Swimlane has announced new capabilities in the Swimlane Turbine platform that allow security teams to run their own detection logic against large volumes of data and send resulting alerts directly into Swimlane AI SOC for investigation and response.
Google started working on in-house silicon in 2013, but didn't begin selling its tensor processing units (TPUs) to outside customers until the second quarter of this year. Now the company is tapping Oracle's cloud as another avenue for selling TPUs as it tries to bolster competition with Nvidia in the AI chip market.
Bayes is available today to Monte Carlo customers directly in the platform, in Slack, Microsoft Teams, and through MCP, so teams can work with it in the tools they already use. To learn more, visit montecarlo.ai.
Major banks are quietly blocking AI agents from customer accounts, and one well-known financial advisor says even that response may not go far enough to protect your retirement savings.
Novel view synthesis and 3D reconstruction have advanced rapidly with radiance fields and splatting, yet their memory and computational demands limit their use in resource-constrained settings. We introduce Hexels: novel planar hexagonal surface elements with a first-order spatial color field for efficient 3D scene and 2D image representation. Hexels exploit locally planar regions with smoothly varying appearance using fewer, larger primitives. Their hexagonal support provides an area- and boundary-efficient domain, while the local color field captures appearance variation within each primitive. Across 34 scenes, Hexels improve PSNR over memory-matched 2DGS by 4.53-7.38 dB across datasets while requiring lower training VRAM. We further ablate Hexels by showing that per primitive, Hexels outperform 2DGS by upto 4.74 dB while using lower training VRAM and having 2.31 times faster FPS. Even in unconstrained settings, Hexels offers complementary quality-memory trade-offs. For efficient 3D surface reconstruction, Hexels reduce error by 28.9% compared with memory-matched 2DGS across 15 DTU scans. On 6 Tanks and Temples surface scenes, Hexels achieves the highest mean F1 among compared…
In Nanbeige4.3, we take a foundational step toward pushing the boundaries of lightweight agents. Our strategy centers on building a high-capacity teacher agent first, which will subsequently anchor the distillation of advanced agentic capabilities into smaller models.
Artificial Analysis overhauled its Intelligence Index, causing Kimi K3.1 Pro to drop five spots in the rankings. The article New Artificial Analysis index widens the gap between open and closed models appeared first on The Decoder .
Long-running AI agents outlive initiating processes through credentials, delegated tasks, queues, callbacks, reservations, and provider-side operations. Cancellation, process exit, and credential revocation neither close every pre-cut carrier nor distinguish independently…
Embedding-based retrieval on user-generated content at the trillion-document scale exposes a sharp conflict between two production demands: deep, expressive personalization for queries with rich user intent, and broad coverage of a massive inventory under fixed latency and…
Drug mechanism-of-action (MoA) modeling commonly relies on perturbational transcriptomes, but matched microRNA (miRNA) measurements are often unavailable. Inferred regulatory features offer a scalable way to reuse these data. Here, we present MIRCID, a framework comparing gene…
A panel's human-equivalent size is target-specific. Matching a fixed 32-judge panel to empirical human label distributions on three ChaosNLI tasks yields two distinct effective sizes: distributional-error matching gives $ν_{\mathrm{MSE}}=2.304$, $3.750$, and $3.445$, whereas…