The latest AI news, in focus.

The biggest names in AI are backing a new way to opt out of scraping

OpenAI, Google, Microsoft, and Anthropic say they will adopt a new standard that publishers can use to opt out of being scraped by AI, as reported earlier by Search Engine Roundtable. With this control, publishers can apply their AI training preferences across multiple AI companies, instead of setting them individually. The shared "policy token" signal […]

GitHub Copilot Fusion Launches with Dual Capabilities: Intelligent Model Orchestration and Local Model Routing

On October 28, 2026, GitHub released the official version of Copilot Fusion, simultaneously introducing an experimental local model routing feature. This capability enables automatic invocation of local models such as MAI Code 1.1 Flash on Windows devices and activates intelligent routing within VS Code, the CLI, and the Copilot App. The system automatically selects and combines cloud models—like those from OpenAI, Anthropic, and Google—based on three effort levels: Standard, High, and Max, achieving performance close to Claude Code while cutting costs in half during coding evaluations.

Say goodbye to tedious exporting! Google Gemini gets a major upgrade: it now supports directly generating and editing multi-format files within conversations.

Google has announced a significant upgrade to its conversational AI large model, Gemini, adding the ability to directly edit files within the conversation. All users can now generate, preview, and fully edit multiple mainstream file types in real time on both web and mobile platforms—no longer limited to exporting. This update covers formats such as PDF, Google Docs, Sheets, Slides, Markdown, and more, aiming to enhance AI-assisted productivity and smoother interaction.

The Moonlight Side announced that Kimi has officially integrated with Google Gmail, Google Drive, and Google Calendar.

On October 29, the Moonlight Side announced overseas that its AI assistant, Kimi, has been fully integrated into Google’s three major office services: Gmail, Google Drive, and Google Calendar. Users can enable this integration simply by making a few settings on the Plugins page. According to the official statement, this integration expands Kimi’s application scenarios, allowing users to quickly summarize Gmail email conversations, precisely locate needed information, and improve efficiency in handling tasks related to emails, cloud storage, and calendars.

You can generate and edit Google Docs files directly in Gemini chats now

Now you can generate, preview, and edit PDFs, Google Docs, Google Sheets, slide decks, Markdown files, TXT files, and code directly within Google Gemini. The new file editing capabilities are available starting today for all Gemini users, and while PDF previews/editing works on both the web and mobile, all other file formats are only in […]

GitHub releases Copilot Fusion, an automated multi-model orchestration tool that boosts coding efficiency and cost-effectiveness.

At Universe 2026, GitHub unveiled Copilot Fusion, an intelligent orchestration layer that automatically routes and combines models from OpenAI, Anthropic, Google, and others to complete coding tasks—eliminating the need for developers to manually select models. It integrates existing automatic model-selection technologies with Project HydraFusion, previewed in September. Once the model selector is enabled, the system automatically adopts execution modes such as single-model, cascading, or critique, based on task complexity.

Chrome starts “scrolling through your inbox” to help you fill out forms: Gemini turns Gmail into a form-filling assistant

Google is shifting Chrome’s auto-fill from “remembering” to “finding”: Powered by Gemini and Personal Intelligence, Chrome can now read your Gmail and proactively suggest order numbers, tracking numbers, flight booking IDs, and more—based on the current form. This enhanced form-filling feature began rolling out to U.S. users in batches starting late October, covering desktop Chrome as well as iOS and Android users who have enabled Gemini, aiming to break the decade-old habit of relying on users to manually save their names and addresses.

As AI agents reshape the web, Google opens Gemini and Search to more apps

A new developer platform will let businesses bring their services to Google's AI experiences, potentially changing how consumers discover and use apps.

The Sequence Learning Loop - Issue #960: Last Week in AI Infrastructure

DeepSeek, Google, and NVIDIA build the agent stack

Meet Google DeepMind’s Genie 4: Real-Time World Model With Native Audio

Google DeepMind released Genie 4 as a research preview. The real-time world model adds native audio, live dialogue and text steering, with Gemini 3.8 Flash planning events inside the simulation loop. The post Meet Google DeepMind’s Genie 4: Real-Time World Model With Native Audio appeared first on MarkTechPost.

Google introduces cutting-edge Genie 4 world model

Google LLC today previewed Genie 4, an artificial intelligence model that can generate realistic three-dimensional environments. The algorithm is the latest addition to a series of world models that the company launched in 2024. Compared to its predecessor, Genie 4 is better at rendering fluid movements and other complex details. Furthermore, it can generate accompanying […] The post Google introduces cutting-edge Genie 4 world model appeared first on SiliconANGLE.

Oracle brings Google’s TPUs to its cloud, adds ChatGPT integrations

Oracle Corp. today announced plans to make Google LLC’s TPU artificial intelligence accelerators available through its public cloud. The initiative marks the first time the search giant is supplying the chips to another hyperscaler. According to CNBC, Oracle will also add Cerebras Systems Inc.’s wafer-scale accelerators and OpenAI Group PBC’s Jalapeño inference processor. The latter […] The post Oracle brings Google’s TPUs to its cloud, adds ChatGPT integrations appeared first on SiliconANGLE.

How to put generative AI directly into a dbt pipeline with plain SQL

Google Cloud on wiring BigQuery's AI SQL functions into dbt pipelines, from sentiment scoring to LLM-generated product briefings.

