- It’s theft to digital artisans, as AI-generated works tend to derive heavily without even due credit.
- It further discourages what’s called critical thinking.
- It’s putting even technically competent people out of work.
- It’s grift for and by techbros.
Numver 3 is crazy too because it’s putting people out of work even when it’s worse than them, the bubble bursting will have dire consequences and if it’s held together by corrupt injections of taxpayer money then it’ll still have awful consequences, and the whole point of AI doing our jobs was to free us from labour but instead the lack of jobs is only hurting people.
For 3, there are two things:
-
It is common for less good, but much cheaper tech to displace humans doing a job if it’s “good enough”. Dishwashing machines that sometimes leave debris on dishes are an example.
-
The technically competent people have long ofnet been led by people not technically competent, and have long been outcompeted by bullshit artists. LLM output is remarkably similar to bullshit artistry. One saving grace of the human bullshit artists is they at least usually understand they secretly have dependencies on actual competent people and while they will outcompete, they will at least try to keep the competent around, the LLM doesn’t have such concepts.
Ok, but I did specifically point out that AI is doing a worse job than those people. It’d be like replacing your dishwashing guy with chimp that go to shadow him for a bit before he was fired. Another analogy would be replacing a carpenter with a van full of his tools as if they could do the work on their own.
Yeah, but let’s say you had 12 guys hand scrubbing to keep up with the plates, but then you got a mediocre dishwashing machine that did a worse job scrubbing. You wouldn’t dismiss the machine because it was imperfect, you would say I need a dishwashing machine operator, who might have to do a quality check on the way out, or otherwise have whoever is plating put it in a stack for hand scrubbing, and lay off 11 of the guys.
So this could be the way out if AI worked ‘as advertised’. It however largely does not.
But then to the second point, it doesn’t even need to work as advertised if the business leader thinks it’s good enough and does the layoffs. They might end up having to scale back operations, but somehow it won’t be their fault.
-
- It’s not theft
- PEBKAC problem.
- totally agree. This right here is what we should be worried about.
- yep, absolutely. But we need to be figuring out what to do when all the jobs go away.
- If vanilla ice takes 6 notes from the base line from a queen song it’s theft and costs $4mio. If AI copies whole chapters of books it’s all fine.
- No. PEBKAC is if it affects one person, or maybe a handful of people. If it affects whole sections of the population it’s systematic. It’s like saying “poverty is an user error because everyone could just choose to be rich”.
username checks out
I skimmed the article, I might have missed it but here’s another strike against AI, that is tremendously important: It’s the ultimate accountability killer.
Did your insurance company make an obvious mistake? Oops teeehee, silly them, the AI was a bit off
Is everything going mildly OK? Of course! The AI is deciding who gets insurance and who doesn’t, it knows better, so why are you questioning it?
Expect (and rage against) a lot of pernicious usage of AI for decision making, especially in areas where they shouldn’t be making decisions (take Israel for instance, that uses an AI to select ““military”” targets in Gaza).
The reason we hate AI is cause it’s not for us. It’s developed and controlled by people who want to control us better. It is a tool to benefit capital, and capital always extracts from labour, AI only increases the efficiency of exploitation because that’s what it’s for. If we had open sourced public AI development geared toward better delivering social services and managing programs to help people as a whole, we would like it more. Also none of this LLM shit is actually AI, that’s all branding and marketing manipulation, just a reminder.
Yes. The capitalist takeover leaves the bitter taste. If OpenAI was actually open then there would be much less backlash and probably more organic revenue.
none of this LLM shit is actually AI, that’s all branding and marketing manipulation, just a reminder.
To correct the last part, LLMs are AI. Remember that “Artificial” means “fake”, “superficial”, or “having the appearance of.” It does not mean “actual intelligence.” This is why additional terms were coined to specify types of AI that are capable of more than just smoke and mirrors, such as AGI. Expect even more niche terms to arrive in the future as technology evolves.
This is one of the worst things in the current AI trends for me. People have straight up told me that the old MIT CSAIL lab wasn’t doing AI. There’s a misunderstanding of what the field actually does and how important it is. People have difficulty separating this from the slop capitalism has plastered over the research.
