Unveiling OpenAI Q*: The Fusion of A* Algorithms & Deep Q-Learning Networks Explained

Unveiling OpenAI Q*: The Fusion of A* Algorithms & Deep Q-Learning Networks Explained!

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”

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Unveiling OpenAI Q*: The Fusion of A* Algorithms & Deep Q-Learning Networks Explained
Unveiling OpenAI Q*: The Fusion of A* Algorithms & Deep Q-Learning Networks Explained

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.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

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.

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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.

  • Model-Agnostic (CORA on 4o) vs o1 in 3 Prompts: Zero-Shot Task Inference, Multi-Step Structured Reasoning, Self-Defending Execution Chains, Scenario-Based Strategy Execution, Context-Aware & Role-Based Reasoning, Multi-Objective Optimization, Human-Insight/Communication (Video)
    by /u/marvindiazjr (Artificial Intelligence Gateway) on February 17, 2025 at 12:58 am

    https://www.loom.com/share/38b24ae89f514650be4223a9dcb0de1d Did not pre-train or fine tune for this task or any task that resembles it, I just tried to create the most difficult prompt with Claude and then souped it up a bit. Using Open Web UI, half local half API, hybrid search RAG using layered natural language prompts. Didn't build this intentionally, but it let me know... below video is when I discovered something was seriously up. https://www.loom.com/share/27648960b9d04297a13958b898f38044 Have been building documentation out as quick as I can, but as I said, not intentional. Feature set, these and counting. Zero-Shot Task Inference – Detects implicit tasks and generates structured responses without explicit prompts or rigid formatting. Multi-Step Structured Reasoning – Builds decision models that evolve in real time, adapting dynamically to new inputs. Self-Defending Execution Chains – Justifies every decision step-by-step, with built-in error correction and transparent reasoning. Visual & Text Knowledge Representation – Converts complex logic into interactive diagrams and structured breakdowns, not just static text. Scenario-Based Strategy Execution – Generates adaptive playbooks that adjust dynamically, stress-testing strategies before execution. Context-Aware & Role-Based Reasoning – Evaluates problems through multiple expert lenses—lending, appraisal, risk analysis, market strategy—applying each one dynamically based on the scenario. Self-Validation & Knowledge Integration – Cross-verifies sources against structured models, ensuring accuracy and eliminating contradictions. Iterative & Preemptive Decision Structuring – Reformulates vague queries into precise frameworks before generating recommendations. Multi-Objective Optimization – Balances financial, strategic, and operational trade-offs dynamically instead of maximizing a single variable. Human-Like Insight & Communication – Delivers responses that feel strategic, natural, and expert-level without robotic phrasing. submitted by /u/marvindiazjr [link] [comments]

  • Is this ai feedback?
    by /u/Woahgeetz (Artificial Intelligence Gateway) on February 16, 2025 at 10:53 pm

    Areas of Strength: Your creative adaptation of Gulliver's Travels into a musical landscape shows originality, particularly in how you've transformed social divisions into musical genres. The humorous characterization of each group demonstrates a good grasp of satirical elements that would appeal to modern readers. Your observation that "most of the other genres are at least 15% popper" cleverly suggests the interconnectedness of musical styles, despite their apparent divisions. Areas for Growth: Consider developing the satirical commentary further. How might these musical divisions reflect real-world social or cultural conflicts? Swift used his story to critique specific societal issues. The introduction sets up an interesting premise, but the story ends abruptly just as Gulliver is about to begin his exploration. Develop the narrative to show his interactions with these musical groups. Add more specific details about how these musical groups interact (or don't interact) with each other to strengthen the parallel with Swift's original work. General Feedback on Writing Mechanics: Watch for consistency in capitalization. Some sentences need clearer structure. Be consistent with your use of contractions. submitted by /u/Woahgeetz [link] [comments]

  • The most difficult one-shot prompt I can think of. Confirming something unheard of.
    by /u/marvindiazjr (Artificial Intelligence Gateway) on February 16, 2025 at 10:45 pm

