Unlike other, more famous large language models such as OpenAI's GPT-3 and Google's LaMDA, BLOOM (which stands for BigScience Large Open-science Open-access Multilingual Language Model) is designed to be as transparent as possible, with researchers sharing details about the data it was trained on, the challenges in its development, and the way they evaluated its performance. OpenAI and Google have not shared their code or made their models available to the public, and external researchers have very little understanding of how these models are trained. BLOOM was created over the last year by over 1,000 volunteer researchers in a project called BigScience, which was coordinated by AI startup Hugging Face using funding from the French government. It officially launched on July 12. The researchers hope developing an open-access LLM that performs as well as other leading models will lead to long-lasting changes in the culture of AI development and help democratize access to cutting-edge AI technology for researchers around the world.

Last month, a group of Cosmopolitan editors, alongside digital artist Karen X. Cheng and members of artificial intelligence research lab OpenAI, created the first-ever magazine cover designed by artificial intelligence. This is the first-ever magazine cover generated using DALLE-2. Words I never thought I'd be saying? An image I generated is the cover of @cosmopolitan for their first ever AI-generated magazine cover #dalle #dalle2 pic.twitter.com/x2oqiNMRVx Recently, OpenAI's GPT-3 also published a research thesis on itself.

The results may look fascinating, but what's cooler is that this comes from OpenAI's DALL-E 2, founded by Elon Musk. So in a way, credit for this Apple Car goes to Tesla's Elon Musk?! Mmm?? Designed by Dall-E 2 based on a text prompt from designer, educator, and YouTuber John Mauriello, this Apple Car is fascinating for two prime reasons – the car's design itself, but more importantly, the underlying AI technology that ended up creating the car. The genesis for this idea came from Marques Brownlee's own efforts with DALL-E 2. In a YouTube video, Brownlee demonstrated how simply typing the words "Apple Car" resulted in a car that looked like the apple fruit. This became a starting point for Mauriello, who instead, decided to tweak the prompt a little to get more specialized results. Mauriello told the AI to design a "Minimalist Sportscar inspired by a MacBook and a Magic Mouse, built out of aluminum and glass", while also specifying it to design something in the style of Apple's former design head, Jony Ive.

You type in words – however nonsensical or disjointed – and the algorithm creates a unique image based on your search. This is Dall-E 2, a startlingly advanced, image-generating AI trained on 250 million images, named after the surrealist artist Salvador Dalí and Pixar's Wall-E. While use of Dall-E 2 is currently limited to a narrow pool of people, Dall-E mini (or Craiyon) is a free, unrelated version that is open to the public. Drawing on 15m images, Dall-E mini's algorithm offers a smorgasbord of surreal images, complete with absurd compositions and blurred human forms. Already, trends have emerged: nuclear explosions, dumpster fires, toilets and giant eyeballs abound. On a dedicated Reddit thread, people delight in the images generated by the free, low-resolution version, which range from amusing (Kim Jong-un lego) to dark (The Last Supper by Salvador Dali), hellish (synchronized swimming in lava) and deeply disturbing (Steve Jobs introducing a guillotine). Like other machine-learning networks, this AI model seems biased in its images of people – who appear, perhaps unsurprisingly, overwhelmingly white and mostly male.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. In this article, we will look at how you can combine the text generation capabilities of GPT-3 with the creative image generation part of DALL.E to produce a piece of art that would have required days if not months, with the conventional setup Without further ado, let's write a poem on unstructured data in the style of Shakespear using GPT3TextGeneration Executor and generate the illustrations for the same using DALL.E-Flow.

Around the same time that Alan Turing was shaping his theories of machine intelligence in Manchester, another future giant of the computing world, Douglas Engelbart, was developing an alternative computing paradigm over 5,000 miles away in the Bay Area. Engelbart believed that computers, with their ability to synthesize and manipulate vast quantities of information, should help humans solve problems, rather than remove them from the problem-solving loop. This ideology is now known as augmented intelligence. Engelbart's contributions to the field (both as a PhD student at UC Berkeley and at SRI in the decades after) were perhaps best exemplified through "The Mother of All Demos" in 1968, where he unveiled for the first time many of the computing features we now take for granted -- the mouse, GUIs, hyperlinks, word processing, version control, and even video conferencing -- in a single demonstration. Although it's enticing to think about artificial intelligence passing human equivalency tests like Turing's Imitation Game (or maybe something more sophisticated for today's generalist AI models), we really should be thinking about how Engelbart's ideas translate to our modern AI era. Put another way, how do we build the next Mother of All Demos?

Last year, researchers from the OpenAI consortium (an Elon Musk's and Sam Altman's non-profit organization) developed an advanced artificial intelligence model to generate colorful and artistic images based on specific keywords and texts.

In August 2020, I wrote about the stunning storytelling prowess of another LLM, GPT3 (bit.ly/3RbHfbB). The Generative Pre-trained Transformer Version 3, I wrote, was being heralded as the first step towards the holy grail of AGI (Artificial General Intelligence), where a machine has the capacity to understand or learn any intellectual task that a human being can. GPT has been trained on a massive body of text, mined for statistical regularities or parameters or connections between different nodes in its neural network. The scale is gargantuan, with 175 billion parameters; all of Wikipedia comprises just 0.6% of its training data! GPT-3 was developed by OpenAI too, and with DALL-E, it took this to another level.

I typed "gorilla in a grass skirt having a ball" in a search box on Craiyon.com and the site threw up images of a good looking gorilla, wearing a very Hawaiian grass skirt. But its version of having a ball was not to have a party, but to hold a large colourful ball in its arms. While DALL-E Mini, the original name of Craiyon, is fantastic, it has still got some way to go. It is the open source, free and slightly attenuated version of its mother neural network programme DALL-E 2, created by OpenAI. DALL-E, along with Imagen released by Google Brain to go one up OpenAI, are the latest AI LLMS (large language models), which are stretching the boundaries of what AI can do.

OpenAI, a so-called research and deployment company, is pioneering the technology with its program Dall-E 2, released in April to a closed beta audience. The program takes in huge amounts of images with corresponding descriptions in order to learn how to visually identify objects (think "cat") and the relationships between objects (think "cat driving a car"). When you enter a prompt, it calls from this data to create its best approximation of your request. The model can even identify and replicate different artists' styles (think "cat driving a car in the style of Jack Kirby").