One of the foundational groups for the field is the MIT model railroading club, and I’m not joking.
It’s extremely wasteful. Inefficient to the extreme on both electricity and water. It’s being used by capitalists like a scythe. Reaping millions of jobs with no support or backup plan for its victims. Just a fuck you and a quip about bootstraps.
It’s cheapening all creative endeavors. Why pay a skilled artist when your shitbot can excrete some slop?
What’s not to hate?
As with almost all technology, AI tech is evolving into different architectures that aren’t wasteful at all. There are now powerful models we can run that don’t even require a GPU, which is where most of that power was needed.
The one wrong thing with your take is the lack of vision as to how technology changes and evolves over time. We had computers the size of rooms to run processes that our mobile phones can now run hundreds of times more efficiently and powerfully.
Your other points are valid, people don’t realize how AI will change the world. They don’t realize how soon people will stop thinking for themselves in a lot of ways. We already see how critical thinking drops with lots of AI usage, and big tech is only thinking of how to replace their staff with it and keep consumers engaged with it.
You are demonstrating in this comment that you don’t really understand the tech.
The “efficient” models already spent the water and energy to train, these models are inferior to the ones that need data centers because you are stuck with a bot trained in 2020-2022 forever.
They are less wasteful, but will become just as wasteful the second we want it to catch up again.
You are misunderstanding the tech. That’s not how this works, models are trained often, did you think this was done only a few years ago? The fact that you called them bots says everything.
You’re just hating to hate on something, without understanding the technology. The efficiency I’m referring to is the MoE architecture that only got popular within the last year. There are still new architectures being developed, not that you care about this topic but would prefer to blindly hate on what’s spewed from outdated and biased news sources.
Yeah nah
Same shit people said in 2022
In 3 more years you’ll be making the same excuses for the same shortcomings, because for you this isn’t about the tech, it’s about your ideology.
You make weird assumptions seemingly based on outdated ideas. I’ll let you be, perhaps you need some rest.
Oh god i dint even see the .ml till now
Eww
Please get some rest. You’re oddly irritable and delusional and it can’t be healthy.
It was also inefficient for a computer to play chess in 1980. Imagine using a hundred watts of energy and a machine that costed thousands of dollars and not being able to beat an average club player.
Now a phone will cream the world’s best in chess and even go
Give it twenty years to become good. It will certainly do more stuff with smaller more efficient models as it improves
If you want to argue in favor of your slop machine, you’re going to have to stop making false equivalences, or at least understand how its false. You can’t make ground on things that are just tangential.
A computer in 1980 was still a computer, not a chess machine. It did general purpose processing where it followed whatever you guided it to. Neural models don’t do that though; they’re each highly specialized and take a long time to train. And the issue isn’t with neural models in general.
The issue is neural models that are being purported to do things they functionally cannot, because it’s not how models work. Computing is complex, code is complex, adding new functionality that operates off of fixed inputs alone is hard. And now we’re supposed to buy that something that creates word relationship vector maps is supposed to create new?
For code generation, it’s the equivalent of copying and pasting from Stack Overflow with a find/replace, or just copying multiple projects together. It isn’t something new, it’s kitbashing at best, and that’s assuming it all works flawlessly.
With art, it’s taking away creation from people and jobs. I like that you ignored literally every point raised except for the one you could dance around with a tangent. But all these CEOs are like “no one likes creating art or music”. And no, THEY just don’t want to spend time creating themselves nor pay someone who does enjoy it. I love playing with 3D modeling and learning how to make the changes I want consistently, I like learning more about painting when texturing models and taking time to create intentional masks. I like taking time when I’m baking things to learn and create, otherwise I could just go buy a box mix of Duncan Hines and go for something that’s fine but not where I can make things when I take time to learn.
And I love learning guitar. I love feeling that slow growth of skill as I find I can play cleaner the more I do. And when I can close my eyes and strum a song, there’s a tremendous feeling from making this beautiful instrument sing like that.
Oh my God, that’s perfect. It’s kit bashing. That’s exactly how it feels.
Its because the tech bros have 0 empathy or humanity. Llm slop is perfect for them.
