It does, but in my experience, it’s way worse for recollection.
Electronic devices are superior when it comes to storing and organizing data, which makes it a better tool if you prefer to use active recall as a memorisation method.
I had literal books worth of notes until switching to a tablet (a stylus keeps the benefits of writing, btw). And going over them when preparing for exams was an absolute nightmare.
A little background info:
Russia’s been sponsoring one of its oligarchs’ business by eliminating their competition.
First, they restricted YouTube’s speed to an unusable state to force people to switch to RuTube (they didn’t)
Now they’re trying to force people to switch from WhatsApp (and potentially Telegram) to MAX, which they want to be Russia’s version of WeChat.
Add the fact that our politicians are obsessed with controlling all of the media and you’ll get the gist of it.
People don’t predict values to determine their answers to questions…
Also, it’s called neural network, not because it works exactly like neurons but because it’s somewhat similar. They don’t “run on neural networks”, they’re called like that because it’s more than one regression model where information is being passed on from one to another, sort of like a chain of neurons, but not exactly. It’s just a different name for a transformer model.
I don’t know enough to properly compare it to actual neurons, but at the very least, they seem to be significantly more deterministic and way way more complex.
Literally, go to chatgpt and try to test its common reasoning. Then try to argue with it. Open a new chat and do the exact same questions and points. You’ll see exactly what I’m talking about.
Alzheimer’s is an entirely different story, and no, it’s not stochastic. Seizures are stochastic, at least they look like that, which they may actually not be.
It’s not. It’s a math formula that predicts an output based on its parameters that it deduced from training data.
Say you have following sets of data.
We can calculate a regression model using those numbers to predict what Y would equal to if X was 4.
I won’t go into much detail, but
Y = 2 + 1x + e
e in an ideal world = 0 (which it is, in this case), that’s our model’s error, which is typically set to be within 5% or 1% (at least in econometrics). b0 = 2, this is our model’s bias. And b1 = 1, this is our parameter that determines how much of an input X does when predicting Y.
If x = 4, then
Y = 2 + 1×4 + 0 = 6
Our model just predicted that if X is 4, then Y is 6.
In a nutshell, that’s what AI does, but instead of numbers, it’s tokens (think symbols, words, pixels), and the formula is much much more complex.
This isn’t intelligence and not deduction. It’s only prediction. This is the reason why AI often fails at common sense. The error builds up, and you end up with nonsense, and since it’s not thinking, it will be just as confidently incorrect as it would be if it was correct.
Companies calling it “AI” is pure marketing.
The “why” goes a little further than that.
In actuality, it’s because of fundamental properties of operations
a + b = b + a
a×b = b×a
(a + b) + c = a + (b + c)
(a×b)×c = a×(b×c)
a + 0 = a
a×1 = a
If you know that, then PEMDAS and such are useless because they’re derived from those properties but do not fully encompass them.
Eg.
3×2×(2+2) = 3×(4+4) = 12+12 = 24
This is a correct solution that is improper if you’re strictly adhering to PEMDAS rule as I’ve done multiplication before parenthesis from right to left.
I could even go completely out of order by doing 3×2×(2+2) = 2×(6+6) and it will still be correct
I believe I’ve seen them announce that already, so, yeah