What’s different about investing in this than investing in say a young researcher’s startup, or Ilya’s superintelligence? In both those cases, if a model architecture isn’t working out, I believe they will pivot. In YL’s case, I’m not sure that is true.

In that light, this bet is a bet on YL’s current view of the world. If his view is accurate, this is very good for Europe. If inaccurate, then this is sort of a nothing-burger; company will likely exit for roughly the investment amount - that money would not have gone to smaller European startups anyway - it’s a wash.

FWIW, I don’t think the original complaint about auto-regression “errors exist, errors always multiply under sequential token choice, ergo errors are endemic and this architecture sucks” is intellectually that compelling. Here: “world model errors exist, world model errors will always multiply under sequential token choice, ergo world model errors are endemic and this architecture sucks.” See what I did there?

On the other hand, we have a lot of unused training tokens in videos, I’d like very much to talk to a model with excellent ‘world’ knowledge and frontier textual capabilities, and I hope this goes well. Either way, as you say, Europe needs a frontier model company and this could be it.

My main concern with Lecunn are the amount of times he has repeatedly told people software is open source when it’s license directly violates the open source definition.

If you think that LLMs are sufficient and RSI is imminent (<1 year), this is horrible for Europe. It is a distracting boondoggle exactly at the wrong time.

If you're looking to learn about JEPA, LeCun's vision document "A Path Towards Autonomous Machine Intelligence" is long but sketches out a very comprehensive vision of AI research: https://openreview.net/pdf?id=BZ5a1r-kVsf

Training JEPA models within reach, even for startups. For example, we're a 3-person startup who trained a health timeseries JEPA. There are JEPA models for computer vision and (even) for LLMs.

You don't need a $1B seed round to do interesting things here. We need more interesting, orthogonal ideas in AI. So I think it's good we're going to have a heavyweight lab in Europe alongside the US and China.

Tech is ultimately a red herring as far as what's needed to keep the EU competitive. The EU has a trillion dollar hole[0] to fill if they want to replace US military presence, and current net import over 50% of their energy. Unfortunately the current situation in Iran is not helping either of these as they constrains energy further and risks requiring military intervention.

0. https://www.wsj.com/world/europe/europes-1-trillion-race-to-...

As the other commenter pointed out, this is 1B seed.

The giant seed round proves investors were willing to fund Mira Murati, not that the company had built anything durable.

Within months, it had already lost cofounder Andrew Tulloch to Meta, then cofounders Barret Zoph and Luke Metz plus researcher Sam Schoenholz to OpenAI; WIRED also reported that at least three other researchers left. At that point, citing it as evidence of real competitive momentum feels weak.

Looks like you appended the original URL to the end

The fundamental problem with today's LLMs that will prevent them from achieving human level intelligence, and creativity, is that they are trained to predict training set continuations, which creates two very major limitations:

1) They are fundamentally a COPYING technology, not a learning or creative one. Of course, as we can see, copying in this fashion will get you an extremely long way, especially since it's deep patterns (not surface level text) being copied and recombined in novel ways. But, not all the way to AGI.

2) They are not grounded, therefore they are going to hallucinate.

The animal intelligence approach, the path to AGI, is also predictive, but what you predict is the external world, the future, not training set continuations. When your predictions are wrong (per perceptual feedback) you take this as a learning signal to update your predictions to do better next time a similar situation arises. This is fundamentally a LEARNING architecture, not a COPYING one. You are learning about the real world, not auto-regressively copying the actions that someone else took (training set continuations).

Since the animal is also acting in the external world that it is predicting, and learning about, this means that it is learning the external effects of it's own actions, i.e. it is learning how to DO things - how to achieve given outcomes. When put together with reasoning/planning, this allows it to plan a sequence of actions that should achieve a given external result ("goal").

Since the animal is predicting the real world, based on perceptual inputs from the real world, this means that it's predictions are grounded in reality, which is necessary to prevent hallucinations.

So, to come back to "world models", yes an animal intelligence/AGI built this way will learn a model of how the world works - how it evolves, and how it reacts (how to control it), but this behavioral model has little in common with the internal generative abstractions that an LLM will have learnt, and it is confusing to use the same name "world model" to refer to them both.

World models and vision seems like a great use case for robotics which I can imagine that being the main driver of AMI.

I agree with you; there should be more diversity in investments in EU startups, but ¯\_(ツ)_/¯ not my money.

