Архив рубрики: Startups

Twelve Labs lands $12M for AI that understands the context of videos

To Jae Lee, a data scientist by training, it never made sense that video — which has become an enormous part of our lives, what with the rise of platforms like TikTok, Vimeo and YouTube — was difficult to search across due to the technical barriers posed by context understanding. Searching the titles, descriptions and tags of videos was always easy enough, requiring no more than a basic algorithm. But searching within videos for specific moments and scenes was long beyond the capabilities of tech, particularly if those moments and scenes weren’t labeled in an obvious way.
To solve this problem, Lee, alongside friends from the tech industry, built a cloud service for video search and understanding. It became Twelve Labs, which went on to raise $17 million in venture capital — $12 million of which came from a seed extension round that closed today. Radical Ventures led the extension with participation from Index Ventures, WndrCo, Spring Ventures, Weights & Biases CEO Lukas Biewald and others, Lee told TechCrunch in an email.
“The vision of Twelve Labs is to help developers build programs that can see, listen, and understand the world as we do by giving them the most powerful video understanding infrastructure,” Lee said.
A demo of the Twelve Labs platform’s capabilities. Image Credits: Twelve Labs
Twelve Labs, which is currently in closed beta, uses AI to attempt to extract “rich information” from videos such as movement and actions, objects and people, sound, text on screen, and speech to identify the relationships between them. The platform converts these various elements into mathematical representations called “vectors” and forms “temporal connections” between frames, enabling applications like video scene search.
“As a part of achieving the company’s vision to help developers create intelligent video applications, the Twelve Labs team is building ‘foundation models’ for multimodal video understanding,” Lee said. “Developers will be able to access these models through a suite of APIs, performing not only semantic search but also other tasks such as long-form video ‘chapterization,’ summary generation and video question and answering.”
Google takes a similar approach to video understanding with its MUM AI system, which the company uses to power video recommendations across Google Search and YouTube by picking out subjects in videos (e.g., “acrylic painting materials”) based on the audio, text and visual content. But while the tech might be comparable, Twelve Labs is one of the first vendors to market with it; Google has opted to keep MUM internal, declining to make it available through a public-facing API.
That being said, Google, as well as Microsoft and Amazon, offer services (i.e., Google Cloud Video AI, Azure Video Indexer and AWS Rekognition) that recognize objects, places and actions in videos and extract rich metadata at the frame level. There’s also Reminiz, a French computer vision startup that claims to be able to index any type of video and add tags to both recorded and live-streamed content. But Lee asserts that Twelve Labs is sufficiently differentiated — in part because its platform allows customers to fine-tune the AI to specific categories of video content.
Mockup of API for fine-tuning the model to work better with salad-related content. Image Credits: Twelve Labs
“What we’ve found is that narrow AI products built to detect specific problems show high accuracy in their ideal scenarios in a controlled setting, but don’t scale so well to messy real-world data,” Lee said. “They act more as a rule-based system, and therefore lack the ability to generalize when variances occur. We also see this as a limitation rooted in lack of context understanding. Understanding of context is what gives humans the unique ability to make generalizations across seemingly different situations in the real world, and this is where Twelve Labs stands alone.”
Beyond search, Lee says Twelve Labs’ technology can drive things like ad insertion and content moderation, intelligently figuring out, for example, which videos showing knives are violent versus instructional. It can also be used for media analytics and real-time feedback, he says, and to automatically generate highlight reels from videos.
A little over a year after its founding (March 2021), Twelve Labs has paying customers — Lee wouldn’t reveal how many exactly — and a multiyear contract with Oracle to train AI models using Oracle’s cloud infrastructure. Looking ahead, the startup plans to invest in building out its tech and expanding its team. (Lee declined to reveal the current size of Twelve Labs’ workforce, but LinkedIn data shows it’s roughly 18 people.)
“For most companies, despite the huge value that can be attained through large models, it really does not make sense for them to train, operate and maintain these models themselves. By leveraging a Twelve Labs platform, any organization can leverage powerful video understanding capabilities with just a few intuitive API calls,” Lee said. “The future direction of AI innovation is heading straight towards multimodal video understanding, and Twelve Labs is well positioned to push the boundaries even further in 2023.”
