Fingers crossed.
I believe this about as much as I believed the “We’re about to experience the AI singularity” morons.
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:
- Copilot can’t read my mind and figure out what I’m trying to do.
- I’m working on an uncommon problem where the typical solutions don’t work
- Copilot is unable to tell when it doesn’t “know” the answer, because of course it’s just simulating communication and doesn’t really know anything.
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.
So you use other people’s open source code without crediting the authors or respecting their license conditions? Good for you, parasite.
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.
Programmers don’t have the luxury of using inferior toolsets.
Very frequently, yes. As well as closed source code and intellectual property of all kinds. Anyone who tells you otherwise is a liar.
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.Oh god yes, ran into this asking for a shell.nix file with a handful of tricky dependencies. It kept trying to do this insanely complicated temporary pull and build from git instead of just a 6 line file asking for the right packages.
“This code is giving me a return value of X instead of Y”
“Ah the reason you’re having trouble is because you initialized this list with brackets instead of
new().”“How would a syntax error give me an incorrect return”
“You’re right, thanks for correcting me!”
“Ok so like… The problem though.”
Yeah, once you have to question its answer, it’s all over. It got stuck and gave you the next best answer in it’s weights which was absolutely wrong.
You can always restart the convo, re-insert the code and say what’s wrong in a slightly different way and hope the random noise generator leads it down a better path :)
I’m doing some stuff with translation now, and I’m finding you can restart the session, run the same prompt and get better or worse versions of a translation. After a few runs, you can take all the output and ask it to rank each translation on correctness and critique them. I’m still not completely happy with the output, but it does seem that sometime if you MUST get AI to answer the question, there can be value in making it answer it across more than one session.
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.
The fact is that major tech companies are investing billions in this.
They have literally invested billions in every single hype cycle of the last few decades that turned out to be a pile of crap in hindsight. This is a bad argument.
And which are those? There is no technology all major tech companies have invested in like AI AFAIK.
Maybe the dot com wave way back, but are you arguing the Internet came to nothing?
“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.
With current stat prediction models?
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.
Of course! It’s not like animals have jet engines!
Human brains are merely the proof that such energy efficiencies are possible for intelligence. It’s likely we can match or go far beyond that, probably not by emulating biology directly. (Though we certainly may use it as inspiration while we figure out the underlying principles.)
I think I’ve heard about enough of experts predicting the future lately.
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?
Because in some eyes, infinite rapid growth is the only measure of success.
Hope?
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.
There’s a pretty big difference between chatGPT and the science/medicine AIs.
And keep in mind that for LLMs and other chatbots, it’s not that they aren’t useful at all but that they aren’t useful enough to justify their costs. Microsoft is struggling to get significant uptake for Copilot addons in Microsoft 365, and this is when AI companies are still in their “sell below cost and light VC money on fire to survive long enough to gain market share” phase. What happens when the VC money dries up and AI companies have to double their prices (or more) in order to make enough revenue to cover their costs?
Nothing to argue with there. I agree. Many companies will go out of business. Fortunately we’ll still have the llama3’s and mistral’s laying around that I can run locally. On the other hand cost justification is a difficult equation with many variables, so maybe it is or will be in some cases worth the cost. I’m just saying there is some merit.
The merits are real. I do understand the deep mistrust people have for tech companies, but there’s far too much throwing out of the baby with the bath water.
As a solo developer, LLMs are a game-changer. They’ve allowed me to make amazing progress on some of my own projects that I’ve been stuck on for ages.
But it’s not just technical subjects that benefit from LLMs. ChatGPT has been a great travel guide for me. I uploaded a pic of some architecture in Berlin and it went into the history of it, I asked it about some damage to an old church in Spain - turned out to be from the Spanish civil war, where revolutionaries had been mowed down by Franco’s firing squads.
Just today, I was getting help from an LLM for an email to a Portuguese removals company. I sent my message in English with a Portuguese translation, but the guy just replied back with a single sentence in broken English:
“Yes a can , need tho mow m3 you need delivery after e gif the price”
The first bit is pretty obviously “Yes I can” but I couldn’t really be sure what he was trying to say with the rest of it. So I asked ChatGPT who responded:
It seems he’s saying he can handle the delivery but needs to know the total volume (in cubic meters) of your items before he can provide a price. Here’s how I’d interpret it:
“Yes, I can [do the delivery]. I need to know the [volume] in m³ for delivery, and then I’ll give you the price.”
Thanks to LLMs, I’m able to accomplish so many things that would have previously taken multiple internet searches and way more effort.
People pay real money for smartphones.
People pay real Money for AIaaS as well…
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.
so long, see you all in the next hype. Any guesses?
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!
Get a few more hours of sleep
I woke up at 4 this morning. The fridge made a big ice maker noise that sounded like a door getting slammed. Anyway here I am shit posting and reading shit posts.
Tradwives
“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!
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.
It’s had all the signs of a bubble for the last few years.
It’s gonna crash like a self driving tesla. It’s gonna fall apart like a cybertrukkk.
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.
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.
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?
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.
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…
learning is the basis of all known intelligence. LLMs have learned something very specific, AGI would need to be built by generalising the core functionality of learning not as an outgrowth of fully formed LLMs.
and yes the current approach is very much using a brick to open a lock and that’s why it’s … ahem … hit a brick wall.
Yeah, 20 something years ago when I was trying to learn PHP of all things, I really wanted to make a chat bot that could learn what words are… I barely got anywhere but I was trying to program the understanding of sentence structure and feeding it a dictionary of words… My goal was to have it output something on its own …
I see these things become less resource intensive and hopefully running not on some random server…
I found the files… It was closer to 15 years ago…
Trying to invent artificial intelligence to learn php is quite funny lol
I’m amazed I still have the files… But yeah this was before all this shit was big… If I had a better drive I would have ended up more evil than zuck … my plan was to collect data on everyone who used the thing and be able to build profiles on everyone based on what information you gave the chat … And that’s all I can really remember… But it’s probably for the best…
Also a bit sadistic to be honest. Bringing a new form of life into the world only to subject it to PHP.
Right, so AIs don’t really know what words are. All they see are tokens. The tokens could be words and letters, but they could also be image/video features, audio waveforms, or anything else.
deleted by creator
