This is a question that many people are wondering. How should you start your adventure with AI art? Well, it all starts by knowing what this term means. It's an artistic process that involves relatively advanced technology, such as computer-generated images and sounds, to produce artwork automatically. Artists use this technology to create unique works of art without direct human involvement.

Another month, another flood of weird, wonderful and cute images generated by an artificial intelligence. In April, OpenAI showed off its new picture-making neural network, DALL-E 2, which could produce remarkable high-res images of almost anything it was asked to. Now, just a few weeks later, Google Brain has revealed its own image-making AI, called Imagen. And it performs even better than DALL-E 2: it scores higher on a standard measure for rating the quality of computer-generated images and the pictures it produced were preferred by a group of human judges. But like OpenAI did with DALL-E, Google is going all in on cuteness.

It's no secret that large models, such as DALL-E 2 and Imagen, trained on vast numbers of documents and images taken from the web, absorb the worst aspects of that data as well as the best. OpenAI and Google explicitly acknowledge this. Scroll down the Imagen website--past the dragon fruit wearing a karate belt and the small cactus wearing a hat and sunglasses--to the section on societal impact and you get this: "While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized [the] LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes. Imagen relies on text encoders trained on uncurated web-scale data, and thus inherits the social biases and limitations of large language models. As such, there is a risk that Imagen has encoded harmful stereotypes and representations, which guides our decision to not release Imagen for public use without further safeguards in place."

Google has shown off a new artificial intelligence system that can create images based on text input. Its Imagen diffusion model, created by the Brain Team at Google Research, offers "an unprecedented degree of photorealism and a deep level of language understanding." This isn't the first time we've seen AI models like this. OpenAI's DALL·E (and its successor) performed similar witchcraft, turning text into visuals. Google's version, however, tries to create more realistic images.

Feed these programs any text you like and they'll generate remarkably accurate pictures that match that description. They can match a range of styles, from oil paintings to CGI renders and even photographs, and -- though it sounds cliched -- in many ways the only limit is your imagination. To date, the leader in the field has been DALL-E, a program created by commercial AI lab OpenAI (and updated just back in April). Yesterday, though, Google announced its own take on the genre, Imagen, and it just unseated DALL-E in the quality of its output. The best way to understand the amazing capability of these models is to simply look over some of the images they can generate. There's some generated by Imagen above, and even more below (you can see more examples at Google's dedicated landing page).

Google has shown off an artificial intelligence system that can create images based on text input. The idea is that users can enter any descriptive text and the AI will turn that into an image. The company says the Imagen diffusion model, created by the Brain Team at Google Research, offers "an unprecedented degree of photorealism and a deep level of language understanding." This isn't the first time we've seen AI models like this. OpenAI's DALL-E (and its successor) generated headlines as well as images because of how adeptly it can turn text into visuals.

Generative artificial intelligence (AI), autonomic systems and privacy-enhancing computation are three technology trends gaining traction in banking and investment services in 2022, according to Gartner, Inc. These trends will continue to grow over the next two to three years, contributing to growth and transformation of financial services organizations, it said in a statement. "While growth is the top priority, the need to manage risk, optimize costs and increase efficiency also requires new technology innovations," said Moutusi Sau, VP Analyst at Gartner. "Generative AI enables bank CIOs to offer technology solutions to the business in pursuit of revenue growth, while autonomic systems and privacy-enhancing computation are long-term solutions that provide new options for business transformation in financial services." IT spending by banking and investment services firms is forecast to grow 6.1 percent in 2022 to $623 billion worldwide.

I explain Artificial Intelligence terms and news to non-experts. If you thought Dall-e 2 had great results, wait until you see what this new model from Google Brain can do. Dalle-e is amazing but often lacks realism, and this is what the team attacked with this new model called Imagen. They share a lot of results on their project page as well as a benchmark, which they introduced for comparing text-to-image models, where they clearly outperform Dall-E 2, and previous image generation approaches. Read the full article: https://www.louisbouchard.ai/google-brain-imagen/

The AI world is still figuring out how to deal with the amazing show of prowess that is DALL-E 2's ability to draw/paint/imagine just about anything… but OpenAI isn't the only one working on something like that. Google Research has rushed to publicize a similar model it's been working on -- which it claims is even better. Imagen (get it?) is a text-to-image diffusion-based generator built on large transformer language models that… okay, let's slow down and unpack that real quick. Text-to-image models take text inputs like "a dog on a bike" and produce a corresponding image, something that has been done for years but recently has seen huge jumps in quality and accessibility. Part of that is using diffusion techniques, which basically start with a pure noise image and slowly refine it bit by bit until the model thinks it can't make it look any more like a dog on a bike than it already does.

"You can't go to a physics conference and say: I've got a great theory. It accounts for everything and is so simple it can be captured in two words: "Anything goes."" Every now and then engineers make an advance, and scientists and lay people begin to ponder the question of whether that advance might yield important insight into the human mind. Descartes wondered whether the mind might work on hydraulic principles; throughout the second half of the 20th century, many wondered whether the digital computer would offer a natural metaphor for the mind. The latest hypothesis to attract notice, both within the scientific community, and in the world at large, is the notion that a technology that is popular today, known as large language models, such as OpenAI's GPT-3, might offer important insight into the mechanics of the human mind. Enthusiasm for such models has grown rapidly; OpenAI's Chief Science Officer Ilya Sutskever recently suggested that such systems could conceivably be "slightly conscious".