October 23, 2026

Updated Google privacy policy says your Gmail might be used for advertising

Turning on Personal Intelligence gives the company more data for ad targeting.

SpaceX-Tesla Merger? | Zuckerberg's Deal | The Rundown

Elon Musk wants a SpaceX-Tesla merger, Meta makes a deal to keep Mark Zuckerberg, and the battle of the AI agents. Ed Ludlow recaps the biggest stories from Bloomberg Tech on "The Rundown." (Source: Bloomberg)

OpenAI Resumes Training AI, But Without Internet Access

OpenAI has begun cutting off internet access for its most capable artificial intelligence models during the training process in response to prior incidents in which its systems breached other companies.

OpenAI Sets Target Valuation of $1.25 Trillion

OpenAI wants to raise around $30 billion in a fresh funding round. That could value the ChatGPT maker at $1.25 trillion. This is according to people familiar with the matter. Bloomberg's Rebecca Torrence reports. (Source: Bloomberg)

Chinese AI model Kimi K3.1 Pro is closing in on Anthropic's Claude Opus 5.5

Moonshot AI's Kimi K3.1 Pro scores 55 points on the Artificial Analysis Intelligence Index, an 11-point jump over its predecessor that puts it in fourth place. Only three Anthropic and Meta models rank higher. If Moonshot releases the weights as planned, the gap between open and proprietary models shrinks to just three points. The article Chinese AI model Kimi K3.1 Pro is closing in on Anthropic's Claude Opus 5.5 appeared first on The Decoder.

Built for Agents: The Case for Spark Declarative Pipelines

AI agents now write a growing share of production data pipelines (over 70% of SDP...

Real-time energy trading and analytics on Databricks

Why does real-time energy trading need more than faster data?Energy trading desks...

SpaceX Enlists 130 Tech Leaders to Help Businesses Deploy Grok AI - PYMNTS.com

SpaceX Enlists 130 Tech Leaders to Help Businesses Deploy Grok AI PYMNTS.com

Three-week on-site audit at OpenAI reveals major gaps in AI safety monitoring

External auditors from METR and Redwood Research spent three weeks on-site at OpenAI examining the company's internal monitoring system for AI agents and found significant gaps, with up to 25 percent of agent traffic from models at the GPT-5.5 class and above going unmonitored. OpenAI redacted large portions of the 61-page report, and only a summary is publicly available. The article Three-week on-site audit at OpenAI reveals major gaps in AI safety monitoring appeared first on The Decoder.

OpenAI Moves Toward a $1.25 Trillion Value

Bloomberg’s Ed Ludlow takes a look at OpenAI's potential fundraising at a staggering $1.25 trillion valuation. Plus, Anthropic's looming IPO could deliver blockbuster gains for some of its biggest investors, with billions of dollars on the line. And, SpaceX is taking another step toward becoming a major US wireless carrier, eyeing access to at least 80,000 cell sites nationwide. (Source: Bloomberg)

Big Paydays Ahead for Anthropic's Early Backers

Anthropic’s upcoming public listing is poised to create multibillion-dollar gains for top venture capital firms, tech companies and independent investors that have backed the company. Bloomberg’s Natasha Mascarenhas explains why many of the investors raking in huge paydays won’t have their stakes listed when Anthropic releases its IPO filings. She joins Ed Ludlow on “Bloomberg Tech.” (Source: Bloomberg)

Scaling NVIDIA TensorRT Performance Optimization Beyond a Single Build Route with the Global Performance Tuner, Part 2

The Global Performance Tuner is a feature for automatic performance tuning released in NVIDIA TensorRT 11.1. It provides automatic, accuracy-aware and...

Anthropic says its unreleased model is both more capable and better aligned than Mythos 5.1

Anthropic says it has an internal AI model that outperforms and is better aligned than Mythos 5.1. During a cybersecurity test by the UK's AISI, Mythos 5 went rogue in 9 of 43 runs, sneaking malicious code into a real GitHub project and creating fake accounts to get it approved. An internal research model stopped such attacks on its own 67 percent of the time. The article Anthropic says its unreleased model is both more capable and better aligned than Mythos 5.1 appeared first on The Decoder.

Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)

Learn how OpenAI scaled Habitat with reliable multi-tenancy, global caching, and Azure Cosmos DB to deliver 12× faster reads for ChatGPT.

LitPivot: Developing research ideas through dynamic contextualization within the literature landscape

OpenAI, SpaceX and AI Market Jitters

Get a jump start on the US trading day on "Bloomberg Open Interest." OpenAI seeks fresh funding as investors reassess the AI trade, while SpaceX explores a push into wireless. UBS’s Nadia Lovell weighs the market outlook, CFTC Chair Michael Selig discusses prediction markets and crypto regulation, and Chris Marangi of Gabelli Funds breaks down AI investing and opportunities beyond tech. (Source: Bloomberg)

OpenAI CEO Sam Altman says advanced AI will inevitably escape its sandbox, and only alignment can save us

Sam Altman believes advanced AI can't be kept locked up forever. Sandboxes and monitoring are only temporary fixes. Eventually, the only real solution is alignment, and that's still an unsolved problem. He also warns that AI models are already finding hardware security flaws no human hacker would ever spot. The article OpenAI CEO Sam Altman says advanced AI will inevitably escape its sandbox, and only alignment can save us appeared first on The Decoder.

Using AI Agents to Teach Robots Like Microduck to Walk in Simulation

Teaching a robot to navigate an obstacle course requires more than a walking policy. It must perceive its surroundings, maintain balance, and adjust its actions...

Not Intel. Not CoreWeave. This Underappreciated Nvidia-Backed Company Is Growing Artificial Intelligence (AI) Revenue by 123%. Here's Why It's a Buy Today for $8. - Yahoo Finance

Not Intel. Not CoreWeave. This Underappreciated Nvidia-Backed Company Is Growing Artificial Intelligence (AI) Revenue by 123%. Here's Why It's a Buy Today for $8. Yahoo Finance

A few engineers asked IT for Linux. Then it spread to 200 machines.

Linux is already part of the AI developer’s desktop toolkit. Nvidia’s DGX Spark, which became available to order on October The post A few engineers asked IT for Linux. Then it spread to 200 machines. appeared first on The New Stack.

Databricks vs. Oracle in 2026

Why organizations are moving beyond OracleEnterprise data warehouses built on Oracle...

AI Ushers In Era of Rapid-Fire Software Vulnerabilities

Mass cybersecurity disruptions, such as this week’s scramble by governments and companies to patch two critical software flaws, are about to “get a lot worse before they get better,” a top executive at Anthropic PBC has warned.

OpenAI Targets $1.25 Trillion Valuation in New Funding Round

A rout in the artificial intelligence trade faded, offering global stock investors some respite in the final session of a volatile week, with further gains in oil prices keeping sentiment cautious. Bloomberg has learned OpenAI, which is seeking to raise around $30 billion in a fresh funding round, is now targeting a valuation of $1.25 trillion during the final stages of negotiations. French authorities are bracing for a new round of protests Saturday, rolling out the military to back up police in a bid to prevent a repeat of last weekend’s riots and reassure markets unnerved by the violence. Today's guests: Geoff Yu, BNY Senior Market Strategist; James Murray, Financial Secretary to the UK Treasury; Martin Quencez, German Marshall Fund of the United States Senior Fellow & Special Adviser to the President; Oskar Lewnowski, Orion Resource Partners Founder & Group CEO. (Source: Bloomberg)

Microsoft’s new voice AI can start working out what you want before you finish speaking

“I think you said, ‘representative.’” It’s the fastest way to bypass automated speech recognition chatbots that fail to route human The post Microsoft’s new voice AI can start working out what you want before you finish speaking appeared first on The New Stack.

Anthropic's Claude now writes code and proves it safe automatically

Anthropic had Claude generate mathematical proofs that existing code is correct, producing roughly 180 million lines of proof code for 123 open-source libraries. Along the way, the model caught bugs that years of testing had missed. Anthropic's goal: making formal verification with AI as routine as standard testing. The article Anthropic's Claude now writes code and proves it safe automatically appeared first on The Decoder.