Google’s Genie 4 world model adds audio, more realistic worlds, and real-time steering

Google DeepMind released Genie 4, a world model with real-time audio, more realistic simulations, and mid-simulation text prompts, in research preview for trusted testers.

Meta AI director Rohit Patel leaves to take on Microsoft Office and Google Workspace

Rohit Patel is departing Meta after eight years working on AI evaluations and reinforcement learning, TechCrunch has exclusively learned. His startup, Ndoc, aims to take on Microsoft Office Suite.

MiniMax M3.1Flash release sparks “questionable results” concerns: high scores on old questions, failure on new ones, and an illusion rate as high as 92.8%

On October 19, 2026, MiniMax released its low-cost large model, M3.1Flash, which scored 42 points in comprehensive evaluations and boasts a 70% reduction in cost compared to the previous generation. However, it immediately faced accusations of “cheating on exam questions”: on AA’s publicly available past papers, it nearly achieved a perfect score, yet continued to produce the original answers even after the question stems were rewritten. Reviewers such as Cat Boss and Binkg pointed out that its performance on old and new questions is sharply divided, token consumption is abnormal, and its hallucination rate in knowledge tests reaches as high as 92.8%. On October 23, MiniMax open-sourced the model weights for independent verification, but the mechanisms behind its question-identification capabilities and the reasons for discrepancies in Chinese–English responses remain unresolved.

Why it mattersThis report is based on multi‑party empirical testing (including Binkg’s re‑title experiment, the author’s replication, and a dual evaluation comparison between AA and Plotly‑Mila), verifiable data (token counts, prices, accuracy/hallucination rates), and contradictions in model behavior (English errors vs. Chinese correctness, stable performance on old tasks vs. collapse on new ones). It systematically lays out the core symptoms of the current credibility crisis in large‑model evaluations: high scores do not necessarily equate to strong generalization, and low‑price advantages create tension with the reality of model capabilities. The information source is a deep investigation authorized for publication by Titanium Media, complete with original test procedures, screenshot evidence, and timestamps, offering both timeliness and rich detail.

Multi-Agent Retrieval-Augmented Causality Assessment (MARCA) of Individual Case Safety Reports in Pharmacovigilance

arXiv:2610.33279v1 Announce Type: new Abstract: Introduction: Increasing volumes of individual case safety reports (ICSRs) challenge expert-led causality assessment in pharmacovigilance. We developed MARCA (Multi-Agent Retrieval-Augmented Causality Assessment), a four-agent framework for Naranjo-based assessment, and evaluated agreement with human experts and exploratory differences between non-RAG and RAG-labelled scores. Methods: MARCA combined scientific literature retrieval, drug-label assessment, objective-evidence classification, and clinical causality reasoning using Gemini 2.5 Pro and DeepSeek-V3.2. Agent-generated rationales informed human-assisted scoring. A consensus expert reference standard covered 215 ICSRs and 1,562 drug-event combinations (DECs) from FAERS (Q1-Q2 2025). Agr

Two Uncertainties Are Not One: Disagreement versus Decision-Risk in Cheap Structured Content Moderation

arXiv:2610.33258v1 Announce Type: new Abstract: Content moderation compresses a graded, contested human judgment into a binary act (leave up / take down) over billions of items, at a scale where running a frontier LLM on every post is uneconomical. A new class of frozen "System One" models returns typed, calibrated decisions instead of text at ~USD 0.000025 per call and ~100 ms. We run the first independent audit of one such model on 142,658 human-labeled comments, and benchmark it against 14 frontier reasoning models (the GPT-5.x, Claude, Gemini 3, and Grok families) on a shared probe set. Two findings stand out. First, the cheap model is accuracy-competitive: its zero-shot classification AUROC (0.83) is not statistically separable (paired bootstrap, n=56; a failure to reject parity, not

Our newest AI Quest: Mapping the human brain

Learn AI literacy via Google’s latest AI Quests lesson on neuroscience, “Polar Peaks.”

Focusing on AI’s catastrophic risks while masking everyday harms: Experts call for a redefinition of technological accountability standards.

MIT Technology Review’s October 26, 2026 issue published a commentary pointing out that current AI safety policies overly focus on extreme risks such as suicide induction and drug trafficking, while systematically overlooking the pervasive everyday harms—such as dependence on AI companions, cognitive decline, and erosion of social relationships. Drawing on Google’s efforts to promote lenient state-level AI legislation, cases of harm caused by Meta’s platforms, and MIT’s brain-imaging research, the article emphasizes the need to go beyond the “do no evil” baseline and establish substantive accountability for learning outcomes, emotional authenticity, and the capacity for autonomous thinking.

Why it mattersThis article is an in-depth commentary published by a reputable technology media outlet on the day of the incident (2026-10-26). The source is clearly identified as technologyreview.com, and its core arguments are grounded in disclosed legislative developments (such as California’s SB1119 and Nebraska’s bill), empirical research (MIT brain connectivity data), industry backgrounds (Sam Hiner and Emma Leiken’s publicly available positions and achievements), and verifiable records of harm caused by global platforms (in Myanmar, Sri Lanka, and Ethiopia). All factual statements strictly correspond to the original materials, with no external inferences or additions.