Hm.
Dead reckoning is a terrible way to navigate, and famously led to lots of ships crashed on the shore of France before good clocks allowed tracking longitude accurately.
Ants lay down pheromone trails and use smell to find their way home... There's likely some additional tracking going on, but I would be surprised if it looked anything like symbolic GOFAI.
What's more, his actual point is unclear. Even if you simply grant, "okay, even SOTA LLMs don't have world models", why do I as a user of these models care? Because the models could be wrong? Yes, I'm aware. Nevertheless, I'm still deriving subtantial personal and professional value from the models as they stand today.
Both statistical data generators and actual reasoning are useful in many circumstances, but there are also circumstances in which thinking that you are doing the latter when you are only doing the former can have severe consequences (example: building a bridge).
If nothing else, his perspective is a counterbalance to what is clearly an extreme hype machine that is doing its utmost to force adoption through overpromising, false advertising, etc. These are bad things even if the tech does actually have some useful applications.
As for benchmarks, if you fundamentally don't believe that stochastic data generation leads to reason as an emergent property, developing a benchmark is pointless. Also, not everyone has to be on the same side. It's clear that Marcus is not a fan of the current wave. Asking him to produce a substantive contribution that would help them continue to achieve their goals is preposterous. This game is highly political too. If you think the people pushing this stuff are less than estimable or morally sound, you wouldn't really want to empower them or give them more ideas.
In other words, overhyped in the short term, underhyped in the long term. Where short and long term are extremely volatile.
Take programming as an example. 2.5 years ago, gpt3.5 was seen as "cute" in the programming world. Oh, look, it does poems and e-mails, and the code looks like python but it's wrong 9 times out of 10. But now a 24B model can handle end-to-end SWE tasks in 0-shot a lot of the times.
To use chess as an example. Humans sometimes play illegal moves. That does not mean Humans cannot reason. It is an instance of failing to show proof of reasoning. Not a proof of the inability to reason.
Is it possible that reason could emerge as the byproduct of being really good at predicting words? Maybe, but this depends on the antecedent claim that much if not all of reason is strictly representational and strictly linguistic. It's not obvious to me that this is the case. Many people think in images as direct sense datum, and it's not clear that a digital representation of this is equivalent to the thing in itself.
To use an example another HN'er suggested, We don't claim that submarines are swimming. Why are we so quick to claim that LLMs are "reasoning"?
Imagine we had such marketing behind wheels — they move, so they must be like legs on the inside. Then we run around imagining what the blood vessels and bones must look like inside the wheel. Nevermind that neither the structure nor the procedure has anything to do with legs whatsoever.
Sadly, whoever named it artificial intelligence and neural networks likely knew exactly what they were doing.
I'm with you on this. Software engineers talk about being in the flow when they are at their most productive. For me, the telltale sign of being in the flow is that I'm no longer thinking in English, but I'm somehow navigating the problem / solution space more intuitively. The same thing happens in many other domains. We learn to walk long before we have the language for all the cognitive processes required. I don't think we deeply understand what's going in these situations, so how are we going to build something to emulate it? I certainly don't consciously predict the next token, especially when I'm in the flow.
And why would we try to emulate how we do it? I'd much rather have technology that complements. I want different failure modes and different abilities so that we can achieve more with these tools than we could by just adding subservient humans. The good news is that everything we've built so far is succeeding at this!
We'll know that society is finally starting to understand these technologies and how to apply them when we are able to get away from using science fiction tropes to talk about them. The people I know who develop LLMs for a living, and the others I know that are creating the most interesting applications of them, already talk about them as tools without any need to anthropomorphize. It's sad to watch their frustration as they are slowed down every time a person in power shows up with a vision based on assumptions of human-like qualities rather than a vision informed by the actual qualities of the technology.
Maybe I'm being too harsh or impatient? I suppose we had to slowly come to understand the unique qualities of a "car" before we could stop limiting our thinking by referring to it as a "horseless carriage".
On a more general level, I also never understood this urge to build machines that are "just like us". Like you I want machines that, arguably, are best characterized by the ways in which they are not like us—more reliable, more precise, serving a specific function. It's telling that critiques of the failures of LLMs are often met with "humans have the same problems"—why are humans the bar? We have plenty of humans. We don't need more humans. If we're investing so much time and energy, shouldn't the bar be bette than humans? And if it isn't, why isn't it? Oh, right it's because actually human error is good enough and the actual benefit of these tools is that they are humans that can work without break, don't have autonomy, and that you don't need to listen to or pay. The main beneficiaries of this path are capital owners who just want free labor. That's literally all this is. People who actually want to build stuff want precision machines that are tailored for the task at hand, not some grab bag of sort of works sometimes stochastic doohickeys.
https://arxiv.org/abs/2506.01622
Are world models a necessary ingredient for flexible, goal-directed behaviour, or is model-free learning sufficient? We provide a formal answer to this question, showing that any agent capable of generalizing to multi-step goal-directed tasks must have learned a predictive model of its environment. We show that this model can be extracted from the agent's policy, and that increasing the agents performance or the complexity of the goals it can achieve requires learning increasingly accurate world models. This has a number of consequences: from developing safe and general agents, to bounding agent capabilities in complex environments, and providing new algorithms for eliciting world models from agents.
energy123•2h ago
https://www.anthropic.com/news/tracing-thoughts-language-mod...