Blogs / World Model in Artificial Intelligence: The Key to Achieving AGI

World Model in Artificial Intelligence: The Key to Achieving AGI

World Model در هوش مصنوعی: کلید دستیابی به AGI

Introduction

Imagine a child seeing a ball roll off a table for the first time. Without any special training, they know the ball will move downward and likely roll on the ground. This ability to predict the behavior of the physical world is the result of what cognitive science and artificial intelligence call a World Model. While humans build these models naturally over time, creating such capability in AI systems is one of the most challenging yet promising research paths toward achieving Artificial General Intelligence (AGI).
In recent years, with remarkable advances in deep learning and language models, researchers' attention to the concept of World Models has intensified. These models enable AI systems to not only learn from past data but also predict the future, plan, and adapt to new environments.

What is a World Model? Definition and Fundamental Concepts

A World Model is an internal representation of the environment that an AI system creates to simulate the external world within itself. Simply put, this model allows an AI agent to simulate the consequences of different actions before executing them in the real world. This process is similar to what the human brain does when planning daily activities.
At its core, a World Model consists of several key components:
Transition Model: This component predicts how the environment's state will change if the agent takes a specific action. For example, if a robot moves its hand toward an object, the transition model predicts that the object will be displaced from its current position.
Observation Model: This component determines what the agent will observe in each state. This includes understanding sensors, cameras, and other input devices.
Reward Model: In reinforcement learning, this model predicts how much reward or penalty will result from taking a specific action in a given state.
The fundamental difference between World Models and traditional machine learning approaches is that instead of learning a direct mapping from input to output, the system creates a general understanding of how its environment works. This approach allows AI models to perform better in new situations they haven't encountered before.

History and Evolution of World Models in AI

The idea of World Models in AI is not a new concept. Its roots go back decades, when cognitive science and robotics researchers realized that intelligent systems need an internal representation of their environment. In the 1980s and 1990s, concepts like "cognitive maps" and "internal models" in robots were discussed.
However, the real renaissance of World Models has occurred in recent years. One important milestone was the publication of the famous "World Models" paper in 2018 by David Ha and Jürgen Schmidhuber, which demonstrated how neural networks could be used to learn compressed representations of environments.
Yann LeCun, Chief AI Scientist at Meta and Turing Award winner, is one of the main pioneers in reviving this concept. He believes that current AI systems, especially large language models, lack true understanding of the world and merely operate based on statistical pattern matching. LeCun proposes that to reach AGI, we must build systems that learn like children, through interaction with the environment and building internal models.
The I-JEPA approach introduced by Meta is the first practical step in this direction. This architecture uses Joint-Embedding Predictive Architecture to learn internal representations of the world without needing to reconstruct complete images pixel by pixel.

Architectures and Implementation Methods for World Models

Implementing World Models in practice can be done in various ways, each with its own advantages and limitations:

Variational Autoencoders (VAE) for World Models

One common approach is using VAE to learn compressed representations of environmental states. In this method, an encoder maps images or sensory data to a lower-dimensional latent space, and then a decoder can reconstruct the original image from this compressed representation. This latent space acts as the "memory" of the World Model.

Recurrent Neural Networks and LSTM

To model the temporal dynamics of environments, recurrent architectures like LSTM and GRU are used. These networks can process temporal sequences and predict how the environment's state changes over time. Recurrent neural networks are very suitable for this purpose because they can retain past information in their memory.

Transformer-based World Models

With the emergence of Transformer architecture and Attention mechanism, researchers have begun using these architectures to build World Models. Transformers can model long-range dependencies better than RNNs, making them suitable for predicting complex sequences.

Diffusion Models for Environment Generation

Diffusion models, which have recently achieved remarkable success in image and video generation, are now also being used to create World Models. These models can learn the probability distribution of future environmental states and generate different scenarios.

JEPA and Self-Supervised Learning

The Joint-Embedding Predictive Architecture proposed by Yann LeCun uses self-supervised learning. In this method, instead of reconstructing the complete image, the model only predicts hidden parts in an abstract representation space. This approach is more efficient and leads to learning more meaningful representations.

