But in trying to do so, it has demonstrated something else: the most popular content on Facebook is often awful, recycled generic memes.
It’s not necessarily surprising that reposting already popular memes gets views on Facebook, but “it’s imperative to monitor where the attention garnered by this content is directed” in order to catch attempts to funnel this attention into grifts, extremism, and disinformation, says Karan Lala, a fellow and the editor in chief at the Integrity Institute, an organization founded by former employees of Facebook’s integrity team to research and advise the public on the inner workings of social media platforms. Lala recently published research into the spam economy on Facebook.
The top 20 posts by views on Facebook in the most recent report are overwhelmingly reposted memes that were originally created for other platforms. A lot of the pages responsible for them belong to Instagram viral content accounts with names like Ideas365 or Factsdailyy. There are two pro–Johnny Depp meme reposts on the list, with nearly 100 million views between them. Two of the 20 top-viewed posts are not listed on the report because Meta removed them for violating its policies on intellectual property or inauthentic behavior.
The main issue here is not necessarily one of safety: the most popular content on Facebook feels more like boomer bait than it does something designed to attract engagement from the younger audiences Meta is courting. But as Lala notes, relatively benign meme accounts and potentially harmful accounts that are posting memes in order to drive attention somewhere specific are difficult to distinguish on the surface.
Ideas365 and Factsdailyy appear similar at first: they are both Instagram meme accounts getting enormous amounts of views on Facebook. They each post about a half-dozen short videos a day. Their content is generic. But looking closer, Lala noted some key differences: Factsdailyy’s bio contains contact information, and each post credits the source of the meme it’s reposting. At a cursory glance, this account is likely just a normal old meme account.
By contrast, Ideas365—the page that posted the Family Feud video at the top of Facebook’s most-viewed list for this quarter—drives traffic to a site selling courses for making money selling things on Amazon. While the account does credit the source of some memes, it’s using the attention those memes grab to advertise questionable services. Its featured stories advertise a “mentorship” program that promises to teach students how to create automated Instagram accounts for profit. “The user behind the account mentions owning over 250 theme pages on Instagram and earning ‘hundreds of thousands a month’ from their phone. This is also complete with ostentatious videos of the user’s many luxury cars,” Lala added.
There’s nothing inherently wrong with being a spammy meme page, of course. The harm here isn’t that the account is using short-form videos on Meta to get people to sign up for an expensive course, says Lala: “As we approach election season, it is important to note that this attention could be just as easily directed toward disinformation or other harms using similar tactics.” Last year, MIT Technology Review revealed the extent to which global content farms have become adept at using Meta’s own incentive structures to profit directly from popular content, whether that’s memes about a celebrity breakup or misinformation on a divisive issue.
Meta also provides data on the top-viewed external links and domains. In this report, five of the top 20 links were removed for inauthentic behavior (the top link was, of course, to TikTok). And the list of most widely viewed domains—perhaps the part of this report that is designed to most directly counter CrowdTangle’s data—showed a mix of competitors like YouTube and TikTok, mainstream news sites, and GoFundMe.
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.