Testing stock prediction models for security flaws 📈 pic.twitter.com/XO9AQCglr1
— IBM Data, AI & Automation (@IBMData) 16 novembre 2022
Testing stock prediction models for security flaws
SAFETY
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Testing Stock Prediction Models for Security Flaws
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Self-examination in AI science models for error detection
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what would be cool with science models like Galactica would be to add self examination of the inputs like the coming Codex model, where the model can inspect it’s theorem proofs or essays and spot errors
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Concerns about GPT alignment with human values and safety
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"Finally, we are very concerned that this GPT could be unaligned with humans. This would be bad. We want this to be a nice GPT that deeply loves all humans and is always considerate and helpful. Thanks"
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Mastodon’s Safety-First Design Built by Underrepresented Communities
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Mastodon was built by sensible, thoughtful people, many from underrepresented communities who had very clear needs around safety and moderation – they've put a lot of careful work into that, and the community there continues to invest in it I'm optimistic!
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OpenAI Emphasizes Safety as Core Pride and Track Record
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we do take safety extremely seriously, and it's what i'm most proud of openai for. i am sure we will make some very wrong decisions along the way, but i think our track record as a whole will really hold up.
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Tesla Autopilot Safety Data Transparency Concerns
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https://
nytimes.com/2022/06/08/tec
hnology/tesla-autopilot-safety-data.html
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Tesla FSD Still Falls Short of Required Reliability Standards
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Thorough story on FSD in a neighborhood where Tesla's CEO has sent engineers to solve previous failures noted by the car owner. Appears to be at about one 9 to my eye. Needs to be at about six 9s to be trusted. Each 9 is ten times harder than the previous. https://
nytimes.com/interactive/20
22/11/14/technology/tesla-self-driving-flaws.html
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ICLR 2023 Ethics Committee Responds on AI Ethics
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The voice of reason, finally… The #iclr2023 ethics committee responded.
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CRINGE Loss: Learning What Language Not to Model
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The CRINGE Loss: Learning what language not to model Adolphs et al.: https://
arxiv.org/abs/2211.05826 #ArtificialIntelligence #DeepLearning #MachineLearning