Meta AI and KAUST Researchers Propose Neural Computers That Fold Computation, Memory, and I/O Into One Learned Model

Standard prompt attacks are merely the beginning. A structured framework to map and mitigate the backend attack vectors of agentic workflows.  The post The AI Agent Security Surface: What Gets Exposed When You Add Tools and Memory appeared first on Towards Data Science.

Optimizing artificial intelligence pipelines requires moving beyond surface-level hardware adjustments to fundamentally alter how models process data. While engineers often implement basic toggle-away efficiencies inside the training loop, achieving permanent cost reductions requires architectural changes directly inside the neural network. As I have previously argued, the science is solved, but the engineering is broken; true FinOps maturity demands deep, model-level interventions. The following 12 architectural cuts will drastically lower the unit economics of your AI pipeline. Redesigning the training foundation 1. Fine-tune, don’t train from scratch Training a foundation model from scratch is computationally prohibitive and rarely necessary for standard enterprise applications. Instead of burning millions of dollars on raw compute, engineering teams should download highly capable, publicly available open-weight models. This baseline transfer learning approach is the mandatory first

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.

Learn which seven OpenCode plugins add memory, search, Gemini, terminal control, analytics, and reusable skills to make AI coding workflows stronger.

Holy moly, I am flying around my phone this week. It’s a wild feeling — especially since Google’s grand I/O gala, which is traditionally the time when shapeshifting new Android additions are supposed to command our attention, isn’t for another couple weeks yet. These days, though, we’ve reached a point where many of the most interesting and non-AI-gobblydegook Android innovations aren’t even coming from Google itself but rather from third-party apps, add-ons, and crafty configuring (a fancy way of saying “good old-fashioned geeky tinkering”). And that’s absolutely the case with this latest superpower I’ve just been granted. It’s an on-demand desktop-style taskbar that makes it delightfully swift ‘n’ simple to switch over to any other app on your favorite Android gadget without first having to head back to your home screen and then poke around to find it. Instead, you just summon that taskbar — or even set it to be always visible, if you’d rather — and, exactly like on a desktop compute

Meta 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.

Chinese market research firm Sigmaintell expects Apple to be the only company to see growth in the laptop market this year. Overall, Sigmaintel predicts global notebook shipments will reach 181.1 million units this year, a decline of 8%. That drop will, in part, be caused by memory and component shortages and also by slowing market demand. That’s going to damage all of the notebook vendors, bar Apple,.  Apple laptop sales expected to rise more than 20% Sigmaintell calculates Apple will ship 28 million laptop in the year, up 21.7% from 2025. This puts Apple in third place in laptop shipments, a demand the company will be able to meet despite component shortages because of the efficient use of memory inherent to its systems. That memory efficiency acts as a protection against the impact of climbing costs, even as competitors struggle with the affects on their business. Apple’s incoming CEO, John Ternan, is being presented as a hardware man, so he will no doubt be pleased to experience th

A stateless AI agent has no memory of previous calls.