![Stop telling me AI is the future [Still Vreni]](https://lemmy.world/pictrs/image/4bb4b8e1-242a-4f66-ae86-60cf2e04f996.jpeg){kind=link}
Transcript
Title text: This is how you all fucking sound
[A smug tech bro wearing a sideways cap, watch, chain around his neck stands in front of a data center by a lake with dead fish. A smoke stack blows pollution into the air]
Tech bro: AI is already here, there’s no going back.
[A smug man in a suit with cigarette in hand stands in a restaurant while two disgruntled diners cough from the smoke]
Suit: Smoking indoors is already here, there’s no going back.
[A smug man in a top hat and suit stands in a factory with two sad and dirty children]
Hat: Child labor is already here, there’s no going back.
[A smug plantation owner stands in front of a field with with two angry slaves]
Plantation owner: The Atlantic Slave trade is already here, there’s no going back.
Such a fallacy. Anything that falls under the umbrella of machine learning will contribute to future AI. We certainly won’t improve LLMs such that they become AGI, but all of it contributes.
And, whether or not future AI even uses traditional silicon computing is also irrelevant.
What matters is improved understanding of mathematics, neurons, chemistry, electronics, etc. That all happens each step of the way, even if the next technology is completely different.
All of which have absolutely nothing to do with what we are currently calling AI.
Doing with it, sure, but the creation of LLMs, and the algorithms behind them, especially the training, are what I’m talking about. It’s a lot of very impressive, complicated math
I think it’s pretty pathetic that “fuck AI” has become the trendy, cool thing. It really misses the mark. It should be fuck capitalism and the sociopathic CEOs abusing AI and shoving it down our throats. AI is not the problem.
It’s actually just a lot of pretty simple maths from decades ago, but it’s a lot of it. The big changes in those decades have been the feasibility of doing enough of that simple maths to achieve anything useful, and domain-specific network architecture stuff that’s rarely transferable, e.g. LLMs are possible because of the invention of the transformer architecture in 2017, and that’s also turned out to be useful for a few things like image generation and protein folding simulation, but not for all neural network based techniques, and then most of the things that have made successive LLMs better haven’t also been useful for the few other transformer-architecture-based neural networks. Most not-LLM AI isn’t going to be meaningfully easier to create than it would have been had the world got bored after GPT-2 and we’d only focussed on doing image and video generation.
Transformer is useful for damn near anything. At the end of the day, what we consider intelligence is the ability to predict what comes next, whether that is what our senses will tell us next or what the next hypothesis to test should be based on the data we have seen so far.
It’s not damn near anything. There’s loads of stuff that computers can do much more quickly and more accurately without it just by virtue of computers already being fast and effective at maths and obeying logic. With or without the transformer architecture, a neural network is never going to be as fast or reliable at, for example, summing a collection of numbers as just adding them would be, and loads of real-world tasks are like this, hence why we’ve built billions of computers even before the transformer architecture was invented.
Also, in particular, I didn’t say that the transformer architecture wasn’t useful for things that aren’t LLMs, I said that most of the work done specifically to improve LLMs has no applications outside LLMs, so the next big leap towards making computers intelligent isn’t helped more by working on LLMs than it would be by working on any other kind of AI.