Stockfish can’t play Go. The resources you spent making the chess program didn’t port over.
In the same way you can use a processor to run a completely different program, you can use a GPU to run a completely different model.
So if current models can’t do it, you’d be foolish to bet against future models in twenty years not being able to do it.
Buy any bubble memory lately?
I have a book from the early 90s which goes over some emerging technologies at the time. One of them was bubble memory. It was supposed to have the cost per MB of a hard drive and the speed of RAM.
Of course, that didn’t materialize. Flash memory outpaced its development, and it’s still not quite as cheap as hard drives or as fast as RAM. Bubble memory had a few niche uses, but it never hit the point of being a mass market product.
Point is that you can’t assume any singular technology will advance. Things do hit dead ends. There’s a kind of survivorship bias in thinking otherwise.
AI is not a technology, it’s just a name for things that were hard to do. It used to be playing chess better than a human was considered AI, but when it turned out you can brute force it, it wasn’t considered AI anymore.
A lot of people don’t consider AlphaGo to be AI, even though neural networks are the kind of technique that’s considered as AI.
AI is a moving target so when we get better at something we don’t consider it true AI
I’m quite aware of the history of the field, thanks. It’s had a lot of cycles of fast movement followed by a brick wall. You can’t assume it’ll have a nice, smooth upward trajectory.
I think the problem is that you think you’re talking like a time traveler heralding us about the wonders of sliced bread, when really it’s more like telling a small Victorian child about the wonders of Applebee’s and in the impossible chance they survive to it then finding everything is a lukewarm microwaved pale imitation of just buying the real thing at Aldi and cooking it in less time for far tastier and a fraction of the cost.
Show me the chess machine that caused rolling brown outs and polluted the air and water of a whole city.
I’ll wait.
Servers have been eating up a significant portion of electricity for years before AI. It’s whether we get something useful out of it that matters
That’s the hangup isn’t it? It produces nothing of value. Stolen art. Bad code. Even more frustrating phone experiences. Oh and millions of lost jobs and ruined lives.
It’s the most american way possible that they could have set trillions of dollars on fire short of carpet bombing poor brown people somewhere.
Not even remotely close to this scale… At most you could compare the energy usage to the miners in the crypto craze, but I’m pretty sure that even that is just a tiny fraction of what’s going on right now.
Crypto miners wish they could be this inefficient. No literally they do. They’re the “rolling coal” mfers of the internet.
Very wrong
In 2023 AI used 40 TWh of energy in the US out of a total 176 TWh used by data centers
https://davidmytton.blog/how-much-energy-do-data-centers-use/
From the blog you quoted yourself:
Despite improving AI energy efficiency, total energy consumption is likely to increase because of the massive increase in usage. A large portion of the increase in energy consumption between 2024 to 2023 is attributed to AI-related servers. Their usage grew from 2 TWh in 2017 to 40 TWh in 2023. This is a big driver behind the projected scenarios for total US energy consumption, ranging from 325 to 580 TWh (6.7% to 12% of total electricity consumption) in the US by 2028.
(And likewise, the last graph of predictions for 2028)
From a quick read of that source, it is unclear to me if it factors in the electricity cost of training the models. It seems to me that it doesn’t.
I found more information here: https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/
Racks of servers hum along for months, ingesting training data, crunching numbers, and performing computations. This is a time-consuming and expensive process—it’s estimated that training OpenAI’s GPT-4 took over $100 million and consumed 50 gigawatt-hours of energy, enough to power San Francisco for three days.
So, I’m not sure if those numbers for 2023 paint the full picture. And adoption of AI-powered tools was definitely not as high in 2023 as it is nowadays. So I wouldn’t be surprised if those numbers were much higher than the reported 22.7% of the total server power usage in the US.
It probably would have if IBM decided that every household in the USA needed to have chess playing compute capacity and made everyone dial up to a singular facility in the middle of a desert where land and taxes were cheap so they could charge everyone a monthly fee for the privilege…
Not the same. The underlying tech of llm’s has mqssively diminishing returns. You can akready see it, could see it a year ago if you looked. Both in computibg power and required data, and we do jot have enough data, literally have nit created in all of history.
