If you're starting a reading group on Large Language Models (LLMs), what is one research paper you will want added to the reading list? Researchers: Feel free to recommend your own paper too!
@andrewyng
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Generative AI Impact: Images vs Text Which Will Dominate
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Which type of Generative AI do you think will have a bigger commercial impact: Generating images (e.g., diffusion algorithms, stable diffusion) or text (e.g., LLMs, ChatGPT)?
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Interactive Visualizations Help Build Mathematical Intuition
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3/This specialization was designed with numerous interactive visualizations to help you see how the math works. Math isn’t about memorizing formulas; it’s about sharpening your intuition. I hope you enjoy the specialization!
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Essential Math Topics for Machine Learning Success
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2/I’ve often said “don’t worry about it” when it comes to math, because math shouldn’t hold anyone back from making progress in ML. And, understanding some key topics in linear algebra, calculus, and prob & stats will help you better get learning algorithms to work.
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Math for Machine Learning and Data Science Course Launches on Coursera
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1/Math for Machine Learning and Data Science is now available on Coursera! Taught by @luis_likes_math
, this gives an intuitive understanding of the most important math concepts for AI. -
Large Language Models and Their Potential to Disrupt Search Engines
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Do large language models like ChatGPT have a shot at disrupting search engines? I share my take here. (Short answer: Yes. But the details are complicated.)
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AI community shares diverse visions for the future
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1/2 Thanks everyone that replied to this. Lots of great ideas here! The diversity of our hopes for AI is a sign of how large our field is — we collectively want AI to go lots of places.
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Andrew Ng Thanks Contributors to The Batch Newsletter
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7/Exciting times ahead, and thank you Yoshua Bengio, @AlonHalevy
, @douwekiela
, @_beenkim
, and @Reza_Zadeh for writing for The Batch! -
Generative AI and Active Learning: AI Systems Selecting Data for Labeling
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6/Reza Zadeh sees generative AI bringing progress to active learning, where a system picks its own examples to be labeled to improve the data. With generative AI, he sees a potential revolution in algorithms generating new data to request to be labeled.
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AI Explainability: From Engineering to Fundamental Scientific Principles
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5/Been Kim discusses AI explainability. AI has taken an engineering-centric approach, where researchers devise techniques via trial and error, and she urges developing fundamental scientific principles that make explanations more trustworthy and accurate.