I think AI will be the missing key. The ability to micromanage millions of inputs at once and respond with control corrections in microseconds can push this over the top. I’ve read of some progress on this front already.
I don’t think it’ll be LLMs (which is what a lot of people jump to when you mention “AI”), they have much higher latencies than microseconds. It will be AI of some sort, but probably won’t be considered AI due to the AI effect:
The AI effect is the discounting of the behavior of an artificial intelligence program as not “real” intelligence.
The author Pamela McCorduck writes: “It’s part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, ‘that’s not thinking’.”
Researcher Rodney Brooks stated: “Every time we figure out a piece of it, it stops being magical; we say, ‘Oh, that’s just a computation.’”
LLMs might be useful for researchers diving down a particular research/experiment rabbit hole.
We’re very close. I think it was only a few years ago that we first got more energy back than we put in. That’s a big milestone.
At the risk of getting roughed up in the replies…
I think AI will be the missing key. The ability to micromanage millions of inputs at once and respond with control corrections in microseconds can push this over the top. I’ve read of some progress on this front already.
I don’t think it’ll be LLMs (which is what a lot of people jump to when you mention “AI”), they have much higher latencies than microseconds. It will be AI of some sort, but probably won’t be considered AI due to the AI effect:
LLMs might be useful for researchers diving down a particular research/experiment rabbit hole.
Machine Learning in this case instead of LLMs. Fields like microbiology have been seeing waves of discoveries with the latest ML approaches.
Is there a difference between a neural net and ml?
Neural nets aren’t the only way to do ML. They are, however, by far the most popular.
ML includes other models like random forest in addition to neural nets.