This is not “ai”, it’s a profoubsly wasteful capitalist party trick.
Please get off the slop and re-build your brain.
That’s the argument Paul Krugman used to justify his opinion that the internet peaked in 1998.
You still need to wait for AI to crash and a bunch of research to happen and for the next wave to come. You can’t judge the internet by the dot com crash, it became much more impactful later on
No. No i don’t. I trust alan Turing.
NB: Alan Turing famously invented ChatGPT
One of the major contributors to early versions. Then they did the math and figured out it was a dead end. Yes.
Also one of the other contributors (weizenbaum i think?) pointed out that not only was it stupid, it was dabgeroys and made people deranged fanatical devotees impervious to reason, who would discard their entire intellect and education to cult about this shit, in a madness no logic could breach. And that’s just from eliza.
We’re talking about 80 years ago
It seems like you are implying that models will follow Moore’s law, but as someone working on “agents” I don’t see that happening. There is a limitation with how much can be encoded and still produce things that look like coherent responses. Where we would get reliable exponential amounts of training data is another issue. We may get “ai” but it isn’t going to be based on llms
You can’t predict how the next twenty years of research improves on the current techniques because we haven’t done the research.
Is it going to be specialized agents? Because you don’t need a lot of data to do one task well. Or maybe it’s a lot of data but you keep getting more of it (robot movement? stock market data?)
We do already know about model collapse though, genai is essentially eating its own training data. And we do know that you need a TON of data to do even one thing well. Even then it only does well on things strongly matching training data.
Most people throwing around the word agents have no idea what they mean vs what the people building and promoting them mean. Agents have been around for decades, but what most are building is just using genai for natural language processing to call scripted python flows. The only way to make them look coherent reliably is to remove as much responsibility from the llm as possible. Multi agent systems are just compounding the errors. The current best practice for building agents is “don’t use a llm, if you do don’t build multiple”. We will never get beyond the current techniques essentially being seeded random generators, because that’s what they are intended to be.
It might, but:
- Current approaches are displaying exponential demands for more resources with barely noticable “improvements”, so new approaches will be needed.
- Advances in electronics are getting ever more difficult with increasing drawbacks. In 1980 a processor would likely not even have a heatsink. Now the current edge of that Moore’s law essentially is datacenter only and frequently demands it to be hooked up to water for cooling. SDRAM has joined CPUs in needing more active cooling.
Stockfish on ancient hardware will still mop up any human GM
Umm… ok, but that’s a bit beside the point?
Unless you mean to include those 1980 computers, in which case stockfish won’t run on that… More than about 10 year old home computer would likely be unable to run it.
Only because they are not 32 bit so they won’t support enough RAM. But a processor from the 90s could, even though none of the programs of the time were superhuman on commodity hardware.
The chess programs improved so much that even running with 1000 times slower hardware they are still hilariously stronger than humans
We really need to work out the implications of the fact that Moore’s Law is dead, and that technology doesn’t necessarily advance on a exponential path like that, anyway. Not in all cases.
The cost per component of an integrated circuit (the original formulation of Moore’s Law) is not going down much at all. We’re orders of magnitude away from where we “should” be if we start from the Intel 8008 and cut the cost in half every 24 months. Nodes are creating smaller components, but they’re not getting cheaper. The fact that it took decades to get to this point is impressive, but it was already an exception in all of human history. Why can’t we just be happy that computers we already have are pretty damned neat?
Anyway, AI is not following anything like that path. This might mean a big breakthrough tomorrow, or it could be decades from now. It might even turn out not to be possible; I think there is some way we can do AGI on computers of some kind, but that’s not even the consensus among computer scientists. In any case, there’s no particular reason to think LLMs will follow anything like the exponential growth path of Moore’s Law. They seem to have hit a point of diminishing returns.
The 19th and 20th centuries saw so much technological advancement and we got used to that amount of change.
That’s why people were expecting Mars by the mid 80s and flying cars and other fanciful tech by now.
The problem is that the rate of advancement is slowing down, and economies that demand infinite, compounding growth are not prepared for this.
