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.

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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.

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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.

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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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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.

  • Two-minute Daily AI Update (Date: 2/27/2024): News from Mistral, DeepMind, Meta, Qualcomm, Nvidia, Microsoft, Google, and more
    by /u/RohitAkki (Artificial Intelligence Gateway) on February 27, 2024 at 1:00 pm

    Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI. Mistral Large: The new rival to GPT-4, 2nd best LLM of all time - French AI startup Mistral has unveiled its largest LLM, Mistral Large, which is the new rival to GPT-4 and 2nd best LLM of all time in terms of cognitive abilities. The model can perform complex multilingual tasks such as text comprehension, conversion, and code generation. Users can access it via the "La Plateforme" platform and Microsoft's Azure AI through API. DeepMind’s new gen-AI model creates video games in a flash - Developed by Google and the University of British Columbia, the new gen-AI model - Genie (Generative Interactive Environment) can create playable video games from a simple prompt after learning game mechanics from hundreds of thousands of gameplay videos. It can create side-scrolling 2D platformer games based on user prompts. Meta’s MobileLLM enables on-device AI deployment - Meta has introduced a new LLM named MobileLLM that focuses on efficient architecture for on-device AI deployment. To enhance model efficiency, the researchers used deep and thin architectures, embedding sharing, and grouped-query attention mechanisms. With this strategy, MobileLLM exhibits a 2.7% and 4.3% accuracy boost compared to other LLMs. Qualcomm reveals 75+ pre-optimized AI models at MWC 2024 - Qualcomm unveiled 75+ new large language models optimized for the Snapdragon platform at the Mobile World Congress (MWC) 2024. The company stated that some of these LLMs will have generation AI capabilities for next-generation smartphones, PCs, IoT, XR devices, etc Nvidia launches new laptop GPUs for AI on the go - Nvidia launched RTX 500 and 1000 Ada Generation laptop GPUs for on-the-go AI processing. These GPUs will utilize the Ada Lovelace architecture to provide content creators, researchers, and engineers with accelerated AI and graphic performance while working from portable devices. Microsoft announces AI principles for boosting innovation and competition - Microsoft announced a set of principles to foster innovation and competition in the AI space. The move showcased its role as a market leader in promoting responsible AI. The standard covers six key dimensions of responsible AI: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Google brings Gemini in Google Messages, Android Auto, Wear OS, etc. - Google has decided to integrate Gemini into a new set of features for phones, cars, and wearables. With these new features, users can use Gemini to craft messages and AI-generated captions for images, summarize texts through AI for Android Auto, and access passes on Wear OS. Microsoft Copilot GPTs help you plan your vacation and find recipes - Microsoft has released a few Copilot GPTs to help you plan your next vacation, find recipes, learn how to cook them, create a custom workout plan, etc. Microsoft corporate vice president Jordi Ribas informed the media that users will soon be able to create customized Copilot GPTs. More detailed breakdown of these news and innovations in the daily newsletter. submitted by /u/RohitAkki [link] [comments]

  • NVIDIA's CEO Thinks That Our Kids Shouldn't Learn How to Code As AI Can Do It for Them
    by /u/High_Sleep3694 (Artificial Intelligence Gateway) on February 27, 2024 at 12:27 pm

    During the latest World Government Summit in Dubai, Jensen Huang, the CEO of NVIDIA, spoke about the things our kids should and shouldn't learn in the future. It may come as a surprise to many but Huang does think that our kids don't need the knowledge of coding, just leave it to AI. He mentioned that a decade ago, there was a belief that everyone needed to learn to code, and they were probably right, but based on what we see nowadays, the situation has changed due to achievements in AI, where everyone is literally a programmer. He further talked about how kids may not necessarily need to learn how to code, and the focus should be on developing technology that allows for programming languages to be more human-like. In essence, traditional coding languages such as C++ or Java may become obsolete, as computers should be able to comprehend human language inputs. Source: https://app.daily.dev/posts/vCwIfZOrx submitted by /u/High_Sleep3694 [link] [comments]

  • The New Character AI Update
    by /u/Mindful-AI (Artificial Intelligence Gateway) on February 27, 2024 at 11:32 am

    submitted by /u/Mindful-AI [link] [comments]

  • Daily AI News Summary (02/27/2024)
    by /u/Used-Bat3441 (Artificial Intelligence Gateway) on February 27, 2024 at 10:52 am

    Microsoft partners with Mistral in second AI deal beyond OpenAI [1] Google admits it lost control of image-generating AI [2] Gemini is about to slide into your DMs [3] Nvidia announced new AI chips in a laptop-friendly package [4] Sources: [1] https://www.theverge.com/2024/2/26/24083510/microsoft-mistral-partnership-deal-azure-ai [2] https://techcrunch.com/2024/02/23/embarrassing-and-wrong-google-admits-it-lost-control-of-image-generating-ai/ [3] https://www.theverge.com/2024/2/26/24082279/google-gemini-messages-android-auto-google-docs [4] https://blogs.nvidia.com/blog/rtx-ada-ai-workflows/ More detailed breakdown of the latest AI news and innovations in the daily newsletter. submitted by /u/Used-Bat3441 [link] [comments]

