Rabbit AI: Large Action Models (LAMs)

The Large Action Models (LAMs) are advanced artificial intelligence systems that are capable of understanding the human intention and predicting actions. In this article, we will be covering the fundamentals, working and architecture of the Large Action Models.

We have all heard about Generative AI & LLMs, used them, and seen their tremendous impact across various industries, especially in tasks like conversation bots, image generation, and customer service. They provide great information regarding asked queries. They mainly work by predicting the next word that should be there using natural language processing techniques. You must have used tools like ChatGPT, MidJourney, and Bard, which are the most common examples of Generative AIs and Large Language Models. These tools are fostering innovation in different kinds of tasks like content creation, website designing, and text-to-image / video generation, and the list keeps on growing.

However, there is one area where all these LLM models lack, and that is taking “ACTIONS” based on the commands given by the user. These models can provide detailed steps to perform a task but cannot perform the task on your behalf. The aim of the article is to cover the fundamentals of this cutting-edge technology and its applications.

What is Action Model Learning?

Action Model Learning is a form of Inductive Reasoning used in Artificial Intelligence. Where an AI model learns new things by their agent’s observations. In this type of learning a model learns how to perform a task by observing another model performing the same task. It might sound like Reinforcement learning, but it is different from Reinforcement Learning where the model is trained using a reward and punishment mechanism. When it predicts a correct output, it gets rewarded and when it predicts a wrong output it gets punished. Instead, Action Model Learning uses reasoning about actions rather than conducting trials in the real world. Any correct input/output pairs are never presented in action model learning, nor are imprecise action models explicitly corrected.

Action model learning has various benefits. It can help AI agents learn how to perform tasks more efficiently by observing how others are performing them. It can also help agents utilize their knowledge of new scenarios and improve their ability to plan and execute actions.

What is Pattern Recognition?

Patterns are everywhere in this world. Humans learn things based on patterns. Like: You searched for an article on the internet related to technology and found an article from GFG, you searched for articles on many topics, and you found that all articles are very insightful. So, your mind built a pattern that articles at GFG are insightful and correct. And you’ll always read from GFG from now onwards. Now coming to the digital world, here everything is a pattern. Whether it is the color of the fonts you’re looking at or the background behind these fonts everything is a pattern. You can see a pattern physically or represent it mathematically. Whole Artificial Intelligence is based on this pattern recognition. Pattern recognition is a process of finding patterns in the data using machine learning algorithms and labeling them into classes based on the extracted patterns or knowledge already gained. Pattern recognition is used in various tasks like Image processing, Image Segmentation, Computer Vision, Seismic analysis, Speech Recognition, Fingerprint Recognition, etc. Pattern recognition usage possibilities are endless.

What is Neuro-symbolic programming?

Neuro-symbolic programming is a kind of Artificial Intelligence that combines Neural Networks and Symbolic AI that explicitly capture pre-existing human knowledge, together to address the limitations/weaknesses of both models and combine their strengths. That way we create an AI that is capable of performing reasoning, learning, and cognitive modeling. A model created after combining these two technologies is modular, interpretable, amenable to symbolic analysis, and can naturally incorporate rich inductive biases expressed in symbolic form. It is used in various domains like natural language understanding, robotics, scientific discovery, etc.

What are Large Action Models?

Large Action Models (LAMs) are the latest development in the world of Artificial Intelligence. LAMs use agents to perform actions. The agents are software entities capable of independently executing tasks, moving beyond merely responding to human queries and actively contributing to the achievement of specific goals. LAMs integrate the linguistic proficiency of LLMs with the ability to autonomously perform tasks and make decisions, marking a significant shift.

The architecture of Large Action Models is structured based on the simulation of applications and human actions they are intended to replicate. Unlike a mere textual representation, LAMs can effectively simulate the composition of diverse applications and the corresponding human actions performed on them without the need for a temporary demonstration. This capability is facilitated by advancements in neuro-symbolic programming and pattern recognition.

An AI model can provide you with a detailed process of how you can order food online, but it can’t place an order for you. Even our smartphones with existing conversational models like Alexa, Siri, and Cortana are not capable of doing all sorts of tasks. We also have something called AI agents that can be trained to perform a specific task, but they could be more feasible. And these things open up a whole new area of possibilities where Large Action Models (LAMs) come into action. LAMs are a super advanced version of LLMs operating at approximately 10x the speed of general LLMs. They are advanced computational models designed to handle complex and sophisticated actions in various domains.