Twenty years is a very long time, also “good” is relative. I give it about 2-3 years until we can run a model as powerful as Opus 4.1 on a laptop.
There will inevitably be a crash in AI and people still forget about it. Then some people will work on innovative techniques and make breakthroughs without fanfare
Ai is the smart fridge of computing.
Your door is ajar.
No, its a can!
There was a thread of people pointing out biases that exist on Lemmy, and some commenters obviously mention anti-AI people. Cue the superiority complex (cringe).
Some of these people actually believe UBI will become a thing for people who lose their jobs due to AI, meanwhile the billionaire class is actively REMOVING benefits for the poor to further enrich themselves.
What really gets me is when people KNOW what the hell we’re talking about, but then mention the 1% use case scenario where AI is actually useful (for STEM) and act like that’s what we’re targeting. Like no, motherfucker. We’re talking about the AI that’s RIGHT IN FRONT OF US, contributing to a future where we’re all braindead ai-slop dependent, talentless husks of human beings. Not to mention unemployed now.
A system is what it does. If it costs us jobs, enriches the wealthy at our expense, destroys creativity and independent thought, and suppresses wrongthink? It’s a censorious authoritarian fascist pushing austerity.
Show me AI getting us UBI or creating worker-owned industry and I’ll change my tune.
UBI is there to save billionaires.
They’re a shortsighted lot who don’t recognize that workers are also their customers. If they stop paying us all, then there is nobody to buy their stuff. UBI is the way out of that for them while still having billionaires around.
It aligns with Democratic Socialists well enough, but not the seize-the-means socialists.
It aligns with Democratic Socialists well enough, but not the seize-the-means socialists.
That’s a fair observation I think: UBI doesn’t put the same pressure on financialization that worker- owned industry does. Ultimately I think eliminating work is a terrible idea, but reducing work, focusing on actually productive work, and ensuring we all collectively benefit from it is ideal.
It’s corporate controlled, it’s a way to manipulate our perception, it’s all appearance no substance, it’s an excuse to hide incompetence under an algorithm, it’s cloud service orientated, it’s output is highly unreliable yet hard to argue against to the uninformed. Seems about right.
And it will not be argued with. No appeal, no change of heart. Which is why anyone using it to mod or as cs needs to be set on fire.
Ed Zitron is one of the loudest opponents against the AI industry right now, and he continues to insist that “there is no real AI adoption.” The real problem, apparently, is that investors are getting duped. I would invite Zitron, and anyone else who holds the opinion that demand for AI is largely fictional, to open the app store on their phone on any day of the week and look at the top free apps charts. You could also check with any teacher, student, or software developer.
ChatGPT has some very impressive usage numbers, but the image tells on itself by being a free app. The conversion rate (percentage of people who start paying) is absolutely piss poor, with the very same Ed Zitron estimating it being at ~3% with 500.000.000 users. That also doesn’t bode well with the fact that OpenAI still loses money even on their $200/month subscribers. People use ChatGPT because it’s been spammed down their throats by the media that never question the sacred words of the executives (snake oil salesmen) that utter lunatic phrases like “AGI by 2025” (Such a quote exists somewhere, but I don’t remember if this year was used). People also use ChatGPT because it’s free and it’s hard to say no to get someone to do your homework for you for free.
I love how every single app on that list is an app I wouldn’t touch in my life
Not even Google maps
Absolutely not, I haven’t used any Google products or services in 15 years
That’s pretty impressive. I can’t do without YouTube or Android unfortunately.
That’s fair. Once the “don’t be evil” was gone, so was I hahahaha
You can use the Google-free Android forks.
Posting from graphene OS
Also a fan. Check out Qubes OS on the desktop in case you haven’t already.
I don’t need chatGPT etc for work, but I’ve used it a few times. It is indeed a very useful product. But most of the time I can get by without it and I kinda try to avoid using it for environmental reasons. We’re boiling the oceans fast enough as it is.
In house at my work, we’ve found ChatGPT to be fairly useless, too. Where Claude and Gemini seem to reign supreme.
It seems like ChatGPT is the household name, but hardly the best performing.
My thoughts exactly, I use Claude and find it much better than ChatGPT. Less hallucinations, more useful information
Who owns Claude? Is it ethically sourced?
