TSMC has a factory in the USA now, ASML too. OpenAI, Google, xAI and Nvidia are natively in the USA.
While no other country is even close to build AI on their own.
Is the USA going to "own" the world by becoming the keeper of AI? Or is there an alternative future that has a probability > 0?
So far, I haven't seen any indication that this is the case. And I'd say, hyped up speculations by people financially incentivized to hype AI should be taken with an entire mine full of salt.
Second, how could AI not be the deciding geopolitical factor of the future? You expect progress to stop and AI not to achieve and surpass human intelligence?
A word generator is not intelligence. There’s no “thinking” involved here.
To surpass human intelligence, you’d first need to actually develop intelligence, and llms will not be it.
But how are animals with nerve-centres or brains different? What do we think us humans do differently so we are not just very big probabilistic prediction systems?
A completely different tack: if we develop the technology to engineer animal-style nerves and form them into big lumps called 'brains', in what way is that not artificial and intelligence? And if we can do that, what is to stop that manufactured brain from not being twice or ten times larger than a humans?
People think LLMs are intelligent because intelligence is latent within the text they digest, process and regurgitate. Their performance reflects this trick.
Reasoning models are trying to address that now, but monologueing in token-space still feels more like a hack than a real solution, but it does improve their performance a good bit nonetheless.
In practical terms all this means is that current LLMs still need a hell of a lot of hand holding and fail at anything more complex, even if their "System 1" thinking is good enough for the task (e.g. they can write Tetris in 30sec no problem, but they can't write SuperMarioBros at all, since that has numerous levels that would blow the context window size).
In current LLM neural networks, the signal proceeds in one direction, from input, through the layers, to output. To the extend that LLM's have memory and feedback loops, it's that they write the output of the process to text, and then read that text and process it again though their unidirectional calculations.
Animal brains have circular signals and feedback loops.
There are Recurrent Neural Network (RNN) architectures, but current LLM's are not these.
I see this statement thrown around a lot and I don't understand why. We don't process information like computers do. We don't learn like they do, either. We have huge portions of our brains dedicated to communication and problem solving. Clearly we're not stochastic parrots.
> if we develop the technology to engineer animal-style nerves and form them into big lumps called 'brains'
I think y'all vastly underestimate how complex and difficult a task this is.
It's not even "draw a circle, draw the rest of the owl", it's "draw a circle, build the rest of the Dyson sphere".
It's easy to _say_ it, it's easy to picture it, but actually doing it? We're basically at zero.
On Internet comment sections, that's not clear to me. Memes are incredibly infectious, and we can see by looking at, say, a thread about Nvidia. It's inevitable that someone is going to ask about a moat. In a thread about LLMs, the likelihood of stoichastic parrots getting a mention approaches one, as the thread gets longer. what does it all mean?
If every single human on earth was an identical clone with the same cultural upbringing and similar language conversation choices and opinions and feelings, they still wouldn't work like an LLM and still wouldn't be stochastic parots.