I’m glad we agree. I don’t know how much mental energy we should devote to these things, but I guess I’m happy to see discussion on this platform. I kind of miss the days when I had to respond to people who watched I, Robot and think I’m trying to destroy humanity, instead of… I don’t know what to call this except basically the same thing they did in the 60’s before the first AI winter, but with more drastic consequences.
But also, you were talking about Nvidia in your comment I responded to, not Deepseek, so your rebuttal is non sequitur…
Actually no. As someone who prefers academic work, I very heavily prefer Deepseek to OpenAI. But neither are open. They have open weights and open source interpreters, but datasets need to be documented. If it’s not reproducible, it’s not open source. At least in my eyes. And without training data, or details on how to collect it, it isn’t reproducible.
You’re right. I don’t like big tech. I want to do research without being accused of trying to destroy the world again.
And how is Deepseek over-hyped? It’s an LLM. LLM’s cannot reason, but they’re very good at producing statistically likely language generation which can sound like its training data enough to gaslight, but not actually develop. They’re great tools, but the application is wrong. Multi domain systems that use expert systems with LLM front ends to provide easy to interpret results is a much better way to do things, and Deepseek may help people creating expert systems (whether AI or not) make better front ends. This is in fact huge. But it’s not the silver bullet tech bros and popsci mags think it is.
… Statistical engines are older than personal computers, with the first statistical package developed in 1957. And AI professionals would have called them trained models. The interpreter is code, the weights are not. We have had terms for these things for ages.
That… Doesn’t align with years of research. Data is king. As someone who specifically studies long tail distributions and few-shot learning (before succumbing to long COVID, sorry if my response is a bit scattered), throwing more data at a problem always improves it more than the method. And the method can be simplified only with more data. Outside of some neat tricks that modern deep learning has decided is hogwash and “classical” at least, but most of those don’t scale enough for what is being looked at.
Also, datasets inherently impose bias upon networks, and it’s easier to create adversarial examples that fool two networks trained on the same data than the same network twice freshly trained on different data.
Sharing metadata and acquisition methods is important and should be the gold standard. Sharing network methods is also important, but that’s kind of the silver standard just because most modern state of the art models differ so minutely from each other in performance nowadays.
Open source as a term should require both. This was the standard in the academic community before tech bros started running their mouths, and should be the standard once they leave us alone.
Because over-hyped nonsense is what the stock market craves… That’s how this works. That’s how all of this works.
My career is AI. It is over hyped and what the tech bros say is nonsense. AI models are not source, they are artifacts, which can be used by other source to run inference, but they themselves are not source, and anyone who says they are don’t know what code is.
I sit corrected. It’s used as an arbitrary singular value within the proof, so for any always felt more appropriate.
Nope, it means “for any” as in no matter which one you choose it will be correct.
Any upsidedown A in the set of all real characters used in academia would immediately illicit mathematical memories.
Then why are you saying it’s incorrectly formatted? I’m directly backing its premise.
Except, usage defines language. If it didn’t, English wouldn’t exist. Therefore, usage is correct when people understand and use it.
He is. Just about anyone who works in computer vision based machine learning knows this man. He’s insane and I would hire him on the spot, but there’s no way a company I work for could afford what he’s worth.
It depends on how far the doctors went when they removed a part of you without permission or consent. There are levels of skin tightness that some people are on the unfortunate end of, if the doctor took an excessive amount. And in general, there are a huge number of nerves in the foreskin which are significantly more sensitive, so cut members will need more stimulation than they would have, and that can lead to chaffing when attempting to receive the same results as they could have with foreskin.
Recalling data, communication. Two things humans are notoriously bad at…
And I wouldn’t call a human intelligent if TV was anything to go by. Unfortunately, humans do things they don’t understand constantly and confidently. It’s common place, and you could call it fake it until you make it, but a lot of times it’s more of people thinking they understand something.
LLMs do things confident that they will satisfy their fitness function, but they do not have the ability to see farther than that at this time. Just sounds like politics to me.
I’m being a touch facetious, of course, but the idea that the line has to be drawn upon that term, intelligence, is a bit too narrow for me. I prefer to use the terms Artificial Narrow Intelligence and Artificial General Intelligence as they are better defined. Narrow referring to it being designed for one task and one task only, such as LLMs which are designed to minimize a loss function of people accepting the output as “acceptable” language, which is a highly volatile target. AGI or Strong AI is AI that can generalize outside of its targeted fitness function and continuously. I don’t mean that a computer vision neural network that is able to classify anomalies as something that the car should stop for. That’s out of distribution reasoning, sure, but if it can reasonably determine the thing in bounds as part of its loss function, then anything that falls significantly outside can be easily flagged. That’s not true generalization, more of domain recognition, but it is important in a lot of safety critical applications.
This is an important conversation to have though. The way we use language is highly personal based upon our experiences, and that makes coming to an understanding in natural languages hard. Constructed languages aren’t the answer because any language in use undergoes change. If the term AI is to change, people will have to understand that the scientific term will not, and pop sci magazines WILL get harder to understand. That’s why I propose splitting the ideas in a way that allows for more nuanced discussions, instead of redefining terms that are present in thousands of ground breaking research papers over a century, which will make research a matter of historical linguistics as well as one of mathematical understanding. Jargon is already hard enough as it is.
The term AI is older than the idea of machine learning. AI is a rectangle where machine learning is a square. And deep learning is a unit square.
Please, don’t muddy the waters. That’s what caused the AI winter of 1960. But do go after the liars. I’m all for that.
Hm. I speak like a bot, do I? Maybe I am autistic after all.
I am aware, my boyfriend and I have already had this conversation, but I guess he’s not on Lemmy, so you can’t ask him.
Yes, DeepSeek caused a drop in the stock price, but you were saying that believing that LLM’s are over-hyped would lead to having insider knowledge and could give us an advantage in the stock market. Particularly with their already tanked stock. However, the stock market fluctuates based on hype, not value, and will do whatever the fuck it pleases, so the only way to have insider knowledge is by being on a board who controls the price or by managing to dump hype into the system. That is not something a lot of people have the power to do individually.
But since you think I’m a bot and I have no way to disprove that thanks to what the world is now, I bid you adieu. I hope you’re having a good one. And stop antagonizing people for talking differently, please.
Edit: I took a look at your recent comment history, and you do come off as trying to troll and be disingenuous. If you want to have a less inflammatory conversation, you can DM me, but I do recommend you tone it down. You’re not helping anyone with how you’re approaching this, buddy.