WALTHAM, Mass.--(BUSINESS WIRE)--BostonGene, the developer of the leading AI foundation model for tumor and immune biology, today announced a strategic collaboration with AstraZeneca, a global, science-led biopharmaceutical company that focuses on the discovery, development and commercialization of prescription medicines in Oncology, Rare Diseases and BioPharmaceuticals, to advance oncology drug development using BostonGene's multimodal AI platform. The collaboration leverages BostonGene's foun. ...

Foundation Model Strategy - Netflix is leveraging foundation models for personalized recommendations [1] - The strategy is based on work by Yesu Feng, a staff research scientist/engineer at Netflix, focused on generative foundation models [1] - Prior to Netflix, Feng worked on feed and marketplace optimization at LinkedIn and Uber, respectively [1] Industry Focus - The application of foundation models aims to improve personalized recommendations [1] - The discussion took place at the AI Engineer World's Fair in San Francisco [1]

Core Insights - Tempus AI (TEM) is experiencing significant growth in its Data and Services segment, with a 43.2% year-over-year revenue increase to $61.9 million in Q1 2025, driven by a 58% growth in its Insights data licensing business [1][7] - The company has secured major contracts, including a $200 million licensing agreement with AstraZeneca (AZN) and Pathos, which has increased AZN's total remaining contract value to over $1 billion [2][7] - Tempus has expanded collaborations with key pharmaceutical companies, including Illumina and Boehringer Ingelheim, enhancing its position in biomarker development and oncology applications [3][7] Financial Performance - Gross profit for Tempus outpaced revenue growth, increasing by 65.2% with only a modest 3% rise in the cost of revenues [1] - Year-to-date, Tempus AI shares have surged 102.5%, significantly outperforming the industry average growth of 18% [6] Competitive Landscape - Competitors like ICON (ICLR) and IQVIA (IQV) are also experiencing growth, but Tempus AI's performance in securing contracts and expanding its service offerings positions it favorably in the market [4][5] - Tempus currently trades at a forward 12-month Price-to-Sales (P/S) ratio of 8.47X, compared to the industry average of 5.83X, indicating a premium valuation [8]

Core Insights - The article discusses the convergence of life sciences and digital internet technologies through AI for Science, highlighting the transformative potential of large models in accelerating scientific discovery [3][6]. - It emphasizes the shift from traditional trial-and-error methods in drug development, which typically require 10 years and $2 billion, to automated processes enabled by AI, significantly reducing costs and time [7][8]. Group 1: Background and Framework - The 1950s saw two revolutions: Shannon and Turing's information theory laid the groundwork for the digital revolution, while Watson and Crick's discovery of the DNA double helix initiated the information age in biology [6]. - The article introduces a mapping framework for understanding AI in life sciences, with axes representing Generalist vs. Specialist and Tech vs. Bio, assessing the breadth and depth of startups in biopharmaceutical development [9][11]. Group 2: Biology Foundation Models - AlphaFold 3 represents a milestone in AI for science, solving the long-standing challenge of protein structure prediction, which previously took months or years [14]. - Isomorphic Labs, a spinoff from Google DeepMind, has secured significant partnerships with Eli Lilly and Novartis, validating its technology's commercial value [15]. - Other models like ESM3 and Evo2 are exploring different paths in biological foundation models, focusing on multi-modal inputs and genome language modeling [17][22]. Group 3: AI Scientist and Automation - The AI Scientist concept aims to automate research processes, addressing the inefficiencies of traditional biological research, which is often lengthy and costly [24]. - FutureHouse is developing a multi-agent system to enhance research efficiency, demonstrating the potential for AI to significantly increase productivity in scientific discovery [38]. Group 4: AI-native Therapeutics - AI-native therapeutics companies aim to integrate AI throughout the drug discovery and clinical development process, focusing on complex therapies like RNA and cell therapies [40]. - Companies like Xaira Therapeutics and Generate Biomedicines are building comprehensive platforms that leverage AI for end-to-end drug development, aiming to reduce time and costs associated with traditional methods [49][51]. Group 5: AI Empowered Solutions - Companies in this category focus on optimizing specific stages of drug development using AI, such as drug repurposing and clinical trial acceleration [68][75]. - Tahoe Therapeutics has released a large single-cell perturbation dataset, enhancing AI model training and drug discovery processes [64]. Group 6: Conclusion - The article concludes that the integration of foundation models and automated AI scientists is driving exponential advancements in scientific exploration, shifting value from traditional CROs to AI-native companies [78].

Core Insights - NVIDIA has launched a portfolio of technologies aimed at enhancing humanoid robot development, including the NVIDIA Isaac GR00T N1, which is the first open and fully customizable foundation model for humanoid reasoning and skills [1][3][19] Group 1: Technology Overview - The GR00T N1 model features a dual-system architecture inspired by human cognition, consisting of a fast-thinking action model ("System 1") and a slow-thinking decision-making model ("System 2") [4][5] - GR00T N1 can generalize across common tasks and perform multistep tasks, applicable in areas such as material handling, packaging, and inspection [6] - NVIDIA has introduced the Isaac GR00T Blueprint for synthetic data generation, which allows developers to create large amounts of synthetic motion data from limited human demonstrations [15][16] Group 2: Collaborations and Partnerships - NVIDIA is collaborating with Google DeepMind and Disney Research to develop Newton, an open-source physics engine designed to enhance robot learning and task handling precision [9][10] - The collaboration aims to accelerate robotics machine learning workloads by over 70 times through the development of MuJoCo-Warp [11] - Disney Research plans to utilize Newton to advance its robotic character platform, enhancing the expressiveness of next-generation entertainment robots [12][13] Group 3: Performance and Data Generation - NVIDIA generated 780,000 synthetic trajectories in 11 hours, equating to 6,500 hours of human demonstration data, which improved GR00T N1's performance by 40% when combined with real data [16] - The GR00T N1 dataset is being released as part of a larger open-source physical AI dataset, now available on Hugging Face [17] Group 4: Availability and Future Developments - The GR00T N1 training data and task evaluation scenarios are available for download, along with the Isaac GR00T Blueprint for synthetic manipulation motion generation [20] - The Newton physics engine is expected to be available later in the year, further enhancing the capabilities of humanoid robots [21]