And even if you think the chance is zero, unless you also think there is a zero chance they will be capable of pivoting quickly, it might still be beneficial.

I think his views are largely flawed, but chances are there will still be lots of useful science coming out of it as well. Even if current architectures can achieve AGI, it does not mean there can't also be better, cheaper, more effective ways of doing the same things, and so exploring the space more broadly can still be of significant value.

I won't comment on Yann LeCun or his current technical strategy, but if you can avoid sunk cost fallacy and pivot nimbly I don't think it is bad for Europe at all. It is "1 billion dollars for an AI research lab", not "1 billion dollars to do X".

Might be to be close to some of Yann's collaborators like Xavier Bresson at NUS

Models build up this big knowledge base by predicting continuations. But then their RL stage gives rewards for completing problems successfully. This requires learning and generalisation to do well, and indeed RL marked a turning point in LLM performance.

A year after RL was made to work, LLMs can now operate in agent harnesses over 100s of tool calls to complete non-trivial tasks. They can recover from their own mistakes. They can write 1000s of lines of code that works. I think it’s no longer fair to categorise LLMs as just continuation-predictors.

In the last step of training LLMs, reinforcement learning from verified rewards, LLMs are trained to maximize the probability of solving problems using their own output, depending on a reward signal akin to winning in Go. It's not just imitating human written text.

Fwiw, I agree that world models and some kind of learning from interacting with physical reality, rather than massive amounts of digitized gym environments is likely necessary for a breakthrough for AGI.

Using the term autoregressive models instead might help.

But often passion and freedom to explore are often more important than resources

It sounds like you are imagining tacking a world model onto an LLM. That's one approach but not what LeCun advocates for.

I pretty strongly think it will only benefit the rich and powerful while further oppressing and devaluing everyone else. I tend to think this is an obvious outcome and it would be obviously very bad (for most of us)

So I wonder if you just think you will be one of the few who benefit at the expense of others, or do you truly believe AI will benefit all of humanity?

Of course now we know this was delusional and it seems almost funny in retrospect. I feel the same way when I hear that 'just scale language models' suddenly created something that's true AGI, indistinguishable from human intelligence.

Wait, we have another acronym to track. Is this the same/different than AGI and/or ASI?

Of course, each relevant newspaper on those areas highlight that it's coming to their place, but it really seems to be distributed.

Or you're using Cloudflare DNS.

What current LLMs lack is inner motivation to create something on their own without being prompted. To think in their free time (whatever that means for batch, on demand processing), to reflect and learn, eventually to self modify.

I have a simple brain, limited knowledge, limited attention span, limited context memory. Yet I create stuff based what I see, read online. Nothing special, sometimes more based on someone else's project, sometimes on my own ideas which I have no doubt aren't that unique among 8 billions of other people. Yet consulting with AI provides me with more ideas applicable to my current vision of what I want to achieve. Sure it's mostly based on generally known (not always known to me) good practices. But my thoughts are the same way, only more limited by what I have slowly learned so far in my life.

I don't think it makes sense conceptually unless you're literally referring to discovering new physical things like elements or something.

Humans are remixers of ideas. That's all we do all the time. Our thoughts and actions are dictated by our environment and memories; everything must necessarily be built up from pre-existing parts.

Meta absolutely has (or at least had) a word class industry AI lab and has published a ton of great work and open source models (granted their LLM open source stuff failed to keep up with chinese models in 2024/2025 ; their other open source stuff for thins like segmentation don't get enough credit though). Yann's main role was Chief AI Scientist, not any sort of product role, and as far as I can tell he did a great job building up and leading a research group within Meta.

He deserved a lot of credit for pushing Meta to very open to publishing research and open sourcing models trained on large scale data.

Just as one example, Meta (together with NYU) just published "Beyond Language Modeling: An Exploration of Multimodal Pretraining" (https://arxiv.org/pdf/2603.03276) which has a ton of large-experiment backed insights.

Yann did seem to end up with a bit of an inflated ego, but I still consider him a great research lead. Context: I did a PhD focused on AI, and Meta's group had a similar pedigree as Google AI/Deepmind as far as places to go do an internship or go to after graduation.

That's true for 99% of the scientists, but dismissing their opinion based on them not having done world shattering / ground breaking research is probably not the way to go.

> I sincerely wish we will see more competition

I really wish we don't, science isn't markets.

