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Welcome to Startups Weekly, a nuanced take on this week's startup news and trends. To get this in your inbox, subscribe here. It's Kyle, filling in this issue for Natasha, who's taking a much needed break from the news cycle (and the spectacle that's become Twitter). While it's my first Startups Weekly column, you've likely seen me on TC here and there, covering chiefly venture, AI and enterprise-related items. It's a real pleasure to round up this week's startup news -- partially because it doesn't center around Musk shenanigans.

New users can start creating straight away. Lessons learned from deployment and improvements to our safety systems make wider availability possible. Starting today, we are removing the waitlist for the DALL·E beta so users can sign up and start using it immediately. More than 1.5M users are now actively creating over 2M images a day with DALL·E--from artists and creative directors to authors and architects--with over 100K users sharing their creations and feedback in our Discord community. Responsibly scaling a system as powerful and complex as DALL·E--while learning about all the creative ways it can be used and misused--has required an iterative deployment approach.

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?