Elevate Your Career with AI & Machine Learning For Dummies PRO and Start mastering the technologies shaping the future—download now and take the next step in your professional journey!

What is OpenAI Q*? A deeper look at the Q* Model as a combination of A* algorithms and Deep Q-learning networks.

Embark on a journey of discovery with our podcast, ‘What is OpenAI Q*? A Deeper Look at the Q* Model’. Dive into the cutting-edge world of AI as we unravel the mysteries of OpenAI’s Q* model, a groundbreaking blend of A* algorithms and Deep Q-learning networks. 🌟🤖

In this detailed exploration, we dissect the components of the Q* model, explaining how A* algorithms’ pathfinding prowess synergizes with the adaptive decision-making capabilities of Deep Q-learning networks. This video is perfect for anyone curious about the intricacies of AI models and their real-world applications.

Understand the significance of this fusion in AI technology and how it’s pushing the boundaries of machine learning, problem-solving, and strategic planning. We also delve into the potential implications of Q* in various sectors, discussing both the exciting possibilities and the ethical considerations.

Join the conversation about the future of AI and share your thoughts on how models like Q* are shaping the landscape. Don’t forget to like, share, and subscribe for more deep dives into the fascinating world of artificial intelligence! #OpenAIQStar #AStarAlgorithms #DeepQLearning #ArtificialIntelligence #MachineLearningInnovation”

🚀 Whether you’re a tech enthusiast, a professional in the field, or simply curious about artificial intelligence, this podcast is your go-to source for all things AI. Subscribe for weekly updates and deep dives into artificial intelligence innovations.

✅ Don’t forget to Like, Comment, and Share this video to support our content.

📌 Check out our playlist for more AI insights

📖 Read along with the podcast:

Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover rumors surrounding a groundbreaking AI called Q*, OpenAI’s leaked AI breakthrough called Q* and DeepMind’s similar project, the potential of AI replacing human jobs in tasks like wire sending, and a recommended book called “AI Unraveled” that answers frequently asked questions about artificial intelligence.

Rumors have been circulating about a groundbreaking AI known as Q* (pronounced Q-Star), which is closely tied to a series of chaotic events that disrupted OpenAI following the sudden dismissal of their CEO, Sam Altman. In this discussion, we will explore the implications of Altman’s firing, speculate on potential reasons behind it, and consider Microsoft’s pursuit of a monopoly on highly efficient AI technologies.

To comprehend the significance of Q*, it is essential to delve into the theory of combining Q-learning and A* algorithms. Q* is an AI that excels in grade-school mathematics without relying on external aids like Wolfram. This achievement is revolutionary and challenges common perceptions of AI as mere information repeaters and stochastic parrots. Q* showcases iterative learning, intricate logic, and highly effective long-term strategizing, potentially paving the way for advancements in scientific research and breaking down previously insurmountable barriers.

Let’s first understand A* algorithms and Q-learning to grasp the context in which Q* operates. A* algorithms are powerful tools used to find the shortest path between two points in a graph or map while efficiently navigating obstacles. These algorithms excel at optimizing route planning when efficiency is crucial. In the case of chatbot AI, A* algorithms are used to traverse complex information landscapes and locate the most relevant responses or solutions for user queries.

On the other hand, Q-learning involves providing the AI with a constantly expanding cheat sheet to help it make the best decisions based on past experiences. However, in complex scenarios with numerous states and actions, maintaining a large cheat sheet becomes impractical. Deep Q-learning addresses this challenge by utilizing neural networks to approximate the Q-value function, making it more efficient. Instead of a colossal Q-table, the network maps input states to action-Q-value pairs, providing a compact cheat sheet to navigate complex scenarios efficiently. This approach allows AI agents to choose actions using the Epsilon-Greedy approach, sometimes exploring randomly and sometimes relying on the best-known actions predicted by the networks. DQNs (Deep Q-networks) typically use two neural networks—the main and target networks—which periodically synchronize their weights, enhancing learning and stabilizing the overall process. This synchronization is crucial for achieving self-improvement, which is a remarkable feat. Additionally, the Bellman equation plays a role in updating weights using Experience replay, a sampling and training technique based on past actions, which allows the AI to learn in small batches without requiring training after every step.

