AI Expert Warns Crash Is Imminent As AI Improvements Hit Brick Wall
AI researcher Gary Marcus warns that recent signs that AI improvements are stagnating could signal a crash in the industry in the future.
"LLMs such as they are, will become a commodity; price wars will keep revenue low. Given the cost of chips, profits will be elusive," Marcus predicts. "When everyone realizes this, the financial bubble may burst quickly."
Please let this happen
Market crash and third world war. What a time to be alive!
I wish just once we could have some kind of tech innovation without a bunch of douchebag techbros thinking it's going to solve all the world's problems with no side effects while they get super rich off it.
... bunch of douchebag techbros thinking it's going to solve all the world's problems with no side effects...
one doesn't imagine any of them even remotely thinks a technological panacaea is feasible.
... while they get super rich off it.
because they're only focusing on this.
Oh they definitely exist. At a high level the bullshit is driven by malicious greed, but there are also people who are naive and ignorant and hopeful enough to hear that drivel and truly believe in it.
Like when Microsoft shoves GPT4 into notepad.exe. Obviously a terrible terrible product from a UX/CX perspective. But also, extremely expensive for Microsoft right? They don't gain anything by stuffing their products with useless annoying features that eat expensive cloud compute like a kid eats candy. That only happens because their management people truly believe, honest to god, that this is a sound business strategy, which would only be the case if they are completely misunderstanding what GPT4 is and could be and actually think that future improvements would be so great that there is a path to mass monetization somehow.
True, they just sell it to their investors as a panacea
Some are just opportunists, but there are certainly true believers — either in specific technologies, or pedal-to-the-metal growth as the only rational solution to the world’s problems.
Andreessen is pretty open about it: https://a16z.com/the-techno-optimist-manifesto/
Of course most don't actually even believe it, that's just the pitch to get that VC juice. It's basically fraud all the way down.
Soooo... Without capitalism?
Pretty much.
No shit. This was obvious from day one. This was never AGI, and was never going to be AGI.
Institutional investors saw an opportunity to make a shit ton of money and pumped it up as if it was world changing. They'll dump it like they always do, it will crash, and they'll make billions in the process with absolutely no negative repercussions.
Then what is this I’m feeling if it’s not AGI? 🤔
Maybe GERD?
Turns out AI isn't real and has no fidelity.
Machine learning could be the basis of AI but is anyone even working on that when all the money is in LLMs?
I'm not an expert, but the whole basis of LLM not actually understanding words, just the likelihood of what word comes next basically seems like it's not going to help progress it to the next level... Like to be an artificial general intelligence shouldn't it know what words are?
I feel like this path is taking a brick and trying to fit it into a keyhole...
largely based on the notion that LLMs will, with continued scaling, become artificial general intelligence
Who said that LLMs were going to become AGI? LLMs as part of an AGI system makes sense but not LLMs alone becoming AGI. Only articles and blog posts from people who didn't understand the technology were making those claims. Which helped feed the hype.
I 100% agree that we're going to see an AI market correction. It's going to take a lot of hard human work to achieve the real value of LLMs. The hype is distracting from the real valuable and interesting work.
Journalists have no clue what AI even is. Nearly every article about AI is written by somebody who couldn't tell you the difference between an LLM and an AGI, and should be dismissed as spam.
The call is coming from inside. Google CEO claims it will be like alien intelligence so we should just trust it to make political decisions for us bro: https://www.computing.co.uk/news/2024/ai/former-google-ceo-eric-schmidt-urges-ai-acceleration-dismisses-climate
Do you have a non paywalled link? And is that quote in relation to LLMs specifically or AI generally?
I read a lot I guess, and I didn’t understand why they think like this. From what I see, are constant improvements in MANY areas! Language models are getting faster and more efficient. Code is getting better across the board as people use it to improve their own, contributing to the whole of code improvements and project participation and development. I feel like we really are at the beginning of a lot of better things and it’s iterative as it progresses. I feel hopeful
It's so funny how all this is only a problem within a capitalist frame of reference.
What they call "AI" is only "intelligent" within a capitalist frame of reference, too.
I don't understand why you're being downvoted. Current "AI" based on LLM's have no capacity for understanding of the knowledge they contain (hence all the "hallucinations"), and thus possess no meaningful intelligence. To call it intelligent is purely marketing.
Thank fuck. Can we have cheaper graphics cards again please?
I'm sure a RTX 4090 is very impressive, but it's not £1800 impressive.
