First, we know very little about Q* or Q Star, and the little we know (publicly) draws from multiple sources and involves a lot of speculation, so take everything in this article with a grain of salt.
With this disclaimer out of the way, let’s dive right in:
Where Does the Name Q* (Q-Star) Come From?
The Origin of Q* (Q-Star) most likely combines Q-Learning and the A-Star pathfinding algorithm.
- The Q in Q* refers to Q-learning, a machine learning algorithm used in reinforcement learning, where a computer program (agent) learns from experience like playing a video game.
- The Star * in Q* comes from the A* search algorithm, used for finding the shortest paths in computer science problems.
The name sounds like something ChatGPT could’ve come up with. 😉 Let’s have a quick look at both Q-Learning and the A-Star algorithm next. 👇
Q-learning Explained: Q-learning is a reinforcement learning method where computers are taught by rewarding good decisions and penalizing bad ones. It involves an environment (like a game), an agent (the AI), and a process of learning through trial and error to improve decision-making in various scenarios.
The process has different components, such as the environment in which the AI agent operates, the states and actions available to the agent, and the Q-table.
To better understand Q-learning, consider the example of training a pet. You reward good decisions and penalize bad ones, guiding the pet to improve its behavior.
Q-learning operates on a similar principle, using six crucial steps:
- Environment and Agent: In Q-learning, there’s an environment (e.g., a video game or maze) and an agent (an AI or computer program) that needs to learn how to navigate it.
- States and Actions: The environment consists of different states and actions that the agent can take, much like choosing to move left or right in a game.
- Q-Table: This table acts as a “cheat sheet” that advises the agent on the best action in each state. Initially, it’s filled with guesses as the agent is still unfamiliar with the environment.
- Learning by Doing: The agent explores the environment, receiving feedback on its actions. Positive actions earn rewards, while negative actions earn penalties. This feedback helps the agent update the Q-table and learn from experience.
- Updating the Q-Table: The table is refreshed using a formula that considers current rewards and potential future rewards, ensuring long-term consequences are factored into the agent’s actions.
- Improving Over Time: With enough exploration and learning, the Q-table becomes increasingly accurate, and the agent gets better at predicting which actions will yield the highest rewards in different states.
Q-learning is like playing a complex video game, where you gradually learn the best moves and strategies to achieve the highest score.
The Q-table serves as the learning mechanism for the AI model, enabling it to update its knowledge as it interacts with its surroundings. The agent learns to navigate its environment efficiently while keeping long-term consequences in mind.
Just so you’ve seen it, here’s the Q-Learning algorithm more formally — while the formulas look scary, it’s actually very simple when compared to other machine learning algorithms:
Q-Learning is dynamic and interactive, continuously learning and adapting from new data and user interactions. It optimizes decision-making, can address biases in training data, and is goal-oriented, making it suitable for tasks with clear objectives.
In this example from the University of Chicago, the goal is for a robot arm to place each colored dumbbell in front of the correct numbered block. Just enjoy the visuals for now. 😉 👇
What’s A* (A-Star)?
A-star search (A)* is a widely used pathfinding and graph traversal algorithm that helps an AI model find the shortest route between two points. Think of it as providing a set of instructions for navigating a maze or solving complex problems.
The A* (A-star) algorithm is a smart and efficient way to find the shortest path from one point to another.
It does this by combining the actual distance you’ve already traveled with an estimate of the distance to the destination, always choosing the path that looks shortest overall.
This helps A* quickly determine the best route, avoiding longer paths and saving time.
🧑💻 But what has a route-finding algorithm to do with anything?
The A* algorithm can be applied to optimization problems beyond mapping by conceptualizing the problem as a search through a space of possible solutions.
For instance, consider a manufacturing process optimization problem where the goal is to maximize efficiency while minimizing costs. Each point in the search space represents a different set of process parameters (like machine speeds, material choices, etc.).
A* can navigate this space by evaluating each set of parameters based on a cost function (analogous to distance in mapping). The algorithm estimates the best path to the optimal solution, considering both the current cost and an estimate of future costs (similar to estimating the remaining distance to a destination in mapping).
By selecting the path with the lowest estimated total cost, A* efficiently zeroes in on the most promising solutions, avoiding less optimal paths. This approach can be adapted to various optimization scenarios, such as scheduling, resource allocation, and network design.
Or, possibly, large language model and generative AI… 👇
Q* (Q-Star) for Language Models
Now, it’s getting more speculative. I will be wrong. The only question is, how much wrong?
How do we apply Q-Learning and the A* algorithm to AI training and large language models?
Despite the advancements in LLMs, there are still limitations.
🧑💻 A significant challenge with LLMs is their lack of creative problem-solving capabilities. They mainly mimic the data they’ve ingested, resulting in the reproduction of human ingenuity found in their training data. In order to achieve real creativity, LLMs should search through spaces of possibilities to identify hidden gems. Current models fall short in this aspect.
Current LLMs mimic the data they’ve seen from humans, limiting their capabilities in terms of searching through spaces of possibilities or identifying creative solutions.
Another critical factor to consider is the focus on immediate rewards. AI systems need to consider the long-term consequences of their actions to develop superior strategies and become efficient at solving complex tasks.
Q-learning, for instance, incorporates potential future rewards when updating its Q-table, allowing for more thoughtful decision-making in varied scenarios.
In language models, Q* could help the model learn from interactions to improve responses, updating its strategy based on what works well in conversations and adapting to new information and feedback.
This approach enables AI systems to learn from their experiences and make better decisions over time, similar to how we improve our skills when playing video games.
OpenAI’s potential breakthrough in Q-learning could usher in the next evolution in large language models and AI systems. We may witness unprecedented advancements in AI capabilities and problem-solving by overcoming current limitations and leveraging this powerful reinforcement learning method.
Interestingly, a similar idea has been proposed by Google’s DeepMind earlier.
DeepMind’s Gemini, still under development, is an LLM similar to GPT-4 but that is combined with search techniques (tree search!) from AlphaGo, so the system gets new skills such as planning or solving problems.
Here’s what the Deepmind CEO Hassabi says:
“At a high level you can think of Gemini as combining some of the strengths of AlphaGo-type systems with the amazing language capabilities of the large models. […] We also have some new innovations that are going to be pretty interesting.”
Don’t forget, Google DeepMind has also proposed the Transformers idea in the legendary paper “Attention is All You Need” on which OpenAI built its success.
Edit: As I’m about to publish this, a new Wikipedia article has just been published:
While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
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