In January, 2021, the OpenAI consortium -- founded by Elon Musk and financially backed by Microsoft -- unveiled its most ambitious project to date, the DALL-E machine learning system. This ingenious multimodal AI was capable of generating images (albeit, rather cartoonish ones) based on the attributes described by a user -- think "a cat made of sushi" or "an x-ray of a Capybara sitting in a forest." On Wednesday, the consortium unveiled DALL-E's next iteration which boasts higher resolution and lower latency than the original. The first DALL-E (a portmanteau of "Dali," as in the artist, and "WALL-E," as in the animated Disney character) could generate images as well as combine multiple images into a collage, provide varying angles of perspective, and even infer elements of an image -- such as shadowing effects -- from the written description. "Unlike a 3D rendering engine, whose inputs must be specified unambiguously and in complete detail, DALL·E is often able to'fill in the blanks' when the caption implies that the image must contain a certain detail that is not explicitly stated," the OpenAI team wrote in 2021.

Image-generation models like DALL-E have come a long way in just a few years. In 2020, AI2 showed off a neural network that could generate images from prompts such as "Three people play video games on a couch." The results were distorted and blurry, but just about recognizable. Last year, Chinese tech giant Baidu improved on the original DALL-E's image quality with a model called ERNIE-ViLG. DALL-E 2 takes the approach even further.

How and why I got 75Gb of free foreign exchange "Tick" data. Understanding the Bias-Variance tradeoff at three different levels: simple, intermediate and advanced. Overlap is key to a good point cloud alignment.

One of the most exciting aspects of the Metaverse is its potential for scalability. Neil Stephenson's Snow Crash describes a vast world full of amusement parks, houses, entertainment complexes, and worlds within themselves all connected by a virtual street tens of thousands of miles long. Unfortunately, Stephenson's novel is still considered science fiction. The question remains, who will build this enormous world? How will it be populated with content?

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Images used in my articles are Properties of the Respective Organisations and are used here solely for Reference, Illustrative and Educational Purposes Only. It's been almost a year since 11 June 2020, when OpenAI announced GPT-3, the Neural Network that has shaken the AI world. OpewnAI is an organization developed & established by Elon Musk, CEO of Tesla, SpaceX, The Boring Company, & Neuralink. "OpenAI's mission is to ensure that AI benefits all of humanity" -- OpenAI "Our long-term goal is to achieve scalable solutions that will align far more capable AI systems of the future -- a critical part of our mission" -- OpenAI Now that we know the Introduction, let's dive into GPT-3. GPT-3 is a Neural Network Machine Learning Model, which is trained to generate any type of human language text.

This article is part of a VB special issue. Read the full series here: The metaverse - How close are we? Some companies believe that the metaverse -- a yet-to-be-realized, internet-like series of connected worlds -- has enormous potential in the enterprise. For example, it could be used to improve work productivity by allowing employees to train or collaborate in workplace-like virtual environments. Or it could host home and office tours, a boon for a real estate market contending with pandemic travel restrictions.

This week, OpenAI announced an embeddings endpoint (paper) for GPT-3 that allows users to derive dense text embeddings for a given input text at allegedly state-of-the-art performance on several relevant tasks. In this post, I will be reviewing how good these new GPT-3 embeddings really are. Are they really a new state of the art? Dense text embeddings are useful for many tasks, including clustering, topic modeling, deduplication, paraphrase mining and semantic search. As part of my research, I've worked on dense text embeddings since 2019 and released my research as part of the sentence-transformers framework, which provides open & free state-of-the-art text embedding models for many use-cases.

Earlier this month, researchers at the Allen Institute for AI -- a nonprofit founded by late Microsoft cofounder Paul Allen -- released an interactive demo of a system they describe as part of a "new generation" of AI applications that can analyze, search across, and respond to questions about videos "at scale." Called Merlot Reserve, the researchers had the system "watch" 20 million YouTube videos to learn the relationships between images, sounds, and subtitles, allowing it to, for example, answer questions such as "What meal does the person in the video want to eat?" or "Has the boy in this video swam in the ocean before?" Systems that can process and relate information from audio, visuals and text have been around for years. These technologies continue to improve in their ability to understand the world more like humans. San Francisco research lab OpenAI's DALL-E, which was released in 2021, can generate images of objects -- real or imagined -- from simple text descriptions like "an armchair in the shape of an avocado."

One of the most recent advancements in natural language processing (NLP) is the emergence of large language models (LLMs) that are built using vast datasets with enormous amounts of data. There are several LLMs that are available, such as Google's BERT and OpenAI's GPT-2 and GPT-3. With these models, it is possible to generate everything from simple essays to actual financial models with these models. AI startups including OpenAI, Hugging Face, Cohere, AI21 Labs are pushing the boundaries of LLM by training models with billions of parameters. OpenAI Codex is the model based on GPT-3 that powers GitHub Copilot - a tool from GitHub to generate code within mainstream development environments including VS Code, Neovim, JetBrains, and even in the cloud with GitHub Codespaces.