Machine Learning

Explore articles tagged with Machine Learning

#AI#AI Models#Machine Learning

Multi-turn attacks broke AI models 88% of the time — single-turn testing missed it, Cisco AI security lead warns at VB Transform 2026

When Cisco ran 6,986 multi-turn attacks against 15 flagship models, attackers who adapted across the conversation broke through as often as 88. Amy Chang, Cisco's head of AI threat intelligence and security research, brought that finding to the agentic security panel at VB Transform 2026; the number should worry anyone still running single-turn red-teaming programs.

#AI#Anthropic#Claude

Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents

Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception.

#AI#Artificial Intelligence#Anthropic

Latest: Anthropic drops ‘workplace AI agents’ directly inside Slack

Anthropic launched a beta version of its Claude Tag feature for Enterprise and Team tiers, shifting its chat model into shared Slack channels. Moving away from traditional isolated chat boxes, users pull the artificial intelligence model into active group threads by typing @Claude.

#AI#Anthropic#Claude

Three things to watch amid Anthropic’s latest feud with the government

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#AI#Large Language Models#NLP

A startup claims it broke through a bottleneck that’s holding back LLMs

Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade.

#AI#Anthropic#Claude

The AI off switch: How Anthropic’s export controls sparked a global AI sovereignty scramble

Anthropic export controls turned an abstract policy fear into a live one last week: as of June 13, 2026, one US government directive took the company’s two most powerful AI models offline for users everywhere, including, briefly, Anthropic’s own foreign-born employees, and set off alarm bells across Europe and Canada about who really controls the […] The post The AI off switch: How Anthropic’s export controls sparked a global AI sovereignty scramble appeared first on AI News.

#AI Models#Machine Learning#AI

MCP solved tool calling. A2A solved coordination. What solves transport?

The history of distributed computing is one of protocol proliferation followed by consolidation. Common Object Request Broker Architecture (CORBA), Distributed Component Object Model (DCOM), Java remote method invocation (RMI), and early simple object access protocol (SOAP) competed for the enterprise integration market in the late 1990s before representational state transfer (REST) quietly won by being simpler and HTTP-native.

#Anthropic#AI#Claude

Anthropic blocks all public access to Claude Fable 5, Mythos 5 following US government order — what enterprises should do

The US government last night issued an unprecedented export control directive ordering Anthropic to immediately suspend all access to its top-tier Claude Fable 5 and Claude Mythos 5 models for foreign nationals, citing unspecified national security authorities. In response, Anthropic has blocked all public access to both models, globally — meaning no users around the world can access them at this time, even paying enterprise customers and Anthropic employees internally.

#AI#Anthropic#Claude

Anthropic brings Mythos to the masses with Claude Fable 5, its most powerful generally available model ever

Anthropic today launched two new AI models — Claude Fable 5 and Claude Mythos 5 — marking the company’s first broad release of the powerful “Mythos-class” AI capabilities it previously made available only to participating organizations in its restricted cybersecurity program, Project Glasswing, which it announced two months ago. The company says Fable 5, which is the version most users and developers will get starting today, exceeds every Claude model it has previously made generally available —.

#AI#ChatGPT#OpenAI

Researchers trained an open source AI search agent, Harness-1, that outperforms GPT-5.4 on recalling relevant information

A joint research collaboration between researchers at the University of Illinois at Urbana-Champaign (UIUC), UC Berkeley, and the open source AI-native vector database platform Chroma unveiled Harness-1, a 20-billion parameter open-source search agent built atop OpenAI's gpt-oss-20B open source model that fundamentally redesigns how AI executes complex retrieval tasks. Harness-1 achieves a massive leap in performance, scoring 73% average on its ability to recall relevant information correctly f.

#AI#Anthropic#Claude

Anthropic says 80% of its new production code is now authored by Claude — how your enterprise can keep up

Anthropic co-founder and CEO Dario Amodei said it was coming, but it still feels like a milestone: More than 80% of the code merged into Anthropic’s production codebase in May wasn't authored by humans, but by its own AI model, Claude, according to a new report shared by the record-breaking AI startup today. This transformation has triggered an 8x increase in the volume of code shipped per engineer per quarter compared to the company’s 2021–2025 baseline, which the company notes means even more .

#AI#Anthropic#Claude

Anthropic IPO filing marks AI maturing into enterprise utility

Anthropic’s IPO filing marks the maturation of generative AI from a research-heavy venture phase into a stabilised enterprise utility. Model developers operating in private markets have prioritised rapid iteration and maximum compute performance over predictable billing cycles.

#AI#Large Language Models#NLP

AI agents are entering their rebuild era as enterprises confront the reliability problem

As enterprise AI agents move into production, organizations are confronting a growing reliability problem. Many teams are discovering that LLM performance alone does not determine whether agents succeed in production.

#AI#Large Language Models#NLP

MIT's MeMo lets teams swap in a better LLM without retraining — and performance jumps 26%

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits. MeMo, a framework from researchers at multiple universities, encodes new knowledge into a dedicated smaller memory model that operates separately from the main LLM.

#AI#Anthropic#Claude

Anthropic's Claude Opus 4.8 is here with 3X cheaper fast mode and near-Mythos level alignment

Anthropic today released Claude Opus 4.8, an upgrade to its flagship model that ships at the same price as its predecessor, alongside a dramatically cheaper "fast mode" tier and a new feature that lets the model spawn hundreds of parallel subagents for codebase-scale work.

#AI#AI Models#Machine Learning

DataGrail report finds your vendor may be sending data to AI models you never approved

The data processing agreement (DPA) — the bedrock contract companies use to evaluate how vendors handle personal data — can no longer be trusted at face value. That is the central, and arguably most alarming, conclusion of DataGrail's Privacy and AI Trends Report 2026, released today.

#AI#AI Models#Machine Learning

Why prompt debt, retrieval debt, and evaluation debt are quietly reshaping enterprise AI risk

Over the past two decades, technical debt meant outdated architecture, messy code, and poorly maintained documentation. That definition is no longer sufficient in the AI era, where failure modes are more subtle and often non-linear.

#AI#AI Models#Machine Learning

What Your AI agents need a terminal, not just a vector database

When agentic workflows fail, developers often assume the problem lies in the underlying model’s reasoning abilities. In reality, the limited information provided by the retrieval interface is often the primary limiting factor.

#Google AI#Google#Technology

Google's Managed Agents API promises one-call deployment at the cost of execution layer control

At Google I/O, the company unveiled Managed Agents in its Gemini API — a service that promises to collapse weeks of agent deployment work into a single API call. It's also a sign that Google believes its ecosystem, including the newly launched Antigravity CLI, is ready to own the execution layer end-to-end.

#AI#Large Language Models#NLP

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via cosine similarity — is effective for unstructured semantic search.