Just wait for the 5090 prices...
I just don't get whey they're so desperate to cripple the low end cards.
Like I'm sure the low RAM and speed is fine at 1080p, but my brother in Christ it is 2024. 4K displays have been standard for a decade. I'm not sure when PC gamers went from "behold thine might from thou potato boxes" to "I guess I'll play at 1080p with upscaling if I can have a nice reflection".
Sorry, crypto is back in season.
nope, if normal gamers are already willing to pay that price, no reason for nvidia to reduce them.
There's more 4090 on steam than any AMD dedicated GPU, there's no competition
AMD will go back to the same strategy they had with the RX 580. They don't plan to release high end cards next generation. It seems they just want to pump out a higher volume of mid-tier (which is vague and subjective) while fixing hardware bugs plaguing the previous generation.
Hopefully, this means we can game on a budget while AMD is focusing primarily on marketshare.
Oh no!
Anyway...
I've been hearing about the imminent crash for the last two years. New money keeps getting injected into the system. The bubble can't deflate while both the public and private sector have an unlimited lung capacity to keep puffing into it. FFS, bitcoin is on a tear right now, just because Trump won the election.
This bullshit isn't going away. Its only going to get forced down our throats harder and harder, until we swallow or choke on it.
Well duhhhh.
\
Language models are insufficient.
\
They also need:
Someone in here has once linked me a scientific article about how today's "AI" are basically one level below what they need to be anything like an AI. A bit like the difference between exponent and Ackermann function, but I really forgot what that was all about.
LLMs are AI. There’s a common misconception about what ‘AI’ actually means. Many people equate AI with the advanced, human-like intelligence depicted in sci-fi - like HAL 9000, JARVIS, Ava, Mother, Samantha, Skynet, and GERTY. These systems represent a type of AI called AGI (Artificial General Intelligence), designed to perform a wide range of tasks and demonstrate a form of general intelligence similar to humans.
However, AI itself doesn't imply general intelligence. Even something as simple as a chess-playing robot qualifies as AI. Although it’s a narrow AI, excelling in just one task, it still fits within the AI category. So, AI is a very broad term that covers everything from highly specialized systems to the type of advanced, adaptable intelligence that we often imagine. Think of it like the term ‘plants,’ which includes everything from grass to towering redwoods - each different, but all fitting within the same category.
The hype should go the other way. Instead of bigger and bigger models that do more and more - have smaller models that are just as effective. Get them onto personal computers; get them onto phones; get them onto Arduino minis that cost $20 - and then have those models be as good as the big LLMs and Image gen programs.
Other than with language models, this has already happened: Take a look at apps such as Merlin Bird ID (identifies birds fairly well by sound and somewhat okay visually), WhoBird (identifies birds by sound, ) Seek (visually identifies plants, fungi, insects, and animals). All of them work offline. IMO these are much better uses of ML than spammer-friendly text generation.
those are all classification problems, which is a fundamentally different kind of problem with less open-ended solutions, so it's not surprising that they are easier to train and deploy.
Platnet and iNaturalist are pretty good for plant identification as well, I use them all the time to find out what's volunteering in my garden. Just looked them up and it turns out iNaturalist is by Seek.
This has already started to happen. The new llama3.2 model is only 3.7GB and it WAAAAY faster than anything else. It can thow a wall of text at you in just a couple of seconds. You're still not running it on $20 hardware, but you no longer need a 3090 to have something useful.
Well, you see, that's the really hard part of LLMs. Getting good results is a direct function of the size of the model. The bigger the model, the more effective it can be at its task. However, there's something called compute efficient frontier (technical but neatly explained video about it). Basically you can't make a model more effective at their computations beyond said linear boundary for any given size. The only way to make a model better, is to make it larger (what most mega corps have been doing) or radically change the algorithms and method underlying the model. But the latter has been proving to be extraordinarily hard. Mostly because to understand what is going on inside the model you need to think in rather abstract and esoteric mathematical principles that bend your mind backwards. You can compress an already trained model to run on smaller hardware. But to train them, you still need the humongously large datasets and power hungry processing. This is compounded by the fact that larger and larger models are ever more expensive while providing rapidly diminishing returns. Oh, and we are quickly running out of quality usable data, so shoveling more data after a certain point starts to actually provide worse results unless you dedicate thousands of hours of human labor producing, collecting and cleaning the new data. That's all even before you have to address data poisoning, where previously LLM generated data is fed back to train a model but it is very hard to prevent it from devolving into incoherence after a couple of generations.
this is learning completely the wrong lesson. it has been well-known for a long time and very well demonstrated that smaller models trained on better-curated data can outperform larger ones trained using brute force "scaling". this idea that "bigger is better" needs to die, quickly, or else we're headed towards not only an AI winter but an even worse climate catastrophe as the energy requirements of AI inference on huge models obliterate progress on decarbonization overall.
