Generating scenarios for extreme events, without extreme data
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
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Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline
Proof-based general-purpose computing with Magnetar
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
HiRO-ACEv2: Simulating extreme weather at 3 km resolution for a fraction of the cost
Google DeepMind and Singapore Government announce new AI tool to protect citizens against scams
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
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A wayfaring approach for the early concept creation stage of development projects that have a very high degree of intended innovation and thus uncertainty and the importance of including all the involved disciplines (knowledge domains) from the beginning of the project on.
Token-Mol 1.0: tokenized drug design with large language models
Token-Mol is presented, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens, which introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream application...
RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints
RSGPT, a generative model pre-trained on ten billion data points, achieving state-of-the-art performance for synthesis planning, and introduces reinforcement learning to capture the relationships among products, reactants, and templates more accurately.