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What’s next for mRNA vaccines

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What’s next for mRNA vaccines


In the same way that we can train our immune systems to recognize viral proteins, we could also train them to recognize proteins on cancer cells. In theory, this approach could be totally personalized—scientists could study the cells of a specific person’s tumor and create a custom-made treatment that would help that individual’s own immune system defeat the cancer. “It’s a fantastic application of RNA,” says Blakney. “I think there’s huge potential there.”

Cancer vaccines have been trickier to make, partly because there’s often no clear protein target. We can make mRNA for a protein on the outer shell of a virus, such as the spike protein on the virus that causes covid-19. But when our own cells form tumors, there’s often no such obvious target, says Karikó.

Cancer cells probably require a different kind of immune response from that required to protect against a coronavirus, adds Pardi: “We will need to come up with slightly different mRNA vaccines.” Several clinical trials are underway, but “the breakthrough hasn’t happened yet,” he adds.

The next pandemic

Despite their huge promise, mRNA vaccines are unlikely to prevent or treat every disease out there, at least as the technology stands today. For a start, some of these vaccines need to be stored in low-temperature freezers, says Karin Loré, an immunologist at the Karolinska Institute in Stockholm, Sweden. That just isn’t an option in some parts of the world.

And some diseases pose more of a challenge than others. To protect against an infectious  disease, the mRNA in a vaccine will need to code for a relevant protein—a key signal that will give the immune system something to recognize and defend against. For some viruses, like covid-19, finding such a protein is quite straightforward.

But it’s not so easy for others. It might be harder to find good targets for vaccines that protect us against bacterial infections, for example, says Blakney. HIV has also been difficult. “They’ve never found that form of the protein that induces an immune response that works really well for HIV,” says Blakney.

“I don’t want to give the impression that mRNA vaccines will be the solution for everything,” says Loré. Blakney agrees. “We’ve seen the effects that these vaccines can [have], and it’s really exciting,” she says. “But I don’t think that, overnight, all vaccines are going to become RNA vaccines.”

Still, there’s plenty to look forward to. In 2023, we can expect an updated covid-19 vaccine. And researchers are hopeful we’ll see more mRNA vaccines enter clinics in the near future. “I really hope that in the next couple of years, we will have other approved mRNA vaccines against infectious disease,” says Pardi.

He is planning ahead for the next global disease outbreak, which may well involve a flu virus. We don’t know when the next pandemic will hit, “but we have to be ready for it,” he says. “It’s crystal clear that if you start vaccine development in the middle of a pandemic, it’s already too late.”

This story is a part of MIT Technology Review’s What’s Next series, where we look across industries, trends, and technologies to give you a first look at the future.

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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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