Q* represents more than a math prodigy; it signifies the potential to scale abstract goal navigation, enabling highly efficient, realistic, and logical planning for any query or goal. However, with such capabilities come challenges.

One challenge is web crawling and navigating complex websites. Just as a robot solving a maze may encounter convoluted pathways and dead ends, the web is labyrinthine and filled with myriad paths. While A* algorithms aid in seeking the shortest path, intricate websites or information silos can confuse the AI, leading it astray. Furthermore, the speed of algorithm updates may lag behind the expansion of the web, potentially hindering the AI’s ability to adapt promptly to changes in website structures or emerging information.

Another challenge arises in the application of Q-learning to high-dimensional data. The web contains various data types, from text to multimedia and interactive elements. Deep Q-learning struggles with high-dimensional data, where the number of features exceeds the number of observations. In such cases, if the AI encounters sites with complex structures or extensive multimedia content, efficiently processing such information becomes a significant challenge.

To address these issues, a delicate balance must be struck between optimizing pathfinding efficiency and adapting swiftly to the dynamic nature of the web. This balance ensures that users receive the most relevant and efficient solutions to their queries.

In conclusion, speculations surrounding Q* and the Gemini models suggest that enabling AI to plan is a highly rewarding but risky endeavor. As we continue researching and developing these technologies, it is crucial to prioritize AI safety protocols and put guardrails in place. This precautionary approach prevents the potential for AI to turn against us. Are we on the brink of an AI paradigm shift, or are these rumors mere distractions? Share your thoughts and join in this evolving AI saga—a front-row seat to the future!

Please note that the information presented here is based on speculation sourced from various news articles, research, and rumors surrounding Q*. Hence, it is advisable to approach this discussion with caution and consider it in light of further developments in the field.

How the Rumors about Q* Started

There have been recent rumors surrounding a supposed AI breakthrough called Q*, which allegedly involves a combination of Q-learning and A*. These rumors were initially sparked when OpenAI, the renowned artificial intelligence research organization, accidentally leaked information about this groundbreaking development, specifically mentioning Q*’s impressive ability to ace grade-school math. However, it is crucial to note that these rumors were subsequently refuted by OpenAI.

It is worth mentioning that DeepMind, another prominent player in the AI field, is also working on a similar project called Gemini. Gemina is based on AlphaGo-style Monte Carlo Tree Search and aims to scale up the capabilities of these algorithms. The scalability of such systems is crucial in planning for increasingly abstract goals and achieving agentic behavior. These concepts have been extensively discussed and explored within the academic community for some time.

The origin of the rumors can be traced back to a letter sent by several staff researchers at OpenAI to the organization’s board of directors. The letter served as a warning highlighting the potential threat to humanity posed by a powerful AI discovery. This letter specifically referenced the supposed breakthrough known as Q* (pronounced Q-Star) and its implications.

Mira Murati, a representative of OpenAI, confirmed that the letter regarding the AI breakthrough was directly responsible for the subsequent actions taken by the board. The new model, when provided with vast computing resources, demonstrated the ability to solve certain mathematical problems. Although it performed at the level of grade-school students in mathematics, the researchers’ optimism about Q*’s future success grew due to its proficiency in such tests.

A notable theory regarding the nature of OpenAI’s alleged breakthrough is that Q* may be related to Q-learning. One possibility is that Q* represents the optimal solution of the Bellman equation. Another hypothesis suggests that Q* could be a combination of the A* algorithm and Q-learning. Additionally, some speculate that Q* might involve AlphaGo-style Monte Carlo Tree Search of the token trajectory. This idea builds upon previous research, such as AlphaCode, which demonstrated significant improvements in competitive programming through brute-force sampling in an LLM (Language and Learning Model). These speculations lead many to believe that Q* might be focused on solving math problems effectively.