Twelve Labs lands $12M for AI that understands the context of videos by Kyle Wiggers originally published on TechCrunch
Twelve Labs lands $12M for AI that understands the context of videos

Mozilla acquires Active Replica to build on its metaverse vision

An automated status updater for Slack isn’t the only thing Mozilla acquired this week. On Wednesday, the company announced that it snatched up Active Replica, a Vancouver-based startup developing a “web-based metaverse.”
According to Mozilla SVP Imo Udom, Active Replica will support Mozilla’s ongoing work with Hubs, the latter’s VR chatroom service and open source project. Specifically, he sees the Active Replica team working on personalized subscription tiers, improving the onboarding experience and introducing new interaction capabilities in Hubs.
“Together, we see this as a key opportunity to bring even more innovation and creativity to Hubs than we could alone,” Udom said in a blog post. “We will benefit from their unique experience and ability to create amazing experiences that help organizations use virtual spaces to drive impact. They will benefit from our scale, our talent, and our ability to help bring their innovations to the market faster.”
Active Replica was founded in 2020 by Jacob Ervin and Valerian Denis. Ervin is a software engineer by trade, having held roles at AR/VR startups Metaio, Liminal AR and Occipital. Denis has a history in project management — he worked for VR firms including BackLight, which specializes in location-based and immersive VR experiences for brands.
With Active Replica, Ervin and Denis sought to build a platform for virtual events and meetings built on top of Mozilla’s Hubs project. Active Replica sold virtual event packages that included venue design, event planning, live entertainment and tech support.
Prior to the acquisition, Active Replica hadn’t publicly announced outside funding. Ervin and Denis have assumed new jobs at Mozilla within the past several weeks, now working as senior engineering manager and product lead, respectively.
“Mozilla has long advocated for a healthier internet and has been an inspiration to us in its dedication and contributions to the open web. By joining forces with the Mozilla Hubs team, we’re able to further expand on our mission and inspire a new generation of creators, connectors, and builders,” Ervin and Denis said in a statement. “Active Replica will continue to work with our existing customers, partners and community.”
Mozilla launched Hubs in 2018, which it pitched at the time as an “experiment” in “immersive social experiences.” Hubs provides the dev tools and infrastructure necessary to allow users to visit a portal through any browser and collaborate with others in a VR environment. Adhering to web standards, Hubs supports all the usual headsets and goggles (e.g. Oculus Rift, HTC Vive) while remaining open to those without specialized VR hardware on desktops and smartphones.
Hubs recently expanded with the launch of a $20-per-month service that did away with the previously free service, but introduced account management tools, privacy and security features. According to Mozilla, the plan is to roll out additional tiers and reintroduce a free version in the future, along with kits to create custom spaces, avatar and identity options and integrations with existing collaboration tools.
Mozilla’s forays into the metaverse have been met with mixed results. While Hubs is alive and kicking as evidenced by the Active Replica acquisition, Meta shuttered Firefox Reality, its attempt to create a full-featured browser for AR and VR headsets, in February 2022. In explaining why it decided to close up Firefox Reality, Mozilla said that while it does help develop new technologies, like WebVR and WebAR, it doesn’t always continue to host and incubate those technologies long-term.
Mozilla acquires Active Replica to build on its metaverse vision by Kyle Wiggers originally published on TechCrunch
Mozilla acquires Active Replica to build on its metaverse vision

Mark Cuban-backed streaming app Fireside acquires Stremium to bring live, interactive shows to your TV

Mark Cuban-backed streaming app Fireside, which today offers podcasters and other creators a way to host interactive, live shows with audience engagement, will soon expand to the TV’s big screen. Variety reported, and Fireside confirmed, it’s acquired the open streaming TV platform Stremium, which will allow Fireside’s shows to become available to a range of connected TV devices, including Amazon Fire TV, Roku, smart TVs and others.
Deal terms were not disclosed. Cuban retweeted Variety’s reporting but made no other public comment.
