The ROGD paper was not funded by anti-trans zealots. But it arrived at exactly the time people with bad intentions were looking for science to buoy their opinions.
The results were in line with what one might expect given those sources: 76.5% of parents surveyed “believed their child was incorrect in their belief of being transgender.” More than 85% said their child had increased their internet use and/or had trans friends before identifying as trans. The youths themselves had no say in the study, and there’s no telling if they had simply kept their parents in the dark for months or years before coming out. (Littman acknowledges that “parent-child conflict may also explain some of the findings.”)
Arjee Restar, now an assistant professor of epidemiology at the University of Washington, didn’t mince words in her 2020 methodological critique of the paper. Restar noted that Littman chose to describe the “social and peer contagion” hypothesis in the consent document she shared with parents, opening the door for biases in who chose to respond to the survey and how they did so. She also highlighted that Littman asked parents to offer “diagnoses” of their child’s gender dysphoria, which they were unqualified to do without professional training. It’s even possible that Littman’s data could contain multiple responses from the same parent, Restar wrote. Littman told MIT Technology Review that “targeted recruitment [to studies] is a really common practice.” She also called attention to the corrected ROGD paper, which notes that a pro-gender-affirming parents’ Facebook group with 8,000 members posted the study’s recruitment information on its page—although Littman’s study was not designed to be able to discern whether any of them responded.
But politics is blind to nuances in methodology. And the paper was quickly seized by those who were already pushing back against increasing acceptance of trans people. In 2014, a few years before Littman published her ROGD paper, Time magazine had put Laverne Cox, the trans actress from Orange Is the New Black, on its cover and declared a “transgender tipping point.” By 2016, bills across the country that aimed to bar trans people from bathrooms that fit their gender identity failed, and one that succeeded, in North Carolina, cost its Republican governor, Pat McCrory, his job.
Yet by 2018 a renewed backlash was well underway—one that zeroed in on trans youth. The debate about trans youth competing in sports went national, as did a heavily publicized Texas custody battle between a mother who supported her trans child and a father who didn’t. Groups working to further marginalize trans people, like the Alliance Defending Freedom and the Family Research Council, began “printing off bills and introducing them to state legislators,” says Gillian Branstetter, a communications strategist at the American Civil Liberties Union.
The ROGD paper was not funded by anti-trans zealots. But it arrived at exactly the time people with bad intentions were looking for science to buoy their opinions. The paper “laundered what had previously been the rantings of online conspiracy theorists and gave it the resemblance of serious scientific study,” Branstetter says. She believes that if Littman’s paper had not been published, a similar argument would have been made by someone else. Despite its limitations, it has become a crucial weapon in the fight against trans people, largely through online dissemination. “It is astonishing that such a blatantly bad-faith effort has been taken so seriously,” Branstetter says.
Littman plainly rejects that characterization, saying her goal was simply to “find out what’s going on.” “This was a very good-faith attempt,” she says. “As a person I am liberal; I’m pro-LGBT. I saw a phenomenon with my own eyes and I investigated, found that it was different than what was in the scientific literature.”
One reason for the success of Littman’s paper is that it validates the idea that trans kids are new. But Jules Gill-Peterson, an associate professor of history at Johns Hopkins and author of Histories of the Transgender Child, says that is “empirically untrue.” Trans children have only recently started to be discussed in mainstream media, so people assume they weren’t around before, she says, but “there have been children transitioning for as long as there has been transition-related medical technology,” and children were socially transitioning—living as a different gender without any medical or legal interventions—long before that.
Many trans people are young children when they first observe a dissonance between how they are identified and how they identify. The process of transitioning is never simple, but the explanation of their identity might be.
Meta’s new AI can turn text prompts into videos
Although the effect is rather crude, the system offers an early glimpse of what’s coming next for generative artificial intelligence, and it is the next obvious step from the text-to-image AI systems that have caused huge excitement this year.
Meta’s announcement of Make-A-Video, which is not yet being made available to the public, will likely prompt other AI labs to release their own versions. It also raises some big ethical questions.
In the last month alone, AI lab OpenAI has made its latest text-to-image AI system DALL-E available to everyone, and AI startup Stability.AI launched Stable Diffusion, an open-source text-to-image system.