> Understanding world through videos

The word "understanding" is doing a lot of heavy lifting here. I find myself prompting again and again for corrections on an image or a summary and "it" still does not "understand" and keeps doing the same thing over and over again.

Is it a troll? Even if we just ignore Llama, Meta invented and released so many foundational research and open source code. I would say that the computer vision field would be years behind if Meta didn't publish some core research like DETR or MAE.

For a hot minute Meta had a top 3 LLM and open sourced the whole thing, even with LeCunn's reservations around the technology.

At the same time Meta spat out huge breakthroughs in:

- 3d model generation

- Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge breakthrough.

- A whole new class of world modeling techniques (JEPAs)

- SAM (Segment anything)

So I keep wondering: if his idea is really that good — and I genuinely hope it is — why hasn’t it led to anything truly groundbreaking yet? It can’t just be a matter of needing more data or more researchers. You tell me :-D

I can't read the article, but American investors investing into European companies, isn't US the one missing out here? Or does "Europe" "win" when European investors invest in US companies? How does that work in your head?

Almost certainly the IP will be held in Singapore for tax reasons.

Europe in general has been tightening up their rules / taxes / laws around startups / companies especially tech and remote.

It's been less friendly. these days.

It's not a zero sum game, IMO. It will benefit some, be neutral for others, negative for others.

For instance, improved productivity could be good (and doesn't have to result in layoffs, Jevon's paradox will come into play, IMO, with increased demand). Easier/better/faster scientific research could be good too. Not everyone would benefit from those, but not everyone has to for it to be generally good.

Autonomous AI-powered drone swarms could be bad, or could result in a Mutually Assured Destruction stalemate.

Sure LLMs are getting better and better, and at least for me more and more useful, and more and more correct. Arguably better than humans at many tasks yet terribly lacking behind in some others.

Coding wise, one of the things it does “best”, it still has many issues: For me still some of the biggest issues are still lack of initiative and lack of reliable memory. When I do use it to write code the first manifests for me by often sticking to a suboptimal yet overly complex approach quite often. And lack of memory in that I have to keep reminding it of edge cases (else it often breaks functionality), or to stop reinventing the wheel instead of using functions/classes already implemented in the project.

All that can be mitigated by careful prompting, but no matter the claim about information recall accuracy I still find that even with that information in the prompt it is quite unreliable.

And more generally the simple fact that when you talk to one the only way to “store” these memories is externally (ie not by updating the weights), is kinda like dealing with someone that can’t retain memories and has to keep writing things down to even get a small chance to cope. I get that updating the weights is possible in theory but just not practical, still.

The need for a military is tightly coupled with the EU's need for energy. You can see this in the immediate impact that the war in Iran has had on Germany's natural gas prices [0]. But already unable to defend itself from Russia, EU countries are in a tough spot since they can't really afford to expend military resources defending their energy needs, and yet also don't have the energy independence to ignore these military engagements without risk. Meanwhile Russia has spend the last 4 years transition to a wartime economy and is getting hungry for expanded resource acquisition.

The world hasn't fundamentally changed since the stone age: humans need resources to survive and if there aren't enough people for those resources then violence will decide who has access the them.

0. https://tradingeconomics.com/commodity/germany-natural-gas-t...

Have they changed something on their end?

The problem is, idk if we're ready to have millions of distinct, evolving, self-executing models running wild without guardrails. It seems like a contradiction: you can't achieve true cognition from a machine while artificially restricting its boundaries, and you can't lift the boundaries without impacting safety.

Virtual simulations are not substitutable for the physical world. They are fundamentally different theory problems that have almost no overlap in applicability. You could in principle create a simulation with the same mathematical properties as the physical world but no one has ever done that. I'm not sure if we even know how.

Physical world dynamics are metastable and non-linear at every resolution. The models we do build are created from sparse irregular samples with large error rates; you often have to do complex inference to know if a piece of data even represents something real. All of this largely breaks the assumptions of our tidy sampling theorems in mathematics. The problem of physical world inference has been studied for a couple decades in the defense and mapping industries; we already have a pretty good understanding of why LLM-style AI is uniquely bad at inference in this domain, and it mostly comes down to the architectural inability to represent it.

Grounded estimates of the minimum quantity of training data required to build a reliable model of physical world dynamics, given the above properties, is many exabytes. This data exists, so that is not a problem. The models will be orders of magnitude larger than current LLMs. Even if you solve the computer science and theory problems around representation so that learning and inference is efficient, few people are prepared for the scale of it.

