The 0.1% figure comes from OpenAI: https://twitter.com/eevee/status/1410037309848752128 https://twitter.com/eevee/status/1410037309848752128 (person disagrees w

The 0.1% figure comes from OpenAI: https://twitter.com/eevee/status/1410037309848752128 https://twitter.com/eevee/status/1410037309848752128 (person disagrees w/ me, the image is what's relevant). I talk about Lodash one-liners because it makes it pretty obvious that Copilot is not just copy-pasting code (which would be copyright infringement). It's quite unlikely (even if we consider all variables to have the same name), that Copilot is copy-pasting an exact copy of some other snippet, given that the snippet is very specific to my problems. (I'm not asking it to write a generic math function, I'm asking it to use lodash on data structures in my codebase to accomplish a very specific outcome). By quite unlikely I mean (1 / (2 ^ 32)), if we consider the one-liner to be composed of 32 different AST nodes. > Is a Markov chain "Creative"? That's essentially what you're describing here. Have you read the paper that (eventually) inspired Copilot? "Attention is All You Need". It's not a Markov chain. There's many, many, many different steps / layers. I think if you know & understand the fundamental building block that is responsible for it working (the "transformer"), then a lot of the worries around plagiarism go away. Also. What is coding if not a search problem over a very large space? I'm searching for the next N lines to write over the space of all possible lines. To guide my search I use things like my prior knowledge. In my head, this knowledge is encoded using neurons. In Copilot, the knowledge is encoded in parameters. Abstract concepts in my head are encoded in neurons. In Copilot's "head", abstract concepts are encoded using "embedding vectors" of 512 bytes (maybe more / less, not sure). Yeah, maybe I use more than 512 bytes to encode a concept, but still, I don't see a huge difference. If I'm not plagiarizing, then I can't imagine Copilot is (except for in the 0.1% of cases where it's over-fitting)