AI-Generated Fashion Is Next Wave of DIY Design

CALA reimagines DALL-E as a clothing designer’s ultimate smart sketch pad

4 min read
Fashion platform CALA's new generative AI tool displays multiple AI-generated jackets.

Fashion platform CALA is first to use the DALL-E API.

CALA

Could AI inspire your next ugly holiday sweater?

As odd as it may sound, recent advancements in machine learning have made it possible. CALA, an “operating system for fashion” that helps designers sketch, prototype, and produce new products, is the first service to implement OpenAI’s DALL-E API. Its new generative AI tool is live and free to try.

“The use case is enabling anyone to get their idea across without a full sketch or 3D renders, by having DALL-E generate ideas via text inputs,” says Andrew Wyatt, cofounder and CEO at CALA. “It’s a continuance of us democratizing access in an industry that’s historically been very insular.”

DALL-E for e-fashion?

Founded in 2016, CALA is a fashion platform built for designers looking for an accessible way to turn ideas into tangible products. The service is available through both its website and a mobile app. Anyone can sign up and try the platform for free—so I did.

It’s broadly similar to AI art generators such as DALL-E 2 and Stable Diffusion but customized to fit CALA’s platform. Instead of entering a text prompt in a single, long string of text, designers are guided to first select a base style, such as a sweater, blouse, or tote, from a list of 25 options. Designers then use generative AI to modify the style through two textual prompts. The first describes the design based on adjectives and materials, while the other describes desired trims and features such as cuffs or zippers.

“We want to prevent the situation where someone comes in, they type in ‘brown shirt,’ and they’re, like, this sucks.”
—Andrew Wyatt, CALA

Wyatt believes this alternative user interface will help designers zone in on important features and avoid duds. “What we’re kind of doing here, is we built a UI on top of the prompt engineering. Our goal here is to get people to a meaningful result as quickly as possible.” This, Wyatt hopes, will nudge designers away from dead-end or unappealing results. “We want to prevent the situation where someone comes in, they type in ‘brown shirt,’ and they’re, like, this sucks.”

I saw the results of this tactic in my own, messy effort to make a Halloween sweater. Fashion design is, admittedly, well out of my comfort zone, but I found the tool approachable. The entire process, including the time spent waiting for results to appear, took less than a minute. CALA presents six results at a time, any of which can then be inserted into the design platform for further iteration.

Fashion platform CALA's generative AI tool displaying multiple suggestions for an ugly halloween sweater. CALA's generative AI tool offered ideas for an ugly halloween sweater. I like the one on the bottom left. CALA

CALA’s implementation shouldn’t be misunderstood as a one-click design tool. Designers still need to bring their own skills and learn how to use CALA’s platform. However, Wyatt hopes AI will significantly reduce the barrier to entry for new designers and give veteran designers a way to overcome creative roadblocks.

“We want to let people take an idea and just follow the rabbit trail, through variation after variation after variation,” says Wyatt. “We think it’s going to help people come up with way crazier and different concepts.”

Ease of use could drive DALL-E’s surge

CALA’s tool is the first live, public implementation of OpenAI’s DALL-E API by a third party. The API is not currently available to the public and doesn’t have a release date.

This isn’t OpenAI’s first rodeo. GPT-3, the company’s deep learning language model, was released as an API in 2020 and was quickly adopted by third parties. GPT-3 is now used by dozens of companies and organizations including Copysmith and MessageBird. Microsoft acquired a license to use the GPT-3 model for Microsoft Power Apps and the Azure OpenAI Service.

Luke Miller, product manager at OpenAI, says the company learned valuable lessons from GPT-3’s rollout. “Each deployment teaches us more about safety, engineering, and, ultimately, how our technology can create value in the world,” says Miller. “Since releasing the GPT-3 API, we’ve made a number of improvements to our safeguards. For example, we announced an updated moderation endpoint in August and we’re continuing to find ways we can improve.”

CALA’s experience with the DALL-E API hints ease of use will prove the key driver of the API’s adoption once it’s made available to the public. Wyatt says his company’s engineers put the API to use in just a few weeks.

“We sort of did some high-res concepting that we passed off to [OpenAI] for feedback about eight weeks ago. Then the total build and polish was less than a month,” says Wyatt. “I could see this being a meaningful integration in a multitude of different products.”

CALA's generative AI tool offering input options that will generate AI fashion suggestions. CALA’s use of the DALL-E API is different from the tool found on DALL·E’s own website.CALA

In fact, the flood of tools based on DALL-E has already begun. Shutterstock, a service which offers stock photos, images, and videos, plans to implement the DALL-E API “in the coming months.” Shutterstock paired this announcement with a framework to compensate artists on the platform when their work is used to train AI models. Microsoft is bringing DALL-E to its Azure OpenAI Service, too, though access is currently invite-only.

“We’ve always felt the future, especially within fashion, is kind of moving towards AI-powered design and automated production,” says Wyatt. “We just thought it was going to be, you know, five years from now. Over the last six months, just seeing the amount of progress...[we] think there’s just going to be tremendous innovation over the next couple of years.”

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Will AI Steal Submarines’ Stealth?

Better detection will make the oceans transparent—and perhaps doom mutually assured destruction

11 min read
A photo of a submarine in the water under a partly cloudy sky.

The Virginia-class fast attack submarine USS Virginia cruises through the Mediterranean in 2010. Back then, it could effectively disappear just by diving.

U.S. Navy

Submarines are valued primarily for their ability to hide. The assurance that submarines would likely survive the first missile strike in a nuclear war and thus be able to respond by launching missiles in a second strike is key to the strategy of deterrence known as mutually assured destruction. Any new technology that might render the oceans effectively transparent, making it trivial to spot lurking submarines, could thus undermine the peace of the world. For nearly a century, naval engineers have striven to develop ever-faster, ever-quieter submarines. But they have worked just as hard at advancing a wide array of radar, sonar, and other technologies designed to detect, target, and eliminate enemy submarines.

The balance seemed to turn with the emergence of nuclear-powered submarines in the early 1960s. In a 2015 study for the Center for Strategic and Budgetary Assessment, Bryan Clark, a naval specialist now at the Hudson Institute, noted that the ability of these boats to remain submerged for long periods of time made them “nearly impossible to find with radar and active sonar.” But even these stealthy submarines produce subtle, very-low-frequency noises that can be picked up from far away by networks of acoustic hydrophone arrays mounted to the seafloor.

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