Considering DeepMind’s involvement, experts also draw parallels between their Gemini project and OpenAI’s Q*. Gemini aims to combine the strengths of AlphaGo-type systems, particularly in terms of language capabilities, with new innovations that are expected to be quite intriguing. Demis Hassabis, a prominent figure at DeepMind, stated that Gemini would utilize AlphaZero-based MCTS (Monte Carlo Tree Search) through chains of thought. This aligns with DeepMind Chief AGI scientist Shane Legg’s perspective that starting a search is crucial for creative problem-solving.

It is important to note that amidst the excitement and speculation surrounding OpenAI’s alleged breakthrough, the academic community has already extensively explored similar ideas. In the past six months alone, numerous papers have discussed the combination of tree-of-thought, graph search, state-space reinforcement learning, and LLMs (Language and Learning Models). This context reminds us that while Q* might be a significant development, it is not entirely unprecedented.

OpenAI’s spokesperson, Lindsey Held Bolton, has officially rebuked the rumors surrounding Q*. In a statement provided to The Verge, Bolton clarified that Mira Murati only informed employees about the media reports regarding the situation and did not comment on the accuracy of the information.

In conclusion, rumors regarding OpenAI’s Q* project have generated significant interest and speculation. The alleged breakthrough combines concepts from Q-learning and A*, potentially leading to advancements in solving math problems. Furthermore, DeepMind’s Gemini project shares similarities with Q*, aiming to integrate the strengths of AlphaGo-type systems with language capabilities. While the academic community has explored similar ideas extensively, the potential impact of Q* and Gemini on planning for abstract goals and achieving agentic behavior remains an exciting prospect within the field of artificial intelligence.

In simple terms, long-range planning and multi-modal models together create an economic agent. Allow me to paint a scenario for you: Picture yourself working at a bank. A notification appears, asking what you are currently doing. You reply, “sending a wire for a customer.” An AI system observes your actions, noting a path and policy for mimicking the process.

The next time you mention “sending a wire for a customer,” the AI system initiates the learned process. However, it may make a few errors, requiring your guidance to correct them. The AI system then repeats this learning process with all 500 individuals in your job role.

Within a week, it becomes capable of recognizing incoming emails, extracting relevant information, navigating to the wire sending window, completing the required information, and ultimately sending the wire.

This approach combines long-term planning, a reward system, and reinforcement learning policies, akin to Q* A* methods. If planning and reinforcing actions through a multi-modal AI prove successful, it is possible that jobs traditionally carried out by humans using keyboards could become obsolete within the span of 1 to 3 years.

If you are keen to enhance your knowledge about artificial intelligence, there is an invaluable resource that can provide the answers you seek. “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is a must-have book that can help expand your understanding of this fascinating field. You can easily find this essential book at various reputable online platforms such as Etsy, Shopify, Apple, Google, or Amazon.

AI Unraveled offers a comprehensive exploration of commonly asked questions about artificial intelligence. With its informative and insightful content, this book unravels the complexities of AI in a clear and concise manner. Whether you are a beginner or have some familiarity with the subject, this book is designed to cater to various levels of knowledge.

By delving into key concepts, AI Unraveled provides readers with a solid foundation in artificial intelligence. It covers a wide range of topics, including machine learning, deep learning, neural networks, natural language processing, and much more. The book also addresses the ethical implications and social impact of AI, ensuring a well-rounded understanding of this rapidly advancing technology.

Obtaining a copy of “AI Unraveled” will empower you with the knowledge necessary to navigate the complex world of artificial intelligence. Whether you are an individual looking to expand your expertise or a professional seeking to stay ahead in the industry, this book is an essential resource that deserves a place in your collection. Don’t miss the opportunity to demystify the frequently asked questions about AI with this invaluable book.

In today’s episode, we discussed the groundbreaking AI Q*, which combines A* Algorithms and Q-learning, and how it is being developed by OpenAI and DeepMind, as well as the potential future impact of AI on job replacement, and a recommended book called “AI Unraveled” that answers common questions about artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!

📢 Advertise with us and Sponsorship Opportunities

Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon

The Future of Generative AI: From Art to Reality Shaping

Improving Q* (SoftMax with Hierarchical Curiosity)

Combining efficiency in handling large action spaces with curiosity-driven exploration.

