The conventional wisdom, well captured recently by Ethan Mollick, is that LLMs are advancing exponentially. A few days ago, in very popular blog post, Mollick claimed that “the current best estimates of the rate of improvement in Large Language models show capabilities doubling every 5 to 14 months”:
pretty hype for third ai winter tbh
It’s not going to happen. The previous AI winters happened because hardware just wasn’t there to do the math necessary.
Right now we absolutely have the hardware. Besides, AI is more than just ChatGPT. Object recognition and image/video analytics have been big business for about a decade now, and still growing strong. The technology is proven, matured, and well established at this point.
And there are other well established segments of AI that are, for the most part, boring to the average person. Stuff like dataset analytics, processing large amounts of data (scientific data, financial stuff, etc).
LLMs may have reached a point of diminishing returns (though I sincerely doubt it) but LLMs are a fraction of the whole AI industry. And transformer models are not the only kind of known model and there’s nonstop research happening at break neck speed.
There will never be another AI winter. The hype train will slow down eventually, but never another winter.
What we haven’t hit yet is the point of diminishing returns for model efficiency. Small, locally run models are still progressing rapidly, which means we’re going to see improvements for the everyday person instead of just for corporations with huge GPU clusters.
That in turn allows more scientists with lower budgets to experiment on LLMs, increasing the chances of the next major innovation.
Exactly. We’re still very early days with this stuff.
The next few years will be wild.
I think increasingly specialized models and analog systems that run them will be increasingly prevalent.
LLMs at their current scales don’t do enough to be worth their enormous cost… And adding more data is increasingly difficult.
That said: the gains on LLMs have always been linear based on recent research. Emergence was always illusory.
I’d like to read the research you alluded to. What research specifically did you have in mind?
I think it depends on how you define AI winter. To me, the hype dying down is quite a winter. Hype dying -> less interest in AI in general. But will development stop? Of course not, the same as the previous AI winter, AI researchers didn’t stop. But there are a decreasing number of them eventually.
But that’s not how the industry defines AI winter. You’re thinking of hype in the context of public perception, but that’s not what matters.
Previous AI interest was about huge investments into research with the hope of a return on that investment. But since it didn’t pan out, the interest (from investors) dried up and progress drastically slowed down.
GPUs are what made the difference. Finally AI research could produce meaningful results and that’s where we’re at now.
Previously AI research could not exist without external financial support. Today AI is fully self-sustaining, meaning companies using AI are making a profit while also directing some of that money back into research and development.
And we’re not talking chump change, we’re talking hundreds of billions. Nvidia has effectively pivoted from a gaming hardware company to the number one AI accelerator manufacturer in the world.
There’s also a number of companies that have started developing and making analogue AI accelerators. In many cases the so the same workload for a fraction of the energy costs of a digital one (like the H100).
There’s so much happening every day and it keeps getting faster and faster. It is NOT slowing down anytime soon, and at this point it will never stop.
How can I verify this?
Look at the number of companies offering AI based video surveillance. That sector alone is worth tens of billions each year and still growing.
Just about every large company is using AI in some way. Google and Microsoft are using AI in their backend systems for things like spam filtering.
You’re thinking of AI as “ChatGPT” but the market for AI has been established well before ChatGPT became popular. It’s just the “new” guy on the scene that the news cycle is going crazy over.
I’m interested in LLMs and how they are being used because that’s what large sums of money is being thrown at with very uncertain future returns.
I have no idea how LLMs are being used by private companies to generate profit.
What I do know is that other forms of AI are employed in cybersecurity, fintech, video surveillance, spam filtering, etc.
The AI video surveillance market is huge and constantly growing. This is a proven and mature segment worth tens of billions every year and constantly growing.
I find it interesting that you keep driving the point about LLMs and constantly ignore my point that AI is way bigger than just LLMs, and that AI is making billions of dollars for companies every year.
Oh, I see. If that’s how we would define it then yes of course. I mean, I already saw upscaler and other “AI” technologies being used on consumer hardware. That is actually useful AI. LLM usefulness compared to their resource consumption is IMHO not worth it.
If you worked in that industry you’d have a different opinion. Using LLMs to write poetry or make stories is frivolous, but there are other applications that aren’t.
Some companies are using them to find new and better drugs, to solve diseases, invent new materials, etc.
Then there’s the consideration that a number of companies are coming out with AI accelerators that are analogue based and use a tiny fraction of the energy current systems use for the same workloads.
I work in the field for a company with 40k staff and over 6 million customers.
We have about 100 dedicated data science professionals and we have 1 LLM we use for our chatbots vs a few hundred ML models running.
LLMs are overhyped and not delivering as much as people claim, most businesses doing LLM will not exist in 2-5 years because Amazon, Google and Microsoft will offer it all cheaper or free.
They are great at generating content but honestly most content is crap because it’s AI rejuvenating something it’s been trained on. They are our next gen spam for the most part.
I absolutely agree it’s overhyped, but that doesn’t mean useless. And these systems are getting better everyday. And the money isn’t going to be in these massive models. It’s going to be in smaller domain specific models. MoE models show better results over models that are 10x larger. It’s still 100% early days.
I somewhat agree with this, but since the LLM hype train started just over a year ago, smaller open source fine-tuned models have been keeping ahead of the big players that are too big to shift quickly. Google even mentioned in an internal memo that the open source community had accomplished in a few months what they thought was literally impossible and could never happen (to prune and quantize models and fine-tune them to get results very close to larger models).
And there are always more companies that spring up around a new tech than the number that continue to exist after a few years. That’s been the case for decades now.
Well, this is actually demonstrably false. There are many thorough examples of how LLMs can generate novel data, even papers written on the subject. But beyond generating new and novel data, the use for LLMs are more than that. They are able to discern patterns, perform analysis, summarize data, problem solve, etc. All of which have various applications.
But ultimately, how is “regurgitating something it’s been trained on” any different from how we learn? The reality is that we ourselves can only generate things based on things we’ve learned. The difference is that we learn basically about everything. And we have a constant stream of input from all our senses as well as ideas/thoughts shared with other people.
Edit: a great example of how we can’t “generate” something outside of what we’ve learned is that we are 100% incapable of visualizing a 4 dimensional object. And I mean visualize in your mind’s eye like you can with any other kind of shape or object. You can close your eyes right now and see a cube or sphere, but you are incapable of visualizing a hyper-cube or a hyper-sphere, even though we can describe them mathematically and even render them with software by projecting them onto a 3D virtual environment (like how a photo is a 2D representation of a 3D environment).
/End-Edit
It’s not an exaggeration that neural networks are trained the same way biologic neural networks (aka brains) are trained. But there’s obviously a huge difference in the inner workings.
Maybe the last gen models, definitely not the current gen SOTA models, and the models coming in the next few years will only get better. 10 years from now is going to look wild.
deleted by creator