Genie: Google DeepMind's Breakthrough in World Models

One of the most exciting recent advances in World Models is the introduction of the Genie series of models by Google DeepMind. These models demonstrate how World Models can learn complex interactive environments from video data.
Genie 2, introduced in late 2024, has the ability to simulate virtual worlds where various actions like jumping, swimming, and picking up objects can be performed. This model was trained on extensive video datasets and shows interesting emergent capabilities including understanding physics, complex interactions, and maintaining stability over time.
Genie 3, introduced in August 2025, is a major leap forward. This model can generate diverse and complex interactive environments with just a text prompt. DeepMind introduces this model as an important milestone on the path to AGI. Genie 3 demonstrates that a World Model can:
  • Build rich, interactive 3D environments
  • Simulate realistic physics
  • Have high diversity in generated environments
  • Serve as a training environment for AI agents
These models show that World Models are not just a theoretical concept but a practical technology that can be used in real applications such as robot training, scenario simulation, and even video game creation.

The Role of World Models in the Path to AGI

The main question is: why are World Models so critical for achieving Artificial General Intelligence or AGI?
Understanding Causality Instead of Correlation: Current large language models like GPT and Gemini, while very powerful in generating text and answering questions, lack true understanding of causal relationships in the world. They operate based on statistical patterns in their training data. A true World Model must be able to understand why one event causes another, not just that they often occur together.
Planning and Reasoning: One key feature of general intelligence is the ability to plan for long-term goals. With a World Model, an AI agent can simulate different scenarios in its mind and choose the best course of action. This is similar to chain of thought used in advanced reasoning models.
Efficient Learning: Humans can learn with very few examples. A child understands the concept of gravity after seeing objects fall a few times. This efficient learning is possible due to the World Model in the human brain. Current AI systems require millions of training examples, but with World Models, this number can be drastically reduced.
Transfer Learning and Generalization: A good World Model should be able to transfer knowledge learned in one domain to other domains. For example, if a robot learns how to pick up a cup, it should be able to apply the same skill to picking up similar objects with different shapes and sizes.
Intuitive Physics Understanding: Humans have intuitive understanding of basic physics - we know objects can't pass through each other, heavier objects fall faster, etc. This intuitive understanding is the result of our internal World Model and is essential for AGI.
Interaction with the Real World: For AGI to operate in the real world, it must have a model of it. Future intelligent robots must be able to predict the consequences of their actions and work in dynamic, unpredictable environments.

Practical Applications of World Models in Various Industries

World Models are important not only for AGI research but also have numerous practical applications across various industries:

Robotics and Automation

In robotics, World Models help robots operate in complex and changing environments. An industrial robot with a World Model can handle new objects without needing to be reprogrammed. In autonomous vehicles, World Models help the system predict the behavior of other vehicles and pedestrians.

Video Games and Virtual Reality

Creating video games with AI is one of the exciting applications of World Models. Using these models, dynamic and responsive game environments can be built that automatically respond to player actions. NPCs (non-player characters) can also be designed to be smarter with more realistic behaviors.

Simulation and Prediction

In predictive modeling, World Models can be used to simulate complex systems such as weather, urban traffic, or financial markets. These models can examine various "what if" scenarios and help with better decision-making.

Training and Simulation

World Models can be used to build realistic training simulators. For example, in training pilots or surgeons, simulated environments can be created that accurately replicate the behavior of real systems.

Business and Finance

In financial markets, World Models can be used to model market behavior and predict price changes. Also in AI financial analysis, they can help identify patterns and trends.

Smart Agriculture

In smart agriculture, World Models can be used to predict plant growth, weather conditions, and optimize resource consumption.

Current Challenges and Limitations of World Models

Despite remarkable progress, World Models still face serious challenges:
Computational Complexity: Building and training accurate World Models for complex environments requires enormous computational resources. For example, accurately simulating the physics of a 3D environment in real-time is very expensive.
Scalability: The more complex and larger the environment, the harder it is to build a World Model for it. The real world has countless variables and states that modeling all of them is impossible.
Uncertainty: The real world is inherently uncertain and many phenomena are random. World Models must be able to handle this uncertainty and model the probability distribution of possible outcomes.
Training Data: To learn an accurate World Model, diverse and rich data is needed. Collecting this data, especially for rare or dangerous situations, is challenging.
AI Hallucination: Like language models that sometimes generate incorrect information, World Models may also make inaccurate predictions about the environment, especially in situations less seen in training data.
Interpretability: Understanding how a World Model makes decisions and why it makes specific predictions is still a fundamental challenge. This issue is very important for critical applications like medicine or autonomous vehicles.
Time Constraints: World Models must be fast enough to predict in real-time. In many applications like robotics, even a few milliseconds of delay can be problematic.

