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Here’s how a Twitter engineer says it will break in the coming weeks

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Here’s how a Twitter engineer says it will break in the coming weeks


This is particularly problematic, says Krueger, for a site like Twitter, which can have unforeseen spikes in user traffic and interest. Krueger contrasts Twitter with online retail sites, where companies can prepare for big traffic events like Black Friday with some predictability. “When it comes to Twitter, they have the possibility of having a Black Friday on any given day at any time of the day,” he says. “At any given day, some news event can happen that can have significant impact on the conversation.” Responding to that is harder to do when you lay off up to 80% of your SREs—a figure Krueger says has been bandied about within the industry but which MIT Technology Review has been unable to confirm. The Twitter engineer agreed that the percentage sounded “plausible.”

That engineer doesn’t see a route out of the issue—other than reversing the layoffs (which the company has reportedly already attempted to roll back somewhat). “If we’re going to be pushing at a breakneck pace, then things will break,” he says. “There’s no way around that. We’re accumulating technical debt much faster than before—almost as fast as we’re accumulating financial debt.” 

The list grows longer

He presents a dystopian future where issues pile up as the backlog of maintenance tasks and fixes grows longer and longer. “Things will be broken. Things will be broken more often. Things will be broken for longer periods of time. Things will be broken in more severe ways,” he says. “Everything will compound until, eventually, it’s not usable.”

Twitter’s collapse into an unusable wreck is some time off, the engineer says, but the telltale signs of process rot are already there. It starts with the small things: “Bugs in whatever part of whatever client they’re using; whatever service in the back end they’re trying to use. They’ll be small annoyances to start, but as the back-end fixes are being delayed, things will accumulate until people will eventually just give up.”

Krueger says that Twitter won’t blink out of life, but we’ll start to see a greater number of tweets not loading, and accounts coming into and out of existence seemingly at a whim. “I would expect anything that’s writing data on the back end to possibly have slowness, timeouts, and a lot more subtle types of failure conditions,” he says. “But they’re often more insidious. And they also generally take a lot more effort to track down and resolve. If you don’t have enough engineers, that’s going to be a significant problem.” 

The juddering manual retweets and faltering follower counts are indications that this is already happening. Twitter engineers have designed fail-safes that the platform can fall back on so that the functionality doesn’t go totally offline but cut-down versions are provided instead. That’s what we’re seeing, says Krueger.

Alongside the minor malfunctions, the Twitter engineer believes that there’ll be significant outages on the horizon, thanks in part to Musk’s drive to reduce Twitter’s cloud computing server load in an attempt to claw back up to $3 million a day in infrastructure costs. Reuters reports that this project, which came from Musk’s war room, is called the “Deep Cuts Plan.” One of Reuters’s sources called the idea “delusional,” while Alan Woodward, a cybersecurity professor at the University of Surrey, says that “unless they’ve massively overengineered the current system, the risk of poorer capacity and availability seems a logical conclusion.”

Brain drain

Meanwhile, when things do go kaput, there’s no longer the institutional knowledge to quickly fix issues as they arise. “A lot of the people I saw who were leaving after Friday have been there nine, 10, 11 years, which is just ridiculous for a tech company,” says the Twitter engineer. As those individuals walked out of Twitter offices, decades of knowledge about how its systems worked disappeared with them. (Those within Twitter, and those watching from the sidelines, have previously argued that Twitter’s knowledge base is overly concentrated in the minds of a handful of programmers, some of whom have been fired.)



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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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