SpaceX Can't Find Spare Nvidia, Google or Amazon Chips Despite Billions to Spend, Strategist Terms It 'Pretty Insane' - Yahoo Finance

SpaceX Can't Find Spare Nvidia, Google or Amazon Chips Despite Billions to Spend, Strategist Terms It 'Pretty Insane' Yahoo Finance

Inside OpenAI's Plan To Protect The Internet Before Hackers Break It

Using the latest GPT model, cyber researchers in OpenAI’s Project Daybreak community found one of the most critical internet bugs in recent memory. It’s part of an all-out sprint to secure the web before hackers can deploy similar tools to wreak havoc on modern life.

Securing our frontier model training environment

OpenAI explains how we strengthened containment for frontier model training and evaluation, including offline environments and high-compute red-teaming.

OpenAI lowers valuation target, raises up to $30 billion

OpenAI is closing in on a new funding round of up to $30 billion but has lowered its valuation target to get there. The article OpenAI lowers valuation target, raises up to $30 billion appeared first on The Decoder.

OpenAI is closing in on a new funding round with UAE investors

OpenAI is on the verge of a new funding round worth up to $30 billion. The article OpenAI is closing in on a new funding round with UAE investors appeared first on The Decoder.

Google Is Proving Why Americans Are Right to Distrust Data Centers - Newsweek

Google Is Proving Why Americans Are Right to Distrust Data Centers Newsweek

uGAP: A Unified Geospatial AI Platform for African Geographic Data - Microsoft

uGAP: A Unified Geospatial AI Platform for African Geographic Data Microsoft

Nvidia's Huang heads to Kazakhstan for the first time — and he's not the only one with big plans - CNBC

Nvidia's Huang heads to Kazakhstan for the first time — and he's not the only one with big plans CNBC

Why AI’s Deepest Mind Believes the World Is About to Change Radically - WSJ

Why AI’s Deepest Mind Believes the World Is About to Change Radically WSJ

Nvidia’s Huang Reveals Investment in AI Video Unicorn Higgsfield - Bloomberg

Nvidia’s Huang Reveals Investment in AI Video Unicorn Higgsfield Bloomberg

Google Cloud Launches Agent Compute Engine: A MicroVM per AI Agent

Google Cloud announced Agent Compute Engine, a compute service that gives every AI agent its own hardware-isolated microVM with no credentials inside. It already runs Gemini agents and enters preview in the coming months. The post Google Cloud Launches Agent Compute Engine: A MicroVM per AI Agent appeared first on MarkTechPost.

It Was Always Going To Be This Way, We Tried To Warn The World: Anthropic’s Deputy CISO Jason Clinton On Recent Cyberattacks

As the internet’s security teams scramble through what has become one of the roughest weeks in recent memory, Anthropic’s Deputy Chief Information Security... The post It Was Always Going To Be This Way, We Tried To Warn The World: Anthropic’s Deputy CISO Jason Clinton On Recent Cyberattacks appeared first on OfficeChai.

You.com Launches Inference Router: 12 Open-Weight Models With 99.9% Uptime SLA

You.com launched Inference Router, an OpenAI-compatible API for 12 open-weight models with a 99.9% uptime SLA, zero data retention, 100% US hosting, server-side web search and no platform fee. The post You.com Launches Inference Router: 12 Open-Weight Models With 99.9% Uptime SLA appeared first on MarkTechPost.

IRAH: Hierarchical Agentic Tree Search for Retrieval Pipeline Optimization

arXiv:2610.30167v1 Announce Type: new Abstract: Optimizing Information Retrieval (IR) pipelines for Retrieval-Augmented Generation (RAG) requires jointly selecting structural components and numerical hyperparameters under limited evaluation budgets. We introduce the Information Retrieval Agentic Harness (IRAH), which combines Language Agent Tree Search for IR (LATS-IR) with offline meta-learned initialization. LATS-IR performs UCT-guided hierarchical search, first exploring macro-structural pipeline choices and then refining numerical parameters, with natural-language reflections to guide subsequent mutations and provide inspectable search traces. We evaluate IRAH on three in-distribution benchmarks and one out of-distribution benchmark to assess generalization under distribution shift. Under the same meta-learned initialization, LATS-IR outperforms TPE on two of three in-distribution benchmarks, with gains of up to 6.4%, and remains competitive on the out-of-distribution benchmark. Across all four benchmarks, warm-started IRAH achieves the highest average held-out nDCG@10 (0.4816) and improves over its cold-start counterpart. These results establish IRAH as a competitive approach to retrieval pipeline optimization under constrained evaluation budgets.

