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Big Tech could help Iranian protesters by using an old tool

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Big Tech could help Iranian protesters by using an old tool


But these workarounds aren’t enough. Though the first Starlink satellites have been smuggled into Iran, restoring the internet will likely require several thousand more. Signal tells MIT Technology Review that it has been vexed by “Iranian telecommunications providers preventing some SMS validation codes from being delivered.” And Iran has already detected and shut down Google’s VPN, which is what happens when any single VPN grows too popular (plus, unlike most VPNs, Outline costs money).

What’s more, “there’s no reliable mechanism for Iranian users to find these proxies,” Nima Fatemi, head of global cybersecurity nonprofit Kandoo, points out. They’re being promoted on social media networks that are themselves banned in Iran. “While I appreciate their effort,” he adds, “it feels half-baked and half-assed.”

There is something more that Big Tech could do, according to some pro-democracy activists and experts on digital freedom. But it has received little attention—even though it’s something several major service providers offered until just a few years ago.

“One thing people don’t talk about is domain fronting,” says Mahsa Alimardani, an internet researcher at the University of Oxford and Article19, a human rights organization focused on freedom of expression and information. It’s a technique developers used for years to skirt internet restrictions like those that have made it incredibly difficult for Iranians to communicate safely. In essence, domain fronting allows apps to disguise traffic directed towards them; for instance, when someone types a site into a web browser, this technique steps into that bit of browser-to-site communication and can scramble what the computer sees on the backend to disguise the end site’s true identity.

In the days of domain fronting, “cloud platforms were used for circumvention,” Alimardani explains. From 2016 to 2018, secure messaging apps like Telegram and Signal used the cloud hosting infrastructure of Google, Amazon, and Microsoft—which most of the web runs on—to disguise user traffic and successfully thwart bans and surveillance in Russia and across the Middle East.

But Google and Amazon discontinued the practice in 2018, following pushback from the Russian government and citing security concerns about how it could be abused by hackers. Now activists who work at the intersection of human rights and technology say reinstating the technique, with some tweaks, is a tool Big Tech could use to quickly get Iranians back online.

Domain fronting “is a good place to start” if tech giants really want to help, Alimardani says. “They need to be investing in helping with circumvention technology, and having stamped out domain fronting is really not a good look.”

Domain fronting could be a critical tool to help protesters and activists stay in touch with each other for planning and safety purposes, and to allow them to update worried family and friends during a dangerous period. “We recognize the possibility that we might not come back home every time we go out,” says Elmira, an Iranian woman in her 30s who asked to be identified only by her first name for security reasons.

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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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