slowly. Our reflexes and analytical capabilities are pretty slow in comparison to the potential rate of development.
That’s what the AUTOGPTs do (as well as others, there’s so many now) they pick apart the task into smaller things and feed the results back in, building up a final result, and that works a lot better than just a one time mass input. The biggest advantage and main reason for these being developed was to keep the LLM on course without deviation.
Thanks, I didn’t know that. I guess I need to broaden my reading.
It changes so much so fast. For a video source to grasp the latest stuff I’d recommend the Youtube channel “AI Explained”.
Thanks, I’ll check it out.