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Dynalang: A Multimodal World Model Capable of Predicting Future Events
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Dynalang: A Multimodal World Model Capable of Predicting Future Events

Hayo News
Hayo News
August 6th, 2023
View OriginalTranslated by Google

Dynalang: A project developed by a research team at the University of California, Berkeley, whose goal is to understand human language instructions and relate these language instructions to the world it sees, and then build a multi-model capable of predicting future situations or events model of the ecological world and perform specific tasks based on it.

Imagine that you tell a robot to go to the kitchen to get a glass of water, it not only needs to understand your words, but also needs to know where the kitchen is, what the glass looks like, where the water is, etc.

Not only can Dynalang understand and carry out such instructions, but it can also learn from language about how the world works. For example, by reading books and watching videos, it can learn that roads are slippery after rain, so be careful when driving. Such information can help it predict what might happen in the future and make decisions accordingly.

Unlike traditional robotic agents, Dynalang can also use past language experience to predict future language and visual situations. This means it can learn from past experiences and use that learning to better understand new situations it may encounter in the future.

Overall, Dynalang is a smart robotic agent that can learn how to act in the world by understanding people's language and observing the world. Whether performing simple tasks or understanding complex world rules, it can continuously improve its abilities by learning language and observation.

How Dynalang works

1. DreamerV3-based world modeling: Dynalang is based on a DreamerV3 model, a reinforcement learning (RL) agent. It continuously learns from experience data collected by the agent as it acts in the environment.

2. How the model works

  • Compressing text and images: The world model compresses text and images at each time step into a latent representation.
  • Reconstruction and Prediction: From this representation, the model is trained to reconstruct the original observation, predict the reward, and predict the representation for the next time step.
  • Anticipating the world: Intuitively, the world model learns what it should expect to see in the world based on what it reads in the text.

3. Dynalang's action choice

  • Training Policy Network: Dynalang selects actions by training a policy network on top of a compressed world model representation.
  • Imagined Unfolding: It trains on imagined unfoldings of the world model and learns to take actions that maximize the predicted reward.

4. Unified video and text modeling:

  • Single Multimodal Stream: Unlike previous multimodal models, Dynalang models video and text as a unified sequence, consuming one image frame and one text token at a time. This is similar to how people receive input in the real world.
  • Pre-training and improving RL performance: Modeling everything as a sequence enables the model to be pre-trained on text data like a language model and improves RL performance.

Application Scenario

Dynalang is designed so that it can improve performance in a variety of tasks, including environment description, game rules and instructions, etc. By using language prompts, navigating through real scanned houses, etc., Dynalang demonstrates its diverse language utilization capabilities.

Application scenario: 1. Virtual assistants and chatbots: By understanding natural language and interacting with the visual world, Dynalang can be used to create more advanced virtual assistants and chatbots that can understand and respond to more complex queries and instructions.

2. Autonomous driving and robot navigation: In terms of autonomous driving and robot navigation, Dynalang can be used to understand environmental descriptions, rules and instructions, and make decisions accordingly. Its multimodal learning and future prediction capabilities enable it to navigate complex and dynamic environments.

3. Game AI and simulation: In the game and simulation environment, Dynalang can be used as an intelligent NPC (non-player character), understand the player's instructions, game rules and environment description, and act accordingly.

4. Assisted decision-making and predictive analysis: Dynalang's future prediction capabilities make it suitable for auxiliary decision-making and predictive analysis, such as predicting future trends and outcomes in financial, medical or supply chain management.

5. Multimodal learning and research: As an advanced multimodal learning framework, Dynalang can also be used in academic and industrial research to explore how to combine information from text, images and other modalities for better understanding and operate the world.

6. Accessible technology: Dynalang's ability to combine language and vision may help in the development of accessible technology, such as providing environmental description and navigation support for the visually impaired.

7. Remote control and monitoring: In remote control and monitoring applications, Dynalang can control and coordinate robots or other automated systems by understanding textual instructions and visual input.

Project address: https://dynalang.github.io/

Paper: https://arxiv.org/abs/2308.01399

Reprinted from 小互View Original

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