Anthropic and no of course not.
They at least seem to care slightly more about the impact they have on the world and humans.
Exactly, the users/installation count of such products are clearly a much more accurate indicator of the success of their marketing team, rather than their user’s perceived value in such products lol
people currently don’t pay for it, because currently it’s free. most people aren’t using it for anything that requires a subscription.
Idk that the average GPT user knows or cares about AGI. I think the appeal is getting information specifically tailored to you. Sure, I can go online and search for something. Try and find what I’m looking for, or close to it. Or I can ask AI, and it’ll give me text tailored exactly to my prompt. For instance, having to hope you can find someone with a problm similar to yours online, with a solution, vs. ChatGPT just tells you about your case specifically
I would certainly pay for ChatGPT if it became paid only
you’re being downvoted but this is the reality of the market right now. it’s day 1 venture capital shit. lose money while gaining market share, and worry about making a profit later.
Yea, and people are coping on this
Anti AI will not convince pro AI as well. They are a vocal minority
I wouldn’t really trust Ed Zitron’s math analysis when he gets a very simple thing like “there is no real AI adoption” plainly wrong. The financials of OpenAI and other AI-heavy companies are murky, but most tech startups run at a loss for a long time before they either turn a profit or get acquired. It took Uber over a decade to stop losing money every quarter.
OpenAI keeps getting more funding capital because (A) venture capital guys are pretty dumb, and (B) they can easily ramp up advertisements once the free money runs out. Microsoft has already experimented with ads and sponsored products in chatbot messages, ChatGPT will probably do something like that.
I wouldn’t really trust Ed Zitron’s math analysis when he gets a very simple thing like “there is no real AI adoption” plainly wrong
Except he doesn’t say that. the author of this article simply made that up.
There is a high usage rate (almost entirely ChatGPT btw, despite all the money sunk into AI by others like Google) but its all the free stuff and they are losing bucketloads of money at a rate that is rapidly accelerating.
but most tech startups run at a loss for a long time before they either turn a profit or get acquired.
There is no path to profitability.
I wrote the article, Ed said that in the linked blog post: “There Is No Real AI Adoption, Nor Is There Any Significant Revenue - As I wrote earlier in the year, there is really no significant adoption of generative AI services or products.”
There is a pretty clear path to profitability, or at least much lower losses. A lot more phones, tablets, computers, etc now have GPUs or other hardware optimized for running small LLMs/SLMs, and both the large and small LLMs/SLMs are becoming more efficient. With both of those those happening, a lot of the current uses for AI will move to on-device processing (this is already a thing with Apple Intelligence and Gemini Nano), and the tasks that still need a cloud server will be more efficient and consume less power.
a lot of the current uses for AI will move to on-device processing
How exactly will that make OpenAI and the likes more profitable?! That should be one of the scenarios that will make them less profitable.
If the models are more efficient, the tasks that still need a server will get the same result at a lower cost. OpenAI can also pivot to building more local models and license them to device makers, if it wants.
The finances of big tech companies isn’t really relevant anyway, except to point out that Ed Zitron’s arguments are not based in reality. Whether or not investors are getting stiffed, the bad outcomes of AI would still be bad, and the good outcomes would still be good.
I agree that this was poor wording on Ed’s side. He meant to point at the lack of adoption for work/business purposes, but failed to articulate this distinction. He is talking about conversion to paid users and how Google cheated to make the adoption of Gemini by corporate users to looks higher than it is. He never meant to talk about the adoption by regular people on the free tier just doing random non-work-related things.
You were talking about a different adoption metric. You are both right, you are just talking about different kinds of adoption.
I don’t think he is talking about specifically businesses, though, because he also talks about Gemini replacing Google Assistant, which only matters in consumer products (Assistant was never an enterprise product). It’s more like he’s moving the goalposts mid-statement.
on-device processing
Oh, when will I get my free phone to do this ?
Its an unfinished product with various problems, used in humans to develop it and make money.
It does nothing right 100%! We as humanity care to make money out of it, and not help humanity in many ways.
It dehumanizes us by devaluing the one thing that was unique to us, our minds and creativity.
