OpenAI just announced a real-time speech API (advanced voice mode) at Dev Day. The “speech wrappers” are coming to replace 20 million global call center jobs…
MACHINE LEARNING
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MPMC Points Beat Low-Discrepancy Methods in Computational Finance
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In computational finance, for example, simulations rely heavily on the quality of the sampling points. In a classical problem from the field in 32 dimensions, MPMC points beat previous state-of-the-art low-discrepancy sampling methods by a factor of 4 to 24.
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MPMC Improves Robotic Path Planning for Autonomous Vehicles
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In robotics, path & motion planning often rely on sampling-based algorithms, which guide robots through decision-making processes. MPMC’s improved uniformity could lead to more efficient robotic navigation & real-time adaptations for things like AVs or drone technology.
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MPMC Points Accessibility Advances in Graph Neural Networks
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Going forward, the team plans to make MPMC points even more accessible to everyone, addressing the current limitation of training a new GNN for every fixed number of points and dimensions. Authors: T. Konstantin Rusch (
@tk_rusch
), Nathan Kirk, Michael M. Bronstein -
AI Advances Point Distribution Uniformity with L2-Discrepancy
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Using AI to generate highly uniform points can be hard because the usual way to measure this is slow to compute & hard to work with. To solve this, the team switched to a quicker, more flexible uniformity measure called L2-discrepancy. For high-dimensional problems, they
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MPMC Generates Optimal Point Sets Faster Than Previous Methods
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MPMC thrives at generating point sets w/optimal or near-optimal discrepancy. It’s super fast & can be trained in a few minutes from scratch, outperforming previous methods in the field.
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MIT’s Graph Neural Networks Optimize Data Point Distribution Uniformly
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How can we distribute data points more uniformly for multi-dimensional problems in robotics, finance, & computational science? MIT CSAIL’s AI-powered method uses graph neural networks to do this, self-optimizing to avoid clumps & voids. This "Message-Passing Monte Carlo
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MPMC Graph Neural Networks Improve Data Point Uniformity
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MPMC uses graph neural networks to allow points to "communicate" for better uniformity.
— MIT CSAIL (@MIT_CSAIL) 1 octobre 2024
Often, the more evenly you can spread out those data points, the more accurately you can simulate complex systems. pic.twitter.com/sfiYLge84yMPMC uses graph neural networks to allow points to "communicate" for better uniformity. Often, the more evenly you can spread out those data points, the more accurately you can simulate complex systems.
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Emu3: single model handles text, images, and videos
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> Emu3: Next-token prediction conquers multimodal tasks This is the most important research in months: we’re now very close to having a single architecture to handle all modalities. The folks at BAAI just released Emu3, a single model that handles text, images, and videos all
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Manish Shah Presents SN40L LLM Solution at RIKEN AI Seminar
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Our own Manish Shah spoke at the 5th Joint Seminar on AI Architectures at RIKEN in Tokyo, showcasing our cutting-edge SN40L solution for large language models. Thanks to Dr. Kentaro Sano and the @RIKEN_AIP_EN team for the insightful discussions! #LLM #AI #MachineLearning