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We’re witnessing the brain death of Twitter

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We’re witnessing the brain death of Twitter


On Monday, December 12, Twitter dissolved its Trust and Safety Council, a wide-ranging group of global civil rights advocates, academics, and experts who have advised the company since 2016. Meanwhile, Musk has welcomed back previously banned high-profile extremists like the white nationalist Patrick Casey. According to data compiled by researcher Travis Brown, others reinstated include Meninist, a “men’s rights” account with more than a million followers; Peter McCullough, a cardiologist who gained a large audience for advocating discredited covid-19 treatments and arguing against receiving the vaccine; and Tim Gionet, a far-right media personality who livestreamed his participation in the January 6 attack on the US Capitol.

Musk’s enthusiasm for eliminating jobs, cutting costs, and undoing Twitter’s safety infrastructure has caused advertisers to leave in droves. At one point, the company reportedly lost the business of half its top 100 advertising clients, and it has missed weekly US ad revenue expectations by as much as 80%. Musk’s behavior now poses difficult questions for the brands that remain. The company has stopped enforcing its policy on covid-19 misinformation.

And as people who like Musk’s vision for Twitter return to posting, others are finding it tougher to justify their presence on the site, declaring hiatuses or announcing their migration elsewhere. According to one estimate, Twitter may have lost a million users in just a few days after Musk took over. Others are giving up on tweeting even if they haven’t deleted their accounts yet. Some of these are high profile: Elton John quit Twitter on December 9, citing the site’s policy changes on misinformation.

MIT Technology Review ran an analysis in Hoaxy, a tool created by Indiana University to show how information spreads on Twitter by looking at both keyword frequency and interactions between individual accounts. The results hint at Musk’s new role in this network: as effectively a hall monitor for the far right.

The tool plots interactions visually, showing the connections between individual Twitter accounts on a specific keyword or hashtag and indicating whether that account is the one amplifying the search term to others or being mentioned by accounts that are doing so. Accounts that are more actively involved in conversations appear as nodes.

Musk was a key “node” of activity around usage of the “groomer” slur—we looked at both “Groomer” and “OK groomer”— from Friday, December 9, through the afternoon of Sunday, December 11, when we ran the analysis. (We also ran a second query on Wednesday, December 14, which showed similar results.) Musk himself has not tweeted the word—which, according to a report from GLAAD and Media Matters, has dramatically increased in frequency and reach during his tenure. Instead, he has been repeatedly tagged into conversations by others who are using it.

Sometimes these users are apparently seeking attention and amplification from the guy who owns Twitter, and implicitly identifying the slur’s recipients as potential targets for harassment. At other times, Musk is tagged in conversations where the slur is used to attack those who directly disagree with him on Twitter—including Jack Dorsey, the company’s cofounder and former CEO, who tweeted at Musk last week to dispute his claim that the company “refused to take action on child exploitation for years!” Musk regularly interacts with a selection of power users and fans, including conservative meme accounts and far-right personalities like Ian Miles Cheong and Andy Ngo.

Increasingly, Musk isn’t just enabling these conversations—he’s joining in. “My pronouns are Prosecute/Fauci,” he tweeted last weekend. When astronaut Scott Kelly publicly pleaded with him not to “mock and promote hate toward already marginalized and at-risk-of-violence members of the #LGBTQ+ community,” Musk replied, ”Forcing your pronouns upon others when they didn’t ask, and implicitly ostracizing those who don’t, is neither good nor kind to anyone.”



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The Download: generative AI for video, and detecting AI text

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The original startup behind Stable Diffusion has launched a generative AI for video


The original startup behind Stable Diffusion has launched a generative AI for video

What’s happened: Runway, the generative AI startup that co-created last year’s breakout text-to-image model Stable Diffusion, has released an AI model that can transform existing videos into new ones by applying styles from a text prompt or reference image.

What it does: In a demo reel posted on its website, Runway shows how the model, called Gen-1, can turn people on a street into claymation puppets, and books stacked on a table into a cityscape at night. Other recent text-to-video models can generate very short video clips from scratch, but because Gen-1adapts existing footage it can produce much longer videos.

Why it matters: Last year’s explosion in generative AI was fueled by the millions of people who got their hands on powerful creative tools for the first time and shared what they made, and Runway hopes Gen-1 will have a similar effect on generated videos. Read the full story.

—Will Douglas Heaven

Why detecting AI-generated text is so difficult (and what to do about it)

Last week, OpenAI unveiled a tool that can detect text produced by its AI system ChatGPT. But if you’re a teacher who fears the coming deluge of ChatGPT-generated essays, don’t get too excited.