But text-to-video AI comes with some even greater challenges. For one, these models need a vast amount of computing power. They are an even bigger computational lift than large text-to-image AI models, which use millions of images to train, because putting together just one short video requires hundreds of images. That means it’s really only large tech companies that can afford to build these systems for the foreseeable future. They’re also trickier to train, because there aren’t large-scale data sets of high-quality videos paired with text.
To work around this, Meta combined data from three open-source image and video data sets to train its model. Standard text-image data sets of labeled still images helped the AI learn what objects are called and what they look like. And a database of videos helped it learn how those objects are supposed to move in the world. The combination of the two approaches helped Make-A-Video, which is described in a non-peer-reviewed paper published today, generate videos from text at scale.
Tanmay Gupta, a computer vision research scientist at the Allen Institute for Artificial Intelligence, says Meta’s results are promising. The videos it’s shared show that the model can capture 3D shapes as the camera rotates. The model also has some notion of depth and understanding of lighting. Gupta says some details and movements are decently done and convincing.
However, “there’s plenty of room for the research community to improve on, especially if these systems are to be used for video editing and professional content creation,” he adds. In particular, it’s still tough to model complex interactions between objects.
In the video generated by the prompt “An artist’s brush painting on a canvas,” the brush moves over the canvas, but strokes on the canvas aren’t realistic. “I would love to see these models succeed at generating a sequence of interactions, such as ‘The man picks up a book from the shelf, puts on his glasses, and sits down to read it while drinking a cup of coffee,’” Gupta says.
How AI is helping birth digital humans that look and sound just like us
Jennifer: And the team has also been exploring how these digital twins can be useful beyond the 2D world of a video conference.
Greg Cross: I guess the.. the big, you know, shift that’s coming right at the moment is the move from the 2D world of the internet, into the 3D world of the metaverse. So, I mean, and that, and that’s something we’ve always thought about and we’ve always been preparing for, I mean, Jack exists in full 3D, um, You know, Jack exists as a full body. So I mean, Jack can, you know, today we have, you know, we’re building augmented reality, prototypes of Jack walking around on a golf course. And, you know, we can go and ask Jack, how, how should we play this hole? Um, so these are some of the things that we are starting to imagine in terms of the way in which digital people, the way in which digital celebrities. Interact with us as we move into the 3D world.
Jennifer: And he thinks this technology can go a lot further.
Greg Cross: Healthcare and education are two amazing applications of this type of technology. And it’s amazing because we don’t have enough real people to deliver healthcare and education in the real world. So, I mean, so you can, you know, you can imagine how you can use a digital workforce to augment. And, and extend the skills and capability, not replace, but extend the skills and, and capabilities of real people.
Jennifer: This episode was produced by Anthony Green with help from Emma Cillekens. It was edited by me and Mat Honan, mixed by Garret Lang… with original music from Jacob Gorski.
If you have an idea for a story or something you’d like to hear, please drop a note to podcasts at technology review dot com.
Thanks for listening… I’m Jennifer Strong.
A bionic pancreas could solve one of the biggest challenges of diabetes
The bionic pancreas, a credit card-sized device called an iLet, monitors a person’s levels around the clock and automatically delivers insulin when needed through a tiny cannula, a thin tube inserted into the body. It is worn constantly, generally on the abdomen. The device determines all insulin doses based on the user’s weight, and the user can’t adjust the doses.
A Harvard Medical School team has submitted its findings from the study, described in the New England Journal of Medicine, to the FDA in the hopes of eventually bringing the product to market in the US. While a team from Boston University and Massachusetts General Hospital first tested the bionic pancreas in 2010, this is the most extensive trial undertaken so far.
The Harvard team, working with other universities, provided 219 people with type 1 diabetes who had used insulin for at least a year with a bionic pancreas device for 13 weeks. The team compared their blood sugar levels with those of 107 diabetic people who used other insulin delivery methods, including injection and insulin pumps, during the same amount of time.
The blood sugar levels of the bionic pancreas group fell from 7.9% to 7.3%, while the standard care group’s levels remained steady at 7.7%. The American Diabetes Association recommends a goal of less than 7.0%, but that’s only met by approximately 20% of people with type 1 diabetes, according to a 2019 study.
Other types of artificial pancreas exist, but they typically require the user to input information before they will deliver insulin, including the amount of carbohydrates they ate in their last meal. Instead, the iLet takes the user’s weight and the type of meal they’re eating, such as breakfast, lunch, or dinner, added by the user via the iLet interface, and it uses an adaptive learning algorithm to deliver insulin automatically.