A company spokesperson confirmed the deal to TechCrunch, noting it was for a combination of IP and talent.
“Fireside has acquired all of Stremium including its full team and intellectual property,” the spokesperson said. “The company is the first interactive web3 streaming platform and the acquisition will help Fireside accelerate delivering on being the only platform that turns creators, celebrities, brands, and IP owners into the studio, networks, and streaming services of the future. Expect other major announcements coming soon on this front,” they added.
Launched just over a year ago, Fireside arrived on the heels of the pandemic-fueled demand for startups offering live entertainment as well as a growing number of startups catering to the creator economy.
Despite some early — and erroneous — comparisons between Fireside and other live audio platforms like Twitter Spaces or Clubhouse, the startup gained traction due to a differentiated feature set that also prioritizes video content. Shows on Fireside’s platform could be streamed live to its app, recorded, saved, or even simulcast to other social networks. The app additionally includes audience engagement tools and other features to aid creators with promotion, editing, measurement, distribution, monetization, and audience growth, all of which are part of Fireside’s end-to-end content production experience. More recently, the company had been exploring web3 technologies, including NFTs.
Co-founded by Cuban, early Yammer employee Mike Ihbe, and former Googler, YouTuber and Node co-founder Falon Fatemi, who sold her last company to SugarCRM, Fireside has managed to attract some high-profile creators like Jay Leno, Michael Dell, Melissa Rivers, Craig Kilborn, and screenwriter and Entourage creator Doug Ellin over the past year.
In a letter to Fireside investors published by Variety, Fatemi shared that the Stremium acquisition would help Fireside to offer a “second screen experience where the audience can use their phones to engage and interact in real-time while watching on their TVs.”
“Imagine watching a live cookalong show with your favorite chef simultaneously on your TV and your phone where you can interact and get invited to talk directly to them and even show them what you are cooking from the palm of your hand,” Fatemi explained. Plus, Stremium’s infrastructure would allow creators to upload, publish, program and distribute their live shows across both mobile and TV, she added. (Stremium confirmed to us the letter’s accuracy.)
TechCrunch this February reported Fireside was in talks to raise a $25 million Series A that valued its business at $125 million. That round has since closed, but Fireside hasn’t yet made a formal announcement about raise, investors, or its valuation. We understand this may be because Fireside is still adding some additional strategic investors to the deal, and it plans to detail the fundraise soon. Of course, the funding may have helped pave the way for Fireside to make this new acquisition.
Other investors in Fireside include the Chainsmokers, HBSE, Goodwater, Animal Capital, and NFL stars Larry Fitzgerald and Kelvin Beachum and former NBA star Baron Davis, in addition to Cuban. Ahead of its Series A, Fireside had raised around $8 million.
Stremium had been developing a service that allowed consumers to aggregate all their favorite channels using their “TV Everywhere” credentials and use a cloud DVR instead of downloading separate streaming apps. It also included a selection of free streaming channels. But the service faced an increasingly competitive landscape where there are now numerous ways to watch free streaming content, like Tubi, Pluto TV, The Roku Chanel, Freevee (formerly IMDb TV), Plex, and more. Meanwhile, cord-cutting is accelerating leaving fewer people with cable TV logins for Stremium to market its services to.
The Stremium website is now pointing visitors to Fireside and confirms the acquisition. Fireside is aiming to release its TV product sometime next year as a result of the deal.
 
 
 
Mark Cuban-backed streaming app Fireside acquires Stremium to bring live, interactive shows to your TV by Sarah Perez originally published on TechCrunch
Mark Cuban-backed streaming app Fireside acquires Stremium to bring live, interactive shows to your TV

SponsorUnited secures $35M investment to build out its database of brand sponsorships

Sponsorships are a multibillion-dollar industry. But data on sponsorships, like who’s sponsoring who, can be tough to come by because of the various forms they take — and channels on which those sponsorships take place (think not only websites and social media posts but also physical signage and even sports team jerseys). For both brands and the recipients of sponsorships, the lack of data presents a challenge. Brands don’t always know how much to charge sponsors, while sponsors aren’t consistently aware of sponsorship deals currently in place.
