/OpenAI

Scaling ChatGPT: Five Real-World Engineering Challenges

- Gergely Orosz Evan Morikawa tl;dr: An interview with Evan Morikawa, who led the OpenAI Applied Engineering team as ChatGPT launched and scaled. Evan reveals the five engineering challenges along with lessons learned. Challenges are: (1) KV Cache & GPU RAM. (2) Optimizing batch size. (3) Finding the right metrics to measure. (4) Finding GPUs wherever they are. (5) Inability to autoscale.  

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Donald Knuth On ChatGPT

- Donald Knuth tl;dr: “Since one of today's popular recreations is to play with ChatGPT, I decided to try my own little experiment, as part of a correspondence with Stephen Wolfram.The results were sufficiently interesting that I passed them onto a few friends the next day, and I've also been mentioning them in conversation when the topic comes up. So I was asked to post the story online.”

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Using GPT-3 To Explain How Code Works

- Simon Willison tl;dr: "One of my favourite uses for the GPT-3 AI language model is generating explanations of how code works. It’s shockingly effective at this: its training set clearly include a vast amount of source code. Simon shows a few recent examples." 

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DALL·E 2

tl;dr: A new AI system that can create realistic images and art from a description in natural language.

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OpenAI Codex

tl;dr: "Our AI system that translates natural language to code... Codex can now interpret simple commands in natural language and execute them on the user’s behalf - making it possible to build a natural language interface to existing applications"

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DALL·E: Creating Images from Text

tl;dr: "We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language."

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