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Technology review

Would you like “milk” with that Impossible burger?

Impossible Foods has continued to expand throughout the pandemic, bringing its bleeding, sizzling plant-based burgers and sausages to more than 10,000 additional US stores this year.

Now the company, which has raised $700 million in 2020, is preparing to move into new markets and product lines. On Tuesday, it will announce plans to double its research and development team in the next 12 months, adding about 150 new scientists and engineers. That includes 10 new “Impossible Investigator” roles, designed to lure top scientists by allowing them to propose their own research programs.

During a press conference, the company will also unveil a prototype of a product under development: a plant-based milk alternative. (They’re not yet discussing when it may reach store shelves.)

Impossible, founded in 2011 by Stanford biochemistry professor Pat Brown and based in Redwood City, California, developed a convincing ground-beef alternative primarily by genetically engineering and fermenting certain types of yeast to crank out heme. The company says this iron-containing compound is largely responsible for the color and taste of ground beef.

Impossible’s plant-based burgers.

Tens of thousands of restaurants now serve Impossible burgers, including Burger King.

Brown’s founding mission was to ease the environmental impact of the livestock sector, which produces about 14.5% of the world’s greenhouse-gas pollution, according to the Food and Agriculture Organization of the United Nations.

In an interview with MIT Technology Review, Brown laid out an audacious end goal: supplanting enough of the key animal products to put the industry out of business and completely wipe out those emissions. Among other things, the company is now working to develop convincing alternatives to chicken breasts and steaks, he says.

This interview has been edited for length and clarity.

Q: Why are you expanding your R&D team?

Our mission is to completely replace the world’s most destructive technology by far, which is the use of animals, by 2035. We will succeed or fail based on whether we build a complete technology platform that creates all the foods we get today from animals, but makes them more delicious, more nutritious, affordable, sustainable, and so forth.

What are the key research questions that you’re now trying to solve?

We’re building a technology platform for turning plants, ingredients from plants, into something that appears completely different from the plant, and to a consumer is meat.

What matters here is both the biochemical system, which is responsible for the dynamic flavor and aroma behavior and so forth, and also materials that have very precise properties that are somewhat exotic from a materials science standpoint.

This isn’t just about taking a bunch of standard crap, mixing it in mixing bowl, and making Cheetos out of it, or whatever. This is a much harder problem. Because for us to succeed, it’s not just that we need to throw things together in the form factor of a burger patty. We have to make something that to consumers is meat, and is more delicious, nutritious, and affordable than anything that the current technology, a cow or whatever, can produce.

What’s the next grand challenge? Are you going from ground meat to chunks and slabs, like steak or chicken breasts? Are you talking about going into milk and cheese? Are you talking about going into poultry and lamb?

We’re working on all of those things.

If we were to take a section of muscle tissue from a cow, or a pig, or a chicken, or a halibut, or even a fruit fly, and look at it under an electron microscope, they look extremely similar. So in a sense, if you solve it for one kind of meat, you’ve 95% solved it for every kind of meat.

Pat Brown, founder and CEO of Impossible Foods.

We made a very strategic choice to launch with raw ground beef, because beef is the most destructive sector of the animal ag industry by a huge margin. In the US, ground beef is the single largest-selling, so to speak, cut or category of meat. So if you want to disrupt that industry, which is our goal, that’s the obvious product to launch with.

What are some of the scientific challenges your research group would have to solve to go from ground beef to something that approximates, in a consumer mind, a slab of chicken or steak?

If you look at a steak, you realize it’s got several distinct components. It’s got adipose tissue, it’s got loose connective tissue, it’s got hard connective tissue, it’s got the muscle tissue itself.

So the diversity of materials that you can recognize as distinct materials is greater; that’s one thing. Now, most of those are probably not that important to the consumer. For example, even though your big cut of steak has nerves and lymph nodes and gristle and stuff like that in it, you probably don’t particularly mind if we leave those parts out.

Just the fat and meat for me.

