How To Build #AIAgents Without Building Risk In The Enterprise by Quang Tuan Dang @Forbes Learn more: bit.ly/4tpRTh8 #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning
→ View original post on X — @ronald_vanloon, 2026-04-07 16:53 UTC
ETHICS
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Legal Framework and Ethical Standards in AI Development
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reference? which law? also whatever happened to taking the moral high ground?
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Gallup Partners With Simile to Replace Polling With AI Simulations
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'Silicon sampling' is replacing real humans in public opinion polls with AI simulations. No phone calls or focus groups. Just a language model role-playing as thousands of people who were never asked what they think. Gallup has partnered with a startup called Simile to build
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Domain-Specific AI Versus General-Purpose Chatbots Debate
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perhaps we should compare calculators to humans? also, if you actually follow my work (eg October 2025 NYT oped) you will know I am a fan of domain specific AI (like Waymo) and skeptical of domain-general AI (like chatbots), so I appreciate your making my case for me.
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Growing Up Means Understanding Multiple Perspectives in Complex Situations
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Big part of growing up is realizing that there are more than two sides in any situation.
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Generative AI: 90% Accuracy, 10% Unacceptable Errors
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Imagine if your car randomly went out of control 10% of the time. That's commercial-grade generative AI web search. rat king 🐀 (@MikeIsaac) glass half full: 90 percent accuracy is an impressive accuracy rate glass half empty: 10 percent error rate for a company that does more than 5 Trillion search queries per year is still a gigantic number nytimes.com/2026/04/07/techn… — https://nitter.net/MikeIsaac/status/2041535422623609174#m [Translated from EN to English]
→ View original post on X — @garymarcus, 2026-04-07 16:16 UTC
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Google’s 10% Error Rate: Shift from Pre-ChatGPT Standards
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10% error rate at any scale would never have been tolerated by Google pre-ChatGPT. The company has fundamentally changed rat king 🐀 (@MikeIsaac) glass half full: 90 percent accuracy is an impressive accuracy rate glass half empty: 10 percent error rate for a company that does more than 5 Trillion search queries per year is still a gigantic number nytimes.com/2026/04/07/techn… — https://nitter.net/MikeIsaac/status/2041535422623609174#m
→ View original post on X — @garymarcus, 2026-04-07 15:51 UTC
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Character Assessment and Objective Standards in AI Leadership
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I agree that the control by a small group is not a good thing. But this wasn’t about that, more to do with Altman’s character. Who decides what character is “good”? You and I prob have different views on that. That’s why it requires a more objective lens in my view
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The challenges of measuring AI performance and reliability
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This article is a case study of why measuring AI performance is so hard. AI Overviews make mistakes. But the same mistakes are in Wikipedia. But the sources are harder to find when using AI. But the AI answers may be better than most people would find. Unclear what it all means.
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AI’s Hybrid Strategy: Sharing Influence and Competitive Control
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A new divide appears. For our free newsletter this week, we discuss how the AI industry is moving toward a new divide, open sourcing enough to spread influence while reserving their strongest models to preserve their strategic edge. @IrenaCronin and I write this newsletter every week. The AI industry is moving toward a hybrid strategy in which companies share enough of their models and tools to build adoption, developer loyalty, and ecosystem influence, while keeping their most advanced systems closed to protect competitive advantage, control risk, and capture more value. Instead of a simple open versus closed divide, AI is increasingly becoming a spectrum shaped by business strategy, safety concerns, and market competition. Read for free at unaligned.io and please subscribe! [Translated from EN to English]
→ View original post on X — @scobleizer, 2026-04-07 15:20 UTC