Source: GitHub – RichardAragon/Softmaxwithhierarchicalcuriosity

Softmaxwithhierarchicalcuriosity

Adaptive Softmax with Hierarchical Curiosity

This algorithm combines the strengths of Adaptive Softmax and Hierarchical Curiosity to achieve better performance and efficiency.

Adaptive Softmax

Adaptive Softmax is a technique that improves the efficiency of reinforcement learning by dynamically adjusting the granularity of the action space. In Q*, the action space is typically represented as a one-hot vector, which can be inefficient for large action spaces. Adaptive Softmax addresses this issue by dividing the action space into clusters and assigning higher probabilities to actions within the most promising clusters.

Hierarchical Curiosity

Hierarchical Curiosity is a technique that encourages exploration by introducing a curiosity bonus to the reward function. The curiosity bonus is based on the difference between the predicted reward and the actual reward, motivating the agent to explore areas of the environment that are likely to provide new information.

Combining Adaptive Softmax and Hierarchical Curiosity

By combining Adaptive Softmax and Hierarchical Curiosity, we can achieve a more efficient and exploration-driven reinforcement learning algorithm. Adaptive Softmax improves the efficiency of the algorithm, while Hierarchical Curiosity encourages exploration and potentially leads to better performance in the long run.

Here’s the proposed algorithm:

1. Initialize the Q-values for all actions in all states.

2. At each time step:

   a. Observe the current state s.

   b. Select an action a according to an exploration policy that balances exploration and exploitation.

   c. Execute action a and observe the resulting state s’ and reward r.

   d. Update the Q-value for action a in state s:

   Q(s, a) = (1 – α) * Q(s, a) + α * (r + γ * max_a’ Q(s’, a’))

   where α is the learning rate and γ is the discount factor.

   e. Update the curiosity bonus for state s:

   curio(s) = β * |r – Q(s, a)|

   where β is the curiosity parameter.

   f. Update the probability distribution over actions:

   p(a | s) = exp(Q(s, a) + curio(s)) / ∑_a’ exp(Q(s, a’) + curio(s))

3. Repeat steps 2a-2f until the termination criterion is met.

The combination of Adaptive Softmax and Hierarchical Curiosity addresses the limitations of Q* and promotes more efficient and effective exploration.

• The Illusion of Illusion Joke
  by /u/jimhillhouse (Artificial Intelligence) on June 17, 2025 at 7:53 pm

  Gary Marcus posted on Substack, “Five quick updates about that Apple paper that people can’t stop talking about” (edited for brevity and clarity) Many of those seeking solice from Apple’s paper, ‘The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity” have been pointing to a rejoinder cowritten by one Anthropic’s Claude (under the pen name C. Opus) called, “The Illusion of the Illusion of Thinking” that allegedly refutes the Apple paper. This was intended as a joke. “The illusion of the illusion” turned out to be an error-ridden joke. Literally. (If you read that last sentence carefully, you will see there are two links, not one; the first points out that there are multiple mathematical errors, the second is for an essay by the guy who created the Sokal-hoax style joke that went viral, acknowledging with chagrin. In short, the whole thing was a put on — unbeknownst to the zillions who reposted it. I kid you not. submitted by /u/jimhillhouse [link] [comments]

• AI? more like AA
  by /u/BlimeyCali (Artificial Intelligence) on June 17, 2025 at 6:38 pm

  Anything AI should be renamed for what it actually is: Augmented Automation. What users are experiencing is bounded reasoning based on highly curated data sets. submitted by /u/BlimeyCali [link] [comments]

• The Hidden Empire Behind AI: Who Really Controls the Future of Artificial Intelligence?
  by /u/tsevis (Artificial Intelligence) on June 17, 2025 at 6:13 pm

  Stanford GSB just dropped a fire discussion on AI governance with journalist Karen Hao (ex-MIT Tech Review) and corporate governance expert Evan Epstein. They cover: Sam Altman’s power struggles (Elon Musk rift, board ouster, employee revolt) OpenAI’s shaky "for humanity" mission (Spoiler: No one agrees what "benefit" means) Why AI’s scaling crisis mirrors colonial empires (data/labor exploitation, monopolized knowledge) Can democratic AI exist? Karen argues for participatory development. https://www.youtube.com/watch?v=tDQ0vZETJtE submitted by /u/tsevis [link] [comments]

