Core Viewpoint - Tevogen Bio Holdings Inc. has successfully developed the alpha version of its AI-driven PredicTcell™ model in collaboration with Microsoft and Databricks, aiming to revolutionize therapeutic development through enhanced target discovery and accelerated clinical research [1][3]. Group 1: Technology and Innovation - The PredicTcell model utilizes a terabyte-scale dataset with nearly a billion genetic and proteomic elements, significantly improving target discovery and reducing protein sequence analysis time from months to hours through advanced machine learning techniques [2][7]. - Tevogen.AI's initiatives are expected to streamline early-stage drug discovery, potentially generating billions in cost savings across the healthcare system and creating substantial top-line revenues for early adopters [7]. Group 2: Future Developments - The company plans to enhance the PredicTcell platform by expanding its datasets to include virology, oncology, and neurology, which may lead to improved accuracy and reduced time for wet lab testing [3][4]. - Additional advancements in clinical trial optimization and patient market analysis through the complementary AdapTcell™ model are anticipated to be announced in future communications [4].

Core Technology & Architecture - The workshop focuses on a GPT-2 inference implementation in Vanilla JS, providing a foundation for understanding modern AI systems like ChatGPT, Claude, DeepSeek, and Llama [1] - It covers key concepts such as converting raw text into tokens, representing semantic meaning through vector embeddings, training neural networks through gradient descent, and generating text with sampling algorithms [1] Educational Focus & Target Audience - The workshop is designed for web developers entering the field of ML and AI, aiming to provide a "missing AI degree" in two hours [1] - Participants will gain an intuitive understanding of how Transformers work, applicable to LLM-powered projects [1] Speaker Expertise - Ishan Anand, an AI consultant and technology executive, specializes in Generative AI and LLMs, and created "Spreadsheets-are-all-you-need" [1] - He has a background as former CTO and co-founder of Layer0 (acquired by Edgio) and VP of Product Management for Edgio, with expertise in web performance, edge computing, and AI/ML [1]

Product Overview - MindsDB is presented as a federated query engine with a built-in MCP server [1] - The platform supports querying data from over 200 sources, including Slack, Gmail, and social platforms [1] - MindsDB offers query capabilities in both SQL and natural language [1] - The platform is 100% open-source and has over 33 thousand stars [1]

Architectural Decision & Strategy - The company highlights a fundamental architectural decision, aligning with the "bitter lesson" principle, favoring general learning approaches scalable with computation over human-labor-intensive methods [1][2][3] - This architectural decision mirrors a similar one made at Tesla, suggesting a pattern in strategic choices [2] - The "bitter lesson" emphasizes that general computation consistently outperforms approaches relying heavily on human knowledge in solving AI problems [3][4] Competitive Landscape & Implications - Other companies like Llama, Gemini, OpenAI, and Anthropic are questioned regarding their reliance on human knowledge, particularly in data labeling, with Llama's investment of 15 billion to buy 49% of Scale AI as an example [5] - The company suggests that a general computational approach, requiring less human labeling, can achieve better and faster results [4] - The company contrasts its approach with others, implying a competitive advantage by embracing scalable, general-purpose computing [5] Scalability & Impact - The company draws a parallel to Travis's approach to food production, suggesting that a general-purpose computing approach can scale solutions to a global level [6] - The company emphasizes the profound importance of its approach, implying its potential to revolutionize various industries [6]

