Guess we could start calling this a 'hallucitation'? If you're curious about the article, it's here. cc @mjnblack https://
businessinsider.com/lex-fridman-po
dcast-anti-woke-elon-musk-ai
…?
@katecrawford
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AI Hallucination: Critical Commentary on Lex Fridman Podcast Episode
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ChatGPT Generates False Information About Critics
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ChatGPT strikes again. A journalist contacted me to research her profile on @lexfridman
. ChatGPT informed her that @_KarenHao and I were his top critics. It cited articles we'd written about him, gave links, and summaries. Only problem: it's all false. Here's what she sent me: -
Nuclear countries risk exchange to limit AI training runs
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Not joking. And this gem: "allied nuclear countries are willing to run some risk of nuclear exchange if that’s what it takes to reduce the risk of large AI training runs."
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ChatGPT plugins and extreme AI safety proposals dominate week
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This week began with ChatGPT plugins and ended with @TIME publishing a call to bomb data centers from the air.
Totally normal times, have a great weekend everyone https://
time.com/6266923/ai-eli
ezer-yudkowsky-open-letter-not-enough/
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AI Training Problems and GPT-4 Secrecy Concerns
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New piece on the problems with AI training – and the secrecy of GPT-4. @NewYorker on the data, labor & energy issues: "leaving aside the question of AGI…it is clear that large-language engines are creating real harms to all of humanity right now.
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ChatGPT Privacy Breach Exposes Sensitive User Data to Other Users
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This is a significant privacy breach. People are encouraged to use ChatGPT as a personal tool, so that means everything from sensitive work tasks to health questions have been exposed to other users.
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Model Safety: Mitigation Without Full Release, Transparency Needed
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There's a lot of ways to mitigate harms without having to publicly release the entire model. There are many papers on auditing, datasheets, transparency etc. With GPT3 we knew the training data. With GPT4 we don't. Without that, we're all looking at shadows in Plato's cave.
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Black Box AI Systems: The Reproducibility and Transparency Crisis
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Without knowing how these systems are built, there is no reproducibility. You can't test or develop mitigations, predict harms, or understand when and where they should not be deployed or trusted. The tools are black boxed.
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Lack of Transparency in AI Model Training Data
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There is a real problem here. Scientists and researchers like me have no way to know what Bard, GPT4, or Sydney are trained on. Companies refuse to say. This matters, because training data is part of the core foundation on which models are built. Science relies on transparency.
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Bard’s False Gmail Training Claim Sparks Public Debate
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In the 12hrs since Bard told me it was trained on Gmail data:
-Google replies (says it's not)
-Elon Musk replies (lol)
-Google adds a 'community note' that this is a Bard error and it's not trained on Gmail
-Some ace memes
What should happen next: Real talk about training data