World Models explained in 10min..

World Models explained in 10min..

Understanding the Limitations of Large Language Models and the Promise of World Models

The Nature of Coin Flipping and LLMs

The speaker introduces the concept of a coin flip, emphasizing that humans understand its 50/50 odds through experience rather than complex notation.

A question is posed regarding whether large language models (LLMs) are inherently flawed in understanding the physical world due to their lack of experiential learning.

Differences Between Human Learning and LLM Training

Unlike humans, LLMs are trained on vast amounts of text tokens over extended periods, lacking sensory experiences that inform human understanding.

The speaker highlights that LLMs only process text, which abstracts away from direct interactions with the physical world.

Introduction to World Models

Around 2018, "world models" emerged as an alternative approach to how AI can simulate and understand environments differently than traditional LLM training.

World models aim to create simulations of the physical world within an AI's architecture instead of relying solely on textual data.

Components of World Models

David Ha's original paper outlines three main components: an environment model, a vision model using variational autoencoders for feature extraction, and a processing model (MDN RNN).

The MDN RNN architecture allows for tracking previous states, enabling predictions based on past observations.

Functionality and Implications of World Models

A controller model samples outputs from both the vision model and MDN RNN to perform actions in simulated environments.

This setup enables agents to learn through simulation rather than direct interaction with real-world environments, potentially leading closer to AGI compared to traditional LLM approaches.

Promising Results and Future Questions

Initial results show promise; world models can learn tasks like driving with significantly fewer parameters than typical LLM architectures.

A critical question remains: Can these world models scale up to match or exceed human-level capabilities?

Resources for Further Learning

BCloud offers educational resources about AI concepts tailored for various skill levels.

The popularity of current LLM frameworks stems from their versatility across multiple tasks beyond simple chat functions.

World Models and Language Models: A Deep Dive

The Role of Yan Lakun in World Models

Yan Lakun, a key figure in the development of world models at Meta, has contributed significantly to JEPA-based models before founding his own company, AMI (Advanced Machine Intelligence), which aims for a valuation of up to $5 billion.

Lakun critiques language models (LMs), arguing they lack understanding of the physical world beyond their autoregressive nature that predicts tokens sequentially.

Language Representation vs. Physical Understanding

While Lakun downplays LMs' capabilities, it is argued that language encompasses more than mere token prediction; it includes grammar and figures of speech that convey abstract meanings about the physical world.

The distinction between pure LMs and world models has blurred with advancements like multimodal models (e.g., GPT-4 from OpenAI and Gemini 1 from Google), which integrate vision and language processing.

Innovations in Multimodal Learning

Vision-Language Action (VLA) represents a new type of world model combining vision transformers with LLMs to generate action tokens, exemplified by Neo, a humanoid released in October 2025.

Despite advancements, critics argue that multimodal LMs still fundamentally lack spatial awareness regarding the physical environment.

Spatial Intelligence Developments

Fei Lee's startup World Labs raised over $230 million to demonstrate spatial intelligence through its product Marble, which creates interactive particle systems representing environments without traditional controllers.

Py founded General Intuition aiming for closer representations of world models capable of interacting with games and simulations.

Contributions from Major Tech Companies

Google has made significant strides with projects like SEMA and Genie 3, creating hyperrealistic worlds for various applications including AI video generation and robotics training.

NVIDIA's open-source platform Cosmos serves as a foundation model for diverse applications such as data augmentation for autonomous vehicles and robots.

Philosophical Questions on Understanding

The discussion raises philosophical questions about whether language models truly exhibit human-like thinking or understanding, prompting further exploration into the implications of such capabilities.