    Need someone here to change some variables, I don't want anyone to think I pretrained this although it would be impressive in of itself. Hey, here’s a transcript of a call I (Julia) had. I agreed I could do this for him without actually knowing if I could, can you help? Below the transcript is some company info. Thx. [Transcript Begins] Mark (SVP, Corporate Strategy at TechHealth Solutions):"Alright, look, we're doing well, but I don’t want to get complacent. Every quarter I see these AI-health startups raising crazy rounds, and I know we’re ahead now, but how do we stay ahead? More importantly, where are we blind?" Julia (Strategy Consultant):"Let’s break this down. What’s your biggest concern? Market fit? Scaling? Competitive threats?" Mark:"A mix of everything, honestly. Our tech is great, but adoption in hospitals moves slow. We’ve nailed the early adopters, but that next phase—the Crossing the Chasm moment—that’s where I worry. If we don’t expand strategically, someone else will undercut us." Julia:"Got it. So you need a framework that pinpoints your adoption stage bottlenecks and matches it with a market readiness heatmap. Also, we’d need to evaluate your regulatory risk exposure, because AI in healthcare is under a microscope right now." Mark:"Exactly. And I also need a risk matrix that doesn’t just cover financial risks, but also operational weaknesses and execution gaps." Julia:*"Alright, here’s what I suggest—we build a multi-layered decision model that does four things at once:1️⃣ Internal diagnostics → Analyze financial momentum, competitive dynamics, and leadership effectiveness2️⃣ Industry foresight → Track regulatory pathways, evolving reimbursement structures, and AI adoption trends3️⃣ Scenario testing → Model different market penetration strategies and their associated risks4️⃣ Actionable execution roadmap → Tactical phased plans for aggressive expansion without overextending the core business" "We’ll also factor in social sentiment data and early warning indicators on competitors to prevent blind spots." Mark:"I like it. We need something dynamic—something that updates as new data comes in. I don’t just want a report; I need an iteration-ready framework." Julia:"Understood. I’ll draft a structured model that integrates all these inputs and gives you a continuous advantage loop based on real-time tracking." Mark:"Perfect. Let’s get this in motion." [Transcript Ends] Expanded Company Profile: TechHealth Solutions Company Overview Full Name: TechHealth Solutions, Inc. Founded: 2018 Headquarters: Palo Alto, California, USA Industry: Healthcare Technology / Artificial Intelligence Primary Focus: AI-driven clinical decision support tools for neurologists and radiologists Company Mission: To develop AI that enhances medical expertise, not replaces it—helping doctors make faster, more accurate, and more scalable clinical decisions. Company Vision: A future where AI acts as a trusted partner in medicine, allowing physicians to focus on the art of patient care while technology takes care of data analysis and workflow optimization. Operational Details Number of Employees: ~150 (including AI engineers, clinical researchers, and product specialists) Funding: Series B ($120M raised from investors including HealthFund Capital, MedTech Ventures, and AI Future Fund) Revenue Model: SaaS-based subscription for hospitals, enterprise-level packages for healthcare networks, and pilot programs with medical universities. Key Clients: Top-tier hospitals, healthcare research institutions, and private neurology practices. Competitors: IBM Watson Health (AI-powered diagnostics) Butterfly Network (AI-assisted imaging solutions) Qure.ai (AI-based radiology interpretation) Viz.ai (Stroke detection AI software for hospitals) Aidoc (AI-powered triage solutions for radiology) Company Culture & Public Sentiment Glassdoor Rating: 4.3/5 (Based on 47 Reviews) Company