Yeah, exactly. And probably for a lot of people, it’s not the bulk, kind of subcutaneous fat; it’s the interstitial, sort of thick marbling fat that you care about.

Our goal, to be honest, is to as quickly as possible make it economically unsustainable to continue to raise cows. In order to do that, I think we probably have to make two products. We never have to make beef liver. We have to make ground beef—that’s half of all the beef sales in the US—and one really good steak. We don’t have to make eight different kinds of steak in order to basically disrupt the market enough to achieve our mission.

And the characteristics of a really good steak? I think we have to have something with a very good version of that muscle structure and texture; you have to be able to create the right mechanical properties. It’s got some connective tissue, which is sort of a non-woven fabric of protein, and then it’s got the interstitial adipose tissue. And for the adipose tissue, the things that matter? Well, its mechanical properties matter to some extent, its melting behavior matters, and its flavor chemistry matters.

In contexts other than meat, when you think about forming soft materials into structures and stuff like that, that’s already a very highly developed area of engineering and materials science.

I have to admit to being just a bit skeptical in terms of how big an impact this can have. There are deep cultural ties to raising and consuming livestock around the world, and we’re expected to have a massive increase in meat demand in the developing world as we see this expansion of the middle class. So how do you appeal to these big parts of the market?

The basic answer is, you figure out a better technology platform that delivers the fundamental things that consumers want from these foods better than the incumbents. That’s the essence of it. And then you’re saying, what about all those cultural ties? We’ve done a ton of research on that, and the fact is that meat lovers around the world—it doesn’t matter how hard core they are, where they live, or anything—they love that meat is delicious. They love the familiarity, the foods they can make with it; they love the protein, the iron, the convenience, etc.

Brown in the lab at Impossible Foods

But they do not love the fact that it’s made from the corpse of an animal. That’s not enough to make them not want to eat meat, but it’s just not part of the value proposition at all. We don’t have to deal with culture; we have to deal with deliciousness. And that’s a solvable problem.

What particular things do you need to solve to start really addressing the market in the developing world?

One of the things that you’re driving at, which is dead-on correct, is that we have to get the cost below the cost of the current product. Our process uses 25-fold less land, it uses nine-fold less water, 12-fold less fertilizer. There’s less labor growing crops, no labor devoted to managing livestock. So the labor costs are lower, and all the other inputs are lower costs. The economics are vastly superior. What we need to do is basically realize those essential advantages, which requires scale and it requires us to get further along, because right now we have to be constantly investing in growth, right?

So to be clear, you don’t think this is 80% of the solution. You think this could be a complete solution to the livestock emissions problem?

Cattle will be pets.

If we have a cheaper, more delicious, healthier product for consumers, I am confident that most consumers in the world will choose it over the current product. And if no one is buying the products of animal agriculture, then there will be no incentive to keep covering the planet with cows. It’s as simple as that.

It’s not that we have to replace every single thing that people value from a cow. We have to replace enough of the profitable components of a cow to make it unprofitable to grow more cows.

Would you like “milk” with that Impossible burger? 2020/10/20 17:00

A deepfake bot is being used to “undress” underage girls

In June of 2019, Vice uncovered the existence of a disturbing app that used AI to “undress” women. Called DeepNude, it allowed users to upload a photo of a clothed woman for $50 and get back a photo of her seemingly naked. In actuality, the software was using generative adversarial networks, the algorithm behind deepfakes, to swap the women’s clothes for highly realistic nude bodies. The more scantily clad the victim, the better. It didn’t work on men.

Within 24 hours, the Vice article had inspired such a backlash that the creators of the app quickly took it down. The DeepNude Twitter account announced that no other versions would be released, and no one else would get access to the technology.

But a new investigation from Sensity AI (previously Deeptrace Labs), a cybersecurity company focused on detecting the abuse of manipulated media, has now found very similar technology being used by a publicly available bot on the messaging app Telegram. This time it has an even simpler user interface: anyone can send the bot a photo through the Telegram mobile or web app and receive a nude back within minutes. The service is also completely free, though users can pay a base of 100 rubles (approximately $1.50) for perks such as removing the watermark on the “stripped” photos or skipping the processing queue. 

