It’s crazy how much fud is flying around, and legitimately buries good open research. It’s also crazy what these giant corporations are explicitly saying what they’re going to do, and that anyone buys it. TSMC’s allegedly calling Sam Altman a ‘podcast bro’ is spot on, and I’d add “manipulative vampire” to that.
Talk to any long-time resident of localllama and similar “local” AI communities who actually dig into this stuff, and you’ll find immense skepticism, not the crypto-like AI bros like you find on linkedin, twitter and such and blot everything out.
The saddest part is, this is going to cause yet another AI winter. The first few ones were caused by genuine over-enthusiasm but this one is purely fuelled by greed.
TSMC are probably making more money than anyone in this goldrush by selling the shovels and picks, so if that’s their opinion, I feel people should listen…
There’s little in the AI business plan other than hurling money at it and hoping job losses ensue.
I really want to like AI, I’d love to have an intelligent AI assistant or something, but I just struggle to find any uses for it outside of some really niche cases or for basic brainstorming tasks. Otherwise, it just feels like alot of work for very little benefit or results that I can’t even trust or use.
I keep Qwen 32B loaded on my desktop pretty much whenever its on, as an (unreliable) assistant to analyze or parse big texts, to do quick chores or write scripts, to bounce ideas off of or even as a offline replacement for google translate (though I specifically use aya 32B for that).
It does “feel” different when the LLM is local, as you can manipulate the prompt syntax so easily, hammer it with multiple requests that come back really fast when it seems to get something wrong, not worry about refusals or data leakage and such.
I receive alerts when people are outside my house, using security cameras, Blue Iris, CodeProject AI, Node-RED and Home Assistant, using a Google Coral for local AI. That’s a good use case for AI since it avoids false positives that occur with regular motion detection.
For real. Being a software engineer with basic knowledge in ML, I’m just sick of companies from every industry being so desperate to cling onto the hype train they’re willing to label anything with AI, even if it has little or nothing to do with it, just to boost their stock value. I would be so uncomfortable being an employee having to do this.
As someone who was working really hard trying to get my company to be able use some classical ML (with very limited amounts of data), with some knowledge on how AI works, and just generally want to do some cool math stuff at work, being asked incessantly to shove AI into any problem that our execs think are “good sells” and be pressured to think about how we can “use AI” was a terrible feel. They now think my work is insufficient and has been tightening the noose on my team.
They’re all built on top of OpenAI which is very unprofitable at the moment. Feels like the whole industry is built on a shaky foundation.
Putting the entire fate of your company in a different company (OpenAI) is not a great business move. I guess the successful AI startups will eventually transition to self-hosted models like Llama, if they survive that long.
After getting my head around the basics of the way LLMs work I thought “people rely on this for information?”, the model seems ok for tasks like summarisation though
I don’t love it for summarization. If I read a summary, my takeaway may be inaccurate.
Brainstorming is incredible. And revision suggestions. And drafting tedious responses, reformatting, parsing.
In all cases, nothing gets attributed to me unless I read every word and am in a position to verify the output. And I internalize nothing directly, besides philosophy or something. Sure can be an amazing starting point especially compared to a blank page.
It’s good for coding if you train it on your own code base. Not great for writing very complex code since the models tend to hallucinate, but it’s great for common patterns, and straightforward questions specific to your code base that can be answered based on existing code (eg “how do I load a user’s most recent order given their email address?”)
It’s wild when you only know how to use SELECT in SQL, but after a dollar worth of prompting and 10 minutes of your time, you can have a significantly complex query you end up using multiple times a week.
When Mr. Altman visited TSMC’s headquarters in Taiwan shortly after he started his fund-raising effort, he told its executives that it would take $7 trillion and many years to build 36 semiconductor plants and additional data centers to fulfill his vision, two people briefed on the conversation said. It was his first visit to one of the multibillion-dollar plants.
TSMC’s executives found the idea so absurd that they took to calling Mr. Altman a “podcasting bro,” one of these people said. Adding just a few more chip-making plants, much less 36, was incredibly risky because of the money involved.
You’re absolutely right. LLMs are good at faking language and sometimes not even great at that. Not sure why I got downvoted but oh well. But AGI will be game changing if it happens.
