MIT

MIT Unveils BoltzGen: A Generative AI for Designing Therapeutic Molecules In a major leap for drug discovery, scientists at MIT have introduced BoltzGen, a generative AI model capable of creating novel protein binders from scratch. This innovative tool is designed to address diseases that have long been considered difficult or impossible to treat, marking a significant shift from predictive biology to generative engineering in medicine. How BoltzGen Unifies Design and Prediction BoltzGen stands out by unifying protein design and structure prediction into a single, powerful process. The model incorporates specific constraints to ensure that the molecules it generates are not only novel but also functional and stable, adhering to the fundamental laws of physics and chemistry. Unlike previous models that were often limited to specific protein types, BoltzGen is engineered as a more general-purpose tool, capable of creating a much wider range of therapeutic molecules. ...

China’s DePTH-GPT: A New AI Model to Revolutionize Deep-Sea Research A team of Chinese scientists has launched a new artificial intelligence model named DePTH-GPT to advance deep-sea exploration. The model utilizes a combination of AI technologies, including deep learning, large language models, and computer vision, to analyze a wide range of data from the deep ocean such as video, topography, and bioacoustics. This new tool is expected to shift deep-sea research from traditional qualitative analysis to a more intelligent and predictive approach. DePTH-GPT has already been used to create an intelligent cognitive system for a seamount and a hydrothermal vent field. Developed as part of the UN Decade of Ocean Science for Sustainable Development, the model will be made available to global research institutions to create cognitive systems for various deep-sea habitats. ...

SAP Boosts Enterprise AI with New Tools and Global Skills Initiative SAP has unveiled a suite of AI-powered enhancements for its SAP Build solution, featuring new data integration capabilities and intelligent automation via Joule Agents. These updates aim to accelerate developer workflows and transform how businesses leverage data and AI. Addressing the global demand for AI talent, SAP has also committed to upskilling 12 million people by 2030 through expanded training programs with partners like Coursera. The company introduced its first relational foundation model, SAP-RPT-1, designed for predicting business outcomes. Furthermore, SAP is expanding its Business Data Cloud ecosystem through a new partnership with Snowflake, enhancing existing integrations with Databricks and Google Cloud. ...

NVIDIA Unveils RLP: A New Era of Reinforcement Learning for AI Pretraining NVIDIA researchers have developed a groundbreaking training objective called Reinforcement Learning Pretraining (RLP), which integrates reinforcement learning directly into the pretraining stage of AI model development. The core concept of RLP treats a short chain-of-thought as an action that is rewarded based on the information gain it provides for the model’s next-token prediction. This innovative method creates a verifier-free and dense reward system that can be applied at scale to standard text streams. Unlike previous methods that depend on sparse signals or external verifiers, RLP’s approach allows for updates at every single token position. Experiments demonstrate that RLP can yield significant improvements in a model’s reasoning capabilities, with one model showing a substantial increase in its average score on scientific reasoning benchmarks. ...

Google Quantum AI Reports Breakthrough in Generative Quantum Advantage Researchers at Google Quantum AI have reported the first experimental evidence of “generative quantum advantage,” a breakthrough demonstrating that quantum computers can learn to generate results that are difficult for classical computers to simulate. Using a 68-qubit superconducting processor, the team trained the quantum computer to generate complex bitstring distributions, compress quantum circuits, and learn quantum states. This moves beyond previous demonstrations of quantum advantage, which focused on producing complex outputs that couldn’t be checked, to now include the ability to learn from data and reliably generate useful results. This development suggests that quantum devices could eventually contribute to generative AI tasks, similar to those powering large language models, with significant implications for both artificial intelligence and quantum computing. ...