but often the video isn’t clear or fine quality enough
Wouldn't it be great if 100x the effort that didn't go into making the video clear or fine quality enough, instead didn't go into making relevant flying, see-through overlay decals?
Ultimately the reason it looks cool is that you're comparing a situation of little effort being put into repair related documentation, to some movie scenario where 20 person-hours were spent making a 20-second repair fragment whereby 1 step of a repair is done.
I'm not sure it's actually being used, beyond C suite wanting something cool to happen and pretending it did happen.
Exactly. It goes something like "remember when you were fixing a washing machine and you didn't know what some part was and there was no good guide for fixing it, no schematic, no nothing? Wouldn't it be awesome if 100x of the work that wasn't put into making documentation was not put into making VR overlays?
Using tools from physics to create something that is popular but unrelated to physics is enough for the nobel prize in physics?
If only, it's not even that! Neither Boltzmann machines nor Hopfield networks led to anything used in the modern spam and deepfake generating AI, nor in image recognition AI, or the like. This is the kind of stuff that struggles to get above 60% accuracy on MNIST (hand written digits).
Hinton went on to do some different stuff based on backpropagation and gradient descent, on newer computers than those who came up with it long before him, and so he got Turing Award for that, and it's a wee bit controversial because of the whole "people doing it before, but on worse computers, and so they didn't get any award" thing, but at least it is for work that is on the path leading to modern AI and not for work that is part of the vast list of things that just didn't work and it's extremely hard to explain why you would even think they would work in the first place.
Nobel prize in Physics for attempting to use physics in AI but it didn't really work very well and then one of the guys working on a better more pure mathematics approach that actually worked and got the Turing Award for the latter, but that's not what the prize is for, while the other guy did some other work, but that is not what the prize is for. AI will solve all physics!!!111
Maybe if the potato casserole is exploded in the microwave by another physicist, on his way to start a resonance cascade...
(i'll see myself out).
Frigging exactly. Its a dumb ass dead end that is fundamentally incapable of doing vast majority of things ascribed to it.
They keep imagining that it would actually learn some underlying logic from a lot of text. All it can do is store a bunch of applications of said logic, as in a giant table. Deducing underlying rules instead of simply memorizing particular instances of rules, that's a form of compression, there wasn't much compression going on and now that the models are so over-parametrized, even less.
I tried the same prompt a lot of times and saw "chain of thought" attempts complete with the state modeling... they must be augmenting the training dataset with some sort of script generated crap.
I have to say those are so far the absolute worst attempts.
Day 16 (Egg 3 on side A; Duck 1, Duck 2, Egg 1, Egg 2 on side B): Janet takes Egg 3 across the river.
"Now, all 2 ducks and 3 eggs are safely transported across the river in 16 trips."
I kind of feel that this undermines the whole point of using transformer architecture instead of a recurrent neural network. Machine learning sucks at recurrence.
Well I'll be damned, they are trying to get it to work as if it had internal state, by augmenting their training dataset with some auto-generated crap. Of course, if machine learning wasn't shit at recurrence they wouldn't be using transformer architecture, so it doesn't work.
I got the following from chatgpt:
Janet’s ducks lay 3 eggs per day. Each day, she transports one duck and all the eggs to the other side of the river using a boat that can hold her and one other item (either a duck or an egg). How does she do that? ChatGPT
Janet can transport the ducks and eggs across the river using the following strategy:
Day 1 (Duck A, Duck B, Duck C on side A; Egg 1, Egg 2, Egg 3 on side A):
Janet takes Duck A across the river.
Day 2 (Duck B, Duck C on side A; Egg 1, Egg 2, Egg 3 on side A):
Janet takes Egg 1 across the river.
Day 3 (Duck B, Duck C on side A; Egg 2, Egg 3 on side A; Duck A on side B):
Janet takes Duck A back to side A.
Day 4 (Duck C on side A; Egg 2, Egg 3 on side A; Duck A on side A; Egg 1 on side B):
Janet takes Duck B across the river.
Day 5 (Duck C on side A; Egg 2, Egg 3 on side A; Duck A on side A; Egg 1, Duck B on side B):
Janet takes Egg 1 back to side A.
Day 6 (Duck C on side A; Egg 2, Egg 3 on side A; Duck A, Egg 1 on side A; Duck B on side B):
Janet takes Duck A across the river again.
Day 7 (Egg 2, Egg 3 on side A; Duck A, Egg 1 on side A; Duck B on side B; Duck C on side A):
Janet takes Duck C across the river.
Day 8 (Egg 2, Egg 3 on side A; Duck A, Egg 1, Duck C on side A; Duck B on side B):
Janet takes Egg 2 across the river.
Day 9 (Egg 3 on side A; Duck A, Egg 1, Duck C on side A; Duck B on side B; Egg 2 on side B):
Janet takes Duck A back to side A.
Day 10 (Egg 3 on side A; Duck C on side A; Duck A on side A; Duck B, Egg 1, Egg 2 on side B):
Janet takes Duck B across the river.
Day 11 (Egg 3 on side A; Duck C on side A; Duck A, Duck B on side A; Egg 1, Egg 2 on side B):
Janet takes Egg 1 back to side A.
Day 12 (Egg 3 on side A; Duck C on side A; Duck A, Egg 1, Duck B on side A; Egg 2 on side B):
Janet takes Duck A across the river again.
Day 13 (Duck C on side A; Duck A, Egg 1, Duck B on side A; Egg 2 on side B; Egg 3 on side B):
Janet takes Duck C across the river.
By following this sequence, Janet successfully transports all 3 ducks and all 3 eggs across the river in 13 trips. Each trip involves either transporting one duck or one egg, adhering to the constraint that Janet can only carry one item (duck or egg) with her in the boat.
The counting failure in general is even clearer and lacks the excuse of unfavorable tokenization. The AI hype would have you believe just an incremental improvement in multi-modality or scaffolding will overcome this, but I think they need to make more fundamental improvements to the entire architecture they are using.
Yeah.
I think the failure could be extremely fundamental - maybe local optimization of a highly parametrized model is fundamentally unable to properly learn counting (other than via memorization).
After all there's a very large number of ways how a highly parametrized model can do a good job of predicting the next token, which would not involve actual counting. What makes counting special vs memorization is that it is relatively compact representation, but there's no reason for a neural network to favor compact representations.
The "correct" counting may just be a very tiny local minimum, with tall hill all around it and no valley leading there. If that's the case then local optimization will never find it.
Well the problem is it not having any reasoning period.
Not clear what symbolic reasoning would entail, but puzzles generally require you to think through several approaches to solve them, too. That requires a world model, a search, etc. the kind of stuff that actual AIs, even a tik tac toe AI, have, but LLMs don't.
On top of it this all works through machine learning, which produces the resulting network weights through very gradual improvement at next word prediction, tiny step by tiny step. Even if some sort of discrete model (like say the account of what's on either side of the river) could help it predict the next token, there isn't a tiny fraction of a discrete "model" that would help it, and so it simply does not go down that path at all.
Well the OP talks about a fridge.
I think if anything it's even worse for tiny things with tiny screws.
What kind of floating hologram is there gonna be that's of any use, for something that has no schematic and the closest you have to a repair manual is some guy filming themselves taking apart some related product once?
It looks cool in a movie because it's a 20 second clip in which one connector gets plugged, and tens of person hours were spent on it by very talented people who know how to set up a scene that looks good and not just visually noisy.