Nonsense. Have you used any of the deep research tools?
Don't fall for the utopia fallacy. Humans also publish junk.
Don't fall for the utopia fallacy. Humans also publish junk.
For the hard topics, the solution is still the same as pre-AI - search for popular survey papers, then start crawling through the citation network and keeping notes. The LLM output had no idea of what was actually impactful vs what was a junk paper in the niche topic I was interested in so I had no other alternative than quality time with Google Scholar.
We are a long way from deep research even approaching a well-written survey paper written by grad student sweat and tears.
Most people are capable of maybe 4 good hours a day of deep knowledge work. Saving 30 minutes is a lot.
I've found getting a personalized report for the basic stuff is incredibly useful. Maybe you're a world class researcher if it only saves you 15-30 minutes, I'm positive it has saved me many hours.
Grad students aren't an inexhaustible resource. Getting a report that's 80% as good in a few minutes for a few dollars is worth it for me.
But, all provenance systems are gamed. I predict the most reliable methods will be cumbersome and not widespread, thus covering little actual content. The easily-gamed systems will be in widespread use, embedded in social media apps, etc.
Questions: 1. Does there exist a data provenance system that is both easy to use and reliable "enough" (for some sufficient definition of "enough")? Can we do bcrypt-style more-bits=more-security and trade time for security?
2. Is there enough of an incentive for the major tech companies to push adoption of such a system? How could this play out?
If you're training an AI, do you want it to get trained on other AIs' output? That might be interesting actually, but I think you might then want to have both, an AI trained on everything, and another trained on everything except other AIs' output. So perhaps an HTML tag for indicating "this is AI-generated" might be a good idea.
But I don’t think that’s a reasonable goal. Pragmatic example: There’s almost no optional HTML tags or optional HTTP Headers which are used anywhere close to 100% of the times they apply.
Also, I think field is already muddy, even before the game starts. Spell checker, grammar.ly, and translation all had AI contributions and likely affect most of human-generated text on the internet. The heuristic of “one drop of AI” is not useful. And any heuristic more complicated than “one drop” introduces too much subjective complexity for a Boolean data type.
Any current technology which can used to accurately detect pre-AI content would necessarily imply that that same technology could be used to train an AI to generate content that could skirt by the AI detector. Sure, there is going to be a lag time, but eventually we will run out of non-AI content.
It's just not accurate to say they only produce shit. Their rapid adoption demonstrates otherwise.
It may be the case that the non-bad things B does outweigh the bad things. That would be an argument in favor of B. The another group doing bad things has no bearing on the justification for B itself.
They also consume it.