The fears of AI-fueled existential risks are based on flawed ideas.
A WSJ piece by Princeton's @random_walker and @sayashk .
RESEARCH
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AI Existential Risk Fears Based on Flawed Ideas
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Teaching Statistics Interactively with webR Technology
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Teaching statistics interactively with webR https://
bit.ly/3PcvhP2
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Refusal Classifier Training with Supervised Data
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@wzhao_nlp is the expert on this and she said she thinks they have a classifier trained for refusal, probably just with some supervised data it would still work well even if it were pretty small (<1B params)
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Bulletpapers: AI Assistant Makes Research Papers Accessible
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First prize went to Bulletpapers: an AI assistant that uses Claude to make the thousands of research papers published every month more accessible. The platform distills down jargon, creates explainer videos, and maps field progress to make breakthroughs more discoverable.
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Building Responsibility Into AI/ML Pipeline From Data to Feedback
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The Context in AI Research (CAIR) team develops novel methods in the context of the entire AI/ML pipeline, from data to end-user feedback. Learn more about how we build responsibility into each step—data collection, model design, deployment and feedback. https://
goo.gle/46c9JZ4 -
OpenAI DevDay: GPT-4 Turbo, Assistants API, Vision Capabilities
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Incredibly proud of the @OpenAI team for all the product launches on DevDay. Here’s a roundup of what’s new for developers: GPT-4 Turbo with 128K context and lower prices, the new Assistants API, Vision capabilities, DALL·E 3 API, TTS API, and more! https://
openai.com/blog/new-model
s-and-developer-products-announced-at-devday
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FAIR-Paris Seeds Paris AI Startup Ecosystem Growth
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An article about the bubbling AI startup ecosystem in Paris and the important role FAIR-Paris played in seeding it.
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Mastering LLM Output Control and Model Parameter Tuning
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That's just the tip of the iceberg! To dig deeper into controlling your LLM outputs and optimally tuning your model's parameters, expand your knowledge with this newsletter iteration: https://
louisbouchard.substack.com/p/our-ai-tutor
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Model Behavior Optimization Through Examples and Parameter Tuning
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Identify your model's desired behavior. Create user-model interaction examples to assess its performance and identify improvement areas. Enhancements can be achieved through adjusting decoding parameters.