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The outgoing White House AI director explains the policy challenges ahead

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The outgoing White House AI director explains the policy challenges ahead


They’re making good progress on this and anticipate having that framework out by the beginning of 2023. There are some nuances here—different people interpret risk differently, so it’s important to come to a common understanding of what risk is and what appropriate approaches to risk mitigation might be, and what potential harms might be.

You’ve talked about the issue of bias in AI. Are there ways that the government can use regulation to help solve that problem? 

There are both regulatory and nonregulatory ways to help. There are a lot of existing laws that already prohibit the use of any kind of system that’s discriminatory, and that would include AI. A good approach is to see how existing law already applies, and then clarify it specifically for AI and determine where the gaps are. 

NIST came out with a report earlier this year on bias in AI. They mentioned a number of approaches that should be considered as it relates to governing in these areas, but a lot of it has to do with best practices. So it’s things like making sure that we’re constantly monitoring the systems, or that we provide opportunities for recourse if people believe that they’ve been harmed. 

It’s making sure that we’re documenting the ways that these systems are trained, and on what data, so that we can make sure that we understand where bias could be creeping in. It’s also about accountability, and making sure that the developers and the users, the implementers of these systems, are accountable when these systems are not developed or used appropriately.

What do you think is the right balance between public and private development of AI? 

The private sector is investing significantly more than the federal government into AI R&D. But the nature of that investment is quite different. The investment that’s happening in the private sector is very much into products or services, whereas the federal government is investing in long-term, cutting-edge research that doesn’t necessarily have a market driver for investment but does potentially open the door to brand-new ways of doing AI. So on the R&D side, it’s very important for the federal government to invest in those areas that don’t have that industry-driving reason to invest. 

Industry can partner with the federal government to help identify what some of those real-world challenges are. That would be fruitful for US federal investment. 

There is so much that the government and industry can learn from each other. The government can learn about best practices or lessons learned that industry has developed for their own companies, and the government can focus on the appropriate guardrails that are needed for AI.

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Meta’s new AI can turn text prompts into videos

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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. 

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How AI is helping birth digital humans that look and sound just like us

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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.

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A bionic pancreas could solve one of the biggest challenges of diabetes

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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.

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