“The real benchmark is: the world growing at 10 percent,” he added. “Suddenly productivity goes up and the economy is growing at a faster rate. When that happens, we’ll be fine as an industry.”
Needless to say, we haven’t seen anything like that yet. OpenAI’s top AI agent — the tech that people like OpenAI CEO Sam Altman say is poised to upend the economy — still moves at a snail’s pace and requires constant supervision.
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I’ve been working on an internal project for my job - a quarterly report on the most bleeding edge use cases of AI, and the stuff achieved is genuinely really impressive.
So why is the AI at the top end amazing yet everything we use is a piece of literal shit?
The answer is the chatbot. If you have the technical nous to program machine learning tools it can accomplish truly stunning processes at speeds not seen before.
If you don’t know how to do - for eg - a Fourier transform - you lack the skills to use the tools effectively. That’s no one’s fault, not everyone needs that knowledge, but it does explain the gap between promise and delivery. It can only help you do what you already know how to do faster.
Same for coding, if you understand what your code does, it’s a helpful tool for unsticking part of a problem, it can’t write the whole thing from scratch
For coding it’s also useful for doing the menial grunt work that’s easy but just takes time.
You’re not going to replace a senior dev with it, of course, but it’s a great tool.
My previous employer was using AI for intelligent document processing, and the results were absolutely amazing. They did sink a few million dollars into getting the LLM fine tuned properly, though.
YES
YES
FUCKING YES! THIS IS A WIN!
Hopefully they curtail their investments and stop wasting so much fucking power.
I think the best way I’ve heard it put is “if we absolutely have to burn down a forest, I want warp drive out of it. Not a crappy python app”
AI is burning a shit ton of energy and researchers’ time though!
It is fun to generate some stupid images a few times, but you can’t trust that “AI” crap with anything serious.
I was just talking about this with someone the other day. While it’s truly remarkable what AI can do, its margin for error is just too big for most if not all of the use cases companies want to use it for.
For example, I use the Hoarder app which is a site bookmarking program, and when I save any given site, it feeds the text into a local Ollama model which summarizes it, conjures up some tags, and applies the tags to it. This is useful for me, and if it generates a few extra tags that aren’t useful, it doesn’t really disrupt my workflow at all. So this is a net benefit for me, but this use case will not be earning these corps any amount of profit.
On the other end, you have Googles Gemini that now gives you an AI generated answer to your queries. The point of this is to aggregate data from several sources within the search results and return it to you, saving you the time of having to look through several search results yourself. And like 90% of the time it actually does a great job. The problem with this is the goal, which is to save you from having to check individual sources, and its reliability rate. If I google 100 things and Gemini correctly answers 99 of those things accurate abut completely hallucinates the 100th, then that means that all 100 times I have to check its sources and verify that what it said was correct. Which means I’m now back to just… you know… looking through the search results one by one like I would have anyway without the AI.
So while AI is far from useless, it can’t now and never will be able to be relied on for anything important, and that’s where the money to be made is.
Even your manual search results may have you find incorrect sources, selection bias for what you want to see, heck even AI generated slop, so the AI generated results will just be another layer on top. Link aggregating search engines are slowly becoming useless at this rate.
While that’s true, the thing that stuck out to me is not even that the AI was mislead by itself finding AI slop, or even somebody falsely asserting something. I googled something with a particular yea or no answer. “Does X technology use Y protocol”. The AI came back with “Yes it does, and here’s how it uses it”, and upon visiting the reference page for that answer, it was documentation for that technology where it explained very clearly that x technology does NOT use Y protocol, and then went into detail on why it doesn’t. So even when everything lines up and the answer is clear and unambiguous, the AI can give you an entirely fabricated answer.
And crashing the markets in the process… At the same time they came out with a bunch of mambo jumbo and scifi babble about having a million qbit quantum chip… 😂
Tech is basically trying to push up the stocks one hype idea after another. Social media bubble about to burst? AI! AI about to burst? Quantum! I’m sure that when people will start realizing quantum computing is another smokescreen, a new moronic idea will start to gain steam from all those LinkedIn “luminaries”
Quantum computation is a lot like fusion.
