An artificial intelligence (AI) technology made by a firm co-founded by billionaire Elon Musk has won praise for its ability to generate coherent stories, novels and even computer code but it remains blind to racism or sexism. GPT-3, as Californian company OpenAI's latest AI language model is known, is capable of completing a dialogue between two people, continuing a series of questions and answers or finishing a Shakespeare-style poem. Start a sentence or text and it completes it for you, basing its response on the gigantic amount of information it has been fed. This could come in useful for customer service, lawyers needing to sum up a legal precedent or for authors in need of inspiration. While the technology is not new and has not yet learnt to reason like a human mind, OpenAI's latest offering has won praise for the way its text resembles human writing.

OpenAI has revealed the projected pricing plans for its API, which lets people use the company's vaunted AI tools on "virtually any English language task." But you're gonna need money to burn if you wanna try it out. The API gives users access to GPT-3, OpenAI's headline-grabbing text generator. The company says developers can apply it "to any language task -- semantic search, summarization, sentiment analysis, content generation, translation, and more -- with only a few examples or by specifying your task in English." The product was initially launched in a free, two-month private beta on July 11.

An artificial intelligence (AI) technology made by a firm co-founded by billionaire Elon Musk has won praise for its ability to generate coherent stories, novels and even computer code but it remains blind to racism or sexism. GPT-3, as Californian company OpenAI's latest AI language model is known, is capable of completing a dialogue between two people, continuing a series of questions and answers or finishing a Shakespeare-style poem. Start a sentence or text and it completes it for you, basing its response on the gigantic amount of information it has been fed. This could come in useful for customer service, lawyers needing to sum up a legal precedent or for authors in need of inspiration. While the technology is not new and has not yet learnt to reason like a human mind, OpenAI's latest offering has won praise for the way its text resembles human writing.

What do NeuraLink and OpenAI have in common? They are both on a quest to synthesize intelligence using a wet lab vs. dry lab approach. Neuralink [wet lab] is trying to interact with the brain and learn straight from the source through implantable brain-machine interfaces. OpenAI [dry lab] is looking to emulate brain functions in the real world. The OpenAI researchers want to democratize access to safe artificial intelligence.

In late April 2020, a company named OpenAI uploaded dozens of new tracks to SoundCloud, all of them matter-of-factly titled like "Hip-hop, in the style of Nas" or "Pop, in the style of Katy Perry." You'd be forgiven for initially thinking the songs were average YouTube covers. A few seconds spent listening to the gargled production, bizarre lyrics, and eerie vocals would definitely change your mind. The songs were all made using an artificial intelligence software called Jukebox, designed by OpenAI, a billion dollar research organization leading the field in AI research. Jukebox isn't your standard Elvis impersonator: After being trained on 1.2 million songs and other data about genres and artists, the neural net has learned to produce original music in the uncannily recognizable style of famous artists like Elton John and Rihanna.

In the past few years, deep generative models, such as generative adversarial networks \autocite{GAN}, variational autoencoders \autocite{vaepaper}, and their variants, have seen wide adoption for the task of modelling complex data distributions. In spite of the outstanding sample quality achieved by those early methods, they model the target distributions \emph{implicitly}, in the sense that the probability density functions induced by them are not explicitly accessible. This fact renders those methods unfit for tasks that require, for example, scoring new instances of data with the learned distributions. Normalizing flows have overcome this limitation by leveraging the change-of-variables formula for probability density functions, and by using transformations designed to have tractable and cheaply computable Jacobians. Although flexible, this framework lacked (until recently \autocites{semisuplearning_nflows, RAD}) a way to introduce discrete structure (such as the one found in mixtures) in the models it allows to construct, in an unsupervised scenario. The present work overcomes this by using normalizing flows as components in a mixture model and devising an end-to-end training procedure for such a model. This procedure is based on variational inference, and uses a variational posterior parameterized by a neural network. As will become clear, this model naturally lends itself to (multimodal) density estimation, semi-supervised learning, and clustering. The proposed model is illustrated on two synthetic datasets, as well as on a real-world dataset. Keywords: Deep generative models, normalizing flows, variational inference, probabilistic modelling, mixture models.

One doesn't have to dig too deep into legal organizations to find people who are skeptical about artificial intelligence. AI is getting tremendous attention and significant venture capital, but AI tools frequently underwhelm in the trenches. Here are a few reasons why that is and why I believe GPT-3, a beta version of which was recently released by the OpenAI Foundation, might be a game changer in legal and other knowledge-focused organizations. GPT-3 is getting a lot of oxygen lately because of its size, scope and capabilities. However, it should be recognized that a significant amount of that attention is due to its association with Elon Musk.

Since OpenAI released GPT-3, you have probably come across examples of impressive and/or problematic content that people have used the model to generate. Here we summarise the outputs of GPT-3 as seen through the eyes of the Twitter-sphere. We're releasing an API for accessing new AI models developed by OpenAI. See how companies are using the API today, or join our waitlist: https://t.co/SvTgaFuTzN First, let me summarize the API documentation.

The only way you can really "debug" humans is conversationally though? If some employee screwed up the assembly line and messed up production, you might call him into the office and ask him why he made that mistake, you could get the same "oh, I'm sorry I got distracted and wasn't paying attention.", There could be some bad synaptic weights that caused some of his neurons to misfire and cause him to make the mistake. This is just explained by "getting distracted". I don't know if you've played around with GPT-3, but if you push it on something it's gotten wrong, it usually gets very defensive and will bullshit it's way out of it, just as well as a human would.

Word has been making its way out from the technology community: The world changed this summer with the rollout of an artificial intelligence system known as GPT-3. Its ability to interact in English and generate coherent writing have been startling hardened experts, who speak of "GPT-3 shock." Where typical AI systems are trained for specific tasks--classifying images, playing Go--GPT-3 can handle tasks it was never specifically trained for. Research released by its maker, San Francisco-based OpenAI, has found that GPT-3 can...