Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! According to new Gartner research, two types of emerging artificial intelligence (AI) -- emotion and generative AI -- are both reaching the peak of the digital advertising hype cycle. This is thanks to AI's expansion into targeting, measurement, identity resolution and even generating creative content. "I think one of the key pieces is that the options for marketers have been accelerating," Mike Froggatt, senior director analyst in the Gartner marketing practice, told VentureBeat.

DALL-E 2 A new AI system called DALLE 2 can produce art and realistic visuals from an outline given in plain language it's capable of a large range of tasks, like anthropomorphizing objects and animals, convincingly merging seemingly unrelated ideas, producing text, and altering already-existing visuals. DALLE may be a transformer language model, similar to GPT-3. DALLE 2 has the power to enlarge images over the boundaries of the initial canvas, leading to expansive new compositions. While accounting for textures, reflections, and shadows, it can add and take away objects. DALLE 2 has worked out how images relate to the text that describes them.

Reading an article, watching a video on TikTok or YouTube, listening to a podcast while you're out running, you feel you have a reasonable expectation the content you're consuming is created by a human being. There is good reason to assume at least part of what you're consuming was either created by or assisted by an AI or some form of NLP (Natural Language Processor) or machine learning algorithm. Whether it's a TikTok video about a viral trend, an article in a renowned newspaper, or an image accompanying a news story on television, chances are some forms of AI generation has taken place between the idea of the story being created and the story reaching you. It could be the image was generated using DALL·E 2 or another image-generating AI, it could be the title, or lede, or social media text was generated by an NLP, it's quite likely part of or the entire text was written by an AI based on the prompts and prior writings of a human creator, and if you leave your young kids watching YouTube videos, there's a very high chance they'll encounter videos entirely conceived of and generated by an AI. Whereas Web 1.0 was defined by people being able to publish content using HTML, CSS (and eventually JavaScript), and Web 2.0 was defined by people being able to publish content through user-friendly applications that generated the HTML and CSS and JavaScript for them, the next stage of the web is being defined right now.

Note that both AIs are work-in-progress, their features and result qualities may change on almost daily basis. This round turned out to be a good example of DALL·E's better understanding of concepts, while Midjourney produces better looking pictures. Here we see the difference in the approach of the two AIs. Midjourney went for creative concept art, and it wasn't afraid to mix the house into the background mountains. For the same prompt, DALL·E gave us some very realistic real estate pictures.

In the past few years, art made by programs like Midjourney and OpenAI's DALL-E has gotten surprisingly compelling. These programs can translate a text prompt into literally (and controversially) award-winning art. As the tools get more sophisticated, those prompts have become a craft in their own right. And as with any other craft, some creators have started putting them up for sale. PromptBase is at the center of the new trade in prompts for generating specific imagery from image generators, a kind of meta-art market. Launched earlier this summer to both intrigue and criticism, the platform lets "prompt engineers" sell text descriptions that reliably produce a certain art style or subject on a specific AI platform.

Through the looking glass: Since being unveiled by OpenAI in early 2021, DALL-E has been turning heads with its ultra-realistic AI image creations. From jaw-dropping to just downright weird, the text-to-image AI system has wowed the internet. Art lovers can now explore a world outside the confines of a frame. DALL-E's new Outpainting feature can expand a picture beyond its original border. OpenAI has introduced a new tool for DALL-E, enabling it to imagine a world beyond the confines of a frame.

The internet had a collective feel-good moment with the introduction of DALL-E, an artificial intelligence-based image generator inspired by artist Salvador Dali and the lovable robot WALL-E that uses natural language to produce whatever mysterious and beautiful image your heart desires. Seeing typed-out inputs like "smiling gopher holding an ice cream cone" instantly spring to life clearly resonated with the world. Getting said smiling gopher and attributes to pop up on your screen is not a small task. DALL-E 2 uses something called a diffusion model, where it tries to encode the entire text into one description to generate an image. But once the text has a lot of more details, it's hard for a single description to capture it all.

I was young when I first listened to the song Video Killed the Radio Star by the Buggles. I thought it was a fun and catchy song, almost like a jingle, even though I had no idea what the song was all about. But that was ok, I was just a young boy listening to the radio and watching videos on VCR machines. I didn't really pay much attention to the title or the lyrics. It was much later I realised what the lyrics actually meant. In my mind and in my car We can't rewind, we've gone too far Pictures came and broke your heart Put the blame on VCR The song was part of the Age of Plastic album, which had themes of nostalgia, anxiety of the effects of modern technology. While the album was released more than 40 years ago (it was released in 1980) these themes still ring true and clear.

It's been a while since a particular piece of technology has felt as fraught, efficient, and consequential as today's text-to-image AI art-generation tools like DALL-E 2 and Midjourney. The reason for this is twofold: The tools continue to grow in popularity because they are fairly easy to use, and they do something cool by conjuring almost any image you can dream up in your head. When a text prompt comes to life as you envisioned (or better than you envisioned), it feels a bit like magic. When technologies feel like magic, adoption rates tick up rapidly. The second (and more important) reason is that the AI art tools are evolving quickly--often faster than the moral and ethical debates around the technology. In just the last month, the company Stability AI released Stable Diffusion: a free, open-source tool trained off of three massive datasets, including more than 2 billion images.

We've found we can improve language model behavior with respect to specific behavioral values by fine-tuning on a curated dataset of 100 examples of those values. We also found that this process becomes more effective as models get larger. While the technique is still nascent, we're looking for OpenAI API users who would like to try it out and are excited to find ways to use these and other techniques in production use cases. Our approach aims to give language model operators the tools to narrow this universal set of behaviors to a constrained set of values. While OpenAI provides guardrails and monitoring to ensure that model use-cases are compatible with our Charter, we view selecting the exact set of Charter-compatible values for the model as a choice that our users must face for their specific applications.