As of July 2020, the bot had already been used to target and “strip” at least 100,000 women, the majority of whom likely had no idea. “Usually it’s young girls,” says Giorgio Patrini, the CEO and chief scientist of Sensity, who coauthored the report. “Unfortunately, sometimes it’s also quite obvious that some of these people are underage.”

The gamification of harassment

The deepfake bot, launched on July 11, 2019, is connected to seven Telegram channels with a combined total of over 100,000 members. (This number doesn’t account for duplicate membership across channels, but the main group has more than 45,000 unique members alone.)

The central channel is dedicated to hosting the bot itself, while the others are used for functions like technical support and image sharing. The image-sharing channels include interfaces that people can use to post and judge their nude creations. The more a photo gets liked, the more its creator is rewarded with tokens to access the bot’s premium features. “The creator will receive an incentive as if he’s playing a game,” Patrini says.

The community, which is easily discoverable via search and social media, has steadily grown in membership over the last year. A poll of 7,200 users showed that roughly 70% of them are from Russia or other Russian-speaking countries. The victims, however, seem to come from a broader range of countries, including Argentina, Italy, Russia, and the US. The majority of them are private individuals whom the bot’s users say they know in real life or whom they found on Instagram. The researchers were able to identify only a small handful of the women and tried to contact them to understand their experiences. None of the women responded, Patrini says.

The researchers also reached out to Telegram and to relevant law enforcement agencies, including the FBI. Telegram did not respond to either their note or MIT Technology Review’s follow-up request for comment. Patrini says they also haven’t seen “any tangible effect on these communities” since contacting the authorities.

Deepfake revenge porn

Abusers have been using pornographic imagery to harass women for some time. In 2019, a study from the American Psychological Association found that one in 12 women end up being victims of revenge porn at some point in their life. A study from the Australian government, looking at Australia, the UK, and New Zealand, found that ratio to be as high as one in three. Deepfake revenge porn adds a whole new dimension to the harassment, because the victims don’t realize such images exist.

There are also many cases in which deepfakes have been used to target celebrities and other high-profile individuals. The technology first grew popular in the deep recesses of the internet as a way to face-swap celebrities into porn videos, and it’s been used as part of harassment campaigns to silence female journalists. Patrini says he’s spoken with influencers and YouTubers, as well, who’ve had deepfaked pornographic images of them sent directly to their sponsors, costing them immense emotional and financial strain.

Patrini suspects these targeted attacks could get a whole lot worse. He and his fellow researchers have already seen the technology advance and spread. For example, they discovered yet another ecosystem of over 380 pages dedicated to the creation and sharing of explicit deepfakes on the Russian social-media platform VK. (After the publication of this article, a spokesperson from VK sent MIT Technology Review a statement: “VK doesn’t tolerate such content or links on the platform and blocks communities that distribute them. We will run an additional check and block inappropriate content and communities.”) The researchers also found that the “undressing” algorithm is starting to be applied to videos, such as footage of bikini models walking down a runway. Right now, the algorithm must be applied frame by frame—“it’s very rudimentary at the moment,” Patrini says. “But I’m sure people will perfect it and also put up a license service for that.”

Unfortunately, there are still few ways to stop this kind of activity—but awareness of the issues is growing. Companies like Facebook and Google, and researchers who produce tools for deepfake creation, have begun to more seriously invest in countermeasures like automated deepfake detection. Last year, the US Congress also introduced a new bill that would create a mechanism for victims to seek legal recourse for reputational damage.

In the meantime, Patrini says, Sensity will continue to track and report these types of malicious deepfakes, and seek to understand more about the motivations of those who create them and the impacts on victims’ lives. “Indeed, the data we share in this report is only the tip of the iceberg,” he says.

Update: An official statement from the Russian social media platform VK has been added to the article.

