With its human-level capacity on tasks as diverse as question-answering, translation, and arithmetic, GPT-3 is a game-changer for A.I. This week's brilliant guest, Melanie Subbiah, was a lead author of the GPT-3 paper.
GPT-3 is a natural language processing (NLP) model with 175 billion parameters that has demonstrated unprecedented and remarkable "few-shot learning" on the diverse tasks mentioned above (translation between languages, question-answering, performing three-digit arithmetic) as well as on many more (discussed in the episode).
Melanie's paper sent shockwaves through the mainstream media and was recognized with an Outstanding Paper Award from NeurIPS (the most prestigious machine learning conference) in 2020.
Melanie:
• Developed GPT-3 while she worked as an A.I. engineer at OpenAI, one of the world’s leading A.I. research outfits.
• Previously worked as an A.I. engineer at Apple.
• Is now pursuing a PhD at Columbia University in the City of New York specializing in NLP.
• Holds a bachelor's in computer science from Williams College.
In this episode, Melanie details:
• What GPT-3 is.
• Why applications of GPT-3 have transformed not only the field of data science but also the broader world.
• The strengths and weaknesses of GPT-3, and how these weaknesses might be addressed with future research.
• Whether transformer-based deep learning models spell doom for creative writers.
• How to address the climate change and bias issues that cloud discussions of large natural language models.
• The machine learning tools she’s most excited about.
This episode does have technical elements that will appeal primarily to practicing data scientists, but Melanie and I put an effort into explaining concepts and providing context wherever we could so hopefully much of this fun, laugh-filled episode will be engaging and informative to anyone who’s keen to learn about the start of the art in natural language processing and A.I.
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