I know nothing about anything, but I unfoundedly believe we’re still very far away from the computing power required for that. I think we still underestimate the power of biological brains.
There are people way smarter than me that claim it will be a threshold and would likely grow exponentially after it’s crossed. I guess we won’t know for sure until it happens. I do agree most people get bored easily but if this thing is possible to think for itself without interaction it won’t matter if the humans get bored.
Well there is a very specific architecture “rut” the LLMs people use have fallen into, and even small attempts to break out (like with Jamba) don’t seem to get much interest, unfortunately.
Sure, but LLMs aren’t the only AI being used, nor will they eliminate the other forms of AI. As people see issues with the big LLMs, development focus will change to adopt other approaches.
There is real risk that the hype cycle around LLMs will smother other research in the cradle when the bubble pops.
The hyperscalers are dumping tens of billions of dollars into infrastructure investment every single quarter right now on the promise of LLMs. If LLMs don’t turn into something with a tangible ROI, the term AI will become every bit as radioactive to investors in the future as it is lucrative right now.
Viable paths of research will become much harder to fund if investors get burned because the business model they’re funding right now doesn’t solidify beyond “trust us bro.”
Sure, but those are largely the big tech companies you’re talking about, and research tends to come from universities and private orgs. That funding hasn’t stopped, it just doesn’t get the headlines like massive investments into LLMs currently do. The market goes in cycles, and once it finds something new and promising, it’ll dump money into it until the next hot thing comes along.
There will be massive market consequences if AI fails to deliver on its promises (and I think it will, because the promises are ridiculous), and we get those every so often. If we look back about 25 years, we saw the same thing w/ the dotcom craze, where anything with a website got obscene amounts of funding, even if they didn’t have a viable business model, and we had a massive crash. But important websites survived that bubble bursting, and the market recovered pretty quickly and within a decade we had yet another massive market correction due to another bubble (the housing market, mostly due to corruption in the financial sector).
That’s how the market goes. I think AI will crash, and I think it’ll likely crash in the next 5 years or so, but the underlying technologies will absolutely be a core part of our day-to-day life in the same way the Internet is after the dotcom burst. It’ll also look quite a bit different IMO than what we’re seeing today, and within 10 years of that crash, we’ll likely be beyond where we were just before the crash, at least in terms of overall market capitalization.
It’s a messy cycle, but it seems to work pretty well in aggregate.
Sure, but those are largely the big tech companies you’re talking about, and research tends to come from universities and private orgs.
Well, that’s because the hyperscalers are the only ones who can afford it at this point. Altman has said ChatGPT 4 training cost in the neighborhood of $100M (largely subsidized by Microsoft). The scale of capital being set on fire in the pursuit of LLMs is just staggering. That’s why I think the failure of LLMs will have serious knock-on effects with AI research generally.
To be clear: I don’t disagree with you re: the fact that AI research will continue and will eventually recover. I just think that if the LLM bubble pops, it’s going to set things back for years because it will be much more difficult for researchers to get funded for a long time going forward. It won’t be “LLMs fail and everyone else continues on as normal,” it’s going to be “LLMs fail and have significant collateral damage on the research community.”
As a fervent AI enthusiast, I disagree.
…I’d say it’s 97% hype and marketing.
It’s crazy how much fud is flying around, and legitimately buries good open research. It’s also crazy what these giant corporations are explicitly saying what they’re going to do, and that anyone buys it. TSMC’s allegedly calling Sam Altman a ‘podcast bro’ is spot on, and I’d add “manipulative vampire” to that.
Talk to any long-time resident of localllama and similar “local” AI communities who actually dig into this stuff, and you’ll find immense skepticism, not the crypto-like AI bros like you find on linkedin, twitter and such and blot everything out.
The saddest part is, this is going to cause yet another AI winter. The first few ones were caused by genuine over-enthusiasm but this one is purely fuelled by greed.
TSMC are probably making more money than anyone in this goldrush by selling the shovels and picks, so if that’s their opinion, I feel people should listen…
There’s little in the AI business plan other than hurling money at it and hoping job losses ensue.