The Future of World Models: Vision and Opportunities

Despite challenges, the future of World Models is very promising. Several key trends are emerging:
Integration with New Architectures: Researchers are combining World Models with advanced architectures like Mixture of Experts, Mamba, and RWKV to improve efficiency and performance.
Learning from Less Data: Inspired by how humans learn, researchers are working on methods that can train World Models with less data. Techniques like federated learning and transfer learning are useful in this area.
Multimodal World Models: The future belongs to multimodal models that can combine information from different senses (vision, hearing, touch) to have a more comprehensive understanding of the environment.
Edge AI and World Models: With the advancement of Edge AI, it becomes possible to run World Models on local devices without needing the cloud. This is very important for real-time applications like robots and autonomous vehicles.
Continuous Learning and Adaptation: Future World Models must be able to continuously learn from new experiences and adapt to environmental changes. Self-improving models are a step in this direction.
Standardization and Development Tools: As this field matures, standard frameworks and tools for building and evaluating World Models are expected to emerge, similar to what TensorFlow and PyTorch did for deep learning.
Custom Chips: Hardware companies are designing custom chips for efficient execution of World Models, which can largely solve the computational complexity problem.
Integration with Decision-Making Systems: World Models alone are not enough; they must be integrated with planning, reasoning, and decision-making systems to build truly intelligent AI agents.

Relationship Between World Models and Other Emerging Technologies

World Models don't operate in a vacuum and have close connections with other advanced technologies:
Quantum Computing: Quantum computing can accelerate complex World Model simulations and enable modeling of larger systems. Quantum AI is a promising combination for the future.
Blockchain and Trust: In distributed systems, blockchain and AI can be used to create trustworthy and verified World Models.
Internet of Things: AI and IoT integration enables collecting rich data from numerous sensors, which is essential for training accurate World Models.
Digital Twins: Digital Twins are actually a type of specialized World Model for a specific system or process and can be combined with more general World Models.
Augmented Reality and Metaverse: AI transformation of the metaverse requires accurate World Models that can build realistic and interactive virtual worlds.
Brain-Computer Interface: Brain-computer interface can help us better understand how World Models are built in the human brain and inspire us in designing AI systems.

Comparing World Models with Existing Approaches

To better understand the position of World Models, let's compare them with current dominant approaches:
Versus Large Language Models: ChatGPT, Claude, and Gemini are powerful language models that perform excellently in text generation and answering questions. However, they lack true understanding of the physical world. World Models fill this gap.
Versus Traditional Reinforcement Learning: Reinforcement learning typically operates through trial and error. World Models allow agents to simulate in their minds before acting in the real world, thus learning faster.
Versus Supervised Learning: Supervised learning requires precise data labeling. World Models can learn from unlabeled data and discover hidden patterns.
Versus Unsupervised Learning: Unsupervised learning discovers patterns but doesn't necessarily model causal relationships. World Models, in addition to pattern discovery, also learn cause-and-effect relationships.

Ethical and Social Considerations

Like any powerful technology, World Models also have their own ethical challenges:
Privacy: Building accurate World Models of human behavior may require collecting sensitive data. Ethics in AI must be considered.
Misuse: Powerful World Models can be misused to predict and manipulate human behavior. Ensuring AI trustworthiness is critical.
Bias and Fairness: If World Model training data is biased, the model also learns and reinforces these biases. Mechanisms must exist to identify and reduce bias.
Responsibility: If a system equipped with a World Model makes a wrong decision that causes harm, who is responsible? This is an important question that must be answered.
Impact on Employment: With World Model advancement and AI impact on jobs, some professions may undergo major changes. Society must prepare for these transformations.
Control and Security: Systems equipped with advanced World Models can operate autonomously. Ensuring these systems are always under control and their cybersecurity is guaranteed is essential.

Conclusion: World Models as a Bridge to the Future

World Models are not just a fascinating theoretical concept but a real and practical path toward building truly intelligent AI systems. As we've seen, these models are becoming a key element in the path to achieving AGI.
From Genie and I-JEPA to extensive research in new architectures, everything shows that the scientific community is seriously investing in this approach. The existing challenges are significant, but rapid progress shows we're moving in the right direction.
The future of AI belongs to systems that not only learn from past data but also have deep understanding of how the world works, can plan, reason, and adapt in new situations. World Models are the key to achieving this future.
For researchers, developers, and companies, understanding and using World Models is no longer optional but a necessity for staying at the forefront of innovation. For society too, familiarity with this concept and its implications is essential for preparing for upcoming transformations.
World Models are the bridge between today's limited AI and tomorrow's general intelligence - a bridge we are currently crossing.