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.
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.
Table of Content
- What is Action Model Learning?
- What is Pattern Recognition?
- What is Neuro-symbolic programming?
- What are Large Action Models?
- Applications of LAMs
- Working of LAMs
- Technical Aspects of Large Action Models