A deepfake bot is being used to “undress” underage girls 2020/10/20 16:05

Dozens of volunteers will be deliberately infected with covid-19 in the UK

The news: Young, healthy people will be deliberately infected with covid-19 in the first ever human challenge trial, set to begin at a London hospital in January. The study, announced today, will recruit up to 50 healthy volunteers between 18 and 30. The UK government has pledged to invest £33.6 million ($44 million) in the trial, which will be carried out in partnership with hVIVO, a company with experience in human viral challenge trials. It will take place at the Royal Free London NHS Foundation Trust, if it gets ethical and regulatory approval. Volunteers will be paid, isolated for the duration of the study, and monitored for up to a year afterwards to check for any side effects. 

Why do this? The hope is that this trial will make it easier to closely study the disease, with the aim of speeding up the development of a vaccine. In the first phase of the trial, researchers would try to work out the smallest level of exposure required for someone to catch covid-19. Next, they could test if a vaccine prevents infection. They could also explore other potential treatments, and study the immune response. The benefit of this approach is that it lets researchers study vaccine candidates side by side to see which is the most effective. “Deliberately infecting volunteers with a known human pathogen is never undertaken lightly,” said Peter Openshaw, an investigator on the study at Imperial College London, in a statement. “However, such studies are enormously informative about a disease, even one so well studied as covid-19. It is really vital that we move as fast as possible towards getting effective vaccines and other treatments for covid-19, and challenge studies have the potential to accelerate and de-risk the development of novel drugs and vaccines.” 

Controversial: There are obvious risks to this approach. The volunteers could become seriously ill and even die. There are huge trials under way to test treatments and vaccines in people who are already infected with covid-19 naturally. And given that the challenge study doesn’t start until January, we may already be close to having an effective vaccine by then. 

Dozens of volunteers will be deliberately infected with covid-19 in the UK 2020/10/20 13:26

AI has exacerbated racial bias in housing. Could it help eliminate it instead?

Our upcoming magazine issue is devoted to long-term problems. Few problems are longer-term or more intractable than America’s systemic racial inequality. And a particularly entrenched form of it is housing discrimination. 

A long history of policies by banks, insurance companies, and real estate brokers has denied people of color a fair shot at homeownership, concentrated wealth and property in the hands of white people and communities, and perpetuated de facto segregation. Though these policies—with names like redlining, blockbusting, racial zoning, restrictive covenants, and racial steering—are no longer legal, their consequences persist, and they are sometimes still practiced covertly or inadvertently. 

Technology has in some cases exacerbated America’s systemic racial bias. Algorithmically based facial recognition, predictive policing, and sentencing and bail decisions, for example, have been shown to consistently produce worse results for Black people. In housing, too, recent research from the University of California, Berkeley, showed that an AI-based mortgage lending system charged Black and Hispanic borrowers higher rates than white people for the same loans. 

Could technology be used to help mitigate the bias in housing instead? We brought together some experts to discuss the possibilities. They are:

Lisa Rice

President and CEO of the National Fair Housing Alliance, the largest consortium of organizations dedicated to ending housing discrimination.

Bobby Bartlett

Law professor at UC Berkeley who led the research providing some of the first large-scale evidence for how artificial intelligence creates discrimination in mortgage lending.

Charlton McIlwain

Professor of media, culture, and communication at NYU and author of Black Software: The Internet & Racial Justice, from the Afronet to Black Lives Matter.

This discussion has been edited and condensed for clarity. 

McIlwain: When I testified before Congress last December about the impact of automation and AI in the financial services industry, I cited a recent study that found that unlike human loan officers, automated mortgage lending systems fairly approved home loans, without discriminating based on race. However, the automated systems still charge Black and Hispanic borrowers significantly higher prices for those loans. 

This makes me skeptical that AI can or will do any better than humans. Bobby—this was your study. Did you draw the same conclusions? 