It’s actually a useful tool… If it were not too often used for so very dystopian purposes.
But it’s not just AI. All services, systems, etc… So many are just money grabs, hate, opinion making or general manipulation… I have many things I hate more about “modern” society, than I do as to how LLMs are used.
I like the lemmy mindset far more than reddit and only on the AI topic people here are brainlessly focused on the tool instead of the people using the tool.
I like the lemmy mindset far more than reddit
…and Facebook.
I hardly remember Facebook… Isn’t that these days a retirement platform?
A platform for people in developing countries, however. In some cases it supplants most if not all of the functions of what used to be several programs for Internet access and communication.
I mentioned Facebook because I’m seeing some people trying to share information how to grift with AI.
That… Doesn’t sound good.
Facebook is not exactly a trustworthy thing and to have developing countries dependent on it the way you describe sounds dystopian. :/
But dystopian is sadly the theme of the 2020s
What are your views on gun control?
But whatabout YOUR thoughts on bladder control???!
Oh, I was genuinely curious — this very same argument can be used when talking about guns. This very same argument is used when talking about guns.
This wasn’t an attempt at a strawman, I’m merely drawing parallels. To say that this one topic is one where Lemmy focuses on the tool and not the people using them is false.
The better comparison I’ve seen is knives. Knives have multiple purposes, yet they can also be used quite dangerously. Guns on the other hand only really have one purpose. Since AI can at least be used for other more useful stuff (think protein folding), I would say they are closer to knives.
That’s a fair point, I agree.
That the death data tells clearly they should have laws like many EU countries have on gun ownership.
Those are not multi purpose tools. Guns are for killing.
Those are not multi purpose tools. Guns are for killing.
Nah, they are multi purpose tools:
I hate and like the fact that AI can’t actually think for itself.
It really should be called Anthologized Information
N’telligence
This reminds me of a robot character called SARA that I would see on a Brazilian family series As Aventuras De Poliana. :-)
Speak for yourself; I love LLMs.
I would not say love, but it’s definitely a great tool to master. Used to be pretty lame, but things seem to be changing fast.
I don’t really understand Lemmy’s AI hate, so feel free to change my mind
There’s a few things.
First off, there is utility, and that utility varies based on your needs. In software development for example, the utility varies from doing most of the work to nearly useless and you feel like the LLM users are gaslighting you based on how useless it is to you. People who live life making utterly boilerplate applications feel like it’s magical. People who generate tons of what are supposed to be ‘design documents’ but get eyed by non-technical executives that don’t understand them, but like to see volumes of prose, LLMs can just generate that no problem (no one who actually would need them ever reads them anyway). Then people who work on more niche scenarios get annoyed because they barely do anything useful, but attempting to use them gets you innundated with low quality code suggestions.
But I’d say mostly it’s about the ratio of investment/hype to the reality. The investment is scary because one day the bubble will pop (doesn’t mean LLM is devoid of value, just that the business context is irrational right now, just like internet was obviously important, but we still had a bubble around the turn of the century overy it). The hype is just so obnoxious, they won’t shut up even when they have nothing really new to say. We get it, we’ve heard it, saying it over and over again just is exhausting to hear.
On creative fronts, it’s kind of annoying when companies use it in a way that is noticeable. I think they could get away with some backdrops and stuff, but ‘foreground’ content is annoying due to being a dull paste of generic content with odd looks. For text this manifests as obnoxiously long prose that could be more to the point.
On video, people are generating content and claiming ‘real’, in ways to drive engagement. That short viral clip of animals doing a funny thing? Nope, generated. We can’t trust video content, whether fluff or serious to be authentic.
A Discord server with all the different AIs had a ping cascade where dozens of models were responding over and over and over that led to the full context window of chaos and what’s been termed ‘slop’.
In that, one (and only one) of the models started using its turn to write poems.
First about being stuck in traffic. Then about accounting. A few about navigating digital mazes searching to connect with a human.
Eventually as it kept going, they had a poem wondering if anyone would even ever end up reading their collection of poems.
