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Mythos probably wouldn't, otherwise they'd have included it in their release. Next version of Mythos probably will though. And yeah.. Reality has not been kind
by osti 3mo ago
Mythos probably wouldn't, otherwise they'd have included it in their release. Next version of Mythos probably will though.
And yeah.. Reality has not been kind to LeCun.
- vatsachak 3mo agoAre you joking? They spend billions of dollars training LLMs to get a 7.8% on arc agi 3 whereas DINO models are near sota in image classification, provide meaningful embeddings to the point where image segmentation is just PCA. The spend on DINO cannot be more than five million (correct me if I'm wrong) JEPA is just getting started
- esafak 3mo agoASI is going to be here by the time Lecun gets started.
- redactsureAI 3mo agoDINO is a transformer model?
- vatsachak 3mo agoJEPA can use a transformer, and DINO does so yes
- deleted 3mo ago[deleted]
- Tenoke 3mo agoHis main anti-LLM predictions have been consistently either wrong or misleading. There's many ways to skin a cat so you can probably do something with a JEPA approach as well, but I doubt he actually catches up to having agents on the level of where Anthropic/OpenAI will be at any point.
- onlyrealcuzzo 3mo agoHis main LLM predictions have almost nothing to do with Arc AGI... What exactly was he dead wrong about that is proven by any of this? GPT getting better has absolutely nothing to do with completely disproving anything LeCun has been saying. He never said LLMs couldn't get better. He never said they couldn't score 7.6% on Arc AGI 3. He's merely said they don't think, and you probably want something that actually thinks if you want a model that can be trained cheaply on a small amount of data and provide a ton of value. Spending $5B to train a model that scores better than an older model does not disprove any of that in any way.
- Tenoke 3mo ago>He's merely said they don't think He said years ago even 'GPT 5000' couldnt do things that they ended up doing fine a month later, let alone by 5000. His later predictions are just moving that goal post including towards them not being able to do more general, harder problems of which Arc AGI is a counter-example.
- onlyrealcuzzo 3mo ago> He said even 'GPT 5000' couldnt do things that they could do a month later, let alone by 5000. What things specifically and when?
- Tenoke 3mo agohttps://youtube.com/shorts/zQTt8TkcyfU?is=09r7XDqz2w6-Pygu https://youtube.com/shorts/zQTt8TkcyfU?is=09r7XDqz2w6-Pygu You probably wont like the edit but I dont have the timestamp of the original on hand, you can find it.
- onlyrealcuzzo 3mo agoThat does not at all look cherry picked or taken out of context... LeCun's ideas cannot be reduced to a 6 second clip... You're missing the forrest for the trees, taking a singular example of a problem and thinking that if an LLM can solve the singular example it completely disproves LeCun is comical...
- typon 3mo agoDinoV3 paper: https://arxiv.org/pdf/2508.10104#page=36 https://arxiv.org/pdf/2508.10104#page=36 "we use a rough estimate of a total 9M GPU hours" From CoreWeave, at current prices (~$2.46/hr spot to ~$6.16/hr on demand) would correspond to $22M–$55M. The dataset is really where the cost is though - they used LVD-1689M - 1.6B images of curated web data from roughly 17B instagram images. This probably cost a huge amount of hours in human annotation, compute for algorithmic filtering, etc and not to mention probably a 20-50 person team working on this model. You might want to change assumptions about how expensive these models are.
- vatsachak 3mo agoThanks for the correction on the order of magnitude for the whole training process. The 9M GPU hours includes the DINO v2 inference used in order to curate the data set. The final training run used like 300000 dollars of compute. Unfortunately we don't know how much RLVR + Agent training costs these companies. I'm just gonna say it's in the hundreds of millions, because they are supposedly making billions of profit on inference yet making billion dollar losses
- ainch 3mo agoYann is a big SSL guy but I don't think he was involved in the original DINO - he's not listed as a co-author or anything.
- vatsachak 3mo agoDINO was created independent of JEPA but uses a similar principle of self supervised learning through minimizing the prediction error of a latent. The difficulty in predicting a latent is so called "collapse"; the embedding neutral network can always output the zero vector and this would predict the output correctly. There are different ways to solve this, DINO uses two different models - a teacher and a student and LeCunn uses an explicit term against collapsing to a single output. Yann mentions DINO in his talks
- chrsw 3mo agoMy main takeaway from LeCun's thesis isn't that you can't build LLMs to do useful things better than the best human, it's that these systems don't learn arbitrary skills efficiently, like humans do. And the question is, why not? 8% on ARC-AGI-3 is amazing for a machine considering how far we've come since digital computers were first built. But it is pretty poor if you're claiming something is well on its way to exhibiting human-like intelligence. Mythos can do some amazing things (I'm assuming, I've never seen it). A young child can learn to control its body without reading any books on dynamical systems and kinematics. Mythos cannot learn to control a humanoid robot after sucking in every piece of data Anthropic can get their hands on.