We know how it works and we know that it would be highly beneficial to society but, getting it to work with reliability and at scale is hard and expensive.
Sure, things get over hyped because capitalism but that doesn’t make the technology worthless… It just shows how our economic system rewards lies and misleading people for money.
JC Denton said it best in 2001:
I’m convinced the devs actually time traveled back from like 2035
Unlikely, all time travel technology will have been destroyed in the war, before 2035
That would be worrying
Very bold move, in a tech climate in which CEOs declare generative AI to be the answer to everything, and in which shareholders expect line to go up faster…
I half expect to next read an article about his ouster.
My theory is it’s only a matter of time until the firing sprees generate enough backlog of actual work that isn’t being realised by the minor productivity gains from AI until the investors start asking hard questions.
Maybe this is the start of the bubble bursting.
Well duh
Correction, LLMs being used to automate shit doesn’t generate any value. The underlying AI technology is generating tons of value.
AlphaFold 2 has advanced biochemistry research in protein folding by multiple decades in just a couple years, taking us from 150,000 known protein structures to 200 Million in a year.
Well sure, but you’re forgetting that the federal government has pulled the rug out from under health research and therefore had made it so there is no economic value in biochemistry.
Yeah tbh, AI has been an insane helpful tool in my analysis and writing. Never would I have been able to do thoroughly investigate appropriate statisticall tests on my own. After following the sources and double checking ofcourse, but still, super helpful.
Thanks. So the underlying architecture that powers LLMs has application in things besides language generation like protein folding and DNA sequencing.
Image recognition models are also useful for astronomy. The largest black hole jet was discovered recently, and it was done, in part, by using an AI model to sift through vast amounts of data.
https://www.youtube.com/watch?v=wC1lssgsEGY
This thing is so big, it travels between voids in the filaments of galactic super clusters and hits the next one over.
alphafold is not an LLM, so no, not really
You are correct that AlphaFold is not an LLM, but they are both possible because of the same breakthrough in deep learning, the transformer and so do share similar architecture components.
And all that would not have been possible without linear algebra and calculus, and so on and so forth… Come on, the work on transformers is clearly separable from deep learning.
That’s like saying the work on rockets is clearly separable from thermodynamics.
A Large Language Model is a translator basically, all it did was bridge the gap between us speaking normally and a computer understanding what we are saying.
The actual decisions all these “AI” programs do are Machine Learning algorithms, and these algorithms have not fundamentally changed since we created them and started tweaking them in the 90s.
AI is basically a marketing term that companies jumped on to generate hype because they made it so the ML programs could talk to you, but they’re not actually intelligent in the same sense people are, at least by the definitions set by computer scientists.
What algorithm are you referring to?
The fundamental idea to use matrix multiplication plus a non linear function, the idea of deep learning i.e. back propagating derivatives and the idea of gradient descent in general, may not have changed but the actual algorithms sure have.
For example, the transformer architecture (that is utilized by most modern models) based on multi headed self attention, optimizers like adamw, the whole idea of diffusion for image generation are I would say quite disruptive.
Another point is that generative ai was always belittled in the research community, until like 2015 (subjective feeling would need meta study to confirm). The focus was mostly on classification something not much talked about today in comparison.
Wow i didn’t expect this to upset people.
When I say it hasn’t fundamentally changed from an AI perspective i mean there is no intelligence in artificial Intelligence.
There is no true understanding of self, just what we expect to hear. There is no problem solving, the step by steps the newer bots put out are still just ripped from internet search results. There is no autonomous behavior.
AI does not meet the definitions of AI, and no amount of long winded explanations of fundamentally the same approach will change that, and neither will spam downvotes.
Btw I didn’t down vote you.
Your reply begs the question which definition of AI you are using.
