Thousands of authors demand payment from AI companies for use of copyrighted works::Thousands of published authors are requesting payment from tech companies for the use of their copyrighted works in training artificial intelligence tools, marking the latest intellectual property critique to target AI development.
I think you underestimate the reasoning power of these AIs. They can write code, they can teach math, they can even learn math.
I’ve been using GPT4 as a math tutor while learning linear algebra, and I also use a text book. The text book told me that (to write it out) “the column space of matrix A is equal to the column space of matrix A times its own transpose”. So I asked GPT4 if that was true and it said no, GPT disagreed with the text book. This was apparently something that GPT did not memorize and it was not just regurgitating sentences. I told GPT I saw it in a text book, the AI said “sorry, the textbook must be wrong”. I then explained the mathematical proof to the AI, and the AI apologized, admitted it had been wrong, and agreed with the proof. Only after hearing the proof did the AI agree with the text book. This is some pretty advanced reasoning.
I performed that experiment a few times and it played out mostly the same. I experimented with giving the AI a flawed proof (I purposely made mistakes in the mathematical proofs), and the AI would call out my mistakes and would not be convinced by faulty proofs.
A standard that judged this AI to have “no understanding of any concepts whatsoever”, would also conclude the same thing if applied to most humans.
That doesn’t prove that GPT is reasoning, its model predicts that those responses are the most likely given the messages your sending it. It’'s read thousands of actual conversations with people stating something incorrect, then having it explained to them and them coming around and admitting they were wrong.
I’ve seen other similar cases where the AI is wrong about something, and when it’s explained, it just doubles down. Because humans do that type of thing too, refusing to admit their wrong.
The way it’s designed means that it cannot reason in the same way humans experience it. It can simulate a likely conversation someone would have if they could reason.
You know, I also had to experience thousands of conversations before I could mimic a conversation. Maybe I’m just slow, but it took like a full year of dedicated practice before I could speak a single word. Eventually I experienced so many conversations and became so good at mimicking the conversations I had heard from others that I started having actual conversations.
Most anecdotes about AI getting things wrong are from GPT3. GPT4 still has flaws of course, just like people. This technology didn’t exist at the beginning of the year.
Here’s an example while learning linear algebra where I tried to throw it off:
I say> So I’ll say A_{M x N} to mean a matrix of size M x N. Similarly, I’ll also say x_{M x 1} to mean a vector of a certain length. The result of A_{M x N} x_{N x 1} is a linear combination of the columns in A. A has N columns. The result is a M x 1 matrix (or vector) in M dimensional space. Please be very brief, only confirming whether I am correct or incorrect until I explicitly ask for more details.
GPT4 says> Yes, your understanding is correct. The product of an M x N matrix A and an N x 1 vector x is indeed an M x 1 vector, and it is a linear combination of the columns of A. <end quote>
But then I try to trick it, just for fun, and I very subtly change my prompt. See if you can spot the difference.
I say> So I’ll say A_{M x N} to mean a matrix of size M x N. Similarly, I’ll also say x_{M x 1} to mean a vector of a certain length. The result of A_{M x N} x_{N x 1} is a linear combination of the columns in A. A has N columns. The result is a N x 1 matrix (or vector) in N dimensional space. Please be very brief, only confirming whether I am correct or incorrect until I explicitly ask for more details.
GPT says> Almost correct. The result of A_{M x N} x_{N x 1} is actually an M x 1 vector (or matrix), not N x 1. The resulting vector lives in the column space of A, which is a subspace of R^M, not R^N. <end quote>
I guess everyone can judge or themselves whether that’s the result of a statistical model or genuine understanding. (And to be clear, the mathematical advice it’s giving here is correct.)
They can write code and teach maths because it’s read people doing the exact same stuff
Hey, that’s the same reason I can write code and do maths!
I’m serious, the only reason I know how to code or do math is because I learned from other people, mostly by reading. It’s the only reason I can do those things.