[wongarsu]

> People only speak or write down information that needs to be added to a base "world model" that a listener or receiver already has

Which companies try to address with image, video and 3d world capabilities, to add that missing context. "Video generation as world simulators" is what OpenAI once called it

> When people produce text, there is always a motive to do so which influences the contents of the text. This subjective information component of producing the text is interpreted no different from any "world model" information.

Obviously you need not only a model of the world, but also of the messenger, so you can understand how subjective information relates to the speaker and the world. Similar to what humans do

> The other issue in this argument is that you're inverting the implication. You say an accurate world model will produce the best word model, but then suddenly this is used to imply that any good word model is a useful world model. This does not compute

The argument is that training neural networks with gradient descent is a universal optimizer. It will always try to find weights for the neural network that cause it to produce the "best" results on your training data, in the constraints of your architecture, training time, random chance, etc. If you give it training data that is best solved by learning basic math, with a neural architecture that is capable of learning basic math, gradient descent will teach your model basic math. Give it enough training data that is best solved with a solution that involves building a world model, and a neural network that is capable of encoding this, then gradient descent will eventually create a world model.

Of course in reality this is not simple. Gradient descent loves to "cheat" and find unexpected shortcuts that apply to your training data but don't generalize. Just because it should be principally possible doesn't mean it's easy, but it's at least a path that can be monetized along the way, and for the moment seems to have captivated investors

[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.