
Biohub, supported by Mark Zuckerberg and Priscilla Chan, has launched a new open-source model for protein biology. The model, known as ESMFold2, is designed to predict and design proteins, offering state-of-the-art performance that surpasses AlphaFold. It has already demonstrated success in creating binders for cancer and immune disease targets. This development could significantly speed up drug discovery processes, making advanced molecular tools more accessible to researchers globally.
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© MIT News AIMIT is set to establish the Quantum Systems Laboratory (QSL) with support from the Commonwealth of Massachusetts, aiming to position the region as a leader in quantum innovation. The facility will provide state-of-the-art resources for quantum computing and research, integrating quantum sensors and peripherals. This initiative is expected to drive significant advancements in fields like life sciences and defense, while also creating job opportunities and fostering startup growth. By enhancing Massachusetts' quantum capabilities, the QSL aims to secure the state's role in the next era of technological breakthroughs.
© TechCrunch AIRecursive self-improvement (RSI) is emerging as a buzzword in AI, akin to the earlier hype around AGI. The concept involves AI systems that can autonomously upgrade themselves, potentially leading to rapid advancements limited only by available compute power. Notable figures like Richard Socher and Andrej Karpathy are actively pursuing RSI, with projects like Auto-Research and AutoScientist aiming to automate AI research processes. While the industry is not yet close to achieving full RSI, the pursuit is driving significant interest and investment, hinting at a future where AI could independently push its own boundaries.
© NVIDIA BlogNVIDIA's latest research is pushing the boundaries of robotics by enhancing the transition from simulation to real-world applications. At the ICRA conference, NVIDIA showcased eight papers that highlight advancements in robotic perception, reasoning, and action across unpredictable environments. These innovations include multi-arm coordination, adaptive grasping, and navigation across diverse robot bodies, all trained in simulation without real-world data. This approach not only speeds up robotic processes but also improves success rates significantly, marking a step forward in creating adaptable and reliable autonomous robots.