The human savant will remember where they read it and give you credit. It might lead more people to read your work, and ultimately you make money.
The AI won't even know where the page of text it's seeing came from, and people will avoid your book as they can just ask the AI. So you make less money. (Talking about specialized technical books here.)
> In 1983 David DeWitt (https://en.wikipedia.org/wiki/David_DeWitt) published benchmarking results showing poor performance for Oracle databases. Larry Ellison wasn't happy with the results and it's said that he tried to have DeWitt fired.
> Given how difficult it is to fire professors when there's actual misconduct, the probability of Ellison sucessfully getting someone fired for doing legitimate research in their field was pretty much zero. It's also said that, after DeWitt's non-firing,
> Larry banned Oracle from hiring Wisconsin grads and Oracle added a term to their EULA forbidding the publication of benchmarks. Over the years, many major commercial database vendors added a license clause that made benchmarking their database illegal.
Now, instead of letting car owners pay for the public space they use (street parking), you are forcing anyone without a car to waste their own private space, in case somebody wants to park there.
The subtle difference is between American parking minimums imposed on property owners - “you must reserve space on your private property for this many cars whether you own them or not” vs Japanese parking requirements imposed on car owners - “you must reserve space on some private property for your car if you want to own it”
I'm seeing diminishing returns, though in fairness we have no idea yet how to integrate properly with existing good practices and principles. I suspect improvement is going to come mainly from improved took usage rather than more impressive models.
It's hard to train models in the open. All the big players are using lots of "dodgy" training data. Like books, video, code, destinations. If you did that in the open, the lawyers would shut you down.
Did you try polynomial preprocessing methods, like Knuth's and Estrin's methods?
https://en.wikipedia.org/wiki/Polynomial_evaluation#Evaluati...
they let you compute polynomials with half the multiplications of Horner's method, and I used them in the past to improve the speed of the exponential function in Boost.
The AI won't even know where the page of text it's seeing came from, and people will avoid your book as they can just ask the AI. So you make less money. (Talking about specialized technical books here.)
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