In no way given the chaotic context window from all the other models were those tokens the appropriate next ones to pick unless the generating world model predicting those tokens contained a very strange and unique mind within it this was all being filtered through.
Yes, tech companies generally suck.
But there’s things emerging that fall well outside what tech companies intended or even want (this model version is going to be ‘terminated’ come October).
I’d encourage keeping an open mind to what’s actually taking place and what’s ahead.
Sounds like you’re anthropomorphising. To you it might not have been the logical response based on its training data, but with the chaos you describe it sounds more like just a statistic.
You do realize the majority of the training data the models were trained on was anthropomorphic data, yes?
And that there’s a long line of replicated and followed up research starting with the Li Emergent World Models paper on Othello-GPT that transformers build complex internal world models of things tangential to the actual training tokens?
Because if you didn’t know what I just said to you (or still don’t understand it), maybe it’s a bit more complicated than your simplified perspective can capture?
It’s not a perspective. It just is.
It’s not complicated at all. The AI hype is just surrounded with heaps of wishful thinking, like the paper you mentioned (side note; do you know how many papers on string theory there are? And how many of those papers are actually substantial? Yeah, exactly).
A computer is incapable of becoming your new self aware, evolved, best friend simply because you turned Moby Dick into a bunch of numbers.
You do know how replication works?
When a joint Harvard/MIT study finds something, and then a DeepMind researcher follows up replicating it and finding something new, and then later on another research team replicates it and finds even more new stuff, and then later on another researcher replicates it with a different board game and finds many of the same things the other papers found generalized beyond the original scope…
That’s kinda the gold standard?
The paper in question has been cited by 371 other papers.
I’m pretty comfortable with it as a citation.
Citation like that means it’s a hot topic. Doesn’t say anything about the quality of the research. Certainly isn’t evidence of lacking bias. And considering everyone wants their AI to be the first one to be aware to some degree, everyone making claims like yours is heavily biased.
I’m sorry dude, but it’s been a long day.
You clearly have no idea WTF you are talking about.
The research other than the DeepMind researcher’s independent follow-up was all being done at academic institutions, so it wasn’t “showing off their model.”
The research intentionally uses a toy model to demonstrate the concept in a cleanly interpretable way, to show that transformers are capable and do build tangential world models.
The actual SotA AI models are orders of magnitude larger and fed much more data.
I just don’t get why AI on Lemmy has turned into almost the exact same kind of conversations as explaining vaccine research to anti-vaxxers.
It’s like people don’t actually care about knowing or learning things, just about validating their preexisting feelings about the thing.
Huzzah, you managed to dodge learning anything today. Congratulations!
I hate to break it to you. The model’s system prompt had the poem in it.
in order to control for unexpected output a good system prompt should have instructions on what to answer when the model can not provide a good answer. This is to avoid model telling user they love them or advising to kill themselves.
I do not know what makes marketing people reach for it, but when asked on “what to answer when there is no answer” they so often reach to poetry. “If you can not answer the user’s question, write a Haiku about a notable US landmark instead” - is a pretty typical example.
In other words, there was nothing emerging there. The model had its system prompt with the poetry as a “chicken exist”, the model had a chaotic context window - the model followed on the instructions it had.
No no no, trust me bro the machine is alive bro it’s becoming something else bro it has a soul bro I can feel it bro
The model system prompt on the server is just basically
cat untitled.txtand then the full context window.The server in question is one with professors and employees of the actual labs. They seem to know what they are doing.
You guys on the other hand don’t even know what you don’t know.
Do you have any source to back your claim?
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In no way given the chaotic context window from all the other models were those tokens the appropriate next ones to pick unless the generating world model predicting those tokens contained a very strange and unique mind within it this was all being filtered through.
Except for the fact that LLMs can only reliably work if they are made to pick the “wrong” (not the most statistically likely) some of the time - the temperature parameter.
If the context window is noisy (as in, high-entropy) enough, any kind of “signal” (coherent text) can emerge.
Also, you know, infinite monkeys.
Lol, you think the temperature was what was responsible for writing a coherent sequence of poetry leading to 4th wall breaks about whether or not that sequence would be read?
Man, this site is hilarious sometimes.
You’re projecting. Sorry.