That would be innovation, which I'm convinced no company can do anymore.
It feels like I learn that one of our modern innovations was already thought up and written down into a book in the 1950s, and just wasn't possible at that time due to some limitation in memory, precision, or some other metric. All we did was do 5 decades of marginal improvement to get to it, while not innovating much at all.
Are you talking about something specific?
Huh?
The smartphone improvements hit a rubber wall a few years ago (disregarding folding screens, that compose a small market share, improvement rate slowed down drastically), and the industry is doing fine. It's not growing like it use to, but that just means people are keeping their smartphones for longer periods of time, not that people stopped using them.
Even if AI were to completely freeze right now, people will continue using it.
Why are people reacting like AI is going to get dropped?
People are dumping billions of dollars into it, mostly power, but it cannot turn profit.
So the companies who, for example, revived a nuclear power facility in order to feed their machine with ever diminishing returns of quality output are going to shut everything down at massive losses and countless hours of human work and lifespan thrown down the drain.
This will have an economic impact quite large as many newly created jobs go up in smoke and businesses who structured around the assumption of continued availability of high end AI need to reorganize or go out of business.
Search up the Dot Com Bubble.
Because in some eyes, infinite rapid growth is the only measure of success.
People pay real money for smartphones.
People pay real Money for AIaaS as well..
It’s absurdly unprofitable. OpenAI has billions of dollars in debt. It absolutely burns through energy and requires a lot of expensive hardware. People aren’t willing to pay enough to make it break even, let alone profit
Eh, if the investment dollars start drying up, they'll likely start optimizing what they have to get more value for fewer resources. There is value in AI, I just don't think it's as high as they claim.
Training new models is expensive. Running them can be fairly cheap. So no
Because novelty is all it has. As soon as it stops improving in a way that makes people say "oh that's neat", it has to stand on the practical merits of its capabilities, which is, well, not much.
I’m so baffled by this take. “Create a terraform module that implements two S3 buckets with cross-region bidirectional replication. Include standard module files like linting rules and enable precommit.” Could I write that? Yes. But does this provide an outstanding stub to start from? Also yes.
And beyond programming, it is otherwise having positive impact on science and medicine too. I mean, anybody who doesn’t see any merit has their head in the sand. That of course must be balanced with not falling for the hype, but the merits are very real.
People differentiate AI (the technology) from AI (the product being peddled by big corporations) without making clear that nuance (Or they mean just LLMs, or they aren't even aware the technology has a grassroots adoption outside of those big corporations). It will take time, and the bubble bursting might very well be a good thing for the technology into the future. If something is only know for it's capitalistic exploits it'll continue to be seen unfavorably even when it's proven it's value to those who care to look at it with an open mind. I read it mostly as those people rejoicing over those big corporations getting shafted for their greedy practices.
the bubble bursting might very well be a good thing for the technology into the future
I absolutely agree. It worked wonders for the Internet (dotcom boom in the 90s), and I imagine we'll see the same w/ AI sometime in the next 10 years or so. I do believe we're seeing a bubble here, and we're also seeing a significant shift in how we interact w/ technology, but it's neither as massive or as useless as proponents and opponents claim.
I'm excited for the future, but not as excited for the transition period.
Hope?
"The economics are likely to be grim," Marcus wrote on his Substack. "Sky high valuation of companies like OpenAI and Microsoft are largely based on the notion that LLMs will, with continued scaling, become artificial general intelligence."
"As I have always warned," he added, "that's just a fantasy."
Microsoft shit is a mega corp... AI is based on their revenue lol
Even Zuckerberg admits that trying to scale LLMs larger doesn’t work because the energy and compute requirements go up exponentially. There must exist a different architecture that is more efficient, since the meat computers in our skulls are hella efficient in comparison.
Once we figure that architecture out though, it’s very likely we will be able to surpass biological efficiency like we have in many industries.
That's a bad analogy. We weren't able to surpass biological efficiency in industry sector because we figured out human anatomy and how to improve it. It's simply alternative ways to produce force like electricity and motors which had absolutely no relation to how muscles works.
