프리세일즈 도큐멘토 나라장터 입찰 제안서 다운로드 제공, 시간을 줄여주는 세일즈 문서, 홈페이지 구축 제안서 판매

This is an activation Atlas. It gives us a glimpse into the high-dimensional embedding spaces modern AI models use to organize and make sense of the world. The first model to really see the world like this, AlexNet, was published in 2012 in an 8-page paper that shocked the computer vision community by showing that an old AI idea would work unbelievably well when scaled. The paper's second author, Ilya HK, would go on to co-found OpenAI where he and the OpenAI team would massively scale up this idea again to create ChatGPT. This video is sponsored by KiwiCo; more on them later. If you look under the hood of ChatGPT, you won't find any obvious signs of intelligence; instead, you'll find layer after layer of compute blocks called. Transformers, this is what the T and GPT stand for. Each Transformer performs a set of fixed matrix operations on an input matrix of data and typically returns an output matrix of the same size. To figure out what it's going to say next, Chat GPT breaks apart what you ask, gets into words and word fragments, maps each of these to a vector, and stacks all of these vectors together into a matrix. This matrix is then passed into the first Transformer block, which returns a new matrix of the same size. This operation is then repeated again and again, 96 times in Chat GPT 3.5 and reportedly 120 times in Chat GPT 4. Now here's the absurd part, with a few caveats. The next word or word fragment that Chat GPT says back to you is literally just the..

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