TSMC doesn’t really have official opinions, they take silicon orders for money and shrug happily. Being neutral is good for business.
Altman’s scheme is just a whole other level of crazy though.
Agreed that’s why it’s so dangerous. These tech bros are going to do damage with their shitty products. It seems like it’s Altman’s goal, honestly.
He wants money/power, and he is getting it. The rest of the AI field will forever be haunted by his greed.
I really want to like AI, I’d love to have an intelligent AI assistant or something, but I just struggle to find any uses for it outside of some really niche cases or for basic brainstorming tasks. Otherwise, it just feels like alot of work for very little benefit or results that I can’t even trust or use.
It’s useful.
I keep Qwen 32B loaded on my desktop pretty much whenever its on, as an (unreliable) assistant to analyze or parse big texts, to do quick chores or write scripts, to bounce ideas off of or even as a offline replacement for google translate (though I specifically use aya 32B for that).
It does “feel” different when the LLM is local, as you can manipulate the prompt syntax so easily, hammer it with multiple requests that come back really fast when it seems to get something wrong, not worry about refusals or data leakage and such.
Attractive. You got some pretty solid specs?
Rue the day I cheaped out on RAM. soldered RAMmmm
I receive alerts when people are outside my house, using security cameras, Blue Iris, CodeProject AI, Node-RED and Home Assistant, using a Google Coral for local AI. That’s a good use case for AI since it avoids false positives that occur with regular motion detection.
For real. Being a software engineer with basic knowledge in ML, I’m just sick of companies from every industry being so desperate to cling onto the hype train they’re willing to label anything with AI, even if it has little or nothing to do with it, just to boost their stock value. I would be so uncomfortable being an employee having to do this.
As someone who was working really hard trying to get my company to be able use some classical ML (with very limited amounts of data), with some knowledge on how AI works, and just generally want to do some cool math stuff at work, being asked incessantly to shove AI into any problem that our execs think are “good sells” and be pressured to think about how we can “use AI” was a terrible feel. They now think my work is insufficient and has been tightening the noose on my team.
For sure, it seems like 90% of ai startups are nothing more than front end wrappers for a gpt instance.
They’re all built on top of OpenAI which is very unprofitable at the moment. Feels like the whole industry is built on a shaky foundation.
Putting the entire fate of your company in a different company (OpenAI) is not a great business move. I guess the successful AI startups will eventually transition to self-hosted models like Llama, if they survive that long.
Seriously, I’d love to be enthusiastic about it because it’s genuinely cool what you can do with math.
But the lies that are shoved in our faces are just so fucking much and so fucking egregious that it’s pretty much impossible.
And on top of that LLMs are hugely overshadowing actual interesting approaches for funding.
After getting my head around the basics of the way LLMs work I thought “people rely on this for information?”, the model seems ok for tasks like summarisation though
I don’t love it for summarization. If I read a summary, my takeaway may be inaccurate.
Brainstorming is incredible. And revision suggestions. And drafting tedious responses, reformatting, parsing.
In all cases, nothing gets attributed to me unless I read every word and am in a position to verify the output. And I internalize nothing directly, besides philosophy or something. Sure can be an amazing starting point especially compared to a blank page.
It’s good for coding if you train it on your own code base. Not great for writing very complex code since the models tend to hallucinate, but it’s great for common patterns, and straightforward questions specific to your code base that can be answered based on existing code (eg “how do I load a user’s most recent order given their email address?”)
It’s wild when you only know how to use SELECT in SQL, but after a dollar worth of prompting and 10 minutes of your time, you can have a significantly complex query you end up using multiple times a week.
That and retrieval and the business use cases so far, but even then only if the results can be wrong somewhat frequently.
What’s the source for that? It sounds hilarious
https://web.archive.org/web/20240930204245/https://www.nytimes.com/2024/09/25/business/openai-plan-electricity.html
It’s selling the future, but nobody knows if we can actually get there
It’s selling an anticompetitive dystopia. It’s selling a Facebook monopoly vs selling the Fediverse.
We dont need 7 trillion dollars of datacenters burning the Earth, we need collaborative, open source innovation.
The first part is true … no one cares about the second part of your statement.
