And the brain is made out of neurons that sends electric signals between them and operate muscles.
That doesnt explain how the brain think.

I’m saying that’s fine, but it should be able to reason that it doesn’t know the answer, and say that.

That is of course a big problem. They try to guess too much stuff, but it’s also why it kinda works. Symbolics AI have the opposite problem, they are rarely useful, because they can’t guess stuff, they are rooted in hard logic, and cannot come up with a reasonable guess.
Now humans also try to guess stuff and sometimes get it wrong, it’s required in order to produce results from our thinking and not be stuck in a state where we don’t have enough data to do anything, like a symbolic AI.

Now, this is becoming a spectrum, humans are somewhere in the middle of LLMs and symbolics AI.
LLMs are not completely unable to say what they know and doesnt know, they are just extremely bad at it from our POV.

The probleme with “does it think” is that it doesn’t give any quantity or quality.

I think when you ask an LLM how many 'r’s there are in Strawberry and questions along this line you can see they can’t form judgments.

Like a LLMs you are making the wrong affirmation based lacking knowledge.
Current LLMs input, and output tokens, they dont ever see the individual letters, they see tokens, for straberry, they see 3 tokens:

They dont have any information on what characters are in this tokens. So they come up with something. If you learned a language only by speaking, you’ll be unable to write it down correctly (except purely phonetical systems), instead you’ll come up with what you think the word should be written.

I would also add that if you “form judgments” you probably don’t need to be reminded you formed a judgment immediately after forming one.

You come up with the judgment before you are aware of it: https://www.unsw.edu.au/newsroom/news/2019/03/our-brains-reveal-our-choices-before-were-even-aware-of-them--st

can tell it it made a mistake and it will blindly change it’s answer whether it made a mistake or not. That also doesn’t feel like it’s able to reason or make judgments.

That’s also how the brain can works, it come up with a plausible explanation after having the result.
See the experience which are spoken about here: https://www.youtube.com/watch?v=wfYbgdo8e-8

I showed the same behavior in humans of some behavior you observed in LLMs, does this means that by your definition, humans doesnt think ?

Claiming LLMs can’t think with the current informations available, and calling that not a belief, is claiming to have a response to this philosophy question.
The only sensible answer is saying you don’t know, or being aware and communicating that your statement is a belief.

No it is here because that’s what they claim.
Nobody yet know how it work, we don’t know how LLMs process information.
Anyone who claim it really think, or it isn’t thinking, is believing, this is not something the current ML field know.

You think computation is thinking ?
I asked for your definition of thinking.
The OP talked about belief, then made a statement using a word that is not precisely defined.
If you think computation is thinking then by your definition the LLM is thinking.
But that’s your definition of thinking.

I asked for your definition, I cannot prove something if we do not agree on a definition first.
You also missread what I said, I did not said AI were thinking.
The burden of proof is on the one who made an affirmation.
I’m not the one who made an affirmation which field experts doesn’t know the answer.
But depending of your definition of thinking, some can be answered.

What called my attention is that assessments of AI are becoming polarized and somewhat a matter of belief.

Proceed to write a belief as a statement in the following paragraph

If you think LLMs doesnt think (I won’t argue that they arent extremely dumb), please define what is thinking, before continuing, and if your definition of thinking doesn’t apply to humans, we won’t be able to agree.

The video very clearly answers this. Like, multiple times.

No, they made affirmation, that’s not a proof.
For the first location, they say the loss of water pression AND the sediments are due to the datacenter.
They are getting their water from a well, if a well runs out, you get more sediments.
Is this your “clear answers” ?

We know that it is a tremendous amount of water because we can estimate and we can see the data of towns literally going into extreme droughts right next to data centers.

If this come from your video again, i again doubt your statements.

Datacenters dont make water magically disapear, it have to go somewhere.
You would see a release pipe, so the water is restituted, or vapor cloud, which should be very visible.
But we dont see any vapor cloud.

AI is not the whole cloud, it’s a fraction of the cloud.
The MIT Press article is from 2022, citing 2019 data. Datacenter tech and heat reuse extremely intensified the last years, so this data is clearly out of date.

Go explain to these people why “bigger DCs are actually better”:

Tell me where there is any proof this is meta fault ? Because they are near the datacenter ? Do you have any idea of the amount of water a datacenter consume ?

Not at all. Bigger datacenter usually use lower carbon energy source, try to lower energy spent for cooling and try to recycle the heat.
New datacenter reuse more and more the heat they generate:
https://blog.equinix.com/blog/2024/06/05/what-is-data-center-heat-export-and-how-does-it-work/

Real water polluter are PFAS producer, not datacenters.

Not every datacenter is like elon datacenters.

I’m someone who use LLM, including Claude Code a lot.
But come on, this could be a tiny script that do the same, write it with claude code if you dont want to write it, instead of having claude code run each time, you’ll be deterministic this way !

Torrenting doesn’t deal well with updating files.
And you have another problem: how do you handle bad actors spamming the download ?
That’s probably why github does that.

Wasn’t worth going to movie theater for it.
Not movie quality, even worse than previous seasons i’d say.

I contributed a feature to the .NET JIT without knowing C++.
I really dont know C++, I have at most wrote 300 lines while following a tutorial 6 years ago.

Contributing to big project can earn you more recognition than doing little project from scratch.
You know JS ? Contribute to some libs.
Found a bug in chrome ? Report the bug, learn a bit of C++, and submit a patch to fix it.