As a professional field, climate change adaptation remained neglected, misunderstood, and small through the early 2000s, when Lara Hansen, an ecotoxicologist by training, began working on the subject for the World Wildlife Fund. Hansen and her colleagues would joke that all the world’s adaptation experts and researchers “could fit in an elevator.” But soon, the field began to mushroom. For one thing, it had become clearer that emissions were not dropping—especially after the George W. Bush administration announced in 2001 that it would not implement the Kyoto Protocol, another international agreement to prod countries to rein in atmospheric carbon.
The president’s inaction threw a wrench into international negotiations; partly as a result, when the United Nations forged another treaty called the Marrakesh Accords, they included far more about adaptation than in the past. If the US was going to keep dumping carbon into the sky without limit, then the whole world would have far more things to adapt to.
But environmental groups were still often hesitant to wade into the topic—a missed opportunity, Hansen thinks. “I have long said that adaptation is the gateway drug to mitigation. Because once you see how big the problem will be for your community and how much your way of life will have to change,” she says, “suddenly it’s like, ‘Well, that sucks. It would be a hell of a lot easier to just stop emitting carbon dioxide into the atmosphere.’”
In 2006, in a hotel ballroom in Florida, she led a workshop for a couple hundred people to talk about coral reef conservation, including commercial fishing companies and tourism businesses that were not as familiar with the implications of climate change. That evening, at a local theater, the workshop organizers screened Al Gore’s climate documentary An Inconvenient Truth and aired a video that simulated future floods in south Florida. “I had it zoomed into the Florida Keys,” Hansen recalls, “and you could see that with a two-meter rise in sea level and a Category One hurricane storm surge, the only thing that was still standing in the Florida Keys were a couple of highway bridges and the Key West cemetery.” The audience asked her to replay it three times. Afterward, Hansen said, she heard there was much more interest in mitigation efforts from people in the region.
In the years since, the ranks of adaptation experts have continued to grow exponentially. In 2008, Hansen cofounded an organization called EcoAdapt, a clearinghouse of adaptation reports and lessons, and a convener of experts from around the country. When the Obama administration required federal agencies to develop adaptation plans, it prompted a flurry of other institutions to do the same. “It is actually the thing that probably got more state and local governments thinking about it than anything previously had,” Hansen says.
But adaptation work likely still suffers from some of the constraints it bore in the beginning. Infrastructure, for instance, is built on a slow timeline, and the lag in understanding and acceptance means that planners haven’t necessarily caught up. Burton has noted how some of the railroads in the United Kingdom were ill-suited to withstand the recent heat wave. “The railway lines were designed for what the climate has been over the last 50 years,” he lamented, not what the climate is now and is going to become.
Meta’s new AI can turn text prompts into videos
Although the effect is rather crude, the system offers an early glimpse of what’s coming next for generative artificial intelligence, and it is the next obvious step from the text-to-image AI systems that have caused huge excitement this year.
Meta’s announcement of Make-A-Video, which is not yet being made available to the public, will likely prompt other AI labs to release their own versions. It also raises some big ethical questions.
In the last month alone, AI lab OpenAI has made its latest text-to-image AI system DALL-E available to everyone, and AI startup Stability.AI launched Stable Diffusion, an open-source text-to-image system.
But text-to-video AI comes with some even greater challenges. For one, these models need a vast amount of computing power. They are an even bigger computational lift than large text-to-image AI models, which use millions of images to train, because putting together just one short video requires hundreds of images. That means it’s really only large tech companies that can afford to build these systems for the foreseeable future. They’re also trickier to train, because there aren’t large-scale data sets of high-quality videos paired with text.
To work around this, Meta combined data from three open-source image and video data sets to train its model. Standard text-image data sets of labeled still images helped the AI learn what objects are called and what they look like. And a database of videos helped it learn how those objects are supposed to move in the world. The combination of the two approaches helped Make-A-Video, which is described in a non-peer-reviewed paper published today, generate videos from text at scale.