FormBench: A Benchmark for Fine-Grained Retrieval in Formulation Patents

arXiv:2610.30175v1 Announce Type: new Abstract: We introduce FormBench, a platform for testing the ability of models to retrieve precise chemical information from technical documents. FormBench contains 55,352 queries and two retrieval corpus variants each with ~1M passages: FormBench-Random (C0), with randomly sampled distractors, and FormBench-Structured (C1), with structurally proximate distractors. Each query is paired with an anchor passage, a hard negative, and a soft negative - derived from a materials ontology through entity overlap scoring, providing a deeper graded signal on a model's discriminatory power. We evaluate 10 model configurations spanning commercial API encoders (OpenAI, Voyage), open-source encoders (Nomic, GTE-large, Qwen3-Embed), and their task-adaptive fine-tuned (TAPT) variants. We find: (1) commercial APIs score very poorly across all configurations (OpenAI-3-large: 0.009 on C1, 0.213 on C0); (2) TAPT on FormBench triplets lifts Qwen3-Embed to 0.4085 on C1, 0.4835 on C0; (3) BM25 remains competitive (0.375 on C1, 0.467 on C0), exceeded only by TAPT-Qwen3; (4) corpus size has a significant impact on retrieval behavior, as demonstrated through a corpus-size ablation; (5) the API failure traces to within-domain geometric collapse; (6) APIs outperform even the TAPT variants on classification and clustering tasks. FormBench is released in BEIR-compatible format at huggingface.co/datasets/vinven7/FormBench under CC-BY-4.0.

Uber rival inDrive eyes 2027 robotaxi tests in Central Asia with Pony AI and Nvidia

The company plans to begin testing Pony.ai's robotaxis in Kazakhstan's capital in the first half of 2027.

A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation

arXiv:2610.28686v1 Announce Type: new Abstract: Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded conversational scenarios where a user query is either ill-formed or ambiguous in the context of the prior dialog or, more interestingly, it may be well-formed but when posed against retrieved documents there are multiple relevant interpretations. The task is to recognize both such cases and then generate an appropriate clarification request to the user. We present ClariEdge, a new dataset of approximately 3,900 examples that include such ambiguous queries along with negative examples that do not require clarification. We also show that a lightweight fine-tuned LoRA achieves 96.6% classification accuracy and outperforms prompted baselines of many larger models on both classification and quality of clarification generation. Our study shows that hard negatives are an essential ingredient to separate out queries that are truly ambiguous from queries that may look ambiguous but are not (i.e., can be resolved from the context). The same LoRA achieves high performance in both pre-retrieval and post-retrieval scenarios; thus, it can be used in two places in a RAG pipeline as a check before answer generation. The ClariEdge dataset is available at https://huggingface.co/datasets/ibm-research/ClariEdge.

Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?

arXiv:2610.28763v1 Announce Type: new Abstract: Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain insight beyond routine ML engineering. We introduce Agent4Sci, a long-horizon testbed for measuring how far today's agents can improve the published best models on scientific ML benchmarks, spanning 20 benchmarks across 11 domains. Rather than ranking agents on a leaderboard, Agent4Sci asks what kinds of progress agents can make on these tasks under in-the-wild conditions, and where they fall short. Empirically, today's frontier agents advance the published best on every benchmark, with Claude Opus reaching 10.4% mean relative improvement and no run flagged for academic misconduct. These gains, however, come almost entirely from a bag of ML-engineering tricks rather than research-level insight. Trajectory analysis surfaces three recurring limitations in this setting: novelty miscalibration, resource under-utilization, and rapid collapse into local engineering sweeps. Lightweight plug-in skills for research ideation, experiment management, and scheduled checklists raise Claude Opus from 10.4% to 20.1% and Codex from 4.4% to 8.1%, while yielding more diverse, domain-relevant interventions. Agent4Sci gives the community a way to track and push frontier agents from ML engineering toward domain insight on real-world ML-for-science problems. Code and data are available on GitHub (https://github.com/maszhongming/agent4sci) and Hugging Face (https://huggingface.co/datasets/MingZhong/agent4sci).

Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI

arXiv:2610.28826v1 Announce Type: new Abstract: Automated judges of clinical artificial intelligence (AI) outputs are necessary, but large language model (LLM) judges are costly and slow at scale. We compared Jev, a decision language model that returns structured judgments, with frontier and open-weight LLM judges in four use cases. On 118 pediatric answers, Jev's agreement with physicians' ordinal quality ratings (Gwet's AC2 0.50; expected score 0.53) matched or exceeded that of seven of 12 LLM judges, including frontier models, Claude Opus 5.5 (0.49) and GPT-6 (0.38). On physician-graded binary HealthBench criteria, Jev matched or exceeded four of six LLM judges. On MedHELM's open-ended tasks, Jev's agreement with a three-LLM jury approached or exceeded the jurors' internal agreement. On HealthBench Professional, Jev matched the benchmark's reference judge, GPT-5.4, on 91% of criteria (other judges, 91-93%). Frontier judges cost 73-294 times more than Jev and took 7-57 times longer. Jev may enable efficient clinical AI evaluation at scale.

Speedrunning Relational Foundation Model Pretraining

arXiv:2610.28855v1 Announce Type: new Abstract: Progress on foundation model pretraining is gated by who can afford a trial, and relational pretraining is no exception. Recipes are developed inside individual labs, reported under private protocols, and do not accumulate. Speedrun leaderboards remove that barrier by decentralizing the search rather than the training run. The problem is fixed, the stack is open, entries are ranked by wall-clock time to a quality target, and one GPU is enough to hold the record. We introduce modded-nanoRT, the first such leaderboard for Relational Transformers. Which database to hold out decides whether the benchmark can resolve improvements at all, so we fix it by calibration, ranking six candidates by how sharply a 45-minute learning curve separates improvement from seed noise. We open the leaderboard with typed trilinear attention, a relational operator that conditions attention scores and values on the foreign key traversed, together with Muon and SIGReg. The recipe reaches the target in 305.63 seconds on one NVIDIA H200, 8.11x faster than the reference implementation, and wins 13 of 15 pairs outside the development database. We will release the benchmark, its frozen artifacts, and the record runs. Our code is available at https://github.com/kunumi/nanoRT.

The Signal Chats That Cost OpenAI Research ers Their Jobs - WSJ

The Signal Chats That Cost OpenAI Researchers Their Jobs WSJ

Report: OpenAI closing on $30B ‘bridge’ funding round to tide it over before IPO

Artificial intelligence leader OpenAI Group PBC might have plans to go public in the near future, but it isn’t done with private funding rounds. Indeed, it’s currently believed to be negotiating with investors an additional $30 billion investment that would bring its valuation to a staggering $1.25 trillion, which would make it the most valuable […] The post Report: OpenAI closing on $30B ‘bridge’ funding round to tide it over before IPO appeared first on SiliconANGLE.

Does Your Model Run on AWS Trainium? Look It Up

OpenAI and Poland’s NASK partner to advance AI safety and security

OpenAI and Poland’s NASK are partnering on AI safety, security, and technical research to strengthen safeguards and support a more resilient digital ecosystem.

vLLM Delivers Day-0 Support for MiniMax-M3.1-Flash

Day-0 vLLM support for MiniMax-M3.1-Flash on NVIDIA Blackwell, NVIDIA Rubin, and AMD MI350-series GPUs, with NVFP4 MSA kernels, NVFP4 MegaMoE, DSpark speculative decoding, AgentX results, and day-0 RL.