Frustrated by the sponsorship space’s opaqueness, Bob Lynch, the former VP of corporate partnerships for the Miami Dolphins, in 2017 founded SponsorUnited, a software-as-a-service platform that provides analytics data on the sponsorship industry. SponsorUnited claims to track over a million sponsorships across 250,000 brands, including every U.S.-based major league sports team.
“When I joined the Miami Dolphins after a decade in media, I immediately realized there was significant complexity and a lack of transparency and standardization within sponsorships, making it hard for brands and teams to optimally partner,” Lynch told TechCrunch in an email interview. “Noticing a similar trend in the NBA and arena events while with the Brooklyn Nets, I realized that if you could democratize access to previously inaccessible sponsorship deal data that the entire industry would want access to it.”
Lynch says that SponsorUnited is serving roughly 2,900 brands and properties, including Fortune 500 firms, talent and brand agencies and media companies — and investors seem pleased with the growth so far. SponsorUnited today closed a $35 million Series A funding round led by Spectrum Equity at a postmoney valuation “north of” $100 million. Paired with previous investments from Milwaukee Bucks owner Marc Lasry and San Diego Padres co-owner Ron Fowler, the infusion brings the startup’s total raised to $38.6 million.
“Up to this point, SponsorUnited had raised minimal capital, preferring to stay lean while building our data capture infrastructure and platform,” Lynch said. “But as we’ve gained critical mass beyond properties (e.g., teams and events) with brands, media, agencies and international expansion, we saw an opportunity to further accelerate growth by automating and scaling valuable data.”
Lynch describes SponsorUnited as “the Bloomberg terminal of marketing partnerships.” It’s essentially a search layer on top of a database of sports, esports, music, entertainment and media sponsorship deals, brands and properties. SponsorUnited acquires all the data directly without tapping into third-party sources, and it serves it in a way that allows companies to combine it with other data around sponsorship, including internal spend, return on investment and engagement.
A cursory Google search reveals several companies attempting to solve the same problem as SponsorUnited. There’s GlobalData, the sports-focused SportBusiness and SponsorPitch, to name a few. When asked about these rivals and others, Lynch pointed out that SponsorUnited tracks more categories of sponsorships than most and has invested heavily in its tech stack, which uses both automated and manual methods to compile sponsorship data.
“We have cultivated, refreshed, and expanded a vast repository of information — over five million data points on more than 500 asset types,” Lynch said. “We continue to invest in technology to scale and replicate the processes by which sponsorship data is tracked.”
So what’s next for SponsorUnited? Lynch says he’s tracking trends like sponsorships in the metaverse (to the extent they’re a thing), college athlete deals enabled by last year’s Supreme Court decision, and TikTok’s growing reach with younger audiences. The pandemic was and continues to be a boon for SponsorUnited, he says, as marketing organizations seek to track how deals shift from live events to digital.
In potentially good news for SponsorUnited, a 2021 survey from Caravel Marketing found that 52% of corporations planned to increase their budgets for sports team sponsorships in 2022, with only 16% projecting a decrease in spending. Lynch makes the case that these spenders will be inclined to subscribe to SponsorUnited’s services even if the economy ultimately takes a dip; when budgets tighten, it becomes imperative to discover the right partnerships and “optimize” current sponsorships, he asserts.
“The complexity and number of marketing assets and platforms being bought and sold in this industry is rising at an exponential pace,” Lynch said. “Our data provides valuable insights not only to IT but across the C-suite — chief marketing officers, chief revenue officers, chief customer officers and others.”
Stamford, Connecticut–based SponsorUnited — which isn’t revealing revenue figures — expects to have 100 employees by the end of the year, Lynch added.
SponsorUnited secures $35M investment to build out its database of brand sponsorships by Kyle Wiggers originally published on TechCrunch
SponsorUnited secures $35M investment to build out its database of brand sponsorships

Meet Unstable Diffusion, the group trying to monetize AI porn generators

When Stable Diffusion, the text-to-image AI developed by startup Stability AI, was open sourced earlier this year, it didn’t take long for the internet to wield it for porn-creating purposes. Communities across Reddit and 4chan tapped the AI system to generate realistic and anime-style images of nude characters, mostly women, as well as non-consensual fake nude imagery of celebrities.