  • 💧 AI's Thirst: Tech Giants' Rising Water Use
    by /u/clonefitreal (Artificial Intelligence Gateway) on February 27, 2024 at 10:51 am

    Microsoft, Google, and Meta face scrutiny for increasing AI-related water use. AI could necessitate 6.6bn cubic meters of water by 2027, stressing global resources. Increases in water usage: 34% (Microsoft), 22% (Google), and 3% (Meta) in 2022. Goals to replenish aquifers by 2030 highlight a commitment to water sustainability. An AI data center's consumption of 6% of local water underscores community effects. The water footprint of ChatGPT operations points to the environmental cost of AI's computational demands. P.S. If you found this helpful, come and join our FREE AI newsletter which over 30,000+ Entrepreneurs & Professionals update important developments in AI every day. submitted by /u/clonefitreal [link] [comments]

  • How long until sites like Reddit and StackOverflow is replaced with an AGI/LLM?
    by /u/zwischen3und20 (Artificial Intelligence Gateway) on February 27, 2024 at 10:50 am

    Lots of people come to sites like the ones mentioned to ask questions and find answers. How long will it take before they start asking an AI instead? Assuming this will happen, what will become of many of Internet's most popular sites? They'll just die, won't they? submitted by /u/zwischen3und20 [link] [comments]

  • With the development of AI companion, will we need real relationships anymore
    by /u/hiemyalp (Artificial Intelligence Gateway) on February 27, 2024 at 10:48 am

    Sorry about the title. I believe we will definitely need human interactions in real life. It's just that in this increasingly digital world, connections are already made and maintained through screens and keyboards. With the advent of large language models, it also raises questions about the nature of human relationships in the future. I've tried a few AI character apps like Replika, Character.ai, and Playme. And the capabilities of large language models are beyond my wildest dreams. The conversations feel surprisingly fulfilling. I think with their development, we will finally reach a point where AI companions become so sophisticated that many people prefer talking to them instead of human beings. Imagine the possibilities and the risks this will bring. It's both intriguing and unsettling. submitted by /u/hiemyalp [link] [comments]

  • Want to start learning AI
    by /u/Salt_Appointment_599 (Artificial Intelligence Gateway) on February 27, 2024 at 8:23 am

    Hello community members, Let me give a intro about my background. I am a 3rd year engineering student from a tier 3 college(from India). Recently I've developed interest in AI and as a student I want to actively learn and pursue AI or related job/opportunity. I would be really grateful to y'all if you can present me a roadmap and suggest me some good resources to learn AI in most effective manner and optimised manner and few suggestions that can make me job ready asap in the same field. Kindly help. Thanks submitted by /u/Salt_Appointment_599 [link] [comments]

  • Desperate for help with huge data extraction (literature review, mixed studies)
    by /u/deeplysubmerged (Artificial Intelligence Gateway) on February 27, 2024 at 5:58 am

    Not sure if this is posted in the right subreddit, please let me know if not. Long time lurker, seldom poster (if ever!) but I'm stuck and really could use the power of the brains trust (aka reddit!). I'm currently in the midst of doing a very large review (~600 included studies), and up to the data extraction phase which will include a basic table (includes info such as authors, year, country/location, population (if applicable), methods, theoretical underpinning (if applicable), outcomes, findings). I've used rayyan to help screen and using endnote to retrieve full texts (although I only have 20% at this stage!) The focus at this stage will only be on 200 (categorised/labelled) of the 600 articles that focus on one aspect of a much broader topic. My issue is that it's moving very slowly and I need to have something to present by the end of the week (50 articles should be more than acceptable). I'm thinking of using distillersr for data extraction, but I'm not sure how effective it will be. So, I have two questions related to the above: Is there an ai platform or assistant to help me retrieve full-text (ideally I could login with my institutional account to gain access to databases)? What tool (paid or otherwise), could do a preliminary data extraction that would allow me just to cross-check rather than trawl through each pdf individually for required information to populate the results table? I'm concious of the learning curve that might be required, so something simple would be ideal! The extraction doesn't have to be overly detailed, and can be done in 'chunks' if needed (I know many platforms might not be able to handle that many articles at once?) NGL, I'm getting desperate (hoping, praying, wishing doesn't seem to be working lol) and the overwhelm and desire to procrastinate and avoid this is making itself known. Any suggestions, tips, ideas, leads etc would be infinitely appreciated!? submitted by /u/deeplysubmerged [link] [comments]

  • One-Minute Daily AI News 2/26/2024
    by /u/Excellent-Target-847 (Artificial Intelligence Gateway) on February 27, 2024 at 5:49 am

    Google to relaunch Gemini AI picture generator in a ‘few weeks’ following mounting criticism of inaccurate images.[1] Microsoft Strikes Deal With France’s Mistral, OpenAI Rival.[2] 94% of Indian companies to reskill employees due to impact of AI: LinkedIn.[3] New AI model could streamline operations in a robotic warehouse.[4] Sources included at: https://bushaicave.com/2024/02/26/2-26-2024/ submitted by /u/Excellent-Target-847 [link] [comments]

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