US experts who work in artificial intelligence fields seem to have a much rosier outlook on AI than the rest of us.
In a survey comparing views of a nationally representative sample (5,410) of the general public to a sample of 1,013 AI experts, the Pew Research Center found that “experts are far more positive and enthusiastic about AI than the public” and “far more likely than Americans overall to believe AI will have a very or somewhat positive impact on the United States over the next 20 years” (56 percent vs. 17 percent). And perhaps most glaringly, 76 percent of experts believe these technologies will benefit them personally rather than harm them (15 percent).
The public does not share this confidence. Only about 11 percent of the public says that “they are more excited than concerned about the increased use of AI in daily life.” They’re much more likely (51 percent) to say they’re more concerned than excited, whereas only 15 percent of experts shared that pessimism. Unlike the majority of experts, just 24 percent of the public thinks AI will be good for them, whereas nearly half the public anticipates they will be personally harmed by AI.
Then you must know something the rest of us don’t. I’ve found it marginally useful, but it leads me down useless rabbit holes more than it helps.
I’m about 50/50 between helpful results and “nope, that’s not it, either” out of the various AI tools I have used.
I think it very much depends on what you’re trying to do with it. As a student, or fresh-grad employee in a typical field, it’s probably much more helpful because you are working well trod ground.
As a PhD or other leading edge researcher, possibly in a field without a lot of publications, you’re screwed as far as the really inventive stuff goes, but… if you’ve read “Surely you’re joking, Mr. Feynman!” there’s a bit in there where the Manhattan project researchers (definitely breaking new ground at the time) needed basic stuff, like gears, for what they were doing. The gear catalogs of the day told them a lot about what they needed to know - per the text: if you’re making something that needs gears, pick your gears from the catalog but just avoid the largest and smallest of each family/table - they are there because the next size up or down is getting into some kind of problems engineering wise, so just stay away from the edges and you should have much more reliable results. That’s an engineer’s shortcut for how to use thousands, maybe millions, of man-years of prior gear research, development and engineering and get the desired results just by referencing a catalog.