When the concept artist and illustrator RJ Palmer first witnessed the fine-tuned photorealism of compositions produced by the AI image generator Dall-E 2, his feeling was one of unease. The tool, released by the AI research company OpenAI, showed a marked improvement on 2021's Dall-E, and was quickly followed by rivals such as Stable Diffusion and Midjourney. Type in any surreal prompt, from Kermit the frog in the style of Edvard Munch, to Gollum from The Lord of the Rings feasting on a slice of watermelon, and these tools will return a startlingly accurate depiction moments later. Cosmopolitan trumpeted the world's first AI-generated magazine cover, and technology investors fell over themselves to wave in the new era of "generative AI". The image-generation capabilities have already spread to video, with the release of Google's Imagen Video and Meta's Make-A-Video.

Also of note is the evolution of MLops platforms, which are making it possible to set up complete pipelines for gathering feedback data, versioning datasets and models, and fine-tuning models for specific applications.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. In the wake of last week's release of the DALL-E API, a crowd of startups is sure to follow, racing to build generative AI for the enterprise. I thought of this as I watched coverage of 50,000 runners converging on New York City for its annual marathon yesterday. It reminded me of OpenAI's announcement last week about its Converge program, which will provide 10 early-stage startups with $1 million each and early access to its systems. "I can't think of a more interesting time to start a startup in recent memory," said OpenAI Sam Altman in a tweet about the program. That announcement came just a day before the company released the hotly anticipated DALL-E API in public beta, which means developers can now integrate DALL-E directly into their apps and products -- including many that will likely be used for a host of enterprise use cases.

Who owns these fabulous works of art generated by systems and models like OpenAI's DALL-E or Stability.ai's What about blog content created by tools like GoCharlie or Copy.ai? To engage Ruth's services as an attorney, visit their website at GeekLawFirm.com. This interview does not constitute legal advice or create a client-attorney relationship with anyone. The information contained in this interview is presented on an "as is" basis with no guarantee of completeness, accuracy, usefulness, timeliness, or of the results obtained from the use of this information and without warranty of any kind, express or implied, including, but not limited to warranties of performance, merchantability, or fitness for a particular purpose. While we have taken every reasonable precaution to insure that the content is accurate, errors can occur. In all cases you should consult with a qualified professional familiar with your particular situation for advice concerning specific matters. What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video. Please note the following warning disclosure and disclaimer, this interview does not constitute legal advice or create a client attorney relationship with anyone.

Several text-to-image AI tools are finding the limelight lately – from OpenAI's Dall-e to open-source Stable Diffusion. It is safe to say that text-to-image AI tools are here to stay, thanks to widespread use cases. Now, to ingrate a text-to-image AI tool in a mainstream application, the famous design app Canva has integrated the open-source Stable Diffusion to offer the AI tool to its more than 100 million users. Using free and Pro versions of Canva, users can generate up to 100 images daily, with due safety filters in place. Canva's text-to-image AI presents users with multiple styles, including photo, drawing, painting, pattern and concept art.

In another example of the use of generative AI approaches in the legal sector, Toronto-based Rally has launched a GPT-3 based add-in for Word called Spellbook, which is designed to help lawyers with legal drafting. Spellbook's use of OpenAI's GPT-3 large language model, an AI trained on 45 terabytes of data from books and the internet, is further'tuned' on legal datasets for'optimal contracting performance', they explained. Artificial Lawyer was understandably curious to know some more, especially after recently highlighting the work by PatentPal, which uses a non-GPT-3 generative AI model. This site asked Scott Stevenson, CEO of Rally – which provides its core legal management platform to 110 law firms – about how they are leveraging this technology. When did this start and what is Rally?

NVIDIA's model works better than the rest when it comes to customised prompts, due to the expert denoising system which trains denoisers to maintain fidelity to the textual prompt even in the later stage of the generation process. But, this is not the first time NVIDIA stepped into the waters of text-to-image modelling. Before coming up with eDiffi, NVIDIA used deep learning models to create versions of the GauGAN model. The second version of the model, released in November 2021, was trained on 10 million high-quality landscape images. The application demo allowed users to produce images based on any text input they provide. The GauGAN model is based on generative adversarial networks (GAN), unlike eDiffi, which uses diffusion modelling for generating images. So why did NVIDIA take a departure from using GAN for their text-to-image feature?

Generative AI is in a Cambrian explosion of capability. This is just the beginning, Glimpse AI CEO Alex Cardinell told me in a recent TechFirst podcast. The ultimate thing for AI to create is more of itself. God creating Adam, in an image Dall-E created based on a prompt from the writer. Dall-E has major ... [ ] trouble with fingers still ... "The most exciting part of it all ... is when maybe AI is also at the point where it can start writing the code that will make its own AI even better," Cardinell says.

The world of generative AI is exploding thanks to significant competition in the field of cross-modal generation. Below is a free demo of the largest model. Create any content from music to video. The visible examples in my video were created within seconds.

For years, the only way to create a blockbuster film featuring a Hollywood star and dazzling special effects was at a major studio. The Hollywood giants were the ones that could afford to pay celebrities millions of dollars and license sophisticated software to produce elaborate, special effects-laden films. That's all about to change, and the public is getting a preview thanks to artificial intelligence (AI) tools like OpenAI's DALL-E and Midjourney. Both tools use images scraped from the internet and select datasets like LAION to train their AI models to reconstruct similar yet wholly original imagery using text prompts. The AI images, which vary from photographic realism to mimicking the styles of famous artists, can be generated in as little as 20 to 30 seconds, often producing results that would take a human hours to produce.