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.
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.
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.
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.
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 think we should indict Sam Altman on two sets of charges:
A set of securities fraud charges.
8 billion counts of criminal reckless endangerment.
He's out on podcasts constantly saying the OpenAI is near superintelligent AGI and that there's a good chance that they won't be able to control it, and that human survival is at risk. How is gambling with human extinction not a massive act of planetary-scale criminal reckless endangerment?
So either he is putting the entire planet at risk, or he is lying through his teeth about how far along OpenAI is. If he's telling the truth, he's endangering us all. If he's lying, then he's committing securities fraud in an attempt to defraud shareholders. Either way, he should be in prison. I say we indict him for both simultaneously and let the courts sort it 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.
The AI ecosystem is flooded, we need a good bubble pop to slow down the massive waste of resources that our current info-remix-based-on-what-you-will-likely-react-positively-to shit-tier AI represents.
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.
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."
The scale of capital being set on fire in the pursuit of LLMs is just staggering.
I'm guessing you weren't around in the 90s then? Because the amount of money set on fire on stupid dotcom startups was also staggering. Yet here we are, the winners survived and the market is completely recovered now (took about 15 years because 2008 happened).
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
Maybe. Or if the research is promising enough, investors will dump money into it just like they did with LLMs, and we'll be right back where we are now with ridiculous valuations.
I'm guessing you weren't around in the 90s then? Because the amount of money set on fire on stupid dotcom startups was also staggering.
The scale is very different. OpenAI needs to raise capital at a valuation far higher than any other startup in history just to keep the doors open another 18-24 months. And then continue to do so.
There's also a very large difference between far ranging bad investments and extremely concentrated ones. The current bubble is distinctly the latter. There hasn't really been a bubble completely dependent on massive capital investments by a handful of major players like this before.
There's OpenAI and Anthropic (and by proxy MS/Google/Amazon). Meta is a lesser player. Musk-backed companies are pretty much teetering at the edge of also rans and there's a huge cliff for everything after that.
It's hard for me to imagine investors that don't understand the technology now but getting burned by it being enthusiastic about investing in a new technology they don't understand that promises the same things, but is totally different this time, trust me. Institutional and systemic trauma is real.
(took about 15 years because 2008 happened).
I mean, that's kind of exactly what I'm saying? Not that it's irrecoverable, but that losing a decade plus of progress is significant. I think the disconnect is that you don't seem to think that's a big deal as long as things eventually bounce back. I see that as potentially losing out on a generation worth of researchers and one of the largest opportunity costs associated with the LLM craze.
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.