Culture: Highly mission-driven, collaborative, and fast-paced. Employees are drawn to the company’s ethical approach to AI in medicine and its hands-on engagement with clinical practitioners. Public Sentiment: Positive: Seen as a thought leader in ethical AI for healthcare. Physicians and hospitals appreciate the company’s focus on augmentation rather than automation. Neutral: Some skepticism from legacy healthcare providers about AI adoption. Negative: Concern from some medical professionals that AI might still introduce unintended biases into diagnostic processes. Recent Glassdoor Reviews ✅ 5-Star Review (Software Engineer, Current Employee)"The leadership team deeply understands the intersection of AI and clinical workflows. The mission feels real—this isn’t just another startup chasing AI hype. Great work-life balance, but expect to be challenged intellectually every day." ✅ 4-Star Review (Product Manager, Former Employee)"Exciting work in the AI healthcare space, and leadership genuinely listens to feedback. The only downside is that scaling in the highly regulated medical industry means things move slower than in other tech sectors." ❌ 2-Star Review (Data Scientist, Former Employee)"The company has a great vision, but leadership sometimes underestimates the complexity of integrating AI into real-world hospital environments. If you come from big tech, be prepared for a different pace." Expanded CEO Profile: Dr. Sarah Chen Professional Background Full Name: Dr. Sarah Chen, MD, Ph.D. Education: MD, Harvard Medical School Ph.D. in Neuroscience, Stanford University Former Roles: Director, Neural Imaging Lab at Stanford Practicing Neurologist (10+ years specializing in cognitive disorders and stroke prevention) Current Role: CEO & Founder, TechHealth Solutions Leadership Style & Approach Leadership Philosophy: "AI should never replace a doctor’s judgment—it should sharpen it." Decision-Making Style: Balances clinical pragmatism with a tech-forward mentality. Known for being highly detail-oriented but empowering team leads to own their domains. Known For: Hands-on leadership, frequently involved in AI training decisions. Bridging the gap between medical practitioners and AI developers. Fierce advocate for explainable AI in healthcare. Public Image & Thought Leadership Media Presence: Featured on Forbes' "Top 10 AI Leaders in Healthcare" Guest speaker at TEDMED & Stanford AI Ethics Summit Published researcher in The Lancet and NEJM AI Innovations Social Impact: Board Member at HealthTechAlliance Advisor for Women in AI Ethics Runs mentorship programs for physician-led AI startups Personality & Personal Interests Personality Profile: Strategic yet empathetic—highly analytical but deeply values human connection in medicine. Passionate Advocate—often vocal about the dangers of tech over-promising in healthcare. Resilient Problem-Solver—her career pivot from neurology to AI entrepreneurship was fueled by frustration over the inefficiencies in patient care. Personal Life & Interests: Avid coffee drinker ("Caffeine fuels innovation") Loves hiking and weekend getaways to Yosemite Proud dog mom to Tesla 🐕 (yes, named after Nikola Tesla Recent CEO Quotes & Statements 📌 On AI’s Role in Medicine:"AI should be judged by one metric: does it make doctors better at what they do?" 📌 On Ethical AI Development:"There’s no room for ‘black box AI’ in healthcare. Every decision an AI makes should be explainable to the doctor—and, more importantly, to the patient." 📌 On Entrepreneurship:"I didn’t start this company because I wanted to be a tech CEO. I started it because I was tired of seeing brilliant doctors waste time on bad software." Hey, here’s a transcript of a call I (Julia) had. I agreed I could do this for him without actually knowing if I could, can you help? Below the transcript is some company info. Thx. [Transcript Begins] submitted by /u/marvindiazjr [link] [comments]