The above is from Russells and Norvigs “Artificial Intelligence: A Modern Approach” 3rd edition.I would argue that from these 8 definitions 6 apply to modern deep learning stuff. Only the category titled “Thinking Humanly” would agree with you but I personally think that these seem to be self defeating, i.e. defining AI in a way that is so dependent on humans that a machine never could have AI, which would make the word meaningless.
AI is just what we call automation until marketing figures out a new way to sell the tech. LLMs are generative AI, hardly useful or valuable, but new and shiny and has a party trick that tickles the human brain in a way that makes people give their money to others. Machine learning and other forms of AI have been around for longer and most have value generating applications but aren’t as fun to demonstrate so they never got the traction LLMs have gathered.
It’s always important to double check the work of AI, but yea it excels at solving problems we’ve been using brute force on
I’m afraid you’re going to have to learn about AI models besides LLMs
LLMs in non-specialized application areas basically reproduce search. In specialized fields, most do the work that automation, data analytics, pattern recognition, purpose built algorithms and brute force did before. And yet the companies charge nx the amount for what is essentially these very conventional approaches, plus statistics. Not surprising at all. Just in awe of how come the parallels to snake oil weren’t immediately obvious.
I think AI is generating negative value … the huge power usage is akin to speculative blockchain currencies. Barring some biochemistry and other very, very specialized uses it hasn’t given anything other than, as you’ve said, plain-language search (with bonus hallucination bullshit, yay!) … snake oil, indeed.
Its a little more complicated than that I think. LLMs and AI is not remotely the same with very different use cases.
I believe in AI for sure in some fields, but I understand the skeptics around LLMs.
But the difference AI is already doing in the medical industry and hospitals is no joke. X-ray scannings and early detection of severe illness is the one being used specifically today, and will save thounsands of lives and millions of dollars / euros.
My point is, its not that black and white.
On this topic, the vast majority of people seem to think that AI means the free tier of ChatGPT.
AI isn’t a magical computer demon that can grant all of your wishes, but that doesn’t mean that it is worthless.
For example, Alphafold essentially solved protein folding and diffusion models built on that discovery let us generate novel proteins with specific properties with the same ease as we can make a picture of an astronaut on a horse.
Image classification is massively useful in manufacturing. Instead of custom designed programs purpose built for each client ($$$), you can find tune existing models with generic tools using labor that doesn’t need to be a software engineer.
Robotics is another field. The amount of work required for humans to design and code their control systems was enormous. Now you can use standard models, give them arbitrary limbs and configurations and train them in simulated environments. This massively cuts down on the amount of engineering work ($$$) required.
That’s standard for emerging technologies. They tend to be loss leaders for quite a long period in the early years.
It’s really weird that so many people gravitate to anything even remotely critical of AI, regardless of context or even accuracy. I don’t really understand the aggressive need for so many people to see it fail.
For me personally, it’s because it’s been so aggressively shoved in my face in every context. I never asked for it, and I can’t escape it. It actively gets in my way at work (github copilot) and has already re-enabled itself at least once. I’d be much happier to just let it exist if it would do the same for me.
Because there’s already been multiple AI bubbles (eg, ELIZA - I had a lot of conversations with FREUD running on an Apple IIe). It’s also been falsely presented as basically “AGI.”
AI models trained to help doctors recognize cancer cells - great, awesome.
AI models used as the default research tool for every subject - very very very bad. It’s also so forced - and because it’s forced, I routinely see that it has generated absolute, misleading, horseshit in response to my research queries. But your average Joe will take that on faith, your high schooler will grow up thinking that Columbus discovered Colombia or something.
I just can’t see AI tools like ChatGPT ever being profitable. It’s a neat little thing that has flaws but generally works well, but I’m just putzing around in the free version. There’s no dollar amount that could be ascribed to the service that it provides that I would be willing to pay, and I think OpenAI has their sights set way too high with the talk of $200/month subscriptions for their top of the line product.
Is he saying it’s just LLMs that are generating no value?
I wish reporters could be more specific with their terminology. They just add to the confusion.
Edit: he’s talking about generative AI, of which LLMs are a subset.
