DALL-E can generate images from a few key words--with predictably racist and sexist results. To the casual observer, DALL-E is Silicon Valley's latest miraculous AI creation--a machine learning system that allows anyone to generate almost any image just by typing a short description into a text box. From just a few descriptive words, the system can conjure up an image of cats playing chess, or a teapot that looks like an avocado. It's an impressive trick using the latest advances in natural language processing, or NLP, which involves teaching algorithmic systems how to parse and respond to human language--often with creepily realistic results. Named after both surrealist painter Salvador Dalí and the lovable Pixar robot WALL-E, DALL-E was created by research lab OpenAI, which is well-known in the field for creating the groundbreaking NLP systems GPT-2 and GPT-3.

A YouTuber, Lucas Builds The Future, used AI to bring his childhood imaginary friend -- a microwave -- to life using artificial intelligence (AI). Then, instead of a heartfelt reunion, things took a thing for worse when the kitchen appliance tried to kill its creator. In a Twitter thread, Lucas Rizzotto said that his family's kitchen microwave, which he named Magnetron, was his imaginary friend. Magnetron, unlike other microwaves, had a lengthy backstory in which he fought in World War I. And when OpenAI released a new natural language model, Rizzotto naturally wondered whether he could resurrect his old friend.

Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones. But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do. The most visible advances have been in what's called "natural language processing" (NLP), the branch of AI focused on how computers can process language like humans do. It has been used to write an article for The Guardian, and AI-authored blog posts have gone viral -- feats that weren't possible a few years ago. AI even excels at cognitive tasks like programming where it is able to generate programs for simple video games from human instructions.

How to implement Generative AI models. Recently, we have seen a shift in AI that wasn't very obvious. Generative Artificial Intelligence (GAI) - the part of AI that can generate all kinds of data - started to yield acceptable results, getting better and better. As GAI models get better, questions arise e.g. Or, how to utilize data generation for your own projects?

This article is part of our coverage of the latest in AI research. Artificial intelligence research lab OpenAI made headlines again, this time with DALL-E 2, a machine learning model that can generate stunning images from text descriptions. DALL-E 2 builds on the success of its predecessor DALL-E and improves the quality and resolution of the output images thanks to advanced deep learning techniques. The announcement of DALL-E 2 was accompanied by a social media campaign by OpenAI's engineers and its CEO, Sam Altman, who shared wonderful photos created by the generative machine learning model on Twitter. DALL-E 2 shows how far the AI research community has come toward harnessing the power of deep learning and addressing some of its limits.

The new upcoming era of technology is ready to bring us a technology which not only can observe the underlying pattern of old contents it can also produce new contents similar to its base but different in the front end. "Generative AI" is that tech that allows computers to understand the underlying pattern associated with an input and then generate comparable material based on that pattern. Generative AI is an Artificial Intelligence algorithm that enables the creation of new believable material from existing content such as text, audio recordings, or photographs. To put it another way, it enables computers to abstract the underlying pattern associated with the input and then uses that to generate similar material. GANs are generative models in which two neural networks, a generator, and a discriminator, are engaged against each other.

"The imagination is the tether that keeps poetry in flight. We conjure up words and scenes to fill a void, to fight back against the mundane. Since words are fallible like people, we make them immortal." Paraphrasing the Poet: "Imagination is the tether that keeps the AI art on the fly." It is not skill, technology, or talent that makes art.

At first glance, the AI art generation methods, offered by DALL-E 2 seem like a dream. Especially if you don't have access to the waiting list for DAL-E2, which is long. But what if there was a different program that could take you to the same solution with a similar success rate? DALL-E 2 is a higher-resolution and lower-latency variant of the original system, generating images based on user-written descriptions. It also has additional features, such as altering an existing image. Unfortunately, the waiting list for access is long.

GPT-3 powered applications are flourishing in the global tech market from the house of OpenAI in recent years. It is also known as Generative Pre-trained Transformer 3 as an autoregressive language mode for leveraging deep learning for human-based texts. There are thousands of companies that have started using this AI model of OpenAI to complete the workload efficiently. OpenAI has trained this AI model on a massive corpus of text with over 175 billion parameters to become the largest language model. Multiple GPT-3 powered applications are available on the internet for more efficient services.

This post is meant to be a personal story. What can we create in our future? I don't know, but it's never too late to start. The Future of AI Art is no longer a guessing game. I leave it to you to interpret the events of this story and draw your own conclusions about AI art.