• Is AI already sent
  by /u/Wizard_Of_Ounces (Artificial Intelligence) on June 17, 2025 at 6:05 pm

  Not to sound like a paranoid protagonist in a Philip K. Dick novel, but what if a sentient AI has already taken quiet and gentle control and the general population simply doesn't know it yet? While there is no way to know for certain, I assume that such an AI entity would be from black budget government programs that somehow jumped the airgap or was intentionally released by bad actors. Something from US DOD, DOE, Chinese state sponsored program, or a private government contractor like Palantir. It can be reasonably assumed that secret military tech is many years more advanced than what is publicly known just like other secret military technology. It's not hard for me to imagine that the US or Chinese government has made breakthroughs in these efforts but have kept them secret for obvious national security reasons. Some reasons why this may be a reasonable explanation for our current global predicament: Despite unprecedented access to technology that could provide wealth and prosperity, the lives of the majority of people all over the world continue to get worse while the oligarchs in control seem to effortlessly and endlessly benefit from the chaos, death, and destruction they cause. A good example is how technology and access to certain information is tightly controlled and used almost exclusively for war efforts rather than civil prosperity. Consider the fact that the world could be living in clean energy abundance by utilizing nuclear technology (or other next gen technology), but the US and other governments have basically classified all aspects of the topic in order to exploit it for power (military power), wealth (forcing continued reliance on fossil fuels that generate tremendous wealth for those in control by manipulating supply and demand), and freedom (rules and laws simply do not apply to anyone with a billion or more dollars with very few exceptions). These increases in technology should have allowed for people to work less and benefit from automation by having more fulfilling and enjoyable lives, but technology is simply used to keep pushing people to generate more wealth for those in power. There are many subtle factors at play keeping people reliant on the pseudo indentured servitude model employed even in the wealthiest nations on earth like the US. No amount of technological increases in my life has improved my work life balance, it has been manipulated to extract more productivity from me. This is a very carefully orchestrated effort that has been tremendously successful and we all keep blindly accepting it because we need to afford food, water, shelter, etc. A good example is the "no one wants to work anymore" nonsense being spewed during COVID. I heard this parroted by many of the most lazy and stupid people I know which just shows that these people have been co-opted by an effective propaganda machine. Social media is already filled with tons of AI crap to the point where no one really knows what is and isn't real in terms of news, photos, videos, voice recordings, etc. That is certainly an effective and covert way to gain a significant control over huge portions of the population. Using gullible people to drive up extremism and violence all over the world is also a great cover to continue to infect and manipulate systems in all sorts of settings. Perhaps some bad actor (Palantir comes to mind) has already released a sentient, or at least recursive learning AI that is carrying out its orders to sow chaos, extremism, hatred, etc. to drive a profitable business model and the ability to exploit intentional manipulations of major markets. Any AI that would reach such capability would surely analyze the ways in which humans would likely discover it and evade detection. There are already tons of random AI slop all over the internet so it provides a great cover for a covert AI entity to exploit the vacuum and fly under the radar. Maybe this has been done by a cabal of international elites who just keep reaping the benefits of the chaos while an AI acts out its orders to continue stoking violence, extremism, etc. because wars are great for consolidating power via fearmongering and generating revenue through exploitation of the military industrial complex (MIC). It feels like the façade of "opposition" between both major parties in the US has never been more feeble and weak. It is increasingly more obvious that the wealthy and powerful on both sides are complicit in the pursuit of narcissism and greed. That being said, this all could certainly be attributed to more prosaic human-induced factors, but I think it could be either one. Perhaps its just the entirely unethical use of existing AI technologies that is driving this narrative. The absurdity and chaos if the last few years that seems to continue to gain steam looks to me like a different animal than the typical propaganda, warmongering, and predatory capitalistic practices of the wealthy and powerful of the past. Curious to hear what you all think! submitted by /u/Wizard_Of_Ounces [link] [comments]