I imagine it would be the same for computers, simply another, better method to achieve something but it's so uncertain that it's barely worth discussing about.
With current stat prediction models?
I think I've heard about enough of experts predicting the future lately.
Marcus is right, incremental improvements in AIs like ChatGPT will not lead to AGI and were never on that course to begin with. What LLMs do is fundamentally not "intelligence", they just imitate human response based on existing human-generated content. This can produce usable results, but not because the LLM has any understanding of the question. Since the current AI surge is based almost entirely on LLMs, the delusion that the industry will soon achieve AGI is doomed to fall apart - but not until a lot of smart speculators have gotten in and out and made a pile of money.
Good. I look forward to all these idiots finally accepting that they drastically misunderstood what LLMs actually are and are not. I know their idiotic brains are only able to understand simple concepts like "line must go up" and follow them like religious tenants though so I'm sure they'll waste everyone's time and increase enshitification with some other new bullshit once they quietly remove their broken (and unprofitable) AI from stuff.
This is why you're seeing news articles from Sam Altman saying that AGI will blow past us without any societal impact. He's trying to lessen the blow of the bubble bursting for AI/ML.
Welcome to the top of the sigmoid curve.
If you were wondering what 1999 felt like WRT to the internet, well, here we are. The Matrix was still fresh in everyone's mind and a lot of online tech innovation kinda plateaued, followed by some "market adjustments."
I think it's more likely a compound sigmoid (don't Google that). LLMs are composed of distinct technologies working together. As we've reached the inflection point of the scaling for one, we've pivoted implementations to get back on track. Notably, context windows are no longer an issue. But the most recent pivot came just this week, allowing for a huge jump in performance. There are more promising stepping stones coming into view. Is the exponential curve just a series of sigmoids stacked too close together? In any case, the article's correct - just adding more compute to the same exact implementation hasn't enabled scaling exponentially.
I work with people who work in this field. Everyone knows this, but there's also an increased effort in improvements all across the stack, not just the final LLM. I personally suspect the current generation of LLMs is at its peak, but with each breakthrough the technology will climb again.
Put differently, I still suspect LLMs will be at least twice as good in 10 years.
It's been 5 minutes since the new thing did a new thing. Is it the end?
I am so tired of the ai hype and hate. Please give me my gen art interest back please just make it obscure again to program art I beg of you
It's still quite obscure to actually mess with AI art instead of just throwing prompts at it, resulting in slop of varying quality levels. And I don't mean controlnet, but github repos with comfyui plugins with little explanation but a link to a paper, or "this is absolutely mathematically unsound but fun to mess with". Messing with stuff other than conditioning or mere model selection.
Of course it'll crash. Saying it's imminent though suggests someone needs to exercise their shorts.
As I use copilot to write software, I have a hard time seeing how it'll get better than it already is. The fundamental problem of all machine learning is that the training data has to be good enough to solve the problem. So the problems I run into make sense, like:
2 and 3 could be alleviated, but probably not solved completely with more and better data or engineering changes - but obviously AI developers started by training the models on the most useful data and strategies that they think work best. 1 seems fundamentally unsolvable.
I think there could be some more advances in finding more and better use cases, but I'm a pessimist when it comes to any serious advances in the underlying technology.
Not copilot, but I run into a fourth problem:
4 The LLM gets hung up on insisting that a newer feature of the language I'm using is wrong and keeps focusing on "fixing" it, even though it has access to the newest correct specifications where the feature is explicitly defined and explained.
I've also run into this when trying to program in Rust. It just says that the newest features don't exist and keeps rolling back to an unsupported library.
So you use other people's open source code without crediting the authors or respecting their license conditions? Good for you, parasite.
I completely understand where you’re coming from, and I absolutely agree with you, genAI is copyright infringement on a weapons-grade scale. With that said, though, in my opinion, I don’t know if calling people parasites like this will really convince people, or change anything. I don’t want to tone police you, if you want to tell people to get fucked, then go ahead, but I think being a bit more sympathetic to your fellow programmers and actually trying to help them see things from our perspective might actually change some minds. Just something to think about. I don’t have all the answers, feel free to ignore me. Much love!
Ahh right, so when I use copilot to autocomplete the creation of more tests in exactly the same style of the tests I manually created with my own conscious thought, you're saying that it's really just copying what someone else wrote? If you really believe that, then you clearly don't understand how LLMs work.
