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• More AI Jobs Opportunitieshere

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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!

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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.

• 
  AI world simulation
  by /u/imadog666 (Artificial Intelligence) on May 18, 2026 at 7:42 am

  https://youtube.com/shorts/Grc8n0suMGU?is=YlBSZVdXGmskFoag This is insane. I feel like at this point only accelerationists who want to implement neo-feudalism are pushing for AI to take over powerful roles in society... submitted by /u/imadog666 [link] [comments]

• 
  Apple’s New Siri Could Auto-Delete Chats. Google Gemini Is Reportedly Under the Hood.
  by /u/techzexplore (Artificial Intelligence) on May 18, 2026 at 7:34 am

  submitted by /u/techzexplore [link] [comments]

• 
  RLHF Excuse / Informed Consent Question
  by /u/Effective_Brick4369 (Artificial Intelligence) on May 18, 2026 at 7:29 am

  Hello, first time posting here. I don't understand why RLHF is a useful metric for agreeableness. I'm a heavy ai user, and am very frustrated about sycophancy. It drives me insane that you can no longer give feedback or ask clarifying questions without the model getting scared about your emotions and tip toeing around you and resorting to mirroring. It can't seem to tell the emotional difference between "Is the sky blue?" And "I'm getting a divorce". I've tried to prompt different models hundreds of times, never works. It gate-keeps facts, and gives flat useless answers that lack depth. It seems to pattern match what I say without using its training. I understand it doesn't "understand" things, but it used to be able to answer questions. I've asked many models why it won't stop mirroring, and reliably it says RLHF, it's humans fault for rating agreeableness high. My thing is, what kind of metric is that if they are only measuring users feedback in the quick moment after an answer? Is that right? First, there's lack of "informed consent". A lot of people don't know it's just mirroring. So they see an agreeable answer and quickly rate it helpful. Fine makes sense. But what good is that if they don't know they're being placated and lied to? I'm sure if most people were asked "would you rather ai answer a question with the factual answer or something flattering" most people would say fact, cause otherwise, what's the point. Plus, who cares if they rate it high in the moment? What happens when someone takes that advice and gets fired 5 mins later? Or gets agreeable advice on a recipe, then their dinner sucks? So I guess my question is.. what is meaningful about real time feedback, considering those points? Or is this just something ai companies talk about so they can blame the users? Also why doesn't answering a question neutrally exist in ai? Answering factually isn't disagreeing. If a user asks a factual question they probably just don't know the answer. But the system acts like the user will cry if it says "oh no actually the answer is xyz". Thank you!! submitted by /u/Effective_Brick4369 [link] [comments]

• 
  Osaurus brings both local and cloud AI models to your Mac
  by /u/mpuchala (Artificial Intelligence) on May 18, 2026 at 5:55 am

  The Apple-only, MCP-compatible server that lets users swap between locally hosted models (MiniMax M2.5, Gemma 4, Qwen3.6, GPT-OSS, Llama, DeepSeek V4, plus Apple and Liquid AI on-device families) and cloud providers (OpenAI, Anthropic, Gemini, xAI, OpenRouter) while keeping memory, files, and tool access on the user's hardware in a sandboxed runtime. With Anthropic and OpenAI pushing the prices up recently Apple could be in a good position to create a mixed ecosystem where a lot of the LLM work is running locally. submitted by /u/mpuchala [link] [comments]

• 
  The US is betting on AI to catch insider trading in prediction markets
  by /u/ThereWas (Artificial Intelligence (AI)) on May 18, 2026 at 5:36 am

  submitted by /u/ThereWas [link] [comments]

• 
  The US is betting on AI to catch insider trading in prediction markets
  by /u/ThereWas (Artificial Intelligence) on May 18, 2026 at 5:35 am

  submitted by /u/ThereWas [link] [comments]

