Comment by CamperBob2
1 month ago
OK, now let's talk about what it means to "understand" something.
Let's say a kid who's not unusually gifted/talented at math somehow ends up at the International Math Olympiad. Smart-enough kid, regularly gets 4.0+ grades in normal high school classes, but today Timmy got on the wrong bus. He does have a great calculator in his backpack -- heck, we'll give him a laptop with Mathematica installed -- so he figures, why not, I'll take the test and see how it goes. Spoiler: he doesn't do so well. He has the tools, but he lacks understanding of how and when to apply them.
At the same time, the kid at the next desk also doesn't understand what's going on. She's a bright kid from a talented family -- in fact Alice's old man works for OpenAI -- but she's a bit absent-minded. Alice not only took the wrong bus this morning, but she grabbed the wrong laptop on the way out the door. She shrugs, types in the problems, and copies down what she sees on the screen. She finishes up, turns in the paper, and they give her a gold medal.
My point: any definition of "understanding" you can provide is worthless unless it can somehow account for the two kids' different experiences. One of them has a calculator that does math, the other has a calculator that understands math.
I very much doubt that such a discovery will happen in our lifetime.
So did I, and then AlphaGo happened, and IMO a few years later. At that point I realized I wasn't very good at predicting what was and was not going to be possible, so I stopped trying.
Calculators do not understand math, while both kids understand each other and the world around them. The calculator relies on an external intelligence.
Don't stop trying. Predictability is an indicator of how well a theory describes the universe. That's what science is.
The engineers have long predicted this stuff. LLM tech isn't really new. The size and speed of the machines is new. The more you understand about a topic, the better your predictions.
The more you understand about a topic, the better your predictions.
Indeed.
I'm not sure what your level of expertise is with software but I got a lot out of some free tutorials on developing your own LLM and on ML. These are even available, free, directly from Google among many other sources.
I feel that my expectations surrounding "AI" are much more realistic than they were before building the tools.
If you haven't already, it's very much worth giving them a run through.