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Never say die

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Never say die


This is mostly pretty cool. Beats the alternative, as they say. But one decidedly uncool thing about being almost 50 is that my “bad” LDL cholesterol is too high. The obvious solution here is to improve my diet and get more exercise. But if that were so easy, no one would have high cholesterol, right? Another option might be to go on statins—use medication to bring it down. Lots of people do this, but it’s a treatment, not a cure, and the side effects can be intense. 

Or, just maybe, I could have my genes edited using CRISPR, as a patient in New Zealand recently did, to permanently reduce my LDL. Given that heart disease is a leading cause of death, that could meaningfully prolong my life. There’s still much to learn about the effectiveness of such a treatment, but the mere prospect is pretty amazing. 

The things we can do to alter our bodies today would have sounded like science fiction just a few years ago. Scratch that—some of them still sound like science fiction. Aging and death have long been our inevitable fates, constants of life that begin with birth. But what if aging isn’t inevitable? What if we could slow or even reverse the process? This may be less far-fetched than you imagine.

A flotilla of billionaires and Silicon Valley elites, and even the Saudi royal family, have poured staggering amounts of money into this question, funding startups and research that seek to extend our time on Earth. Antonio Regalado has the story of the quest for medical rejuvenation: researchers want to reprogram our cells to make them young again, letting us lead longer, healthier lives. 

Or even more intriguing: What if we could cheat death and live forever? Because we can. Or at least our digital replicas can, as Charlotte Jee learned when she trained an AI to create simulacra of her parents. The technology she explored is one of several meant to let the living commune with the dead, in this case via Amazon Alexa. 

But even if we can live forever in a smart speaker, at the moment there’s still no way around death. Of course, there are a lot of people working on that. Perhaps you might want to have your corpse preserved at −196 °C in the hopes of being brought back to life again one day. The field of cryonics is still trucking along, even if it is “a hopeless aspiration that reveals an appalling ignorance of biology,” as one scientist puts it. 

For those who have accepted their fate but want their death to help others live longer, there is always the possibility of donating one’s body to science. While that’s something you have surely heard of, it’s nothing like what you have probably imagined. Abby Ohlheiser pulls back the curtain to reveal how this intimate process truly works. 

I hope this issue serves up a lot for you to think about. As always, I’d love to hear your thoughts. You can reach me at mat.honan@technologyreview.com.

See you on the other side,

Mat 

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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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