Human understanding of #AI can't keep up with its advancement, researchers say
by Krystal Kasal @TechXplore_com Learn more: https://
bit.ly/3Sn3Xm9 #ArtificialIntelligence #MachineLearning #ML
@ronald_vanloon
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AI outpaces human understanding, researchers warn
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From Reactive AI to Agentic Partners: Intelligent Systems That Sense, Act, Collaborate
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From Reactive #AI to Agentic Partners: Intelligent Systems That Sense, Act, and Collaborate
— Ronald van Loon (@Ronald_vanLoon) 17 juin 2026
by @tuyasmart#ArtificialIntelligence #Robotics #Innovation #TechForGood #EmergingTech pic.twitter.com/j11YjTb0YuFrom Reactive #AI to Agentic Partners: Intelligent Systems That Sense, Act, and Collaborate
by @tuyasmart #ArtificialIntelligence #Robotics #Innovation #TechForGood #EmergingTech -
Model crumbles due to weak infrastructure and pipelines
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Even a flawlessly tuned model crumbles under real-world production demands if the underlying data pipelines and legacy enterprise infrastructure can't support the required latency and continuous deployment cycles.
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Detecting infrastructure gaps early ensures AI production success
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Spotting infrastructure gaps early makes all the difference when dealing with data volume and legacy bottlenecks. Building that strong foundation is exactly what separates successful production AI from permanent pilots.
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Infrastructure plumbing determines AI pilot scalability and value
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The infrastructure plumbing determines exactly how far an AI pilot can scale. When legacy dependencies and data bottlenecks clog the system, even the most advanced models fail to deliver production value.
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Using AI to turn failed drugs into new medicines
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How I use #AI to turn failed drugs into new medicines
by Emma Ulker @Nature Learn more: https://
bit.ly/4e3c1kA #MedTech #HealthTech #Tech #TechForGood -
AI Performance Is an Infrastructure Issue, Not Just Software
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The PGA Championship is a useful reminder for every technology leader: AI performance is not just a software issue. It is an infrastructure issue. Before asking what AI can automate, predict, or recommend, leaders should ask whether their network can support those decisions
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AI’s enterprise scale depends on data and network under pressure
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At the PGA Championship, the real story was not just “AI at an event.” The real story was what AI requires to be useful at enterprise scale. AI depends on data. Data depends on the network. And under pressure, the question is not: “Can the model find a pattern?” It
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AI Inference: System-Level Time Problem in Post-Moore Era
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My takeaway: AI inference is no longer just a model problem. It's a system-level time problem. For enterprise leaders, AI performance and AI economics are becoming inseparable. Learn more about Tau Scaling and what it means for the post-Moore era: https://
chinaxiv.org/abs/202605.002
24?locale=en
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Why AI inference prioritises low latency over raw compute
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Why does this matter for AI inference specifically? Training = throughput problem. Inference = latency problem. When a user talks to an AI assistant, tokens have to return fast. Latency, memory access, bandwidth, and interconnect all matter, not just raw compute. In large AI