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Businesses must reinvent their processes and workforce to scale agentic AI adoption
Follow ZDNET: Add us as a preferred source on Google. ZDNET's key takeaways * Only 15% of organizations have reached scaled multi-agentic orchestration. * Most business leaders are reevaluating their business models in light of advances in agentic AI. * Scaling agentic AI must begin with sufficient resources to transform the
'Same compute, fewer resources - More compute, same energy': AMD says it is on track to make AI four times…
* AMD says its rack-scale AI systems are now about four times more energy-efficient than its 2024 baseline, beating its own threefold interim target by 33% * The company offers two alternative outcomes for that gain: the same compute in far fewer racks, or twenty times more compute for the same power,
Mercury Research: AMD's share of x86 client CPU shipments tops 30% for the first time, hitting 30.3% in Q2, up from 21.1% two years earlier, vs. Intel's 69.7%
Sponsor Posts Subquadratic: the LLM built for 12M-token reasoning — SubQ can reason across entire codebases and document sets in one pass with no RAG workarounds. Read how SubQ 1.1 Small holds near-perfect retrieval out to 12M tokens. Most carriers track everything. Cape doesn't. — Unlimited talk, text &
CISO's guide to RAG data security risks and protection strategies | TechTarget
Large language models can only generate responses from the data they were trained on. Yet this data might be outdated, might not include proprietary data, can cause hallucinations and could require costly updates. That's where retrieval-augmented generation comes in. RAG overcomes these data limitations by retrieving relevant information