GPT-3 - Language Models are Few-Shot Learners | Paper Explained

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In this video, I cover the famous GPT-3 model.

I first give you some context about the stuff that happened since the paper was first published in May 2020 (hype, anti-hype, limitations, and cool apps), and then I dive deep into explaining the paper.

You'll learn about:
✔️ Useful resources on GPT-3
✔️ Main takeaways from the paper

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✅ "anti-hype" blog: https://lacker.io/ai/2020/07/06/giving-gpt-3-a-turing-test.html
✅ Gwern's blog: https://www.gwern.net/GPT-3
✅ My transformer implementation: https://github.com/gordicaleksa/pytorch-original-transformer
✅ Cool "GPT game": https://play.aidungeon.io/
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⌚️ Timetable:

00:00 GPT (anti)hype, Gwern, prompt programming
04:30 Abstract of the paper
06:50 Architecture, data, compute
12:15 Zero-shot, one-shot, and few-shot learning
18:45 Power-law chart (more compute please)
20:35 Results (machine translation)
23:05 NLI (reasoning is hard)
24:40 Arithmetic
26:25 Word unscrambling
28:40 SAT analogies (how smart are humans?)
30:45 Fake news generation
32:05 Data contamination
35:05 Limitations of the model
37:35 Bias, fairness (broader impact)
44:30 Final thoughts, are we going towards an AGI?

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#gpt3 #fewshotlearning #deeplearning

Видео GPT-3 - Language Models are Few-Shot Learners | Paper Explained канала The AI Epiphany