Variational inference is a fast and scalable alternative to Markov chain Monte Carlo and has been widely applied to posterior inference tasks in statistics and machine learning. A traditional approach for implementing mean-field variational inference (MFVI) is coordinate ascent variational inference (CAVI), which relies crucially on parametric assumptions on complete conditionals. In this paper, we introduce a novel particle-based algorithm for mean-field variational inference, which we term PArticle VI (PAVI). Notably, our algorithm does not rely on parametric assumptions on complete conditionals, and it applies to the nonparametric setting. We provide non-asymptotic finite-particle convergence guarantee for our algorithm. To our knowledge, this is the first end-to-end guarantee for particle-based MFVI.

Last month, software developer Kevin Lacker tested GPT-3, the latest version of an artificial-intelligence language system developed by San Francisco-based software company OpenAI LP. The system isn't yet public, but it set off a firestorm in tech circles after OpenAI gave select researchers and developers access so they could provide feedback. They observed its uncanny and unprecedented ability to answer trivia questions, generate long passages of coherent text, design simple software applications and offer plausible recipes for breakfast burritos. Trained on roughly 300 billion words from across the internet, GPT-3 predicts what is most likely to follow a prompt from a human. But ask it to reason, and it struggles.

Many companies including: OpenAI, Google/DeepMind, Microsoft, and countless others, have started the race for "truly" intelligent AI. For the majority of this article I'll be referencing OpenAI's GPT series of machine learning models. However, the question: "what does it mean to be truly intelligent?" OpenAI have modeled this problem as a text transformer model. This model takes sequences of pieces of words (two character pairs) and tries to predict the next set of word parts.

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.

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.