Understanding transformers: What every leader should know about the architecture powering GenAI - cio.com
<a href="https://news.google.com/rss/articles/CBMi0AFBVV95cUxPOXRoV2pPV2NKN1IyakRmQTg5djJoa3hWeFZCQkVVbVhRb2g4X0JsRktSU25DbjBuTzNLV0owMEVoTDFFTHNTV2JYQTdocjM3RUQ1aVhQOENCbVRyNkRZYzBPb1I0R19yNW1qY2VYRHlNSVMyT19xQm5xQkdsWmRxd0lEZXgtR09lYU1zVHNkODNmLWpvY2hSWVN1dDUwUjhEWFhVaVpKenBkX194S05vWWVnckRjVWNHSUEzcjB3VVFpaTY3WXB2VkczZ3o2SG5o?oc=5" target="_blank">Understanding transformers: What every leader should know about the architecture powering GenAI</a> <font color="#6f6f6f">cio.com</font>
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Tiny Recursive Networks
In this fully connected episode, Daniel and Chris explore the emerging concept of tiny recursive networks introduced by Samsung AI, contrasting them with large transformer based models. They explore how these small models tackle reasoning tasks with fewer parameters, less data, and iterative refinement, matching the giants on specific problems. They also discuss the ethical challenges of emotional manipulation in chatbots. Featuring: Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: Less is More: Recursive Reasoning with Tiny Networks Researchers detail 6 ways chatbots seek to prolong ‘emotionally sensitive events’ Sponsors: Outshift by Cisco - The open source collective building the Internet of Agents. Backed by Outshift by Cisco, AGNT
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arXiv:2603.29075v1 Announce Type: new Abstract: The way we're thinking about generative AI right now is fundamentally individual. We see this not just in how users interact with models but also in how models are built, how they're benchmarked, and how commercial and research strategies using AI are defined. We argue that we should abandon this approach if we're hoping for AI to support groundbreaking innovation and scientific discovery. Drawing on research and formal results in complex systems, organizational behavior, and philosophy of science, we show why we should expect deep intellectual breakthroughs to come from epistemically diverse groups of AI agents working together rather than singular superintelligent agents. Having a diverse team broadens the search for solutions, delays prema
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