Tanmay Gupta, a computer vision research scientist at the Allen Institute for Artificial Intelligence, says Meta’s results are promising. The videos it’s shared show that the model can capture 3D shapes as the camera rotates. The model also has some notion of depth and understanding of lighting. Gupta says some details and movements are decently done and convincing.
However, “there’s plenty of room for the research community to improve on, especially if these systems are to be used for video editing and professional content creation,” he adds. In particular, it’s still tough to model complex interactions between objects.
In the video generated by the prompt “An artist’s brush painting on a canvas,” the brush moves over the canvas, but strokes on the canvas aren’t realistic. “I would love to see these models succeed at generating a sequence of interactions, such as ‘The man picks up a book from the shelf, puts on his glasses, and sits down to read it while drinking a cup of coffee,’” Gupta says.
How AI is helping birth digital humans that look and sound just like us
Jennifer: And the team has also been exploring how these digital twins can be useful beyond the 2D world of a video conference.
Greg Cross: I guess the.. the big, you know, shift that’s coming right at the moment is the move from the 2D world of the internet, into the 3D world of the metaverse. So, I mean, and that, and that’s something we’ve always thought about and we’ve always been preparing for, I mean, Jack exists in full 3D, um, You know, Jack exists as a full body. So I mean, Jack can, you know, today we have, you know, we’re building augmented reality, prototypes of Jack walking around on a golf course. And, you know, we can go and ask Jack, how, how should we play this hole? Um, so these are some of the things that we are starting to imagine in terms of the way in which digital people, the way in which digital celebrities. Interact with us as we move into the 3D world.
Jennifer: And he thinks this technology can go a lot further.
Greg Cross: Healthcare and education are two amazing applications of this type of technology. And it’s amazing because we don’t have enough real people to deliver healthcare and education in the real world. So, I mean, so you can, you know, you can imagine how you can use a digital workforce to augment. And, and extend the skills and capability, not replace, but extend the skills and, and capabilities of real people.
Jennifer: This episode was produced by Anthony Green with help from Emma Cillekens. It was edited by me and Mat Honan, mixed by Garret Lang… with original music from Jacob Gorski.
If you have an idea for a story or something you’d like to hear, please drop a note to podcasts at technology review dot com.
Thanks for listening… I’m Jennifer Strong.
A bionic pancreas could solve one of the biggest challenges of diabetes
The bionic pancreas, a credit card-sized device called an iLet, monitors a person’s levels around the clock and automatically delivers insulin when needed through a tiny cannula, a thin tube inserted into the body. It is worn constantly, generally on the abdomen. The device determines all insulin doses based on the user’s weight, and the user can’t adjust the doses.
A Harvard Medical School team has submitted its findings from the study, described in the New England Journal of Medicine, to the FDA in the hopes of eventually bringing the product to market in the US. While a team from Boston University and Massachusetts General Hospital first tested the bionic pancreas in 2010, this is the most extensive trial undertaken so far.
The Harvard team, working with other universities, provided 219 people with type 1 diabetes who had used insulin for at least a year with a bionic pancreas device for 13 weeks. The team compared their blood sugar levels with those of 107 diabetic people who used other insulin delivery methods, including injection and insulin pumps, during the same amount of time.
The blood sugar levels of the bionic pancreas group fell from 7.9% to 7.3%, while the standard care group’s levels remained steady at 7.7%. The American Diabetes Association recommends a goal of less than 7.0%, but that’s only met by approximately 20% of people with type 1 diabetes, according to a 2019 study.
Other types of artificial pancreas exist, but they typically require the user to input information before they will deliver insulin, including the amount of carbohydrates they ate in their last meal. Instead, the iLet takes the user’s weight and the type of meal they’re eating, such as breakfast, lunch, or dinner, added by the user via the iLet interface, and it uses an adaptive learning algorithm to deliver insulin automatically.