Yep the current iteration is. But should we cross the threshold to full AGI… that’s either gonna be awesome or world ending. Not sure which.
Current LLMs cannot be AGI, no matter how big they are. The fundamental architecture just isn’t right.
You’re absolutely right. LLMs are good at faking language and sometimes not even great at that. Not sure why I got downvoted but oh well. But AGI will be game changing if it happens.
I know nothing about anything, but I unfoundedly believe we’re still very far away from the computing power required for that. I think we still underestimate the power of biological brains.
Very likely. But 4 years ago I would have said we weren’t close to what these LLMs can do now so who knows.
Based on what I’ve witnessed so far, people will play with their AGI units for a bit and then put them down to continue scrolling memes.
Which means it is neither awesome, nor world-ending, but just boring/business as usual.
There are people way smarter than me that claim it will be a threshold and would likely grow exponentially after it’s crossed. I guess we won’t know for sure until it happens. I do agree most people get bored easily but if this thing is possible to think for itself without interaction it won’t matter if the humans get bored.
Ya, it’s like machine learning but better. That’s about it IMO.
Edit: As I have to spell it out: as opposed to (machine learning with) neural networks.
I mean… it is machine learning.
It’s also neural networks, and probably some other CS structures.
AI is a category, and even specific implementations tend to use multiple techniques.
Well there is a very specific architecture “rut” the LLMs people use have fallen into, and even small attempts to break out (like with Jamba) don’t seem to get much interest, unfortunately.
Sure, but LLMs aren’t the only AI being used, nor will they eliminate the other forms of AI. As people see issues with the big LLMs, development focus will change to adopt other approaches.
There is real risk that the hype cycle around LLMs will smother other research in the cradle when the bubble pops.
The hyperscalers are dumping tens of billions of dollars into infrastructure investment every single quarter right now on the promise of LLMs. If LLMs don’t turn into something with a tangible ROI, the term AI will become every bit as radioactive to investors in the future as it is lucrative right now.
Viable paths of research will become much harder to fund if investors get burned because the business model they’re funding right now doesn’t solidify beyond “trust us bro.”
Well you say that, but somehow crypto is still around despite most schemes being (IMO) a much more explicit scam. We have politicans supporting it.
Sure, but those are largely the big tech companies you’re talking about, and research tends to come from universities and private orgs. That funding hasn’t stopped, it just doesn’t get the headlines like massive investments into LLMs currently do. The market goes in cycles, and once it finds something new and promising, it’ll dump money into it until the next hot thing comes along.
There will be massive market consequences if AI fails to deliver on its promises (and I think it will, because the promises are ridiculous), and we get those every so often. If we look back about 25 years, we saw the same thing w/ the dotcom craze, where anything with a website got obscene amounts of funding, even if they didn’t have a viable business model, and we had a massive crash. But important websites survived that bubble bursting, and the market recovered pretty quickly and within a decade we had yet another massive market correction due to another bubble (the housing market, mostly due to corruption in the financial sector).
That’s how the market goes. I think AI will crash, and I think it’ll likely crash in the next 5 years or so, but the underlying technologies will absolutely be a core part of our day-to-day life in the same way the Internet is after the dotcom burst. It’ll also look quite a bit different IMO than what we’re seeing today, and within 10 years of that crash, we’ll likely be beyond where we were just before the crash, at least in terms of overall market capitalization.
It’s a messy cycle, but it seems to work pretty well in aggregate.
Well, that’s because the hyperscalers are the only ones who can afford it at this point. Altman has said ChatGPT 4 training cost in the neighborhood of $100M (largely subsidized by Microsoft). The scale of capital being set on fire in the pursuit of LLMs is just staggering. That’s why I think the failure of LLMs will have serious knock-on effects with AI research generally.
To be clear: I don’t disagree with you re: the fact that AI research will continue and will eventually recover. I just think that if the LLM bubble pops, it’s going to set things back for years because it will be much more difficult for researchers to get funded for a long time going forward. It won’t be “LLMs fail and everyone else continues on as normal,” it’s going to be “LLMs fail and have significant collateral damage on the research community.”
That’s like saying breathing is like turning oxygen into carbon dioxide but better…