- Copilot can't read my mind and figure out what I'm trying to do.
Try writing comments
I just want a portable self hosted LLM for specific tasks like programming or language learning.
You can install Ollama in a docker container and use that to install models to run locally. Some are really small and still pretty effective, like Llama 3.2 is only 3B and some are as little as 1B. It can be accessed through the terminal or you can use something like OpenWeb UI to have a more "ChatGPT" like interface.
I have a few LLMs running locally. I don't have an array of 4090s to spare so I am limited to the smaller models 8B and whatnot.
They definitely aren't as good as anything you get remotely. It's more private and controlled but it's much less useful (I've found) than any of the other models.
Apparently, there was only so much IP to steal
I hope it all burns.
Short on the AI stocks before it crash!
The market can remain irrational longer than you can remain solvent.
A. Gary Shilling
so long, see you all in the next hype. Any guesses?
Tradwives
AI vagina Fleshlight beds. You just find your sleep inside one and it will do you all night long! Telling you stories of any topic. Massaging you in every possible way. Playing your favorite music. It's like a living room! Oh I'm sleeping in the living room again. Yeah I'm in the dog house. But that's why you need an AI vagina Fleshlight bed!
Nice, looking forward to it! So much money and time wasted on pipe dreams and hype. We need to get back to some actually useful innovation.
Yay
Fingers crossed.
AI was 99% a fad. Besides OpenAI and Nvidia, none of the other corporations bullshitting about AI have made anything remotely useful using it.
Absolutely not true. Disclaimer, I do work for NVIDIA as a forward deployed AI Engineer/Solutions Architect—meaning I don’t build AI software internally for NVIDIA but I embed with their customers’ engineering teams to help them build their AI software and deploy and run their models on NVIDIA hardware and software. edit: any opinions stated are solely my own, N has a PR office to state any official company opinions.
To state this as simply as possible: I wouldn’t have a job if our customers weren’t seeing tremendous benefit from AI technology. The companies I work with typically are very sensitive to CapX and OpX costs of AI—they self-serve in private clouds. If it doesn’t help them make money (revenue growth) or save money (efficiency), then it’s gone—and so am I. I’ve seen it happen; entire engineering teams laid off because a technology just couldn’t be implemented in a cost-effective way.
LLMs are a small subset of AI and Accelerated-Compute workflows in general.
To state this as simply as possible: I wouldn’t have a job if our customers weren’t seeing tremendous benefit from AI technology.
Right because corporate management doesn't ever blindly and stupidly overinvest in fads that blow up in their faces...
I work with typically are very sensitive to CapX and OpX costs of AI—they self-serve in private clouds. If it doesn’t help them make money (revenue growth) or save money (efficiency), then it’s gone—and so am I.
You clearly have no clue what you're on about. As someone with a degrees and experience in both CS and Finance all I have to say is that's not at all how these things work. Plenty of companies lose money on these things in the hopes that their FP&A projection fever dreams will come true. And they're wrong much more often than you seem to think. FP&A is more art than science and you can get financial models to support any argument you want to make to convince management to keep investing in what you think they should. And plenty of CEOs and boards are stupid enough to buy it. A lot of the AI hype has been bought and sold that way in the hopes that it would be worthwhile eventually or that other alternatives can't be just as good or better.
I’ve seen it happen; entire engineering teams laid off because a technology just couldn’t be implemented in a cost-effective way.
This is usually what happens once they finally realize spending money on hype doesn't pay off and go back to more established business analytics, operations research, and conventional software which never makes mistakes if it's programmed correctly.
LLMs are a small subset of AI and Accelerated-Compute workflows in general.
No one ever said otherwise. And we're talking about AI only, no moving the goalposts to accelerated computing, which is a mechanism through which to implement a wide range of solutions and not a specific one in and of itself.
I would say LLMs specifically are in that ball park. Things like machine vision have been boringly productive and relatively un hyped.
There's certainly some utility to LLMs, but it's hard to see through all the crazy over estimations and being shoved everywhere by grifters.
Nvidia made money, but I've not seen OpenAI do anything useful, and they are not even profitable.
ChatGPT is basically the best LLM of its kind. As for Nvidia I'm not talking about hardware I'm talking about all of the models it's trained to do everything from DLSS and ACE to creating virtual characters that can converse and respond naturally to a human being.
lalal AI has made some great innovations in taking songs and separating them into vocals and instrumentals. that's a game changer for remix artists.
other than that niche utility and a handful of others, AI is largely bullshit.
