Bayesian purist cope and seeth.
Most machine learning is closer to universal function approximation via autodifferentiation. Backpropagation just lets you create numerical models with insane parameter dimensionality.
Universal function approximation - neural networks.
Auto-differentiation - algorithmic calculation of partial derivatives (aka gradients)
Backpropagation - when using a neural network (or most ML algorithms actually), you find the difference between model prediction and original labels. And the difference is sent back as gradients (of the loss function)
Parameter dimensionality - the “neurons” in the neural network, ie, the weight matrices.
If thats your argument, its worse than Statistics imo. Atleast statistics have solid theorems and proofs (albeit in very controlled distributions). All DL has right now is a bunch of papers published most often by large tech companies which may/may not work for the problem you’re working on.
Universal function approximation theorem is pretty dope tho. Im not saying ML isn’t interesting, some part of it is but most of it is meh. It’s fine.
No, no, everyone knows that a monad is like a burrito.
(Joke is referencing this: https://blog.plover.com/prog/burritos.html )
So what you’re saying is that if you put a burrito inside a burrito it’s still a burrito?
Eh. Even heat is a statistical phenomenon, at some reference frame or another. I’ve developed model-dependent apathy.
Ftfy
The meme would work just the same with the “machine learning” label replaced with “human cognition.”
Have to say that I love how this idea congealed into “popular fact” as soon as peoples paychecks started relying on massive investor buy in to LLMs.
I have a hard time believing that anyone truly convinced that humans operate as stochastic parrots or statistical analysis engines has any significant experience interacting with others human beings.
Less dismissively, are there any studies that actually support this concept?
Speaking as someone whose professional life depends on an understanding of human thoughts, feelings and sensations, I can’t help but have an opinion on this.
To offer an illustrative example
When I’m writing feedback for my students, which is a repetitive task with individual elements, it’s original and different every time.
And yet, anyone reading it would soon learn to recognise my style same as they could learn to recognise someone else’s or how many people have learned to spot text written by AI already.
I think it’s fair to say that this is because we do have a similar system for creating text especially in response to a given prompt, just like these things called AI. This is why people who read a lot develop their writing skills and style.
But, really significant, that’s not all I have. There’s so much more than that going on in a person.
So you’re both right in a way I’d say. This is how humans develop their individual style of expression, through data collection and stochastic methods, happening outside of awareness. As you suggest, just because humans can do this doesn’t mean the two structures are the same.
Idk. There’s something going on in how humans learn which is probably fundamentally different from current ML models.
Sure, humans learn from observing their environments, but they generally don’t need millions of examples to figure something out. They’ve got some kind of heuristics or other ways of learning things that lets them understand many things after seeing them just a few times or even once.
Most of the progress in ML models in recent years has been the discovery that you can get massive improvements with current models by just feeding them more and data. Essentially brute force. But there’s a limit to that, either because there might be a theoretical point where the gains stop, or the more practical issue of only having so much data and compute resources.
There’s almost certainly going to need to be some kind of breakthrough before we’re able to get meaningful further than we are now, let alone matching up to human cognition.
At least, that’s how I understand it from the classes I took in grad school. I’m not an expert by any means.
The big difference between people and LLMs is that an LLM is static. It goes through a learning (training) phase as a singular event. Then going forward it’s locked into that state with no additional learning.
A person is constantly learning. Every moment of every second we have a ton of input feeding into our brains as well as a feedback loop within the mind itself. This creates an incredibly unique system that has never yet been replicated by computers. It makes our brains a dynamic engine as opposed to the static and locked state of an LLM.
I’d love to hear about any studies explaining the mechanism of human cognition.
Right now it’s looking pretty neural-net-like to me. That’s kind of where we got the idea for neural nets from in the first place.
It’s not specifically related, but biological neurons and artificial neurons are quite different in how they function. Neural nets are a crude approximation of the biological version. Doesn’t mean they can’t solve similar problems or achieve similar levels of cognition , just that about the only similarity they have is “network of input/output things”.
At every step of modern computing people have thought that the human brain looks like the latest new thing. This is no different.
Ehhh… It depends on what you mean by human cognition. Usually when tech people are talking about cognition, they’re just talking about a specific cognitive process in neurology.
Tech enthusiasts tend to present human cognition in a reductive manor that for the most part only focuses on the central nervous system. When in reality human cognition includes anyway we interact with the physical world or metaphysical concepts.
There’s something called the mind body problem that’s been mostly a philosophical concept for a long time, but is currently influencing work in medicine and in tech to a lesser degree.
Basically, it questions if it’s appropriate to delineate the mind from the body when it comes to consciousness. There’s a lot of evidence to suggest that that mental phenomenon are a subset of physical phenomenon. Meaning that cognition is reliant on actual physical interactions with our surroundings to develop.
If by “human cognition” you mean "tens of millions of improvised people manually checking and labeling images and text so that the AI can pretend to exist," then yes.
If you mean “it’s a living, thinking being,” then no.
There’s a lot we understand about the brain, but there is so much more we dont understand about the brain and “awareness” in general. It may not be magic, but it certainly isnt 100% understood.
(working with the assumption we mean stuff like ChatGPT) mKay… Tho math and logic is A LOT more than just statistics. At no point did we prove that statistics alone is enough to reach the point of cognition. I’d argue no statistical model can ever reach cognition, simply because it averages too much. The input we train it on is also fundamentally flawed. Feeding it only text skips the entire thinking and processing step of creating an answer. It literally just take texts and predicts on previous answers what’s the most likely text. It’s literally incapable of generating or reasoning in any other way then was already spelled out somewhere in the dataset. At BEST, it’s a chat simulator (or dare I say…language model?), it’s nowhere near an inteligence emulator in any capacity.
This is exactly how I explain the AI (ie what the current AI buzzword refers to) tob common folk.
And what that means in terms of use cases.
When you indiscriminately take human outputs (knowledge? opinions? excrements?) as an input, an average is just a shitty approximation of pleb opinion.
