Chris Summerfield: AI systems mimic human behavior through vast data training, the challenge of continuous learning remains unsolved, and sleep is vital for cognitive function | The Peter McCormack Show

Anthropic has apologized for stealthily throttling its new AI model, Claude Fable 5, with hidden guardrails that undermine both researchers and rivals using it to develop competing systems. The company says it is reversing course and will be more transparent about when the restrictions kick in, even if that means Fable refuses more queries. Fable is the first widely available model in Anthropic's Mythos class of AI systems, a group the company has spent months warning are too dangerous for public release. Anthropic says it has addressed some of those risks by launching Fable with safeguards that prevent it from responding to certain "high-r … Read the full story at The Verge.

DeepMind's initiative could redefine AI collaboration, impacting fields from economics to social sciences by exploring emergent group dynamics. The post Google DeepMind launches $10M research fund to study how AI systems behave in groups appeared first on Crypto Briefing.

Modern AI systems have evolved beyond the simple chatbots that quickly became popular. Now they use semantic tools to manage workflows and link machines to machines, providing a flexible and effective framework for the next generation of business automation. What you used to build in Microsoft’s Power Platform or construct inside Biztalk is now an agent, built around large language models (LLMs) that can parse both your data and the APIs that you want to use your data with, orchestrating workflows with a level of autonomy that traditional tooling can’t match. That shift has offered new opportunities, much like those that came with business platforms like Microsoft Dynamics and Salesforce. Here, tools built to solve one set of business problems could be turned into applications that could be sold to other companies. What worked for you to solve one of your problems could now be an added revenue stream, sold through platform marketplaces that helped customers manage installations and cus

LF AI & Data Foundation, a division of the Linux Foundation, launched a working group on Tuesday that will focus on the development of DocLang, a specification intended to support interoperable document processing across AI and agentic workflows. The working group, founded by premier members IBM, Nvidia and Red Hat, is tasked with the creation of an open, universal, AI-native document format designed to improve how enterprises prepare, exchange, and govern document data for AI systems. Contributors ABBYY and Human Signal will also be involved in its development. The announcement stated, “enterprises today work across a fragmented landscape of document formats, including PDFs, JPEGs, and other file types built primarily for human consumption rather than AI interpretation.” As organizations increasingly rely on generative AI and agentic systems, it said, “this disconnect can introduce complexity, raise costs, and reduce reliability when extracting meaning from business documents.” Mark C

Bitmine Immersion Technologies now controls 4.59% of the entire ethereum supply after accumulating 5.54 million ETH valued at approximately $9.04 billion at current prices. Holdings Overview The Norwalk, Connecticut-based company disclosed total crypto, cash, and “moonshot” holdings of $9.6 billion as of June 7, 2026. The breakdown includes 5,543,872 ETH at $1,630 per token, 204 […]

AI advancements promise to boost productivity, challenging fears of an economic slowdown and improving living standards. The post Julian Jessop: Weak productivity growth since the financial crisis, the AI revolution will drive economic expansion, and excessive regulation hinders corporate performance | The Peter McCormack Show appeared first on Crypto Briefing.

Agentic AI has moved from conference hype to a budget line item. This is where the conversation gets more interesting and more uncomfortable. Unlike traditional AI systems that respond to a single prompt, classify a document, recommend an action, or generate a summary, agentic AI systems are designed to pursue goals. They plan, call tools, inspect results, retry failed steps, consult memory, hand off tasks to other agents, and sometimes critique their own work before producing an answer or taking an action. That extra autonomy is the value proposition. It also introduces the cost problem. A single chatbot interaction may consume a few thousand tokens. A useful agentic workflow can consume hundreds of thousands or millions of tokens per day because it does more than answer a question. It decomposes the problem, retrieves context, reasons through options, invokes APIs, checks the output, and often runs multiple passes before reaching a result. Therefore, the economics need to be understo

The breach highlights the critical need for robust authentication in AI systems, challenging the safety assumptions of large-scale AI deployments. The post Meta’s AI chatbot breach exposes security flaws, impacts high-profile accounts appeared first on Crypto Briefing.