time will tell, but the tragic bombing of the school in Iran looks like it could well have been an AI error of temporal reasoning. viz the AI may have had access to old intel and new intel and failed to give precedence to the new intel.
ETHICS
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From Agent-1 to Superintelligence: AI 2027 Scenario Implications
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From Agent-1 to Superintelligence: Decoding the AI 2027 Scenario and Its Profound Implications https://
linkedin.com/pulse/from-age
nt-1-superintelligence-decoding-ai-2027-its-giuliano-liguori–vb1vf
… via @ingliguori -
AI Agents in Operating Systems Pose Privacy Risks
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📁 Meredith Whittaker, president of Signal, warns that AI agents embedded in operating systems could undermine privacy.
— Jon Hernandez (@JonhernandezIA) 6 mars 2026
To work, they need access to your calendar, files, browser, contacts and messages. That creates a massive gateway into your digital life.
And it can bypass… pic.twitter.com/xOb9ffAgcP📁 Meredith Whittaker, president of Signal, warns that AI agents embedded in operating systems could undermine privacy. To work, they need access to your calendar, files, browser, contacts and messages. That creates a massive gateway into your digital life. And it can bypass the protections encrypted apps rely on.
→ View original post on X — @mer__edith, 2026-03-06 11:30 UTC
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Models Know Longer Isn’t Always Better
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the deeper point here connects to something the field keeps rediscovering. we trained reasoning models to think longer. then we discovered longer doesn't mean better. now this paper shows the models themselves already know that. they're generating stop signals that our inference
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Researchers test AI self-awareness in reasoning
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here's where it gets interesting. the researchers probed whether models internally "know" they're done. they introduced TSearch, which scores partial reasoning traces by cumulative log-probability across the entire chain, not just the next token. when you let the model explore
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Overthinking harms accuracy in AI responses
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and it's not just wasted compute. overthinking actively hurts accuracy. DeepSeek-R1 produces responses 5x longer than Claude 3.7 Sonnet on AIME 2025 with comparable accuracy. QwQ-32B scores 2 percentage points HIGHER with its shortest answers using 31% fewer tokens. 72% of
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RFCS Metric Reveals Early Correct Steps
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first, the problem quantified. the researchers created a metric called RFCS (Ratio of First Correct Step) that tracks where in a chain of thought the correct answer first appears. on MATH-500, across every model tested, the right answer shows up well before the end in over half
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Overthinking in AI: A Sampling Issue
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reasoning models already know when they've solved the problem. we just don't let them stop. new paper from Beihang University and ByteDance shows that the overthinking problem in models like DeepSeek-R1 and Qwen3 isn't a training failure. it's a sampling failure. the fix cuts
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AI Governance: Attribution and Provenance in Creative Processes
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Well said Knut and really interesting framing. As #AI becomes more embedded in creative processes, governance around attribution, provenance and trust may become just as important as traditional ownership frameworks. In fact, this shift from pure ownership toward collaboration,
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Transparent attribution framework aligns AI incentives and provenance tracking
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Interesting point! And agreed that attribution is becoming increasingly feasible technically. A transparent opt-in framework could help align incentives, especially if attribution and provenance can be reliably tracked.