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Why detecting AI-generated text is so difficult (and what to do about it)

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Why detecting AI-generated text is so difficult (and what to do about it)


This tool is OpenAI’s response to the heat it’s gotten from educators, journalists, and others for launching ChatGPT without any ways to detect text it has generated. However, it is still very much a work in progress, and it is woefully unreliable. OpenAI says its AI text detector correctly identifies 26% of AI-written text as “likely AI-written.” 

While OpenAI clearly has a lot more work to do to refine its tool, there’s a limit to just how good it can make it. We’re extremely unlikely to ever get a tool that can spot AI-generated text with 100% certainty. It’s really hard to detect AI-generated text because the whole point of AI language models is to generate fluent and human-seeming text, and the model is mimicking text created by humans, says Muhammad Abdul-Mageed, a professor who oversees research in natural-language processing and machine learning at the University of British Columbia

We are in an arms race to build detection methods that can match the latest, most powerful models, Abdul-Mageed adds. New AI language models are more powerful and better at generating even more fluent language, which quickly makes our existing detection tool kit outdated. 

OpenAI built its detector by creating a whole new AI language model akin to ChatGPT that is specifically trained to detect outputs from models like itself. Although details are sparse, the company apparently trained the model with examples of AI-generated text and examples of human-generated text, and then asked it to spot the AI-generated text. We asked for more information, but OpenAI did not respond. 

Last month, I wrote about another method for detecting text generated by an AI: watermarks. These act as a sort of secret signal in AI-produced text that allows computer programs to detect it as such. 

Researchers at the University of Maryland have developed a neat way of applying watermarks to text generated by AI language models, and they have made it freely available. These watermarks would allow us to tell with almost complete certainty when AI-generated text has been used. 

The trouble is that this method requires AI companies to embed watermarking in their chatbots right from the start. OpenAI is developing these systems but has yet to roll them out in any of its products. Why the delay? One reason might be that it’s not always desirable to have AI-generated text watermarked. 

One of the most promising ways ChatGPT could be integrated into products is as a tool to help people write emails or as an enhanced spell-checker in a word processor. That’s not exactly cheating. But watermarking all AI-generated text would automatically flag these outputs and could lead to wrongful accusations.

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The original startup behind Stable Diffusion has launched a generative AI for video

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The original startup behind Stable Diffusion has launched a generative AI for video


Set up in 2018, Runway has been developing AI-powered video-editing software for several years. Its tools are used by TikTokers and YouTubers as well as mainstream movie and TV studios. The makers of The Late Show with Stephen Colbert used Runway software to edit the show’s graphics; the visual effects team behind the hit movie Everything Everywhere All at Once used the company’s tech to help create certain scenes.  

In 2021, Runway collaborated with researchers at the University of Munich to build the first version of Stable Diffusion. Stability AI, a UK-based startup, then stepped in to pay the computing costs required to train the model on much more data. In 2022, Stability AI took Stable Diffusion mainstream, transforming it from a research project into a global phenomenon. 

But the two companies no longer collaborate. Getty is now taking legal action against Stability AI—claiming that the company used Getty’s images, which appear in Stable Diffusion’s training data, without permission—and Runway is keen to keep its distance.

Gen-1 represents a new start for Runway. It follows a smattering of text-to-video models revealed late last year, including Make-a-Video from Meta and Phenaki from Google, both of which can generate very short video clips from scratch. It is also similar to Dreamix, a generative AI from Google revealed last week, which can create new videos from existing ones by applying specified styles. But at least judging from Runway’s demo reel, Gen-1 appears to be a step up in video quality. Because it transforms existing footage, it can also produce much longer videos than most previous models. (The company says it will post technical details about Gen-1 on its website in the next few days.)   

Unlike Meta and Google, Runway has built its model with customers in mind. “This is one of the first models to be developed really closely with a community of video makers,” says Valenzuela. “It comes with years of insight about how filmmakers and VFX editors actually work on post-production.”

Gen-1, which runs on the cloud via Runway’s website, is being made available to a handful of invited users today and will be launched to everyone on the waitlist in a few weeks.

Last year’s explosion in generative AI was fueled by the millions of people who got their hands on powerful creative tools for the first time and shared what they made with them. Valenzuela hopes that putting Gen-1 into the hands of creative professionals will soon have a similar impact on video.

“We’re really close to having full feature films being generated,” he says. “We’re close to a place where most of the content you’ll see online will be generated.”

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