(source: many years doing frontier R&D on these problems)

While I suspect latter is a real problem (because all mammal brains* are much more example-efficient than all ML), the former is more about productisation than a fundamental thing: the models can be continuously updated already, but that makes it hard to deal with regressions. You kinda want an artefact with a version stamp that doesn't change itself before you release the update, especially as this isn't like normal software where specific features can be toggled on or off in isolation of everything else.

* I think. Also, I'm saying "mammal" because of an absence of evidence (to my *totally amateur* skill level) not evidence of absence.

As for the "just put a vision LLM in a robot body" suggestion: People are trying this (e.g. Physical Intelligence) and it looks like it's extraordinarily hard! The results so far suggest that bolting perception and embodiment onto a language-model core doesn't produce any kind of causal understanding. The architecture behind the integration of sensory streams, persistent object representations, and modeling time and causality is critically important... and that's where world models come in.

If it was a breakthrough, why did Meta acquire Wang and his company? I'm genuinely curious.

>My only contribution was to push for Llama 2 to be open sourced.

Whenever I see people think the model architecture matters much, I think they have a magical view of AI. Progress comes from high quality data, the models are good as they are now. Of course you can still improve the models, but you get much more upside from data, or even better - from interactive environments. The path to AGI is not based on pure thinking, it's based on scaling interaction.

To remain in the same miasma theory of disease analogy, if you think architecture is the key, then look at how humans dealt with pandemics... Black Death in the 14th century killed half of Europe, and none could think of the germ theory of disease. Think about it - it was as desperate a situation as it gets, and none had the simple spark to keep hygiene.

The fact is we are also not smart from the brain alone, we are smart from our experience. Interaction and environment are the scaffolds of intelligence, not the model. For example 1B users do more for an AI company than a better model, they act like human in the loop curators of LLM work.

You can't get Suno to do anything that's not in its training data. It is physically incapable of inventing a new musical genre. No matter how detailed the instructions you give it, and even if you cheat and provide it with actual MP3 examples of what you want it to create, it is impossible.

The same goes for LLMs and invention generally, which is why they've made no important scientific discoveries.

You can learn a lot by playing with Suno.

Einstein’s theory of relativity springs to mind, which is deeply counter-intuitive and relies on the interaction of forces unknowable to our basic Newtonian senses.

There’s an argument that it’s all turtles (someone told him about universes, he read about gravity, etc), but there are novel maths and novel types of math that arise around and for such theories which would indicate an objective positive expansion of understanding and concept volume.

Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude things about the world based on fact and deduction. They only consider what is likely from their training data. They will not foresee/anticipate events, that are unlikely or non-existent in their training data, but are bound to happen due to real world circumstances. They are not intelligent in that way.

Whether humans always apply that much effort to conclude these things is another question. The point is, that humans fundamentally are capable of doing that, while LLMs are structurally not.

The problems are structural/architectural. I think it will take another 2-3 major leaps in architectures, before these AI models reach human level general intelligence, if they ever reach it. So far they can "merely" often "fake it" when things are statistically common in their training data.

Everything is bits to a computer, but text training data captures the flattened, after-the-fact residue of baseline human thought: Someone's written description of how something works. (At best!)

A world model would need to capture the underlying causal, spatial, and temporal structure of reality itself -- the thing itself, that which generates those descriptions.

You can tokenize an image just as easily as a sentence, sure, but a pile of images and text won't give you a relation between the system and the world. A world model, in theory, can. I mean, we ought to be sufficient proof of this, in a sense...

> One major critique LeCun raises is that LLMs operate only in the realm of language, which is a simple, discrete space compared to the continuous, complex physical world we live in. LLMs can solve math problems or answer trivia because such tasks reduce to pattern completion on text, but they lack any meaningful grounding in physical reality. LeCun points out a striking paradox: we now have language models that can pass the bar exam, solve equations, and compute integrals, yet “where is our domestic robot? Where is a robot that’s as good as a cat in the physical world?” Even a house cat effortlessly navigates the 3D world and manipulates objects — abilities that current AI notably lacks. As LeCun observes, “We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.”

The density of information in the spatiotemporal world is very very great, and a technique is needed to compress that down effectively. JEPAs are a promising technique towards that direction, but if you're not reconstructing text or images, it's a bit harder for humans to immediately grok whether the model is learning something effectively.