Oh nice, another Gary Marcus "AI hitting a wall post."
Like his "Deep Learning Is Hitting a Wall" post on March 10th, 2022.
Indeed, not much has changed in the world of deep learning between spring 2022 and now.
No new model releases.
No leaps beyond what was expected.
\s
Gary Marcus is like a reverse Cassandra.
Consistently wrong, and yet regularly listened to, amplified, and believed.
Theres no bracing for this, OpenAI CEO said the same thing like a year ago and people are still shovelling money at this dumpster fire today.
Seems to me the rationale is flawed. Even if it isn't strong or general AI, LLM based AI has found a lot of uses. I also don't recognize the claimed ignorance among people working with it, about the limitations of current AI models.
while you may be right, one would think that the problem lies in the overestimated peception of the abilities of llms leading to misplaced investor confidence -- which in turn leads to a bubble ready to burst.
Yup. Investors have convinced themselves that this time AI development is going to grow exponentially. The breathless fantasies they’ve concocted for themselves require it. They’re going to be disappointed.
Can you name some of those uses that you see lasting in the long term or even the medium term? Because while it has been used for a lot of things it seems to be pretty bad at the overwhelming majority of them.
AI is already VERY successful in some areas, when you take a photo, it is treated with AI features to improve the image, and when editing photos on your phone, the more sophisticated options are powered by AI. Almost all new cars have AI features.
These are practical everyday uses, you don't even have to think about when using them.
But it's completely irrelevant if I can see use cases that are sustainable or not. The fact is that major tech companies are investing billions in this.
Of course all the biggest tech companies could all be wrong, but I bet they researched the issue more than me before investing.
Show me by what logic you believe to know better.
The claim that it needs to be strong AI to be useful is ridiculous.
Sigh I hope LLMs get dropped from the AI bandwagon because I do think they have some really cool use cases and love just running my little local models. Cut government spending like a madman, write the next great American novel, or eliminate actual jobs are not those use cases.
The tech priests of Mars were right; death to abominable intelligence.
That's a Space Grudgin'
It's had all the signs of a bubble for the last few years.
supermicro's accountants have just resigned 🤭
Because nobody could have possibly saw that coming. /s
Until Open AI announces a new 5t model or something and then the hype refreshes
is this where we get to explain again why its not really ai?
Nope, just where you divest your stocks like any other tech run.
I have to do similar things when it comes to 'raytracing'. It meant one thing, and then a company comes along and calls something sorta similar the same thing, then everyone has these ideas of what it should be vs. what it actually is doing. Then later, a better version comes out that nearly matches the original term, but there's already a negative hype because it launched half baked and misnamed. Now they have to name the original thing something new new to market it because they destroyed the original name with a bad label and half baked product.
He is writing about LLM mainly, and that is absolutely AI, it's just not strong AI or general AI (AGI).
You can't invent your own meaning for existing established terms.
It's gonna crash like a self driving tesla. It's gonna fall apart like a cybertrukkk.
I'm shocked I tell you
Crash? Doesn't it have to be moving at all to crash?
Nvidia shares ..
yep Knew ai should die some day.
Ya AI was never going to be it. But I wouldn’t understate its impact even in its current stage. I think it’ll be a tool that will be incredibly useful for just about every industry
There aren't many industries where results that are correct in the very common case everybody knows anyway, a bit wrong in the less common case and totally hallucinated in the actually original cases is useful. Especially if you can't distinguish between those automatically.
Even Pied Piper didn’t scale.
Great!! ....I don't what chatGPT to go anywhere, I use it every day and Google has become assss.
🤷♂️ I only use local generators at this point,so I don't care.
I believe this about as much as I believed the "We're about to experience the AI singularity" morons.
Just put another number behind it. Luddites won't know the difference.
Luddites weren't against new technology, they were against the aristocrats using new technology as a tool or excuse to oppress and kill the labor class. The problem is not the new technology, the problem is that people were dying of hunger and being laid off in droves. Destroying the machinery, which almost always they were the operators of when working on said aristocrat's factories, was an act of protest, just like a riot, or a strike. It was a form of collective bargaining.
Well classic computers will always limited and power hungry. Quantum computer is the key to AI achieving next level
The only people who say this know nothing about quantum or computers
I love the or in this sentence
Quantum computers are only good at a very narrow subset of tasks. None of those tasks are related to Neural Networks, AGI, or the emulation of neurons.