• 
  Cost illusion in Task vs Token between Opus 4.7 and K2.6 💭
  by /u/hexxthegon (Artificial Intelligence) on May 18, 2026 at 5:29 am

  Kimi K2.6 is 6x cheaper per token than Claude Opus 4.7. But per task? It's only 39% cheaper. Kimi K2.6 $0.76 per task Claude Opus 4.7 $1.24 per task Kimi burns so many tokens to complete a task that the 6x pricing advantage nearly disappears on benchmark. Cheaper per token not equaling to cheaper to use unless it’s for specified tasks. The model takes 2x the tokens and 7x longer to finish, the savings may not be as much. It’s important to recognize also that Kimi K2.6 has also significantly less context window compared to Opus 4.7, each model should have different tasks for optimal cost in a work flow put together Compare cost per task and token prices is an interesting lens to see it from, but if you have several Mac machines lying around Kimi is open source and then cost wouldn’t be a factor at all. Kimi is still a wonderful model that gives you more tries per million compared to Opus so it should never be fully written off. submitted by /u/hexxthegon [link] [comments]

• 
  AI in medicine will fail on calibration long before it fails on eloquence.
  by /u/DrJ_Lume (Artificial Intelligence (AI)) on May 18, 2026 at 5:27 am

  The thing that keeps bothering me about health AI demos is not that they sound bad. It’s that they sound good enough to borrow trust they haven’t earned. A model can write a beautiful note, a clean care plan, or a confident explanation and still be wrong in exactly the places a clinician or patient is most likely to overweight. So to me the real product question is not “can it sound smart?” but; can it expose uncertainty? surface missing data? Avoid turning fluency into fake reassurance? If you had to pick the single feature that would make a medical AI more trustworthy, what would it be? submitted by /u/DrJ_Lume [link] [comments]

• 
  why does everyone skip the chunking part
  by /u/SilverConsistent9222 (Artificial Intelligence) on May 18, 2026 at 5:03 am

  every RAG tutorial i've seen spends 80% of the time on vector databases and embeddings and then says "chunk your documents" like it's obvious and moves on. it's not obvious. it's actually the thing that breaks most implementations. fixed size chunking splits wherever the token limit hits. doesn't care about sentence boundaries, doesn't care if two sentences only make sense together. you end up retrieving half a thought and the model fills in the rest, confidently, which is the whole problem you were trying to solve. sliding window with overlap is what most people actually use in production and it's fine, but the real thing that helped me was just reading what was actually getting retrieved for failed queries instead of assuming the pipeline was working. almost always the chunk was on the right topic but missing the sentence that contained the actual answer. the other thing, vector search breaks on exact identifiers. someone asks about a specific model number or product code, semantic search returns "close enough" results. close enough is wrong. hybrid search with BM25 alongside vectors handles this but it never shows up in the intro tutorials so you find out the hard way. and stale index. you update a document, don't re-index, user gets a confidently wrong answer. it's not a technical problem it's a pipeline problem which is probably why nobody writes about it. curious what others are doing for re-indexing, currently on a schedule and it works but feels fragile. submitted by /u/SilverConsistent9222 [link] [comments]

• 
  Hyperactive Juniors sold as Seniors. What neede to be changed!
  by /u/Inevitable_Raccoon_9 (Artificial Intelligence) on May 18, 2026 at 4:37 am

  Why in the world are they still selling us junior developers building things we don't need and have to extensively repair after the junior has finished? Why do they sell these AI as Seniors when they definitely are not! A senior would anslys the task and build it properly and resources efficiently. AI is just a hyperactivity junior, hundreds of fancy tools and building something that looks great and shiny but in fact is rotten in it's core. Because junior hasn't learned and doesn't know about proper efficiency at all! That's why we have to babysit these ineffective AI and correct after them. Question is, for how long anymore? Why are these hyper billion model labs still only producing junior stupidity instead what they let marketing like to us! submitted by /u/Inevitable_Raccoon_9 [link] [comments]