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DALL-E 2, according to OpenAI, will "democratize" the arts.

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Concerns about the disruptive potential of automation and artificial intelligence have long focused on the possibility of computers to replace people in repetitive jobs like flipping hamburgers, accounting, and secretarial work. Any profession requiring creative intelligence—doctor, software engineer, author—seemed secure. However, such stories have been turned on their heads in recent months. A slew of artificial intelligence programs, collectively referred to as “generative AI,” have demonstrated extraordinary skill in using the English language, writing code at a competitive level, producing stunning images from straightforward instructions, and perhaps even assisting in the discovery of new drugs. These applications imply that Silicon Valley still has the ability to rewire the world in stealthy and startling ways, despite the fact that many tech hype bubbles have burst or deflated in the past year.

An understandable response to generative AI is worry; if not even human imagination is secure from computers, then the human intellect appears to be in danger of extinction. Another is to highlight the numerous biases and flaws in these algorithms. However, these new models also arouse a sense of sci-fi awe; perhaps computers won’t replace human creativity so much as enhance or modify it. After all, calculators, computers, and even internet search engines have greatly improved our brains.

The goal of this tool, according to Mark Chen, lead researcher on DALL-E 2, an OpenAI model that turns written prompts into visual art, is to “really democratize image generation for a bunch of people who wouldn’t necessarily classify themselves as artists,” he said yesterday at The Atlantic’s first-ever Progress Summit. “Job displacement and loss are constant concerns with AI, and we don’t want to kind of dismiss these possibilities either. However, we do believe that it is a tool that fosters creativity, and so far, we have observed that artists utilise it in a more creative manner than common consumers. There are numerous similar technology; photographers haven’t been supplanted by smartphone cameras.

Ross Andersen, the deputy editor of The Atlantic, joined Chen for a lengthy discussion about the future of human creativity and artificial intelligence. They talked about how DALL-E 2 operates, the criticism OpenAI has gotten from creatives, and the effects text-to-image systems could have on the creation of a more all-encompassing artificial intelligence.

For the sake of clarity, their interaction has been edited and condensed.

Robert Andersen: Since natural language translation, this new AI technique has caught my attention the most. When some of these tools first appeared on the market, I began recreating pictures of childhood fantasies I had. I was able to share with my children things that had before only existed in my head. Since you invented this technology, I was wondering if you could explain to us a little bit how it works.

Ken Chen: There is a protracted training period. Imagine a very young youngster who you are presenting with a large number of flash cards, each of which has an image and a caption. Perhaps after viewing hundreds of thousands or even millions of them, people begin to associate the word “panda” with a fuzzy animal or something in black and white. As a result, it creates these associations, kind of creates a language of sorts to basically represent language and visuals, and then has the ability to transform it into images.

Andersen: How many pictures were used to train DALL-E 2?

Chen: Many hundreds of millions of pictures. And this is a blend of material that is both publicly available and material that we have licenced from partners.

Andersen: How were those pictures identified?

Chen: Many of the online photographs of things from the natural world contain captions. Many of the partners we collaborate with also offer data that includes annotations that describe the contents of the images.

Andersen: You can create scenes with extremely complicated prompts. How does the thing build an entire scene, and how does it understand how to place objects in the viewing field?

Chen: These algorithms can mix items in new ways that they haven’t seen previously in the training set, even when trained on separate objects like trees and dogs. It can therefore combine all of these elements if you ask for a dog dressed in a suit to appear behind a tree or whatever. And in my opinion, the ability to generalize beyond the data you used to train AI is part of its power.

Andersen: Prompt writing is an art form as well. As a writer, I spend a lot of time considering how to construct word combinations that will evoke vivid images in the reader’s imagination. And in this instance, the reader’s imagination has access to the entire digital library of humanity when using this technology. How has your perspective on prompting changed between DALL-E 1 and DALL-E 2?

Chen: Even up to DALL-E 2, many people used brief, one-sentence descriptions to stimulate the creation of images. However, consumers are now including very particular details, including the textures they desire. And it turns out that the model is able to detect all of these factors and make extremely minute modifications. Personalization is the key; by using all of these words, you are essentially personalizing the result to your preferences.

Andersen: A lot of modern artists have expressed displeasure with this technology. Simon Stlenhag, a contemporary Swedish artist, has a style that I admire, so as I was playing about with creating my dreams, I added his name to the end of it. And in fact, it simply changed the entire scene into this lovely Simon Stlenhag-inspired picture. And I did experience some remorse over it, almost wishing it had been a Spotify model with royalties. Another way to look at it, which is really unfortunate, is that the entire history of art has been about emulating the masters’ techniques and reinterpreting earlier artistic movements. I am aware that this is generating a lot of backlash for you people. Do you see where that is going?

Chen: We don’t want to go around harassing artists or anything of the sort. We have worked closely with the artists throughout the entire release process to understand what they hope to get out of it and how we can make it safer. We want to make sure that we keep working with artists and soliciting their opinions. Many potential solutions are being discussed in this area, such as maybe making it impossible to create in a particular manner. But there’s also this aspect of inspiration that you gain, similar to how individuals pick up skills by imitating greats.

Andersen: Neil Postman once said, “Think of technology development as ecological, as modifying the systems in which people function, rather than thinking of it as additive or subtractive.” I really like that. And in this instance, artists are those people. What changes are you noticing as a result of your conversations with artists? In the wake of these technologies, how will the creative environment look in five or ten years?

Chen: The beautiful thing about DALL-E is that we’ve discovered that artists use these tools more effectively than the average person. Some of the best artwork that has come from these platforms was essentially created by artists. We created this application in order to truly democratize picture creation for a large group of individuals who might not necessarily describe themselves as artists. We don’t want to largely dismiss these possibilities because job loss and displacement are constant concerns with AI. However, we do believe that it is a tool that fosters creativity, and so far, we have observed that artists utilize it in a more creative manner than common consumers. There are many such technologies; smartphone cameras haven’t supplanted professional photographers.

Andersen: Despite how revolutionary DALL-E is, it’s not the only performance at OpenAI. Text-to-text prompts have helped ChatGPT really take the world by storm in recent weeks. Could you briefly discuss how the development of those two items has prompted you to consider the differences between textual and visual creativity? And how can you combine these tools?

Chen: With DALL-E, you may quickly choose the sample you like from a sizable grid of available options. Since you may not always have that luxury with text, the standard for text is rather higher. These kind of models might certainly be utilized in conjunction in the future, in my opinion. You might have a dialog-based interface for producing photos.

Andersen: I’m curious to know if we’ll ever develop an artificial general intelligence, or an AI that can function across a wide range of fields rather than being highly specialised in just one, like a chess-playing AI. Is this a step in the right direction, in your opinion, toward that? Or do you feel like this is a significant advancement?

Chen: OpenAI has always stood out in part because we seek to create artificial general intelligence. Too many of these specific fields are not necessarily of interest to us. DALL-E is largely responsible for this since we needed a means to see how our models see the outside environment. Are they perceiving things the same way we would describe them? To observe what the model is picturing and ensure that it is tuned to how we experience the environment, we created this text interface.

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