But while Reddit quickly shut down many of the subreddits dedicated to AI porn, and communities like NewGrounds, which allows some forms of adult art, banned AI-generated artwork altogether, new forums emerged to fill the gap.
By far the largest is Unstable Diffusion, whose operators are building a business around AI systems tailored to generate high-quality porn. The server’s Patreon — started to keep the server running as well as fund general development — is currently raking in over $2,500 a month from several hundred donors.
“In just two months, our team expanded to over 13 people as well as many consultants and volunteer community moderators,” Arman Chaudhry, one of the members of the Unstable Diffusion admin team, told TechCrunch in a conversation via Discord. “We see the opportunity to make innovations in usability, user experience and expressive power to create tools that professional artists and businesses can benefit from.”
Unsurprisingly, some AI ethicists are as worried as Chaudhry is optimistic. While the use of AI to create porn isn’t new  — TechCrunch covered an AI-porn-generating app just a few months ago — Unstable Diffusion’s models are capable of generating higher-fidelity examples than most. The generated porn could have negative consequences particularly for marginalized groups, the ethicists say, including the artists and adult actors who make a living creating porn to fulfill customers’ fantasies.
A censored image from Unstable Diffusion’s Discord server. Image Credits: Unstable Diffusion
“The risks include placing even more unreasonable expectations on women’s bodies and sexual behavior, violating women’s privacy and copyrights by feeding sexual content they created to train the algorithm without consent and putting women in the porn industry out of a job,” Ravit Dotan, VP of responsible AI at Mission Control, told TechCrunch. “One aspect that I’m particularly worried about is the disparate impact AI-generated porn has on women. For example, a previous AI-based app that can ‘undress’ people works only on women.”
Humble beginnings
Unstable Diffusion got its start in August — around the same time that the Stable Diffusion model was released. Initially a subreddit, it eventually migrated to Discord, where it now has roughly 50,000 members.
“Basically, we’re here to provide support for people interested in making NSFW,” one of the Discord server admins, who goes by the name AshleyEvelyn, wrote in an announcement post from August. “Because the only community currently working on this is 4chan, we hope to provide a more reasonable community which can actually work with the wider AI community.”
Early on, Unstable Diffusion served as a place simply for sharing AI-generated porn — and methods to bypass the content filters of various image-generating apps. Soon, though, several of the server’s admins began exploring ways to build their own AI systems for porn generation on top of existing open source tools.
Stable Diffusion lent itself to their efforts. The model wasn’t built to generate porn per se, but Stability AI doesn’t explicitly prohibit developers from customizing Stable Diffusion to create porn so long as the porn doesn’t violate laws or clearly harm others. Even then, the company has adopted a laissez-faire approach to governance, placing the onus on the AI community to use Stable Diffusion responsibly.
Stability AI didn’t respond to a request for comment.
The Unstable Diffusion admins released a Discord bot to start. Powered by the vanilla Stable Diffusion, it let users generate porn by typing text prompts. But the results weren’t perfect: the nude figures the bot generated often had misplaced limbs and distorted genitalia.
Image Credits: Unstable Diffusion
The reason why was that the out-of-the-box Stable Diffusion hadn’t been exposed to enough examples of porn to “know” how to produce the desired results. Stable Diffusion, like all text-to-image AI systems, was trained on a dataset of billions of captioned images to learn the associations between written concepts and images, like how the word “bird” can refer not only to bluebirds but parakeets and bald eagles in addition to more abstract notions. While many of the images come from copyrighted sources, like Flickr and ArtStation, companies such as Stability AI argue their systems are covered by fair use — a precedent that’s soon to be tested in court.
Only a small percentage of Stable Diffusion’s dataset — about 2.9% — contains NSFW material, giving the model little to go on when it comes to explicit content. So the Unstable Diffusion admins recruited volunteers — mostly members of the Discord server — to create porn datasets for fine-tuning Stable Diffusion, the way you would give it more pictures of couches and chairs if you wanted to make a furniture generation AI.
