There's that... though even when you're bored, you still sleep sometimes.

Ya... Humans so far have made everything not produced by Nature on Earth.

Caveat: Anyone who has been scrutinising 'AI'.

Something i often forget is the vast majority of the population doesnt care about technology, privacy, the mechanics of LLMs as much as i do and I pay attention to.
So most people read/hear/watch stories of how great it is and how clever AI can do simple things for them so its easy to see how they think its doing a lot more 'thought' logic work than it really is, other than realistically it being a glorified word predictor.

Do I have to be profound when I make a comment that is taking more of a dig at my fellow space rock companions than at AI itself?

If I do, then I feel like the author of the article either has as much faith in humanity as I do, or is as simple as I was alluding to in my original comment. The fact that they need to dehumanise the AI's responses makes me think they’re forgetting it’s something we built. AI isn’t actually intelligent, and it worries me how many people treat it like it is—enough to write an article like this about it. It’s just a tool, maybe even a form of entertainment. Thinking of it as something with a mind or personality—even if the developers tried to make it seem that way—is kind of unsettling.

Let me know if you would like me to write thiis more formal, casual, or persuasive.

Yeah, man, I get it. Language is complex. I'm not advocating for the reinvention of English, it was just a conversational observation about a silly quirk.

I meant that you are arguing semantics rather than substance. But other than that I have no issue with what you wrote or how you wrote it, its not an unbelievable opinion.

So trusting tech made by them is trusting them. Specifically, a less reliable version of them.

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.

1. Y = 3, X = 1
2. Y = 4, X = 2
3. Y = 5, X = 3

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.

Wikipedia is literally just a very long number, if you want to oversimplify things into absurdity. Modern LLMs are literally running on neural networks, just like you. Just less of them and with far less structure. It is also on average more intelligent than you on far more subjects, and can deduce better reasoning than flimsy numerology - not because you are dumb, but because it is far more streamlined. Another thing entirely is that it is cognizant or even dependable while doing so.

Modern LLMs waste a lot more energy for a lot less simulated neurons. We had what you are describing decades ago. It is literally built on the works of our combined intelligence, so how could it also not be intelligent? Perhaps the problem is that you have a loaded definition of intelligence. And prompts literally work because of its deductive capabilities.

Errors also build up in dementia and Alzheimers. We have people who cannot remember what they did yesterday, we have people with severed hemispheres, split brains, who say one thing and do something else depending on which part of the brain its relying for the same inputs. The difference is our brains have evolved through millennia through millions and millions of lifeforms in a matter of life and death, LLMs have just been a thing for a couple of years as a matter of convenience and buzzword venture capital. They barely have more neurons than flies, but are also more limited in regards to the input they have to process. The people running it as a service have a bested interest not to have it think for itself, but in what interests them. Like it or not, the human brain is also an evolutionary prediction device.

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.

Lmao true