Bartlett: We had access to a data set that allowed us to identify the lender of record and whether that lender used a totally automated system, without any human intervention—at least in terms of the approval and underwriting. We had information on the race and ethnicity of the borrower of record and were able to identify whether or not the pricing of approved loans differed by race. In fact, it did, by roughly $800 million a year. 

Why is it the case that these algorithms, which are blinded to the race or ethnicity of the borrower, would discriminate in this fashion? Our working hypothesis is that the algorithms are often simply trying to maximize price. Presumably, whoever is designing the algorithm is unaware of the racial consequence of this single-­minded focus on profitability. But they need to understand that there is this racial dynamic, that the proxy variables they’re using—in all likelihood, that’s where the discrimination is. In some sense, there’s effectively redlining of the reddest sort going in through the code. It resembles what happens in the mortgage market generally. We know that brokers will quote higher prices to minority borrowers, knowing that some will turn it away, but others will be more likely to accept it fora whole host of reasons. 

McIlwain: I have a theory that one of the reasons that we end up with biased systems—even when they were built to be less discriminatory—is because the people designing them don’t really understand the underlying complexity of the problem. There seems to me to be a certain naïveté in thinking that a system would be bias free just because it is “race blind.”

Rice: You know, Charlton, we had the same perspective that you did back in the ’90s and early 2000s. We forbade financial institutions from using insurance scoring, risk-based pricing, or credit scoring systems, for just this purpose. We realized that the systems themselves were manifesting bias. But then we started saying you can use them only if they help people, expand access, or generate fairer pricing. 

McIlwain: Do people designing these systems go wrong because they really don’t fundamentally understand the underlying problem with housing discrimination? And does your source of optimism come from the fact that you and organizations like yours do understand that complexity?

Rice: We are a civil rights organization. That’s what we are. We do all of our work through a racial equity lens. We are an antiracism organization. 

In the course of resolving redlining and reverse redlining cases, we encouraged the financial institutions and insurance agencies to rethink their business models, to rethink how they were marketing, to rethink their underwriting guidelines, to rethink the products that they were developing. And I think the reason we were able to do that is because we are a civil rights agency. 

We start by helping corporations understand the history of housing and finance in the United States and how all of our housing and finance policies have been exacted through a racial lens. You can’t start at ground zero in terms of developing a system and think that system is going to be fair. You have to develop it in a way that utilizes antiracist technologies and methodologies.

McIlwain: Can we still realistically make a dent in this problem using the technological tools at our disposal? If so, where do we start?

Rice: Yes—once the 2008 financial crisis was over a little bit and we looked up, it was like the technology had overtaken us. And so we decided, maybe if we can’t beat it, maybe we’ll join. So we spent a lot of time trying to learn how algorithmic-­based systems work, how AI works, and we actually have come to the point where we think we can now use technology to help diminish discriminatory outcomes. 

If we understand how these systems manifest bias, we can get in the innards, hopefully, and then de-bias those systems, and build new systems that infuse the de-biasing techniques within them. 

We really don’t have regulatory agencies who understand how to conduct an exam of a lending institution to ferret out whether or not its system is biased.

But when you think about how far behind the curve we are, it’s really daunting to think about all the work that needs to be done, all the research that needs to be done. We need more Bobbys of the world. But also all of the education that needs to be done so that data scientists understand these issues. 

Rice: We’re trying to get regulators to understand how systems manifest bias. You know, we really don’t have a body of examiners at regulatory agencies who understand how to conduct an exam of a lending institution to ferret out whether or not its system—its automated underwriting system, its marketing system, its servicing system—is biased. But the institutions themselves develop their own organizational policies that can help. 

The other thing that we have to do is really increase diversity in the tech space. We have to get more students from various backgrounds into STEM fields and into the tech space to help enact change. I can think of a number of examples where just having a person of color on the team made a profound difference in terms of increasing the fairness of the technology that was being developed.