Much of the work is ongoing, but Chaudhry tells me that some of it has already come to fruition, including a technique to “repair” distorted faces and arms in AI-generated nudes. “We are recording and addressing challenges that all AI systems run into, namely collecting a diverse dataset that is high in image quality, captioned richly with text, covering the gamut of preferences of our users,” he added.
The custom models power the aforementioned Discord bot and Unstable Diffusion’s work-in-progress, not-yet-public web app, which the admins say will eventually allow people to follow AI-generated porn from specific users.
Growing community
Today, the Unstable Diffusion server hosts AI-generated porn in a range of different art styles, sexual preferences and kinks. There’s a “men-only” channel, a softcore and “safe for work” stream, channels for hentai and furry artwork, a BDSM and “kinky things” subgroup — and even a channel reserved expressly for “nonhuman” nudes. Users in these channels can invoke the bot to generate art that fits the theme, which they can then submit to a “starboard” if they’re especially pleased with the results.
Unstable Diffusion claims to have generated over 4,375,000 images to date. On a semiregular basis, the group hosts competitions that challenge members to recreate images using the bot, the results of which are used in turn to improve Unstable Diffusion’s models.
Image Credits: Unstable Diffusion
As it grows, Unstable Diffusion aspires to be an “ethical” community for AI-generated porn — i.e. one that prohibits content like child pornography, deepfakes and excessive gore. Users of the Discord server must abide by the terms of service and submit to moderation of the images that they generate; Chaudhry claims the server employs a filter to block images containing people in its “named persons” database and has a full-time moderation team.
“We strictly allow only fictional and law-abiding generations, for both SFW and NSFW on our Discord server,” he said. “For professional tools and business applications, we will revisit and work with partners on the moderation and filtration rules that best align with their needs and commitments.”
But one imagines Unstable Diffusion’s systems will become tougher to monitor as they’re made more widely available. Chaudhry didn’t lay out plans for moderating content from the web app or Unstable Diffusion’s forthcoming subscription-based Discord bot, which third-party Discord server owners will be able to deploy within their own communities.
“We need to … think about how safety controls might be subverted when you have an API-mediated version of the system that carries controls preventing misuse,” Abhishek Gupta, the founder and principal researcher at the Montreal AI Ethics Institute, told TechCrunch via email. “Servers like Unstable Diffusion become hotbeds for accumulating a lot of problematic content in a single place, showing both the capabilities of AI systems to generate this type of content and connecting malicious users with each other to further their ‘skills’ in the generation of such content .. At the same time, they also exacerbate the burden placed on content moderation teams, who have to face trauma as they review and remove offensive content.”
A separate but related issue pertains to the artists whose artwork was used to train Unstable Diffusion’s models. As evidenced recently by the artist community’s reaction to DeviantArt’s AI image generator, DreamUp, which was trained on art uploaded to DeviantArt without creators’ knowledge, many artists take issue with AI systems that mimic their styles without giving proper credit or compensation.
Character designers like Hollie Mengert and Greg Rutkowski, whose classical painting styles and fantasy landscapes have become one of the most commonly used prompts in Stable Diffusion, have decried what they see as poor AI imitations that are nevertheless tied to their names. They’ve also expressed concerns that AI-generated art imitating their styles will crowd out their original works, harming their income as people start using AI-generated images for commercial purposes. (Unstable Diffusion grants users full ownership of — and permission to sell — the images they generate.)
Gupta raises another possibility: artists who’d never want their work associated with porn might become collateral damage as users realize certain artists’ names yield better results in Unstable Diffusion prompts — e.g., “nude women in the style of [artist name]”.
Image Credits: Unstable Diffusion
Chaudhry says that Unstable Diffusion is looking at ways to make its models “be more equitable toward the artistic community” and “give back [to] and empower artists.” But he didn’t outline specific steps, like licensing artwork or allowing artists to preclude their work from training datasets.
Artist impact
Of course, there’s a fertile market for adult artists who draw, paint and photograph suggestive works for a living. But if anyone can generate exactly the images they want to see with an AI, what will happen to human artists?
It’s not an imminent threat, necessarily. As adult art communities grapple with the implications of text-to-image generators, Simply finding a platform to publish AI-generated porn beyond the Unstable Diffusion Discord might prove to be a challenge. The furry art community FurAffinity decided to ban AI-generated art altogether, as did Newgrounds, which hosts mature art behind a content filter.
When reached for comment, one of the larger adult content hosts, OnlyFans, left open the possibility that AI art might be allowed on its platform in some form. While it has a strict policy against deepfakes, OnlyFans says that it permits content — including AI-generated content, presumably — as long as the person featured in the content is a verified OnlyFans creator.
Of course, the hosting question might be moot if the quality isn’t up to snuff.
“AI generated art to me, right now, is not very good,” said Milo Wissig, a trans painter who has experimented with how AIs depict erotic art of non-binary and trans people. “For the most part, it seems like it works best as a tool for an artist to work off of… but a lot of people can’t tell the difference and want something fast and cheap.”
For artists working in kink, it’s especially obvious to see where AI falls flat. In the case of bondage, in which tying ropes and knots is a form of art (and safety mechanism) in itself, it’s hard for the AI to replicate something so intricate.
“For kinks, it would be difficult to get an AI to make a specific kind of image that people would want,” Wissig told TechCrunch. “I’m sure it’s very difficult to get the AI to make the ropes make any sense at all.”
The source material behind these AIs can also amplify biases that already exist in traditional erotica – in other words, straight sex between white people is the norm.
“You get images that are pulled from mainstream porn,” said Wissig. “You get the whitest, most hetero stuff that the machine can think up, unless you specify not to do that.”
Image Credits: Milo Wissig
These racial biases have been extensively documented across applications of machine learning, from facial recognition to photo editing.
When it comes to porn, the consequences may not be as stark – yet there is still a special horror to watching as an AI twists and augments ordinary people until they become racialized, gendered caricatures. Even AI models like DALLE-2, which went viral when its mini version was released to the public, have been criticized for disproportionately generating art in European styles.
Last year, Wissig tried using VQGAN to generate images of “sexy queer trans people,” he wrote in an Instagram post. “I had to phrase my terms carefully just to get faces on some of them,” he added.
In the Unstable Diffusion Discord, there is little evidence to support that the AI can adequately represent genderqueer and transgender people. In a channel called “genderqueer-only,” nearly all of the generated images depict traditionally feminine women with penises.
Branching out
Unstable Diffusion isn’t strictly focusing on in-house projects. Technically a part of Equilibrium AI, a company founded by Chaudhry, the group is funding other efforts to create porn-generating AI systems including Waifu Diffusion, a model fine-tuned on anime images.
Chaudhry sees Unstable Diffusion evolving into an organization to support broader AI-powered content generation, sponsoring dev groups and providing tools and resources to help teams build their own systems. He claims that Equilibrium AI secured a spot in a startup accelerator program from an unnamed “large cloud compute provider” that comes with a “five-figure” grant in cloud hardware and compute, which Unstable Diffusion will use to expand its model training infrastructure.
In addition to the grant, Unstable Diffusion will launch a Kickstarter campaign and seek venture funding, Chaudhry says. “We plan to create our own models and fine-tune and combine them for specialized use cases which we shall spin off into new brands and products,” he added.
The group has its work cut out for it. Of all the challenges Unstable Diffusion faces, moderation is perhaps the most immediate — and consequential. Recent history is filled with examples of spectacular failures at adult content moderation. In 2020, MindGeek, Pornhub’s parent company, lost the support of major payment processors after the site site was found to be circulating child porn and sex-trafficking videos.
Will Unstable Diffusion suffer the same fate? It’s not yet clear. But with at least one senator calling on companies to implement stricter content filtering in their AI systems, the group doesn’t appear to be on the steadiest ground.
Meet Unstable Diffusion, the group trying to monetize AI porn generators by Kyle Wiggers originally published on TechCrunch
Meet Unstable Diffusion, the group trying to monetize AI porn generators