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These creepy fake humans herald a new age in AI

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These creepy fake humans herald a new age in AI


Once viewed as less desirable than real data, synthetic data is now seen by some as a panacea. Real data is messy and riddled with bias. New data privacy regulations make it hard to collect. By contrast, synthetic data is pristine and can be used to build more diverse data sets. You can produce perfectly labeled faces, say, of different ages, shapes, and ethnicities to build a face-detection system that works across populations.

But synthetic data has its limitations. If it fails to reflect reality, it could end up producing even worse AI than messy, biased real-world data—or it could simply inherit the same problems. “What I don’t want to do is give the thumbs up to this paradigm and say, ‘Oh, this will solve so many problems,’” says Cathy O’Neil, a data scientist and founder of the algorithmic auditing firm ORCAA. “Because it will also ignore a lot of things.”

Realistic, not real

Deep learning has always been about data. But in the last few years, the AI community has learned that good data is more important than big data. Even small amounts of the right, cleanly labeled data can do more to improve an AI system’s performance than 10 times the amount of uncurated data, or even a more advanced algorithm.

That changes the way companies should approach developing their AI models, says Datagen’s CEO and cofounder, Ofir Chakon. Today, they start by acquiring as much data as possible and then tweak and tune their algorithms for better performance. Instead, they should be doing the opposite: use the same algorithm while improving on the composition of their data.

Datagen also generates fake furniture and indoor environments to put its fake humans in context.

DATAGEN

But collecting real-world data to perform this kind of iterative experimentation is too costly and time intensive. This is where Datagen comes in. With a synthetic data generator, teams can create and test dozens of new data sets a day to identify which one maximizes a model’s performance.

To ensure the realism of its data, Datagen gives its vendors detailed instructions on how many individuals to scan in each age bracket, BMI range, and ethnicity, as well as a set list of actions for them to perform, like walking around a room or drinking a soda. The vendors send back both high-fidelity static images and motion-capture data of those actions. Datagen’s algorithms then expand this data into hundreds of thousands of combinations. The synthesized data is sometimes then checked again. Fake faces are plotted against real faces, for example, to see if they seem realistic.

Datagen is now generating facial expressions to monitor driver alertness in smart cars, body motions to track customers in cashier-free stores, and irises and hand motions to improve the eye- and hand-tracking capabilities of VR headsets. The company says its data has already been used to develop computer-vision systems serving tens of millions of users.

It’s not just synthetic humans that are being mass-manufactured. Click-Ins is a startup that uses synthetic AI to perform automated vehicle inspections. Using design software, it re-creates all car makes and models that its AI needs to recognize and then renders them with different colors, damages, and deformations under different lighting conditions, against different backgrounds. This lets the company update its AI when automakers put out new models, and helps it avoid data privacy violations in countries where license plates are considered private information and thus cannot be present in photos used to train AI.

Click-Ins renders cars of different makes and models against various backgrounds.

CLICK-INS

Mostly.ai works with financial, telecommunications, and insurance companies to provide spreadsheets of fake client data that let companies share their customer database with outside vendors in a legally compliant way. Anonymization can reduce a data set’s richness yet still fail to adequately protect people’s privacy. But synthetic data can be used to generate detailed fake data sets that share the same statistical properties as a company’s real data. It can also be used to simulate data that the company doesn’t yet have, including a more diverse client population or scenarios like fraudulent activity.

Proponents of synthetic data say that it can help evaluate AI as well. In a recent paper published at an AI conference, Suchi Saria, an associate professor of machine learning and health care at Johns Hopkins University, and her coauthors demonstrated how data-generation techniques could be used to extrapolate different patient populations from a single set of data. This could be useful if, for example, a company only had data from New York City’s more youthful population but wanted to understand how its AI performs on an aging population with higher prevalence of diabetes. She’s now starting her own company, Bayesian Health, which will use this technique to help test medical AI systems.

The limits of faking it

But is synthetic data overhyped?

When it comes to privacy, “just because the data is ‘synthetic’ and does not directly correspond to real user data does not mean that it does not encode sensitive information about real people,” says Aaron Roth, a professor of computer and information science at the University of Pennsylvania. Some data generation techniques have been shown to closely reproduce images or text found in the training data, for example, while others are vulnerable to attacks that make them fully regurgitate that data.

This might be fine for a firm like Datagen, whose synthetic data isn’t meant to conceal the identity of the individuals who consented to be scanned. But it would be bad news for companies that offer their solution as a way to protect sensitive financial or patient information.

Research suggests that the combination of two synthetic-data techniques in particular—differential privacy and generative adversarial networks—can produce the strongest privacy protections, says Bernease Herman, a data scientist at the University of Washington eScience Institute. But skeptics worry that this nuance can be lost in the marketing lingo of synthetic-data vendors, which won’t always be forthcoming about what techniques they are using.

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The quest to show that biological sex matters in the immune system

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Sabra Klein and Janna Shapiro look at a specimen on a lightbox.


She ultimately found a postdoctoral position in the lab of one of her thesis committee members. And in the years since, as she has established a lab of her own at the university’s Bloomberg School of Public Health, she has painstakingly made the case that sex—defined by biological attributes such as our sex chromosomes, sex hormones, and reproductive tissues—really does influence immune responses. 