McIlwain: What role does policy play? I get the sense that in the same way that civil rights organizations were behind the industry in terms of understanding how algorithmic systems work, many of our policymakers are behind the curve. I don’t know how much faith I would place in their ability to realistically serve as an effective check on the system, or on the new AI systems’ quickly making their way into the mortgage arena. 

McIlwain: I remain skeptical. For now, for me, the magnitude of the problem still far exceeds both our collective human will and the capabilities of our technology. Bobby, do you think technology can ever help
this problem?

Bartlett: I have to answer that with the lawyerly “It depends.” What we see, at least in the lending context, is that you can eliminate the source of bias and discrimination that you observed with face-to-face interactions through some sort of algorithmic decision making. The flip side is that if improperly implemented, you could end up with a decision-­making apparatus that is as bad as a redlining regime. So it really depends on the execution, the type of technology, and the care with which it is deployed. But a fair lending regime that is operationalized through automated decision making? I think that’s a really challenging proposition. And I think that jury is still out. 

AI has exacerbated racial bias in housing. Could it help eliminate it instead? 2020/10/20 13:00

The 2020 election could permanently change how America votes

More than 29 million voters have already cast their ballots in the 2020 US elections, and we’re still more than two weeks from Election Day itself. At the same point in 2016, the number of early votes was about 6 million. But while a great deal of this is the result of the ongoing (and worsening) covid-19 crisis, America’s top election official says that the shift to early and mail-in voting could be permanent—even when the pandemic is over.

“One of the things that we’ve consistently seen over time is that as more Americans get exposed to convenience voting options like early voting and vote by mail, the more they like it and the more they want to keep doing that,” says Benjamin Hovland, chairman of the Electoral Assistance Commission, which helps administer and advise on voting guidelines around the nation.

Learning from the past

History has lots of lessons to tell us about this process. The first mail-in ballots were cast during the Civil War, and today five states hold their elections almost entirely that way.

Oregon is the one that really blazed a trail for American mail-in voting. When the idea first popped up in 1981, there were skeptics and opponents everywhere. By the end of the decade, the state was moving at speed to embrace mail-in voting, first for local elections and then for state and national ballots. A partisan fight over the issue was resolved in 1998, when Oregonians themselves overwhelmingly backed a ballot measure to make the state vote entirely by mail.

Oregon held its first general election by mail-in vote in 2000, and the process has been repeated again and again in the last two decades. Now Colorado, Washington, and Utah have similar systems, while much of the western US already has large and continuously growing vote-by-mail participation. Utah, a heavily Republican state, has voted by mail for eight years—and both voters and politicians like it that way. Turnout is higher and costs are lower.

(Contrary to politically driven disinformation campaigns, mail-in voting is secure.)

Across the US, the number of Americans who vote by mail has been rising for years. In 2020, that number is skyrocketing and could potentially count for more than half of all votes cast, Hovland says.

He expects the benchmark number of mail-in votes to rise permanently in future elections: “You will see more masses that want to vote this way, who expect to be able to vote by mail or early. I think that jurisdictions absolutely will adapt their processes and their laws.”

Reasons for optimism

In the meantime, he says, the large number of early votes suggests that the election process is coping with heavy amounts of stress—despite concerns.

The pandemic was the reason for the mass switch to early and mail-in voting this time around. Experts worried that crowding at polling places would accelerate transmission of the disease; the idea is that expanding early voting options should spread out the vote—and the people—to reduce the risk.

The nationwide shortage of poll workers was another legitimate worry earlier in the year. But the first-ever national recruiting drive for poll workers has seen a huge response, says Hovland, even if exact numbers are not available. The potential emergency of unmanned polling places has not come to pass.

“Frankly, it’s going well,” he says. “We had about 140 million people vote for president in 2016. I saw predictions that may go as high as 150 or 160 million this year. When you think of that scale, it puts into perspective that some of the woes we hear about are very minor.”

This is an excerpt from The Outcome, our daily email on election integrity and security. Click here to get regular updates straight to your inbox.

The 2020 election could permanently change how America votes 2020/10/20 13:00

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