LLMs have reached a point of diminishing returns
Recent discussions highlight that large language models are facing diminishing returns, with rising training costs and unrealistic expectations leading to unsustainable economic models and potential financial instability in the AI sector.
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Recent discussions in the AI community have confirmed that large language models (LLMs) have reached a point of diminishing returns. Gary Marcus, a prominent critic of the prevailing scaling approach in AI, argues that merely increasing data and computational power will not yield significant improvements in LLM performance. He cites comments from venture capitalist Marc Andreessen and industry editor Amir Efrati, who both acknowledge that advancements in LLMs are slowing down. Marcus warns that the high valuations of companies like OpenAI and Microsoft are based on the unrealistic expectation that LLMs will evolve into artificial general intelligence. He emphasizes that the economic model surrounding LLMs is unsustainable, as the costs of training and scaling continue to rise without corresponding improvements in capabilities. This situation could lead to a financial bubble burst, affecting even major players like Nvidia. Furthermore, Marcus criticizes the media and tech influencers for promoting hype over scientific skepticism, which has influenced U.S. AI policy. He concludes that while LLMs will remain useful tools, they may not meet the inflated expectations set in previous years, and a reevaluation of AI strategies may be necessary to achieve reliable and trustworthy AI.
- LLMs are experiencing diminishing returns in performance improvements.
- High valuations of AI companies are based on unrealistic expectations of LLM capabilities.
- The economic model for LLMs is unsustainable due to rising training costs.
- Media and tech influencers have contributed to the hype surrounding LLMs, impacting AI policy.
- A reevaluation of AI strategies may be needed to achieve reliable AI outcomes.
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35 commentsBut that’s fine, LLMs as-is are amazing without being AGI.
- Then the limits on the amount of curatable data available made the performance gains level off. So they started generating data and that pushed the nose up again.
- Eventually, even with generated data, gains flattened out. So they started increasing inference time. They have now proven that this improves the performance quite a bit.
It's always been a series of S-curves and we have always (sooner or later) innovated to the next level.
Marcus has always been a mouth just trying to take down neural networks.
Someday we will move on from LLMs, large multimodal models, transformers, maybe even neural networks, in order to add new levels and types of intelligence.
But Marcus's mouth will never stop yapping about how it won't work.
I think we are now at the point where we can literally build a digital twin video avatar to handily win a debate with Marcus, and he will continue to deny that any of it really works.
To which Noam Brown added: "I've heard people claim that Sam is just drumming up hype, but from what I've seen everything he's saying matches the ~median view of OpenAI researchers on the ground."
That doesn't mean there's not a lot of juice left to squeeze out of what's available now. Not just from RAG and agent systems, but also integrating neuro-symbolic techniques.
We can do this already just with prompt manipulation and integration with symbolic compute systems: I gave a talk on this at Clojure Conj just the other week (https://youtu.be/OxzUjpihIH4, apologies for the self promotion but I do think it's relevant.).
And that's just using existing LLMs. If we start researching and training them specifically for compatibility with neuro-symbolic data (e.g, directly tokenizing and embedding ontologies and knowledge graphs), it could unlock a tremendous amount of capability.
There’s probably a wall, but what exists might just be good enough for it to not matter much.
But what Marcus seems to be assuming is the impossibility of any fundamental theoretical improvements in the field. I see the reverse; the insights being gained from brute-force models have resulted in a lot of promising research.
Transformers are not the be-all and end-all of models, nor are current training methods the best that can ever be achieved. Discounting any possibility of further theoretical developments seems a bold position to take.
Diminishing returns for investors maybe, but not for humans like me.
Also, new human knowledge is probably only marginally derivative from past knowledge, so we’re not likely to see a vast difference between our knowledge creation and what a system that predicts the next logical thing does.
That’s not a bad thing. We essentially now have indexed logic at scale.
[0] https://www.theinformation.com/articles/openai-shifts-strate...
AI/ML companies are looking to make money by engineering useful systems. It is a fundamental error to assume that scaling LLMs is the only path to "more useful". All of the big players are investigating multimodal predictors and other architectures towards "usefulness".
It has been difficult to have a nuanced public debate about precisely what a model and an intelligent system that incorporates a set of models can accomplish. Some of the difficulty has to do with the hype-cycle and people claiming things that their products cannot do reliably. However, some of it is also because the leading lights (aka public intellectuals) like Marcus have been a tad bit too concerned about proving that they are right, instead of seeking the true nature of the beast.
Meanwhile, the tech is rapidly advancing on fundamental dimensions of reliability and efficiency. So much has been invented in the last few years that we have at least 5 years worth "innovation gas" to drive downstream, vertical-specific innovation.
Crypto again?
1. AI is overvalued;
2. {Many/most/all} AI companies have AI products that don't do what they claim;*
3. AI as a technology is running out of steam;
I'm no fan of Marcus, but I at least want to state his claims as accurately as I can.
To be open, one of my concerns with Marcus he rants a lot. I find it tiresome (I go into more detail in other comments I've made recently.)
So I'll frame it as two questions. First, does Marcus make clear logical arguments? By this I mean does he lay out the premises and the conclusions? Second, independent of the logical (or fallacious) structure of his writing, are Gary Marcus' claims sufficiently clear? Falsifiable? Testable?
Here are some follow-up questions I would put to Marcus, if he's reading this. These correspond to the three points above.
1. How much are AI companies overvalued, if at all, and when will such a "correction" happen?
2. What % of AI companies have products that don't meet their claims. How does such a percentage compare against non-AI companies?
3. What does "running out of steam" mean? What areas of research are doing to hit dead ends? Why? When? Does Marcus carve out exceptions?
Finally, can we disprove anything that Marcus would claim. For example, what would he say, hypothetically speaking, if a future wave of AI technologies make great progress? Would he criticize them as "running out of steam as well?" If he does, isn't he selectively paying attention to the later part of the innovation S-curve while ignoring the beginning?
* You tell me, I haven't yet figured out what he is actually claiming. To be fair, I've been turned off by his writing for a while. Now, I spend much more time reading more thoughtful writers.
Just because openai might be over valued and there are a lot of ai grifters doesn't mean LLMs aren't delivering.
They're astronomically better than they were 2 years ago. And they continue to improve. At some point they might run into a wall, but for now, they're getting better all the time. And real multimodal models are coming down the pipeline.
It's so sad to see Marcus totally lose it. He was once a reasonable person. But his idea of how AI should work was didn't work out. And instead of accepting that and moving forward, or finding a way to adapt, he just decided to turn into a fringe nutjob.
Sure, there is considerable hype around generative AI. There are plenty of flimsy business models. And plenty of overinvestment and misunderstanding of capabilities and risks. But the antidote to this is not more hyperbole.
I would like to find a rational, skeptical, measured version of Marcus. Are you out there?
Same as it ever was, same as it ever was...
To project itself as a sigmoid in ways until it has all the data, the CPU, the literal diplomatic power...
This is what we in the field call the most probably scenario:
"a sneaky fuck"
Related
AI Scaling Myths
The article challenges myths about scaling AI models, emphasizing limitations in data availability and cost. It discusses shifts towards smaller, efficient models and warns against overestimating scaling's role in advancing AGI.
Do AI Companies Work?
AI companies developing large language models face high costs and significant annual losses. Continuous innovation is crucial for competitiveness, as older models quickly lose value to open-source alternatives.