Through research in animal models and humans, Klein and others have shown how and why male and female immune systems respond differently to the flu virus, HIV, and certain cancer therapies, and why most women receive greater protection from vaccines but are also more likely to get severe asthma and autoimmune disorders (something that had been known but not attributed specifically to immune differences). “Work from her laboratory has been instrumental in advancing our understanding of vaccine responses and immune function on males and females,” says immunologist Dawn Newcomb of the Vanderbilt University Medical Center in Nashville, Tennessee. (When referring to people in this article, “male” is used as a shorthand for people with XY chromosomes, a penis, and testicles, and who go through a testosterone-dominated puberty, and “female” is used as a shorthand for people with XX chromosomes and a vulva, and who go through an estrogen-dominated puberty.)

Through her research, as well as the unglamorous labor of arranging symposia and meetings, Klein has helped spearhead a shift in immunology, a field that long thought sex differences didn’t matter. Historically, most trials enrolled only males, resulting in uncounted—and likely uncountable—consequences for public health and medicine. The practice has, for example, caused women to be denied a potentially lifesaving HIV therapy and left them likely to endure worse side effects from drugs and vaccines when given the same dose as men.


Men and women don’t experience infectious or autoimmune diseases in the same way. Women are nine times more likely to get lupus than men, and they have been hospitalized at higher rates for some flu strains. Meanwhile, men are significantly more likely to get tuberculosis and to die of covid-19 than women. 

In the 1990s, scientists often attributed such differences to gender rather than sex—to norms, roles, relationships, behaviors, and other sociocultural factors as opposed to biological differences in the immune system.

For example, even though three times as many women have multiple sclerosis as men, immunologists in the 1990s ignored the idea that this difference could have a biological basis, says Rhonda Voskuhl, a neuroimmunologist at the University of California, Los Angeles. “People would say, ‘Oh, the women just complain more—they’re kind of hysterical,’” Voskuhl says. “You had to convince people that it wasn’t just all subjective or environmental, that it was basic biology. So it was an uphill battle.” 

Sabra Klein (left) and Janna Shapiro in Klein’s laboratory at Johns Hopkins University in Baltimore, Maryland.

ROSEM MORTON

Despite a historical practice of “bikini medicine”—the notion that there are no major differences between the sexes outside the parts that fit under a bikini—we now know that whether you’re looking at your metabolism, heart, or immune system, both biological sex differences and sociocultural gender differences exist. And they both play a role in susceptibility to diseases. For instance, men’s greater propensity to tuberculosis—they are almost twice as likely to get it as women—may be attributed partly to differences in their immune responses and partly to the fact that men are more likely to smoke and to work in mining or construction jobs that expose them to toxic substances, which can impair the lungs’ immune defenses. 

How to tease apart the effects of sex and gender? That’s where animal models come in. “Gender is a social construct that we associate with humans, so animals do not have a gender,” says Chyren Hunter, associate director for basic and translational research at the US National Institutes of Health Office of Research on Women’s Health. Seeing the same effect in both animal models and humans is a good starting point for finding out whether an immune response is modulated by sex. 

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Why can’t tech fix its gender problem?

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From left to right: Gordon MOORE, C. Sheldon ROBERTS, Eugene KLEINER, Robert NOYCE, Victor GRINICH, Julius BLANK, Jean HOERNI and Jay LAST.


Not competing in this Olympics, but still contributing to the industry’s success, were the thousands of women who worked in the Valley’s microchip fabrication plants and other manufacturing facilities from the 1960s to the early 1980s. Some were working-class Asian- and Mexican-Americans whose mothers and grandmothers had worked in the orchards and fruit can­neries of the prewar Valley. Others were recent migrants from the East and Midwest, white and often college educated, needing income and interested in technical work. 

With few other technical jobs available to them in the Valley, women would work for less. The preponderance of women on the lines helped keep the region’s factory wages among the lowest in the country. Women continue to dominate high-tech assembly lines, though now most of the factories are located thousands of miles away. In 1970, one early American-owned Mexican production line employed 600 workers, nearly 90% of whom were female. Half a century later the pattern continued: in 2019, women made up 90% of the workforce in one enormous iPhone assembly plant in India. Female production workers make up 80% of the entire tech workforce of Vietnam. 

Venture: “The Boys Club”

Chipmaking’s fiercely competitive and unusually demanding managerial culture proved to be highly influential, filtering down through the millionaires of the first semiconductor generation as they deployed their wealth and managerial experience in other companies. But venture capital was where semiconductor culture cast its longest shadow. 

The Valley’s original venture capitalists were a tight-knit bunch, mostly young men managing older, much richer men’s money. At first there were so few of them that they’d book a table at a San Francisco restaurant, summoning founders to pitch everyone at once. So many opportunities were flowing it didn’t much matter if a deal went to someone else. Charter members like Silicon Valley venture capitalist Reid Dennis called it “The Group.” Other observers, like journalist John W. Wilson, called it “The Boys Club.”

The men who left the Valley’s first silicon chipmaker, