Renee DiResta, Stanford University; Abhiram Reddy, Georgetown University, and Josh A. Goldstein, Georgetown University

Suppose you’ve spent time on Facebook over the past six months. In that case, you may have noticed photorealistic images that are too good to be true: children holding paintings that look like the work of professional artists, or majestic log cabin interiors that are the stuff of Airbnb dreams.

Others, such as renderings of Jesus made out of crustaceans, are just bizarre.

Like the AI image of the pope in a puffer jacket that went viral in May 2023, these AI-generated images are increasingly prevalent – and popular – on social media platforms. Even as many of them border on the surreal, they’re often used to bait engagement from ordinary users.

Our team of researchers from the Stanford Internet Observatory and Georgetown University’s Center for Security and Emerging Technology investigated over 100 Facebook pages that posted high volumes of AI-generated content. We published the results in March 2024 as a preprint paper, meaning the findings have not yet gone through peer review.

We explored patterns of images, unearthed evidence of coordination between some of the pages, and tried to discern the likely goals of the posters.

Page operators seemed to be posting pictures of AI-generated babies, kitchens or birthday cakes for a range of reasons.

There were content creators innocuously looking to grow their followings with synthetic content; scammers using pages stolen from small businesses to advertise products that don’t seem to exist; and spammers sharing AI-generated images of animals while referring users to websites filled with advertisements, which allow the owners to collect ad revenue without creating high-quality content.

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Our findings suggest that these AI-generated images draw in users – and Facebook’s recommendation algorithm may be organically promoting these posts.

Generative AI meets scams and spam

Internet spammers and scammers are nothing new.

For more than two decades, they’ve used unsolicited bulk email to promote pyramid schemes. They’ve targeted senior citizens while posing as Medicare representatives or computer technicians.

On social media, profiteers have used clickbait articles to drive users to ad-laden websites. Recall the 2016 U.S. presidential election, when Macedonian teenagers shared sensational political memes on Facebook and collected advertising revenue after users visited the URLs they posted. The teens didn’t care who won the election. They just wanted to make a buck.

In the early 2010s, spammers captured people’s attention with ads promising that anyone could lose belly fat or learn a new language with “one weird trick.”

AI-generated content has become another “weird trick.”

It’s visually appealing and cheap to produce, allowing scammers and spammers to generate high volumes of engaging posts. Some of the pages we observed uploaded dozens of unique images per day. In doing so, they followed Meta’s own advice for page creators. Frequent posting, the company suggests, helps creators get the kind of algorithmic pickup that leads their content to appear in the “Feed,” formerly known as the “News Feed.”

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Much of the content is still, in a sense, clickbait: Shrimp Jesus makes people pause to gawk and inspires shares purely because it is so bizarre.

Many users react by liking the post or leaving a comment. This signals to the algorithmic curators that perhaps the content should be pushed into the feeds of even more people.

Some of the more established spammers we observed, likely recognizing this, improved their engagement by pivoting from posting URLs to posting AI-generated images. They would then comment on the post of the AI-generated images with the URLs of the ad-laden content farms they wanted users to click.

But more ordinary creators capitalized on the engagement of AI-generated images, too, without obviously violating platform policies.

Rate ‘my’ work!

When we looked up the posts’ captions on CrowdTangle – a social media monitoring platform owned by Meta and set to sunset in August – we found that they were “copypasta” captions, which means that they were repeated across posts.

Some of the copypasta captions baited interaction by directly asking users to, for instance, rate a “painting” by a first-time artist – even when the image was generated by AI – or to wish an elderly person a happy birthday. Facebook users often replied to AI-generated images with comments of encouragement and congratulations

Algorithms push AI-generated content

Our investigation noticeably altered our own Facebook feeds: Within days of visiting the pages – and without commenting on, liking or following any of the material – Facebook’s algorithm recommended reams of other AI-generated content.

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Interestingly, the fact that we had viewed clusters of, for example, AI-generated miniature cow pages didn’t lead to a short-term increase in recommendations for pages focused on actual miniature cows, normal-sized cows or other farm animals. Rather, the algorithm recommended pages on a range of topics and themes, but with one thing in common: They contained AI-generated images.

In 2022, the technology website Verge detailed an internal Facebook memo about proposed changes to the company’s algorithm.

The algorithm, according to the memo, would become a “discovery-engine,” allowing users to come into contact with posts from individuals and pages they didn’t explicitly seek out, akin to TikTok’s “For You” page.

We analyzed Facebook’s own “Widely Viewed Content Reports,” which lists the most popular content, domains, links, pages and posts on the platform per quarter.

It showed that the proportion of content that users saw from pages and people they don’t follow steadily increased between 2021 and 2023. Changes to the algorithm have allowed more room for AI-generated content to be organically recommended without prior engagement – perhaps explaining our experiences and those of other users.

‘This post was brought to you by AI’

Since Meta currently does not flag AI-generated content by default, we sometimes observed users warning others about scams or spam AI content with infographics.

Meta, however, seems to be aware of potential issues if AI-generated content blends into the information environment without notice. The company has released several announcements about how it plans to deal with AI-generated content.

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In May 2024, Facebook will begin applying a “Made with AI” label to content it can reliably detect as synthetic.

But the devil is in the details. How accurate will the detection models be? What AI-generated content will slip through? What content will be inappropriately flagged? And what will the public make of such labels?

While our work focused on Facebook spam and scams, there are broader implications.

Reporters have written about AI-generated videos targeting kids on YouTube and influencers on TikTok who use generative AI to turn a profit.

Social media platforms will have to reckon with how to treat AI-generated content; it’s certainly possible that user engagement will wane if online worlds become filled with artificially generated posts, images and videos.

Shrimp Jesus may be an obvious fake. But the challenge of assessing what’s real is only heating up.

Renee DiResta, Research Manager of the Stanford Internet Observatory, Stanford University; Abhiram Reddy, Research Assistant at the Center for Security and Emerging Technology, Georgetown University, and Josh A. Goldstein, Research Fellow at the Center for Security and Emerging Technology, Georgetown University

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This article is republished from The Conversation under a Creative Commons license. Read the original article.

The science section of our news blog STM Daily News provides readers with captivating and up-to-date information on the latest scientific discoveries, breakthroughs, and innovations across various fields. We offer engaging and accessible content, ensuring that readers with different levels of scientific knowledge can stay informed. Whether it’s exploring advancements in medicine, astronomy, technology, or environmental sciences, our science section strives to shed light on the intriguing world of scientific exploration and its profound impact on our daily lives. From thought-provoking articles to informative interviews with experts in the field, STM Daily News Science offers a harmonious blend of factual reporting, analysis, and exploration, making it a go-to source for science enthusiasts and curious minds alike. https://stmdailynews.com/category/science/

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Rizwan Virk, Arizona State University

ChatGPT

In May 2024, OpenAI CEO Sam Altman sparked a firestorm by referencing the 2013 movie “Her” to highlight the novelty of the latest iteration of ChatGPT.

Within days, actor Scarlett Johansson, who played the voice of Samantha, the AI girlfriend of the protagonist in the movie “Her,” accused the company of improperly using her voice after she had spurned their offer to make her the voice of ChatGPT’s new virtual assistant. Johansson ended up suing OpenAI and has been invited to testify before Congress.

This tiff highlights a broader interchange between Hollywood and Silicon Valley that’s called the “sci-fi feedback loop.” The subject of my doctoral research, the sci-fi feedback loop explores how science fiction and technological innovation feed off each other. This dynamic is bidirectional and can sometimes play out over many decades, resulting in an ongoing loop.

Fiction sparks dreams of Moon travel

One of the most famous examples of this loop is Moon travel.

Jules Verne’s 1865 novel “From the Earth to the Moon” and the fiction of H.G. Wells inspired one of the first films to visualize such a journey, 1902’s “A Trip to the Moon.”

The fiction of Verne and Wells also influenced future rocket scientists such as Robert Goddard, Hermann Oberth and Oberth’s better-known protégé, Wernher von Braun. The innovations of these men – including the V-2 rocket built by von Braun during World War II – inspired works of science fiction, such as the 1950 film “Destination Moon,” which included a rocket that looked just like the V-2.

Films like “Destination Moon” would then go on to bolster public support for lavish government spending on the space program. https://www.youtube.com/embed/xLVChRVfZ74?wmode=transparent&start=0 The 1902 silent short ‘A Trip to the Moon.’

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Creative symbiosis

The sci-fi feedback loop generally follows the same cycle.

First, the technological climate of a given era will shape that period’s science fiction. For example, the personal computing revolution of the 1970s and 1980s directly inspired the works of cyberpunk writers Neal Stephenson and William Gibson.

Then the sci-fi that emerges will go on to inspire real-world technological innovation. In his 1992 classic “Snow Crash,” Stephenson coined the term “metaverse” to describe a 3-D, video game-like world accessed through virtual reality goggles.

Silicon Valley entrepreneurs and innovators have been trying to build a version of this metaverse ever since. The virtual world of the video game Second Life, released in 2003, took a stab at this: Players lived in virtual homes, went to virtual dance clubs and virtual concerts with virtual girlfriends and boyfriends, and were even paid virtual dollars for showing up at virtual jobs.

This technology seeded yet more fiction; in my research, I discovered that sci-fi novelist Ernest Cline had spent a lot of time playing Second Life, and it inspired the metaverse of his bestselling novel “Ready Player One.”

The cycle continued: Employees of Oculus VR – now known as Meta Reality Labs – were given copies of “Ready Player One” to read as they developed the company’s virtual reality headsets. When Facebook changed its name to Meta in 2021, it did so in the hopes of being at the forefront of building the metaverse, though the company’s grand ambitions have tempered somewhat.

Another sci-fi franchise that has its fingerprints all over this loop is “Star Trek,” which first aired in 1966, right in the middle of the space race.

Steve Perlman, the inventor of Apple’s QuickTime media format and player, said he was inspired by an episode of “Star Trek: The Next Generation,” in which Lt. Commander Data, an android, sifts through multiple streams of audio and video files. And Rob Haitani, the designer of the Palm Pilot’s operating system, has said that the bridge on the Enterprise influenced its interface.

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In my research, I also discovered that the show’s Holodeck – a room that could simulate any environment – influenced both the name and the development of Microsoft’s HoloLens augmented reality glasses.

From ALICE to ‘Her’

Which brings us back to OpenAI and “Her.”

In the movie, the protagonist, Theodore, played by Joaquin Phoenix, acquires an AI assistant, “Samantha,” voiced by Johansson. He begins to develop feelings for Samantha – so much so that he starts to consider her his girlfriend.

ChatGPT-4o, the latest version of the generative AI software, seems to be able to cultivate a similar relationship between user and machine. Not only can ChatGPT-4o speak to you and “understand” you, but it can also do so sympathetically, as a romantic partner would.

There’s little doubt that the depiction of AI in “Her” influenced OpenAI’s developers. In addition to Altman’s tweet, the company’s promotional videos for ChatGPT-4o feature a chatbot speaking with a job candidate before his interview, propping him up and encouraging him – as, well, an AI girlfriend would. The AI featured in the clips, Ars Technica observed, was “disarmingly lifelike,” and willing “to laugh at your jokes and your dumb hat.”

But you might be surprised to learn that a previous generation of chatbots inspired Spike Jonze, the director and screenwriter of “Her,” to write the screenplay in the first place. Nearly a decade before the film’s release, Jonze had interacted with a version of the ALICE chatbot, which was one of the first chatbots to have a defined personality – in ALICE’s case, that of a young woman.

The ALICE chatbot won the Loebner Prize three times, which was awarded annually until 2019 to the AI software that came closest to passing the Turing Test, long seen as a threshold for determining whether artificial intelligence has become indistinguishable from human intelligence.

The sci-fi feedback loop has no expiration date. AI’s ability to form relationships with humans is a theme that continues to be explored in fiction and real life.

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A few years after “Her,” “Blade Runner 2049” featured a virtual girlfriend, Joi, with a holographic body. Well before the latest drama with OpenAI, companies had started developing and pitching virtual girlfriends, a process that will no doubt continue. As science fiction writer and social media critic Cory Doctorow wrote in 2017, “Science fiction does something better than predict the future: It influences it.”

Rizwan Virk, Faculty Associate, PhD Candidate in Human and Social Dimensions of Science and Technology, Arizona State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The science section of our news blog STM Daily News provides readers with captivating and up-to-date information on the latest scientific discoveries, breakthroughs, and innovations across various fields. We offer engaging and accessible content, ensuring that readers with different levels of scientific knowledge can stay informed. Whether it’s exploring advancements in medicine, astronomy, technology, or environmental sciences, our science section strives to shed light on the intriguing world of scientific exploration and its profound impact on our daily lives. From thought-provoking articles to informative interviews with experts in the field, STM Daily News Science offers a harmonious blend of factual reporting, analysis, and exploration, making it a go-to source for science enthusiasts and curious minds alike. https://stmdailynews.com/category/science/

Mahshid Ahmadi, University of Tennessee

Many human activities release pollutants into the air, water and soil. These harmful chemicals threaten the health of both people and the ecosystem. According to the World Health Organization, air pollution causes an estimated 4.2 million deaths annually.

Scientists are looking into solutions, and one potential avenue is a class of materials called photocatalysts. When triggered by light, these materials undergo chemical reactions that initial studies have shown can break down common toxic pollutants.

I am a materials science and engineering researcher at the University of Tennessee. With the help of robots and artificial intelligence, my colleagues and I are making and testing new photocatalysts with the goal of mitigating air pollution.

Breaking down pollutants

The photocatalysts work by generating charged carriers in the presence of light. These charged carriers are tiny particles that can move around and cause chemical reactions. When they come into contact with water and oxygen in the environment, they produce substances called reactive oxygen species. These highly active reactive oxygen species can bond to parts of the pollutants and then either decompose the pollutants or turn them into harmless – or even useful – products.

But some materials used in the photocatalytic process have limitations. For example, they can’t start the reaction unless the light has enough energy – infrared rays with lower energy light, or visible light, won’t trigger the reaction.

Another problem is that the charged particles involved in the reaction can recombine too quickly, which means they join back together before finishing the job. In these cases, the pollutants either do not decompose completely or the process takes a long time to accomplish.

Additionally, the surface of these photocatalysts can sometimes change during or after the photocatalytic reaction, which affects how they work and how efficient they are.

To overcome these limitations, scientists on my team are trying to develop new photocatalytic materials that work efficiently to break down pollutants. We also focus on making sure these materials are nontoxic so that our pollution-cleaning materials aren’t causing further pollution.

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Teeny tiny crystals

Scientists on my team use automated experimentation and artificial intelligence to figure out which photocatalytic materials could be the best candidates to quickly break down pollutants. We’re making and testing materials called hybrid perovskites, which are tiny crystals – they’re about a 10th the thickness of a strand of hair.

These nanocrystals are made of a blend of organic (carbon-based) and inorganic (non-carbon-based) components.

They have a few unique qualities, like their excellent light-absorbing properties, which come from how they’re structured at the atomic level. They’re tiny, but mighty. Optically, they’re amazing too – they interact with light in fascinating ways to generate a large number of tiny charge carriers and trigger photocatalytic reactions.

These materials efficiently transport electrical charges, which allows them to transport light energy and drive the chemical reactions. They’re also used to make solar panels more efficient and in LED lights, which create the vibrant displays you see on TV screens.

There are thousands of potential types of hybrid nanocrystals. So, my team wanted to figure out how to make and test as many as we can quickly, to see which are the best candidates for cleaning up toxic pollutants.

Bringing in robots

Instead of making and testing samples by hand – which takes weeks or months – we’re using smart robots, which can produce and test at least 100 different materials within an hour. These small liquid-handling robots can precisely move, mix and transfer tiny amounts of liquid from one place to another. They’re controlled by a computer that guides their acceleration and accuracy.

We also use machine learning to guide this process. Machine learning algorithms can analyze test data quickly and then learn from that data for the next set of experiments executed by the robots. These machine learning algorithms can quickly identify patterns and insights in collected data that would normally take much longer for a human eye to catch.

Our approach aims to simplify and better understand complex photocatalytic systems, helping to create new strategies and materials. By using automated experimentation guided by machine learning, we can now make these systems easier to analyze and interpret, overcoming challenges that were difficult with traditional methods.

Mahshid Ahmadi, Assistant Professor of Materials Science and Engineering, University of Tennessee

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This article is republished from The Conversation under a Creative Commons license. Read the original article.

The science section of our news blog STM Daily News provides readers with captivating and up-to-date information on the latest scientific discoveries, breakthroughs, and innovations across various fields. We offer engaging and accessible content, ensuring that readers with different levels of scientific knowledge can stay informed. Whether it’s exploring advancements in medicine, astronomy, technology, or environmental sciences, our science section strives to shed light on the intriguing world of scientific exploration and its profound impact on our daily lives. From thought-provoking articles to informative interviews with experts in the field, STM Daily News Science offers a harmonious blend of factual reporting, analysis, and exploration, making it a go-to source for science enthusiasts and curious minds alike. https://stmdailynews.com/category/science/