I think Alexa at home is different – there you are opting into having a microphone in your own personal space – not the same thing as openly wearing a microphone out in the world visibly in front of strangers
@simonw
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Clear Communication Key to Effective AI Model Prompting
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I wonder if part of that is that being "good st prompting" is mainly about being good at clear and explicit communication People with prompts of a less high quality may find they see bigger differences between models
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Google Glass Backlash: Surveillance Concerns and Tech Industry Reactions
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Google Glass wearers were being attacked in San Francisco ten years ago – though that could partly have been a reaction against the tech industry generally, hard to know if it was directly due to surveillance concerns
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Social Acceptance Challenges for Recording Devices Privacy Concerns
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I don't see how these products can get over the social factor that nobody likes the idea that someone else is recording them without their explicit permission Whether or not the devices actually do that doesn't matter – just wearing an inactive microphone or camera is risky
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Misleading public sector employment percentage change graph
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Yeah that original "percentage change" graph feels very deceptive to me, makes it look at first glance like the public sector employs the majority of the working population
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Daily LLM Usage: Claude 3 Opus and GPT-4 Tools
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In a daily basis at the moment it's mainly Claude 3 Opus via my LLM CLI tool and GPT-4 with Code Interpreter via the ChatGPT website and iOS app
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AI-Generated Data in Pre-training: Quality Impact Analysis
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I think synthetic data for fine-tuning is a seoarate issue from whether accidental AI-generated text in pre-training data has an impact on model quality
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Detecting AI-Generated Images and Model Collapse Risks
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I can believe that – detecting images generated by their own image generator feels like a more tractable problem to me than detecting text from their LLMs Also model collapse for images feels more likely to me than for text, though I'm not sure I could explain that intuition
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Haiku vs GPT-3.5 comparison gains traction over flagship models
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I'm finding the comparison between Haiku and GPT 3.5 a whole lot more interesting than the comparison between Opus and GPT-4
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AI Generated Training Data Quality vs Human Data Impact
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No idea! A lot of people seem to believe that accidental AI generated training data is uniquely harmful to models compared to low quality human data – I'm trying to figure out what the latest thinking on that is