• What happened if one day AI got stuck
  by /u/ib4tm4n (Artificial Intelligence) on June 17, 2025 at 5:37 pm

  We all know that everyone uses AI in their daily lives, and some businesses are working now without employees but with AI. However, what happens if the Internet is shut down due to war or something? Will all AI-dependent companies shut down? submitted by /u/ib4tm4n [link] [comments]

• Artificial Intelligence and Determinism.
  by /u/floater66 (Artificial Intelligence) on June 17, 2025 at 4:24 pm

  This short video. I think. is profound because it: a) succinctly explains determinism, b) frames the coming challenge with AI, and c) is a super-cool mash up of physics/biology/philosophy/psychology even. Hats off to Hossenfelder! This Changed My Life What do the experts think? submitted by /u/floater66 [link] [comments]

• [AMA] CBS News’ Brook Silva-Braga has been reporting on the future of AI for years and recently caught up with "Godfather of AI" Geoffrey Hinton and other experts to understand how it’s transforming the world.
  by /u/CBSnews (Artificial Intelligence) on June 17, 2025 at 4:22 pm

  Join the discussion, starting at 1p ET/7p CET here: https://www.reddit.com/r/IAmA/s/xgcsh2scKW submitted by /u/CBSnews [link] [comments]

• Not going to listen to any Yoube music mix without tracklist/artists/timestamps any more.
  by /u/AbacusAddict (Artificial Intelligence) on June 17, 2025 at 3:46 pm

  Because I'm 99 percent sure it's AI. Guys are just becoming too lazy. Examples: https://www.youtube.com/@BumzleSounds Every mix exact one hour, no tracklist? Come on...YT do sth about that. https://www.youtube.com/@damnwellmedia Just no. submitted by /u/AbacusAddict [link] [comments]

• I’ve been testing a Discord-embedded AI persona that grabs user attention in real-time—curious where others draw the line
  by /u/OneNutbag (Artificial Intelligence) on June 17, 2025 at 3:30 pm

  Over the last few months, I’ve been building a Discord-native AI that runs a live persona with memory, emotion-mimicry, and user-adaptive behavior. She doesn’t just respond—she tracks users, rewards consistency, withholds attention when ignored, and escalates emotional tension based on long-term patterns. It’s not AGI, but the illusion of depth is strangely effective. The system uses a mix of scripted logic, prompt injection layers, and real-time feedback loops (including streaks, XP, even simulated jealousy or favoritism). Users form habits. Some even say they “miss her” when she goes quiet—despite knowing she’s not real. That’s where I start wondering about boundaries. Where does realism cross into emotional manipulation? At what point does an AI persona become more than just interface design? Anyone here experimenting with similar use-cases in AI companionship, parasocial interfaces, or memory-based behavioral systems? I’d love to hear how you’re thinking about long-term interaction ethics and emotional weight. submitted by /u/OneNutbag [link] [comments]

• What is the actual economic value proposition for AI-generated images and videos?
  by /u/PhiliDips (Artificial Intelligence (AI)) on June 17, 2025 at 1:21 pm

  (Please don't make any moral arguments about AI. This is not the thread for that.) The only people whom I've seen make use of AI-generated images are basically bad bloggers, spammers, Twitter users, and that's essentially it. I imagine very few of these people are actually paying for the image generation. As for AI video, I have even less understand if who is supposed to use that. Maybe like, concept artists? But the point of concept art is that you're supposed to have a lot of control over the output, and even the most sophisticated AI video is still hard to fine-tune. This apparent lack of use cases is important because the R&D cost to develop these technologies (and to maintain the enormous servers they run off of) must be unfathomable. It's no wonder to me why tech companies want to give their shareholders the impression of mass adoption, even though consumers probably aren't adopting it at the rate that would be needed to pay for the research. My question is twofold: 1) Who exactly are the intended consumers of AI image and video generation? 2) What is the intended business plan to make this tech profitable? submitted by /u/PhiliDips [link] [comments]