[tovej]

You did not address the second issue at all. You are inverting the implication in your argument. Whether gradient descent helps solve the language model problem or not does not help you show that this means it's a useful world model.

Let me illustrate the point using a different argument with the same structure: 1) The best professional chefs are excellent at cutting onions 2) Therefore, if we train a model to cuy onions using gradient descent, that model will be a very good profrssional chef

2) clearly does not follow from 1)

[kenjackson]

I think the commenter is saying that they will combine a world model with the word model. The resulting combination may be sufficient for very solid results.

Note humans generate their own non-complete world model. For example there are sounds and colors we don’t hear or see. Odors we don’t smell. Etc…. We have an incomplete model of the world, but we still have a model that proves useful for us.

[wahern]

> they will combine a world model with the word model.

This takes "world model" far too literally. Audio-visual generative AI models that create non-textual "spaces" are not world models in the sense the previous poster meant. I think what they meant by world model is that the vast majority of the knowledge we rely upon to make decisions is tacit, not something that has been digitized, and not something we even know how to meaningfully digitize and model. And even describing it as tacit knowledge falls short; a substantial part of our world model is rooted in our modes of actions, motivations, etc, and not coupled together in simple recursive input -> output chains. There are dimensions to our reality that, before generative AI, didn't see much systematic introspection. Afterall, we're still mired in endless nature v. nurture debates; we have a very poor understanding about ourselves. In particular, we have extremely poor understanding of how we and our constructed social worlds evolve dynamically, and it's that aspect of our behavior that drives the frontier of exploration and discovery.

OTOH, the "world model" contention feels tautological, so I'm not sure how convincing it can be for people on the other side of the debate.

[pixl97]

Really all you're saying is the human world model is very complex, which is expected as humans are the most intelligent animal.

At no point have I seen anyone here as the question of "What is the minimum viable state of a world model".

We as humans with our ego seem to state that because we are complex, any introspective intelligence must be as complex as us to be as intelligent as us. Which doesn't seem too dissimilar to saying a plane must flap its wings to fly.

[wahern]

Has any generative AI been demonstrated to exhibit the generalized intelligence (e.g. achieving in a non-simulated environment complex tasks or simple tasks in novel environments) of a vertebrate, or even a higher-order non-vertebrate? Serious question--I don't know either way. I've had trouble finding a clear answer; what little I have found is highly qualified and caveated once you get past the abstract, much like attempts in prior AI eras.

[D-Machine]

> e.g. achieving in a non-simulated environment complex tasks or simple tasks in novel environments

I think one could probably argue "yes", to "simple tasks in novel environments". This stuff is super new though.

Note the "Planning" and "Robot Manipulation" parts of V-JEPA 2: https://arxiv.org/pdf/2506.09985:

> Planning: We demonstrate that V-JEPA 2-AC, obtained by post-training V-JEPA 2 with only 62 hours of unlabeled robot manipulation data from the popular Droid dataset, can be deployed in new environments to solve prehensile manipulation tasks using planning with given subgoals. Without training on any additional data from robots in our labs, and without any task-specific training or reward, the model successfully handles prehensile manipulation tasks, such as Grasp and Pick-and-Place with novel objects and in new environments.

[kenjackson]

There is no real bar any more for generalized intelligence. The bars that existed prior to LLMs have largely been met. Now we’re in a state where we are trying to find new bars, but there are none that are convincing.

[D-Machine]

ARC-AGI 2 private test set is one current bar that a large number of people find important and will be convincing to a large amount of people again if LLMs start doing really well on it. Performance degradation on the private set is still huge though and far inferior to human performance.