Core Viewpoint - The article discusses the rapid advancements in embodied intelligence, highlighting its potential to revolutionize various industries by enabling robots to understand language, navigate complex environments, and make intelligent decisions [1]. Group 1: Embodied Intelligence Technology - Embodied intelligence aims to integrate AI systems with physical capabilities, allowing them to perceive and interact with the real world [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are competing in this transformative field [1]. - The potential applications of embodied intelligence span manufacturing, healthcare, service industries, and space exploration [1]. Group 2: Technical Challenges - Achieving true embodied intelligence presents unprecedented technical challenges, requiring advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2]. Group 3: Role of MuJoCo - MuJoCo (Multi-Joint dynamics with Contact) is identified as a critical technology for embodied intelligence, serving as a high-fidelity simulation engine that bridges the virtual and real worlds [3]. - It allows researchers to create realistic virtual robots and environments, enabling millions of trials and learning experiences without risking expensive hardware [5]. - MuJoCo's advantages include high simulation speed, the ability to test extreme scenarios safely, and effective transfer of learned strategies to real-world applications [5]. Group 4: Research and Industry Adoption - MuJoCo has become a standard tool in both academia and industry, with major companies like Google, OpenAI, and DeepMind utilizing it for robot research [7]. - Mastery of MuJoCo positions entities at the forefront of embodied intelligence technology [7]. Group 5: Practical Training and Curriculum - A comprehensive MuJoCo development course has been created, focusing on practical applications and theoretical foundations within the embodied intelligence technology stack [9]. - The course includes project-driven learning, covering topics from physical simulation principles to deep reinforcement learning and Sim-to-Real transfer techniques [9][10]. - Six progressive projects are designed to enhance understanding and application of various technical aspects, ensuring a solid foundation for future research and work [14][15]. Group 6: Expected Outcomes - Upon completion of the course, participants will gain a complete embodied intelligence technology stack, enhancing their technical, engineering, and innovative capabilities [25][26]. - Participants will develop skills in building complex robot simulation environments, understanding core reinforcement learning algorithms, and applying Sim-to-Real transfer techniques [25].

「停止研究 RL 吧，研究者更应该将精力投入到产品开发中，真正推动人工智能大规模发展的关键技术是互联网，而不是像 Transformer 这样的模型架 构。」 前 OpenAI 研究员 Kevin Lu 最近更新了一篇博客长文《The Only lmportant Technology ls The Internet》，直指互联网才是推动人工智能进步的核心技术， 是 next-token 预测的完美补充。 Kevin Lu 认为，没有 Transformer 架构，我们可能也会拥有 GPT-4.5 级别的大模型。在 GPT-4 模型以来，基础模型的能力并没有显著的提升，我们可能会 像 2015-2020 年时代的 RL 研究一样，重蹈覆辙，正在进行无关紧要的 RL 研究。 而互联网提供了丰富而海量的数据来源，这些数据具有多样性、能提供自然的学习课程、代表了人们真正关心的能力，并且是一种经济上可行的规模化部 署技术。相比之下，单靠优化模型结构、手工制作数据集或微调算法，都难以带来模型能力质的飞跃。 有趣的是，Kevin Lu 此前在 OpenAI 任职时的主要研究方向之一正是 RL。在推特上，有博主评论道，「当 ...

Core Insights - Viewbix Inc. announces the launch of an advanced on-premise grammar engine by its subsidiary Metagramm Software Ltd., aimed at enhancing linguistic accuracy for large organizations [1][3] - The new solution is designed to ensure data privacy and compliance with security regulations, differentiating it from generic cloud-based grammar tools [2][8] Company Overview - Metagramm specializes in AI-driven writing assistance tools, with its flagship product, Bubbl, focusing on personalized text generation [5] - Viewbix operates in digital advertising through subsidiaries, providing technological solutions for internet campaign automation and content creation across various platforms [6] Product Features - The grammar engine will be deployed on-premises, allowing total data control and privacy without external data sharing [8] - It will utilize custom language models trained on the user's internal documents and communication guidelines, ensuring industry-specific accuracy [8] - The solution supports multilingual capabilities and is tailored for sectors like finance, law, healthcare, and government, where precise communication is critical [8]

General Information - The content is a wrap-up and call to action to reshare the information [1] - The author shares tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval Augmented Generation (RAGs) daily [1] Technical Focus - The author provides a clear explanation (with visuals) on how to sync GPUs in multi-GPU training [1]

By default, deep learning models only utilize a single GPU for training, even if multiple GPUs are available.An ideal way to train models is to distribute the training workload across multiple GPUs.The graphic depicts four strategies for multi-GPU training👇 https://t.co/M7O1v7LdlQ ...

Gro 4 just dropped and yes Elon was right. It is the smartest model in the world at least currently and it is a pretty significant leap from other Frontier models. So first let me walk you through the progression of the Gro series of models.This was a slide from last night's live stream. We can see Grock 2 which by the way was only like 2 years ago and we have it right here. It was just next token prediction.Here's the amount of compute. And with Grock 3, they 10xed their pre-training compute and it was a r ...