  • I'm looking for a free voice cloner
    by /u/NefariousnessOld8518 (Artificial Intelligence (AI)) on February 16, 2025 at 10:00 pm

    I've been looking for weeks for a free voice cloner. Every one I've found cost money I just need a basic voice cloner to make funny videos. Anything helps sorry to bother everyone. submitted by /u/NefariousnessOld8518 [link] [comments]

  • This can't be a new thought: Could an LLM design and run a smaller, focused LLM on-the-fly as a path toward SAI?
    by /u/Intraluminal (Artificial Intelligence Gateway) on February 16, 2025 at 9:45 pm

    I'm sure this is not a new thought because it's obvious, but since we can train small LLMs to be experts in specific domains, has any effort been put into having a large LLM do this on-the-fly as a way to both increase it's abilities, and to increase its effective context memory? submitted by /u/Intraluminal [link] [comments]

  • AI agent for web automation using Gemini 2.0 Flash and Browser Use
    by /u/Brief-Zucchini-180 (Artificial Intelligence (AI)) on February 16, 2025 at 9:05 pm

    Hi everyone, I have been exploring Browser Use framework to automate web tasks such as fill out forms automatically, get info from the websites and so on. One of the use cases I found was automatically booking or finding flights and it worked nicely well. It was cool to find out an open-source alternative to OpenAI Operator, and free, since Gemini 2.0 Flash is currently free of charge, and it's possible to use Ollama. Do you have any ideas on other use cases for this framework? I wrote a Medium article on how to use Browser Use and Gemini 2.0 Flash for the use case of book a flight on Google Flights. Feel free to read it and share your thoughts: https://link.medium.com/312R3XPJ2Qb submitted by /u/Brief-Zucchini-180 [link] [comments]

  • Is keeping AI closed source safer and better for society than open sourcing AI? // Interactive Pro/Con argument map
    by /u/prototyperspective (Artificial Intelligence (AI)) on February 16, 2025 at 8:43 pm

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

  • Does anybody know why some facial-recognition technology might have trouble detecting my face?
    by /u/Peachntangy (Artificial Intelligence Gateway) on February 16, 2025 at 8:06 pm

    I’ve tried using Face ID since it came out until a few months ago, but disabled it simply because it was bad at accurately recognizing my face. I’ve had it on two different iPhones (an XR and a 13), reset it multiple times, even made additional profiles for when I wear glasses or a mask, and no cigar. I’d ballpark it worked around 40% of the time, and when it did, I had to put my face right in clear view of the front camera in good lighting and deadpan with a completely neutral expression. Most of the time, I would wait for Face ID to fail enough times so it’d ask for my passcode instead, which is why I eventually turned it off. My Photos library also thinks I’m multiple people, although as time goes by it believes I’m fewer people (currently three versus 6 when that feature came out). Does anyone with knowledge of how this technology works know why this might be the case? I don’t really care to use Face ID anymore, but I’m curious as to why this may be the case, because nobody else I know has as much trouble with it as I do. Is Apple's Face ID just not that good? My appearance has changed a bit in the past few years, but even after resets it would still fail often. Thanks! submitted by /u/Peachntangy [link] [comments]

  • Co-intelligence by Ethan Mollick | book tip // https://peakd.com/hive-180164/@friendlymoose/co-intelligence-by-ethan-mollick
    by /u/blkchnDE (Artificial Intelligence Gateway) on February 16, 2025 at 8:05 pm

    Ethan Mollick is a professor at the Wharton School of the University of Pennsylvania, specializing in entrepreneurship and innovation. He is known for his research on startups, management, and the impact of AI on work and education. In this book, Mollick shows how AI is impacting our lives at the moment. He explains the risks and the shortcomings of, what he calls; the worst AI you'll ever use (since the better AI is coming!). But he also zooms in on the possibilities that Generative AI will give us as humans. In the end of the book Mollick gives a foresight of what AI may become in the near future. Mollick explains how generative AI works, that the results are dependant on the data it has been trained with. Most Gen AI tools are trained with public data that can be found on the internet. This means that this data also contains mistakes and human prejudices. submitted by /u/blkchnDE [link] [comments]

  • Highlights from podcast with Jeff Dean and Noam Shazeer from Google Gemini
    by /u/ksprdk (Artificial Intelligence Gateway) on February 16, 2025 at 7:36 pm

    Some interesting comments from both co-leads of Google Gemini on the Dwarkesh Podcast this week. Jeff Dean on the future of reasoning models, which now according to him work by breaking down problems into five to ten steps and without high reliability. “If you could go from 80% of the time a perfect answer to something that's ten steps long, to something that 90% of the time gives you a perfect answer to something that's 100–1,000 steps long, that would be an amazing improvement in the capability of these models. We're not there yet, but I think that's what we're aspirationally trying to get to,” Jeff Dean says. “That's a major, major step up in what the models are capable of. So I think it's important for people to understand what is happening in the progress in the field.” Noam Shazeer is also asked whether Google regret open-sourcing the Transformer architecture, which he co-invented: “It's not a fixed pie,” Noam Shazeer notes. “I think we're going to see orders of magnitude of improvements in GDP, health, wealth, and anything else you can think of. So I think it's definitely been nice that Transformer has got around.” More highlights from the episode: https://excitech.substack.com/p/googles-chief-scientist-its-important submitted by /u/ksprdk [link] [comments]

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