I think that very soon we will see JEPA based language models, but their key domain may very well be in robotics where machines really need to experience and reason about the physical the world differently than a purely text based world.

Creating a startup has to be about a product. When you raise 1B, investors are expecting returns, not papers.

Source: himself https://x.com/ylecun/status/1993840625142436160 (“I never worked on any Llama.”) and a million previous reports and tweets from him.

Lecun introduced backprop for deep learning back in 1989 Hinton published about contrastive divergance in next token prediction in 2002 Alexnet was 2012 Word2vec was 2013 Seq2seq was 2014 AiAYN was 2017 UnicornAI was 2019 Instructgpt was 2022

This makes alot of people think that things are just accelerating and they can be along for the ride. But its the years and years of foundational research that allows this to be done. That toll has to be paid for the successsors of LLMs to be able to reason properly and operate in the world the way humans do. That sowing wont happen as fast as the reaping did. Lecun was to plant those seeds, the others who onky was to eat the fruit dont get that they have to wait

Why would the US miss out here? The US invests in something = the US owns part of something.

This isn't a zero sum game.

As such, They are more likely to talk about singapore news and exaggerate the claims.

Singapore isn't the Key location. From what I am seeing online, France is the major location.

Singapore is just one of the more satellite like offices. They have many offices around the world it seems.

[0]: https://www.sgpbusiness.com/company/Sph-Media-Limited

It already has resulted in layoffs and one of the weakest job markets we've seen in ages

Executives could not have used it as an excuse for layoffs faster, they practically tripped over themselves trying to use it as an excuse to lay people off

Liquid money rich? No.

Can get pulled for big tech packages? Also no, for most of the employees.

AFAIK, big tech didn’t aggressively poach OpenAI-like talent, they did spend 10M+ pay packages but it was for a select few research scientists. Some folks left and came but it boiled down to culture mostly.

Just because RNNs and Transformers both work with enormous datasets doesn't mean that architecture/algorithm is irrelevant, it just suggests that they share underlying primitives. But those primitives may not be the right ones for 'AGI'.

This is literally a description of a zero sum game

What's still missing is the general reasoning ability to plan what to build or how to attack novel problems - how to assess the consequences of deciding to build something a given way, and I doubt that auto-regressively trained LLMs is the way to get there, but there is a huge swathe of apps that are so boilerplate in nature that this isn't the limitation.

I think that LeCun is on the right track to AGI with JEPA - hardly a unique insight, but significant to now have a well funded lab pursuing this approach. Whether they are successful, or timely, will depend if this startup executes as a blue skies research lab, or in more of an urgent engineering mode. I think at this point most of the things needed for AGI are more engineering challenges rather than what I'd consider as research problems.

Top tier scientists aren't gonna be swayed by European state retirement systems.

Can you be a bit more specific at all bounds? Maybe via an example?

Our training data is a lot more diverse than an LLMs. We also leverage our senses as a carrier for communicating abstract ideas using audio and visual channels that may or may not be grounded in reality. We have TV shows, video games, programming languages and all sorts of rich and interesting things we can engage with that do not reflect our fundamental reality.

Like LLMs, we can hallucinate while we sleep or we can delude ourselves with untethered ideas, but UNLIKE LLMs, we can steer our own learning corpus. We can train ourselves with our own untethered “hallucinations” or we can render them in art and share them with others so they can include it in their training corpus.

Our hallucinations are often just erroneous models of the world. When we render it into something that has aesthetic appeal, we might call it art.

If the hallucination helps us understand some aspect of something, we call it a conjecture or hypothesis.

We live in a rich world filled with rich training data. We don’t magically anticipate events not in our training data, but we’re also not void of creativity (“hallucinations”) either.

Most of us are stochastic parrots most of the time. We’ve only gotten this far because there are so many of us and we’ve been on this earth for many generations.

Most of us are dazzled and instinctively driven to mimic the ideas that a small minority of people “hallucinate”.

There is no shame in mimicking or being a stochastic parrot. These are critical features that helped our ancestors survive.

So my question is: when is there enough training data that you can handle 99.99% of the world ?

[1] https://arxiv.org/pdf/2501.00663

[2] https://arxiv.org/pdf/2512.24695

But one might say that the brain is not lossless ... True, good point. But in what way is it lossy? Can that be simulated well enough to learn an Einstein? What gives events significance is very subjective.

"Reading, after a certain age, diverts the mind too much from its creative pursuits. Any man who reads too much and uses his own brain too little falls into lazy habits of thinking".