Applications of LAMs

From tackling simpler tasks like:

• Ordering a Cab
• Ordering Food
• Sending emails
• Scheduling meetings, etc.

To complex tasks like:

• Planning a whole trip abroad, including flight, hotel, and cab bookings, while creating a travel itinerary. This involves various websites and applications.
• On-the-go video/audio translation, etc.

A LAM (Large Action Model) can do all that in a matter of seconds because of its working principle and the architecture it’s designed on. Apart from these applications, LAMs can be utilized in robot motion planning, human-robot interaction, and game development, which will allow for realistic and intelligent behavior of non-player characters (NPCs) and enhance the overall gameplay experience.

Working of LAMs

At its core, LAM utilizes a hierarchical approach to action representation and execution. It breaks down complex actions into smaller sub-actions, allowing for efficient planning and execution. The model leverages the concept of action hierarchies, where higher-level actions are composed of lower-level actions, forming a hierarchical structure.

LAM incorporates a planning component responsible for generating action sequences to achieve a given goal. The planning process involves evaluating the current state, determining the necessary actions, and creating a plan that optimizes the achievement of the desired outcome. This allows for intelligent decision-making and adaptive behavior. Instead of working on app-based interactions (done via AI agents), LAMs use UI (user interface) -based interactions, i.e., generally done via humans.

LAMs utilizes all of the above-mentioned ML Algorithms like Action Based Learning, Pattern Recognition, and Neural-Symbolic Programming. It uses pattern recognition algorithms to analyze and understand complex data. It allows it to identify recurring structures or features in the information provided, enabling it to make informed decisions and predictions based on the observed patterns. After this Neuro-Symbolic AI comes into play that combines the pattern recognition capabilities of neural networks with the logical reasoning of symbolic AI. With this integration LAMs can interpret abstract concepts and perform logical operations. After these two models, Action Model comes into play. That understands human intentions and executes tasks accordingly. It learns from past interactions and adjusts its actions based on feedback, gradually improving its performance over time.

Technical Aspects of Large Action Models

A LAM consists of several key components:

1. Action Representation: LAM employs a formal representation of actions using a combination of high-level symbolic representations and low-level procedural representations. This allows for flexibility and expressiveness in representing a wide range of actions.
2. Action Hierarchy: LAM utilizes a hierarchical structure to represent actions. Actions are organized into a tree-like structure, where higher-level actions encapsulate lower-level actions. This hierarchical organization enables efficient planning and execution of complex actions.
3. Planning Engine: LAM incorporates a powerful planning engine that generates action sequences to achieve desired goals. The planning engine considers the current state, available actions, and the goal to create a plan that maximizes the chances of success.
4. Execution Module: LAM’s execution module executes the generated action sequences. It coordinates the execution of sub-actions, ensuring that the actions are performed in the correct order and with the necessary coordination.
5. Learning and Adaptation: LAM can learn and adapt over time. It can refine its action representations, improve its planning capabilities, and adapt its behavior based on feedback and experience. This learning and adaptation mechanism allows LAM to continuously improve its performance and effectiveness.

Future Scope and Conclusion

LAM has been integrated into a phone-sized standalone AI device called “Rabbit R1” featuring a 2.88-inch touchscreen, a rotating camera, and a scroll wheel/navigation button that can be controlled directly on the device or by voice via a far-field microphone developed in collaboration with Teenage Engineering. It can perform approximately all the above-mentioned tasks like booking a cab, ordering food online, etc. But “Rabbit R1” is not limited to performing these tasks only you can teach Rabbit in one shot to perform any task. You can check out more about the product at Rabbit.

Apart from this device, LAMs have endless potential in various domains:

• Healthcare: To revolutionize patient care through advanced diagnostics and personalized treatment plans.
• Finance: In revolutionalizing risk assessment, fraud detection, and algorithmic trading.
• Automobile: To develop autonomous driving technologies and enhance vehicle safety systems, etc.

In conclusion, LAM (Large Action Models) are commendable pieces of technology that has a lot of potential in coming future. It will in the forefront shaping the future of AI and drive advancements across the industries.