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").

The service has a new responsible AI system that filters out harmful content and helps detect abuse. Additionally, Azure OpenAI Service now offers access to more models, including GPT-3, Codex and embeddings models. Codex can generate code and translate plain language to code, while embeddings make semantic search and other tasks easier. The service also offers new capabilities for customers to fine tune models for more tailored results. Azure OpenAI Service is enabling customers across industries from health care to financial services to manufacturing to quickly perform an array of tasks.

You may be wondering if such a bold headline is true. GPT-3 came out in 2020 and established a new road the whole AI industry has been following in intention and attention since. Tech companies have repeatedly built better, larger models, one after another. But although they've put millions into the task, none of them has fundamentally changed the leading paradigm or the game's rules GPT-3 laid out two years ago. Gopher, Chinchilla, and PaLM (arguably the current podium of large language models) are significantly better than GPT-3 but they are, in essence, more of the same thing.

One of the longstanding worries about the technologies of artificial intelligence is that they will enable surveillance, autocracy and maybe even totalitarianism. These views no longer seem so convincing. The world's autocratic states are not exactly turning these technologies to their advantage, and meanwhile artificial intelligence is taking some individualistic turns. Consider the program called DALL-E, which allows the user to generate digital sketches and images, derived from natural language instructions, using the power of artificial intelligence. The quality and diversity of the images is truly impressive.

The models I use as a replacement are unrestrained. We are at a turning point in the history of art. AI-generated art gives artists a new form of expression, allowing them to explore and create new ideas. Machine Learning Art introduces them in a few articles.