-- Albert Einstein

I like how people are accepting this dubious assertion that Einstein would be "useful" if you surgically removed his hippocampus and engaging with this.

It also calls this Einstein an AGI rather than a disabled human???

https://en.wikipedia.org/wiki/Moravec%27s_paradox

All the things we look at as "Smart" seem to be the things we struggle with, not what is objectively difficult, if that can even be defined.

So when we think about capturing any underlying structure of reality itself, we are constrained by the tools at hand.

The capability of the tool forms the description which grants the level of understanding.

> I wasn't criticising his scientific contribution at all, that's why I started my comment by appraising what he did.

You were criticising his output at Facebook, though, but he was in the research group at facebook, not a product group, so it seems like we did actually see lots of things?

I have no chance in AI industry...

If he still hasn’t produced anything truly meaningful after all these years at Meta, when is that supposed to happen? Yann LeCun has been at Facebook/Meta since December 2013.

Your chronological sequence is interesting, but it refers to a time when the number of researchers and the amount of compute available were a tiny fraction of what they are today.

Personally I don't believe anyone is missing out on anything here.

But rvz earlier claimed that Europe is missing out, because US investors are investing in a European company. That's kind of surprising to me, so asking if they also believe that the US is "missing out" whenever European investors invest in US companies, or if that sentiment only goes one way.

I'm on the contrary believe that the hunt for better data is an attempt to climb the local hill and be stuck there without reaching the global maximum. Interactive environments are good, they can help, but it is just one of possible ways to learn about causality. Is it the best way? I don't think so, it is the easier way: just throw money at the problem and eventually you'll get something that you'll claim to be the goal you chased all this time. And yes, it will have something in it you will be able to call "causal inference" in your marketing.

But current models are notoriously difficult to teach. They eat enormous amount of training data, a human needs much less. They eat enormous amount of energy to train, a human needs much less. It means that the very approach is deficient. It should be possible to do the same with the tiny fraction of data and money.

> The fact is we are also not smart from the brain alone, we are smart from our experience. Interaction and environment are the scaffolds of intelligence, not the model.

Well, I learned English almost all the way to B2 by reading books. I was too lazy to use a dictionary most of the time, so it was not interactive: I didn't interact even with dictionary, I was just reading books. How many books I've read to get to B2? ~10 or so. Well, I read a lot of English in Internet too, and watched some movies. But lets multiply 10 books by 10. Strictly speaking it was not B2, I was almost completely unable to produce English and my pronunciation was not just bad, it was worse. Even now I stumble sometimes on words I cannot pronounce. Like I know the words and I mentally constructed a sentence with it, but I cannot say it, because I don't know how. So to pass B2 I spent some time practicing speech, listening and writing. And learning some stupid topic like "travel" to have a vocabulary to talk about them in length.

How many books does LLM need to consume to get to B2 in a language unknown to it? How many audio records it needs to consume? Life wouldn't be enough for me to read and/or listen so much.

If there was a human who needed to consume as much information as LLM to learn, they would be the stupidest person in all the history of the humanity.

BTW, I went to your website looking for this, but didn't find your blog. I do now see that it's linked in the footer, but I was looking for it in the hamburger menu.

That said, have you considered that “Measure 100+ biomarkers with a single blood draw” combined with "heart health is a solved problem” reads a lot like Theranos?

I think this is true to some extent: we like our tools to be predictable. But we’ve already made one jump by going from deterministic programs to stochastic models. I am sure the moment a self-evolutive AI shows up that clears the "useful enough" threshold we’ll make that jump as well.

Kahneman’s whole framework points the same direction. Most of what people call “reasoning” is fast, associative, pattern-based. The slow, deliberate, step-by-step stuff is effortful and error-prone, and people avoid it when they can. And even when they do engage it, they’re often confabulating a logical-sounding justification for a conclusion they already reached by other means.

So maybe the honest answer is: the gap between what LLMs do and what most humans do most of the time might be smaller than people assume. The story that humans have access to some pure deductive engine and LLMs are just faking it with statistics might be flattering to humans more than it’s accurate.

Where I’d still flag a possible difference is something like adaptability. A person can learn a totally new formal system and start applying its rules, even if clumsily. Whether LLMs can genuinely do that outside their training distribution or just interpolate convincingly is still an open question. But then again, how often do humans actually reason outside their own “training distribution”? Most human insight happens within well-practiced domains.

