11/18/2023 0 Comments Discord best push to talk key![]() Consider looking in the sidebar of subreddits you enjoy, reading the bios of your favorite content creators, or utilizing Server Discovery to find communities that are new to you!īug reports should be shared directly with Discord. Asking for or providing invites to non-official servers is not allowed. No advertising, self-promotion, spamming, code giveaways, selling or trying to buy accounts, trading, or nitro begging. Any NSFW or objectionable content will be removed.Ĥ. Questions about other services, bots, or servers should be directed at their specific support channels. This includes topics that others have recently posted, posts and screenshots of Discord that do not inspire active discussion, content that comes from meme generators, and things of this nature. Controversial topics of discussion unrelated to Discord are not welcome on this subreddit. Absolutely no harassment, witch-hunting, sexism, homophobia, racism, or hate speech will be tolerated. r/discordapp is unofficial & community-run.Ĭome join the r/DiscordApp server! SUBREDDIT RULES Ruleĭo not make personal attacks or use offensive language in addressing others. This pixel prediction task, when performed on large image datasets, resulted in good representations that transferred well to supervised image classification tasks and generative capabilities.Īccording to Sutskever, this supports the view that compression goals like next-pixel prediction, which find regularities in the data, produce useful representations for downstream tasks and go beyond just modeling the data distribution because the pixel prediction task specifically encourages finding long-range dependencies in images to make accurate predictions.Discord is a voice, video, and text communication service to talk and hang out with your friends and communities. iGPT's pixel prediction task as a compressorĪs an example, he discussed iGPT, an OpenAI experiment from 2020, in which the team trained a transformer model to predict the next pixel in images. ![]() He argued that large neural networks trained with gradient descent approximate this optimal compressor, so we can think of unsupervised learning as approximating optimal data compression. Stronger compressors find more shared structure, with Kolmogorov complexity, the length of the shortest program that outputs some data, providing a theoretical optimal compressor, Sutskever told the audience. A good compressor that compresses two data sets together will find and exploit patterns shared between them, just as unsupervised learning finds structure in unlabeled data that helps the main task, he suggested. He suggested thinking about unsupervised learning through the lens of compression. ![]() However unsupervised learning lacks this formal understanding.Ĭheck your inbox or spam folder to confirm your subscription. Supervised learning is well understood mathematically - if the training error is small, and the model capacity is right, learning results are guaranteed. Stronger compressors find more shared structure Since Sutskever couldn't talk about OpenAI's current technical work, he revisited an old idea from 2016 about using compression to understand unsupervised learning, the underlying technique behind ChatGPT and GPT-4. While looking for topics he could talk about at the conference, he realized there was a lot of research he could not talk about, he said - referencing OpenAI's shift from being open to being more secretive about their projects. In a talk at the Simons Institute for the Theory of Computing at UC Berkeley, Ilya Sutskever, co-founder of OpenAI, presented research that tries to rigorously explain unsupervised learning through the lens of compression. The core concept is compression - good compressors can become good predictors. Ilya Sutskever, co-founder of OpenAI, explains why unsupervised learning works and how it relates to supervised learning.
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