Hey, folks! Buckle up because the recent buzz in the AI sphere has been nothing short of an intense rollercoaster. Rumors about a groundbreaking AI, enigmatically named Q* (pronounced Q-Star), have been making waves, closely tied to a chaotic series of events that rocked OpenAI and came to light after the abrupt firing of their CEO - Sam Altman ( u/samaltman ).
There are several questions I would like to entertain, such as the impacts of Sam Altman's firing, the most probable reasons behind it, and the possible monopoly on highly efficient AI technologies that Microsoft is striving to have. However, all these things are too much for 1 Reddit post, so here I will attempt to explain why Q* is a BIG DEAL, as well as go more in-depth on the theory of combining Q-learning and A* algorithms.
At the core of this whirlwind is an AI (Q*) that aces grade-school math but does so without relying on external aids like Wolfram. It may possibly be a paradigm-shattering breakthrough, transcending AI stereotypes of information repeaters and stochastic parrots which showcases iterative learning, intricate logic, and highly effective long-term strategizing.
This milestone isn't just about numbers; it's about unlocking an AI's capacity to navigate the single-answer world of mathematics, potentially revolutionizing reasoning across scientific research realms, and breaking barriers previously thought insurmountable.
What are A* algorithms and Q-learning?:
From both the name and rumored capabilities, the Q* is very likely to be an AI agent that combines A* Algorithms for planning and Q-learning for action optimization. Let me explain.
A* algorithms serve as powerful tools for finding the shortest path between two points in a graph or a map while efficiently navigating obstacles. Their primary purpose lies in optimizing route planning in scenarios where finding the most efficient path is crucial. These algorithms are known to balance accuracy and efficiency with the notable capabilities being: Shortest Path Finding, Adaptability to Obstacles, and their computational Efficiency / Optimality (heuristic estimations).
However, applying A* algorithms to a chatbot AI involves leveraging its pathfinding capabilities in a rather different context. While chatbots typically don’t navigate physical spaces, they do traverse complex information landscapes to find the most relevant responses or solutions to user queries. Hope you see where I´m going with this, but just in case let's talk about Q-learning for a bit.
Connecting the dots even further, let's think of Q-learning as us giving the AI a constantly expanding cheat sheet, helping it decide the best actions based on past experiences. However, in complex scenarios with vast states and actions, maintaining a mammoth cheat sheet becomes unwieldy and hinders our progress toward AGI due to elevated compute requirements. Deep Q-learning steps in, utilizing neural networks to approximate the Q-value function rather than storing it outright.
Instead of a colossal Q-table, the network maps input states to action-Q-value pairs. It's like having a compact cheat sheet tailored to navigate complex scenarios efficiently, giving AI agents the ability to pick actions based on the Epsilon-Greedy approach—sometimes randomly exploring, sometimes relying on the best-known actions predicted by the networks. Normally DQNs (or Deep Q-networks), use two neural networks—the main and target networks—sharing the same architecture but differing in weights. Periodically, their weights synchronize, enhancing learning and stabilizing the process, this last point is highly important to understand as it may become the key to a model being capable of self-improvement which is quite a tall feat to achieve. This point however is driven further if we consider the Bellman equation, which basically states that with each action, the networks update weights using the equation utilizing Experience replay—a sampling and training technique based on past actions— which helps the AI learn in small batches without necessitating training after every step.
I must also mention that Q\'s potential is not just a math whiz but rather* a gateway to scaling abstract goal navigation as we do in our heads when we plan things, however, if achieved at an AI scale we would likely get highly efficient, realistic and logical plans to virtually any query or goal (highly malicious, unethical or downright savage goals included)...
Finally, there are certain pushbacks and challenges to overcome with these systems which I will underline below, HOWEVER, with the recent news surrounding OpenAI, I have a feeling that smarter people have found ways of tackling these challenges efficiently enough to have a huge impact of the industry if word got out.
To better understand possible challenges I would like to give you a hypothetical example of a robot that is tasked with solving a maze, where the starting point is user queries and the endpoint is a perfectly optimized completion of said query, with the maze being the World Wide Web.
Just like a complex maze, the web can be labyrinthine, filled with myriad paths and dead ends. And although the A* algorithm helps the model seek the shortest path, certain intricate websites or information silos can confuse the robot, leading it down convoluted pathways instead of directly to the optimal solution (problems with web crawling on certain sites).
By utilizing A* algorithms the AI is also able to adapt to the ever-evolving landscape of the web, with content updates, new sites, and changing algorithms. However, due to the speed being shorter than the web expansion, it may fall behind as it plans based on an initial representation of the web. When new information emerges or websites alter their structures, the algorithm might fail to adjust promptly, impacting the robot's navigation.
On the other hand, let's talk about the challenges that may arise when applying Q-learning. Firstly it would be limited sample efficiency, where the robot may pivot into a fraction of the web content or stick to a specific subset of websites, it might not gather enough diverse data to make well-informed decisions across the entire breadth of the internet therefore failing to satisfy user query with utmost efficiency.
And secondly, problems may arise when tackling high-dimensional data. The web encompasses a vast array of data types, from text to multimedia, interactive elements, and more. Deep Q-learning struggles with high-dimensional data (That is data where the number of features in a dataset exceeds the number of observations, due to this fact we will never have a deterministic answer). In this case, if our robot encounters sites with complex structures or extensive multimedia content, processing all this information efficiently becomes a significant challenge.
To combat these issues and integrate these approaches one must find a balance between optimizing pathfinding efficiency while swiftly adapting to the dynamic, multifaceted nature of the Web to provide users with the most relevant and efficient solutions to their queries.
To conclude, there are plenty of rumors floating around the Q* and Gemini models as giving AI the ability to plan is highly rewarding due to the increased capabilities however it is also quite a risky move in itself. This point is further supported by the constant reminders that we need better AI safety protocols and guardrails in place before continuing research and risking achieving our goal just for it to turn on us, but I'm sure you've already heard enough of those.So, are we teetering on the brink of a paradigm shift in AI, or are these rumors just a flash in the pan? Share your thoughts on this intricate and evolving AI saga—it's a front-row seat to the future!
I know the post came out lengthy and pretty dense, but I hope this post was helpful to you! Please do remember that this is mere speculation based on multiple news articles, research, and rumors currently speculating regarding the nature of Q*, take the post with a grain of salt :)
Edit: After several requests, I would like to mention an Arxiv paper on a very similar topic I've discussed in the post:
A\ Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks*
( arXiv:2102.04518v2 [cs.AI] )
Let us all push the veil of ignorance back and the frontier of discovery forward.
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