Great work from @williamjurayj
, @jeff_cheng_77
, and @ben_vandurme to push us past just the basic accuracy benchmarking. So next time you get an "I don't know" answer from AI – just remember that could be a good thing. Full study here: https://
arxiv.org/pdf/2502.13962
SAFETY
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AI Model Evaluation Beyond Basic Accuracy Benchmarking Study
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Compute Power Increases Model Confidence and Overconfidence Risks
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– And good news! More compute = more confidence as well. (However, more compute made one model become more confident in wrong answers too. Something to keep an eye to make sure model confidence doesn’t tip into overconfidence.)
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Model Confidence Critical for High-Stakes AI Applications
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– Higher compute and confidence were extra important for model performance when wrong answers had serious consequences (like when they were weighted 20x). You wouldn't want a 51%-sure diagnosis of a terminal illness.
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Johns Hopkins: LLMs Must Know When to Abstain
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You can't just be right, you have to know you're right. Good advice for LLMs, according to new Johns Hopkins research. Sometimes no answer is better than a wrong one – life or death choices in medicine, for example, or big financial decisions.
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AI Privacy Concerns and Cognitive Capabilities
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AI knows our deepest secrets.
— Linus ✦ Ekenstam (@LinusEkenstam) 19 mars 2025
Dreams of flight.
pic.twitter.com/piVsLAeaK0AI knows our deepest secrets. Dreams of flight.
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DPT-Agent System: FSM and Theory of Mind Integration
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DPT-Agent's System 1 employs a Finite-state Machine (FSM) and code-as-policy for fast, intuitive, and controllable decision-making. System 2 integrates Theory of Mind (ToM) and asynchronous reflection to infer human intentions and make reasoning-based autonomous decisions.
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Metaverse Healthcare Communication: Enhancement or Interaction Barrier?
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How the #Metaverse Is Shaping the Future of Healthcare Communication: A Tool for Enhancement or a Barrier to Effective Interaction?
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Superintelligence Strategy Expert Analysis by Leading Researchers
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Superintelligence Strategy: Expert Version Dan Hendrycks, Eric Schmidt, Alexandr Wang: https://
arxiv.org/abs/2503.05628 #ArtificialIntelligence #DeepLearning #MachineLearning -
Differentially Private Synthetic Data Generation via LLM Prompting
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Today we describe an inference-only approach to generating differentially private synthetic data via prompting off-the-shelf large language models with many examples in parallel and aggregating their responses in a privacy-preserving manner. Learn more at https://t.co/lPoft9PXpM pic.twitter.com/YtLRqc2FyI
— Google AI (@GoogleAI) 18 mars 2025Today we describe an inference-only approach to generating differentially private synthetic data via prompting off-the-shelf large language models with many examples in parallel and aggregating their responses in a privacy-preserving manner. Learn more at https://
goo.gle/4izufJT