Why AI Models Are Collapsing And What It Means For The Future Of Technology A new phenomenon called "model collapse" is threatening the #future of #AI as #models trained on AI-generated #data begin to lose touch with reality. Discover what this means for businesses, #technology,
SAFETY
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Caltech Researchers Oppose California AI Safety Bill SB 1047
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A number of my colleagues @Caltech and I have put together a letter voicing our concern regarding CA SB 1047 (AI Safety Act). Sign the letter and show your support! https://
docs.google.com/forms/d/e/1FAI
pQLScgA1GCo241Kfg-S5X2hMVAYivkqPGSnaC0VwvTy11uVJ3OLw/viewform
… We call on @caltech students, postdocs, faculty, staff, and alumni to sign but also leave -
AI Kill Switch Concerns Explored in 2040 Novel
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If you think AIs having a kill switch is a good idea, read http://
2040novel.com. -
Top AI Researchers Support California’s AI Safety Bill
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Well, Yoshua Bengio, Geoffrey Hinton and Stuart Russell actively support the bill. Hardly a outlandishly fringe AI doomsday cult I would say: https://
computing.co.uk/news/4344580/b
ig-ai-names-weigh-endorse-californias-ai-act#:~:text=FourdistinguishedprofessorsofAI,FrontierArtificialIntelligenceModelsAct
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Autonomous taxis coordination challenges in dense urban traffic
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Los taxis autónomos están pensados para interactuar con el tráfico de la ciudad, pero ¿qué pasa cuando se juntan demasiados de estos vehículos en el mismo lugar?
— Juan Merodio (@juanmerodio) 18 août 2024
Un auténtico caos digno de una película de los Monty Python 🤦 pic.twitter.com/twjQMB7YnCLos taxis autónomos están pensados para interactuar con el tráfico de la ciudad, pero ¿qué pasa cuando se juntan demasiados de estos vehículos en el mismo lugar?
Un auténtico caos digno de una película de los Monty Python -
Countdown to Robot Uprising Begins in 72 Hours
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T minus 72 hours and counting to the great robot uprising.
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LLM Output Errors: Accuracy by Chance Not Design
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See, I think that disclaimer is actually misleading. Computer systems don't make mistakes, the output errors. On top of that, if string output by an LLM (once read by a person) evaluates to something accurate, that is by chance.
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Grounding AI Evaluations in Intended Use Cases
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I think it's very important (especially now, but always) for our evaluations to be grounded in the intended use case of the technology. Without that, they aren't really meaningful.
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Synthetic Text Risks: LLM Outputs in Professional Settings
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I'm glad you found the discussion helpful! To be clear, my concerns aren't about synthetic data, but about synthetic text, i.e. setting up systems where lawyer (etc) take the output of an LLM as if it were information.