MarktechPost·
Meta AI team has released NeuralBench, a unified open-source framework for benchmarking NeuroAI models, alongside NeuralBench-EEG v1.0 — the largest open EEG benchmark to date, covering 36 tasks, 94 datasets, and 14 deep learning architectures evaluated under a single standardized interface across 9,478 subjects and 13,603 hours of brain recordings. The post Meta AI Releases NeuralBench: A Unified Open-Source Framework to Benchmark NeuroAI Models Across 36 EEG Tasks and 94 Datasets appeared first on MarkTechPost.
Read full articleMeta Reality Labs releases a new foundation model family for human-centric vision that pushes pose estimation, segmentation, and 3D geometry to new state-of-the-art levels — all from a single backbone. The post Meta AI Releases Sapiens2: A High-Resolution Human-Centric Vision Model for Pose, Segmentation, Normals, Pointmap, and Albedo appeared first on MarkTechPost.
Researchers from Meta AI and the King Abdullah University of Science and Technology (KAUST) have introduced Neural Computers (NCs) — a proposed machine form in which a neural network itself acts as the running computer, rather than as a layer sitting on top of one. The research team presents both a theoretical framework and two […] The post Meta AI and KAUST Researchers Propose Neural Computers That Fold Computation, Memory, and I/O Into One Learned Model appeared first on MarkTechPost.
The app was ranking No. 57 on the App Store just before Meta AI's new model launched. Now it's No. 5 — and rising.
Meta’s new “small and fast” AI model, Muse Spark, is an acknowledgement that as enterprises scale AI systems beyond millions of users and for use on a greater variety of devices, they must make things more efficient and more application-specific. Muse Spark now powers the Meta AI assistant on the web and in the Meta AI app, and the company plans to roll it out across WhatsApp, Instagram, Facebook, Messenger, and the company’s smart glasses. It will also offer select partners access to the underlying technology through an API, initially as a private preview. “We hope to open-source future versions of the model,” it said in a blog post announcing Muse Spark. While Meta did not disclose the model’s size or much about its architecture, it described Muse Spark as being capable of balancing capability with speed. That positioning, even without explicit enterprise deployment guidance, aligns with priorities CIOs and developers are increasingly grappling with as they move generative AI from p
