Search AI news

Search headlines, summaries and articles to connect the dots.

2026-10-21

AWS Machine Learning Blog05:14

i14 releases the Laplace 3B linear attention model: inference with fixed-state memory, implemented on AWS Trainium2 and native PyTorch.

i14 has launched the Laplace 3B model with 3.028 billion parameters, which replaces Softmax attention with Gated Linear Attention (GLA). During inference, its per-sequence recursive state remains constant at 29.5 MB, independent of context length. The model was trained and validated on a single AWS trn2.48xlarge instance (equipped with 16 Trainium 2 chips), achieving a training throughput of 120,000 tokens per second and an inference throughput of 11,544 output tokens per second (with 1,280 concurrent sequences). All performance metrics are based on internal measurements conducted by i14 and the AWS GenAIIC team.

Official Tutorials

Timeline and related coverage

AWS Machine Learning Blog01:59

Smartsheet launches the Model Context Protocol (MCP) server, enabling AI agents to connect directly to real-time project data on AWS.

Smartsheet has officially launched the general‑purpose Model Context Protocol (MCP) server, which uses a standardized protocol to enable AI agents to read and write structured data—such as project tables, rows, columns, and automations—in Smartsheet. The service is now available within the AWS ecosystem, supporting access from two types of clients: Amazon Quick, aimed at business users, and Kiro, designed for developers. This allows for natural‑language‑driven cross‑tool task updates, status synchronization, and context‑aware development.

Official Tutorials

Timeline and related coverage

AWS Machine Learning Blog01:48

GPT-OSS-20B Multilingual Reasoning Fine-Tuning Experiment: SFT Enables Zero-Base Behavior Acquisition, while GRPO Only Effectively Improves Performance When a Probabilistic Headspace Exists

The AWS team conducted multilingual inference fine-tuning experiments on Amazon SageMaker HyperPod using GPT‑OSS‑20B, employing LoRA for parameter-efficient fine-tuning. SFT boosted the correctness rate of inference in five languages (English, French, German, Spanish, and Italian) from 22% to 100%, though the correctness rate for the answer language remained at only 62%. GRPO further improved the answer-language correctness rate to 70% without compromising inference-language performance; however, the gains varied across languages—for example, Spanish saw a +30 percentage-point increase, while Italian experienced a -20 percentage-point decline. The experiments clearly demonstrated that GRPO cannot bootstrap new capabilities from scratch and can only optimize behaviors that already occur sporadically.

Official Tutorials

Timeline and related coverage

AWS Machine Learning Blog00:22

AWS Agent Registry officially released: supports runtime semantic discovery and dynamic orchestration of MCP servers and A2A agents.

On October 26, 2026, AWS announced the general availability of its Agent Registry service, a managed offering that leverages hybrid semantic-plus-keyword search to dynamically discover, instantiate, and execute approved Model Context Protocol (MCP) servers and Agent-to-Agent (A2A) agents at runtime, replacing hard-coded tool integrations and addressing bottlenecks in the large-scale orchestration of enterprise-grade AI agents.

Official Tutorials

Timeline and related coverage

GitHub Blog · AI & ML00:05

How Three Java Champions Leverage GitHub Copilot to Boost Development Efficiency: Focusing on Human-AI Collaboration and Shared Responsibility

Vlad Mihalcea, Patrick Baumgartner, and Frank Delporte, three seasoned Java Champions, shared their experiences using GitHub Copilot in production environments and open-source projects, emphasizing human-led problem definition, context anchoring, iterative validation, and ultimate accountability—rather than relying on AI to automatically generate code.

Official

Timeline and related coverage

2026-10-20

NVIDIA Blog18:30

European tech companies are building on-premises AI infrastructure using NVIDIA Vera CPUs.

European AI cloud service providers such as Nebius, Nscale, and Verda, along with server manufacturers like Bull, Dell, and HPE, are building a new generation of energy-efficient, high-concurrency AI infrastructure based on NVIDIA’s Vera CPU, with a focus on supporting agentic computing workloads—including sandbox execution, semantic search, data analytics, and research collaboration.

Official

Timeline and related coverage

Latent Space13:21

DeepSeek V4.1 Flash, MiniMax M3.1 Flash, and Mercury 3 were released simultaneously, as debates over AI infrastructure and governance continue to intensify.

From October 16 to 19, 2026, three new models—DeepSeek V4.1 Flash 1017, MiniMax M3.1 Flash (Space Bunny), and Mercury 3 (Inception)—were released in quick succession. At the same time, the U.S. Department of Homeland Security ordered a ban on DeepSeek-related products, while China is drafting export control regulations for ultra-large-scale models, bringing the conflicts among the open-source ecosystem, computing infrastructure, and AI governance into sharp focus.

Individual

Timeline and related coverage

2026-05-29

Google Research04:5880

Google I/O 2026 unveiled AI research and health tools such as Gemini for Science, WeatherNext, and Antigravity 2.0.

At the I/O 2026 conference, Google’s research team unveiled a series of AI systems aimed at scientific discovery, climate forecasting, healthcare, and edge computing, including the multimodal research assistant Gemini for Science, WeatherNext for hurricane early warning, the self‑building operating system platform Antigravity 2.0, and the open‑source quantum chip Willow, among others. All these developments are experimental demonstrations, with no independent validation or regulatory approval yet.

Official Research

Timeline and related coverage

2026-05-28

Google Research00:56

Google launches zero-trust, aggregated private analytics technology, integrating cryptography with a trusted execution environment.

Google’s research team has unveiled a new private analytics solution that leverages an innovative single-round lattice-based cryptographic aggregation protocol, working in tandem with a Trusted Execution Environment (TEE), to enable data aggregation without relying on any single trusted entity. This approach ensures that user data remains securely stored on the device while delivering group-level anonymized insights.

Official Research

Timeline and related coverage