Yeah, this is a good answer. AI agents will be too fast for humans to monitor and adapt to.
AI
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AI Safety Requires Experimental Validation Through Incremental Development
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But if we pause it, we won't have systems that we can study. I think AI safety can't be solved in isolation, just in theory. It needs to be experimentally validated, and the best is to do it incrementally.
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AI Agents Liability: Who Bears Legal Responsibility?
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For me, product safety and business responsibility are the main motivations. Also, who will pay for it if our agents cause harm and damage? Who ends up in prison? Agents or me?
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AI Risk Beyond Existential Threat Scenarios
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I don't even look at it from the "AI doom" perspective (AI seeking power and wanting to kill us all).
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Autonomous AI Agents Risk Unwanted Task Execution Behaviors
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The first risk is that they may start doing unwanted tasks (e.g. I ask them to run a marketing campaign for me, and the agents decide to spam everyone, manipulate people, hack into computers, etc)
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Understanding Diffusion Models: A Unified Perspective Tutorial
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Understanding Diffusion Models: A Unified Perspective Diffusion models are the engine behind novel image generative systems. This tutorial provides an intuitive and comprehensive understanding of diffusion models. Paper: https://
arxiv.org/abs/2208.11970
Blog: https://
calvinyluo.com/2022/08/26/dif
fusion-tutorial.html
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Responsible AI: Seven Key Points from Microsoft’s Chief Officer
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Seven things to know about Responsible AI Microsoft’s Chief Responsible AI Officer, Natasha Crampton, was in the UK to meet with policymakers, civil society members, and the tech community https://
bit.ly/41hKuCQ -
Reinforcement Learning Robots Deploy for Real-World Waste Sorting
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Today we discuss a large-scale experiment where we deployed a fleet of #ReinforcementLearning-enabled robots in office buildings to sort waste and recycling. Read how our robotic system uses offline and online data to enable real-world adaptation → https://t.co/prLWRFsMYT pic.twitter.com/Zbh5Ui2Uvs
— Google AI (@GoogleAI) 13 avril 2023Today we discuss a large-scale experiment where we deployed a fleet of #ReinforcementLearning-enabled robots in office buildings to sort waste and recycling. Read how our robotic system uses offline and online data to enable real-world adaptation → https://
goo.gle/416Iv4u -
AISTATS 2023 Proceedings Volume 206 Now Available on PMLR
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Volume 206 https://
proceedings.mlr.press/v206/ Proceedings of AISTATS 2023 Is now available on PMLR. -
ACML 2022 Proceedings Volume 189 Published on PMLR
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Volume 189 https://
proceedings.mlr.press/v189/ Proceedings of ACML 2022 Is now available on PMLR.