That's what I said. Backpropagation cannot be enough; that's not how neurons work in the slightest. When you put biological neurons in a Pong environment they learn to play not through some kind of loss or reward function; they self-organize to avoid unpredictable stimulation. As far as I know, no architecture learns in such an unsupervised way.

https://www.sciencedirect.com/science/article/pii/S089662732...

Quite a big contribution in practice.

It really depends what you mean by 'we'. Laymen? Maybe. But people said it was wrong at the time with perfectly good reasoning. It might not have been accessible to the average person, but that's hardly to say that only hindsight could reveal the correct answer.

The specific biomarkers being predicted are the ones most relevant to heart health, like cholesterol or HbA1c. These tend to be more stable from hour to hour -- they may vary on a timescale of weeks as you modify your diet or take medications.

This sounds very similar to me as to what neurons do (avoid unpredictable stimulation)

I've never heard about the Wason selection task, looked it up, and could tell the right answer right away. But I can also tell you why: because I have some familiarity with formal logic and can, in your words, pattern-match the gotcha that "if x then y" is distinct from "if not x then not y".

In contrast to you, this doesn't make me believe that people are bad at logic or don't really think. It tells me that people are unfamiliar with "gotcha" formalities introduced by logicians that don't match the everyday use of language. If you added a simple additional to the problem, such as "Note that in this context, 'if' only means that...", most people would almost certainly answer it correctly.

Mind you, I'm not arguing that human thinking is necessarily more profound from what what LLMs could ever do. However, judging from the output, LLMs have a tenuous grasp on reality, so I don't think that reductionist arguments along the lines of "humans are just as dumb" are fair. There's a difference that we don't really know how to overcome.

It's like the people who are so hyped up about voice controlled computers. Like you get a linear stream of symbols is a huge downgrade in signals, right? I don't want computer interaction to be yet more simplified and worsened.

Compare with domain experts who do real, complicated work with computers, like animators, 3D modelers, CAD, etc. A mouse with six degrees of freedom, and a strong training in hotkeys to command actions and modes, and a good mental model of how everything is working, and these people are dramatically more productive at manipulating data than anyone else.

Imagine trying to talk a computer through nudging a bunch of vertexes through 3D space while flexibly managing modes of "drag" on connected vertexes. It would be terrible. And no, you would not replace that with a sentence of "Bot, I want you to nudge out the elbow of that model" because that does NOT do the same thing at all. An expert being able to fluidly make their idea reality in real time is just not even remotely close to the instead "Project Manager/mediocre implementer" relationship you get prompting any sort of generative model. The models aren't even built to contain specific "Style", so they certainly won't be opinionated enough to have artistic vision, and a strong understanding of what does and does not work in the right context, or how to navigate "My boss wants something stupid that doesn't work and he's a dumb person so how do I convince him to stop the dumb idea and make him think that was his idea?"

The biggest thing thats missing is actual feedback to their decisions. They have no "idea of that because transformers and embeddings dont model that yet. And langiage descriptions and image representations of feedback arent enough. They are too disjointed. It needs more

You’re so close to getting it and I’m rooting for you

I'm not aware that we have notably different data sources before or after transformers, so what confounding event are you suggesting transformers 'lucked' in to being contemporaneous with?

Also, why are we seeing diminishing returns if only the data matters. Are we running out of data?

f(x)=y' => loss(y',y) => how good was my prediction? Train f through backprop with that error.

While a model trained with reinforcement learning is more similar to this. Where m(y) is the resulting world state of taking an action y the model predicted.

f(x)=y' => m(y')=z => reward(z) => how good was the state I was in based on my actions? Train f with an algorithm like REINFORCE with the reward, as the world m is a non-differentiable black-box.

While a group of neurons is more like predicting what is the resulting word state of taking my action, g(x,y), and trying to learn by both tuning g and the action taken f(x).

f(x)=y' => m(y')=z => g(x,y)=z' => loss(z,z') => how predictable was the results of my actions? Train g normally with backprop, and train f with an algorithm like REINFORCE with negative surprise as a reward.

After talking with GPT5.2 for a little while, it seems like Curiosity-driven Exploration by Self-supervised Prediction[1] might be an architecture similar to the one I described for neurons? But with the twist that f is rewarded by making the prediction error bigger (not smaller!) as a proxy of "curiosity".

[1] https://arxiv.org/pdf/1705.05363