From Mohammad Anis, CEO at jannat enterprises via LinkedIn Jan 15, 2024:
OpenAI has reportedly been working on a new kind of agent, known internally as OpenAI’s New “Q*” (“Q-star”), which marks a major step toward OpenAI’s goal of making systems that are generally better than humans at doing a wide variety of tasks
Remember playing Pac-Man, devouring pellets while navigating the maze to avoid ghosts? That’s roughly how an AI agent learns under Q learning, a revolutionary approach that might just unlock the next level of AI intelligence.
OpenAI’s New Q* (Qstar), rumored to be the secret sauce behind their upcoming GPT-5 model, has sparked a frenzy in the AI world.
But what exactly is Q*, and how could it transform the future of language models like GPT-3 and its successor?
Buckle up, because we’re about to dive into the fascinating realm of Q learning and explore how it might rewrite the rules of AI.
OpenAI’s New Q* (Qstar) Origin
The “Q” in Q* is a nod to Q learning – a type of machine learning used in reinforcement learning.
It’s about rewarding good decisions and penalizing the not-so-good ones, just like training a pet. Now, the “star” part comes from the A* search algorithm,
a tool in computer science for finding the shortest path in mazes or games.
Table: Q Origin Breakdown
Understanding Q Learning: From Pac-Man to AI Mastery
Imagine a robot tasked with cleaning your house. It bumps into walls, stumbles over furniture, but eventually learns the optimal route to get the job done.
That’s the essence of Q learning: teaching machines to learn from their mistakes and experiences, just like humans.
Q* takes this concept a step further by combining Q learning with A search*, an algorithm that finds the shortest path in a maze.
Think of it as a GPS for AI agents, guiding them towards the best decisions for achieving their goals....
....MUCH MORE
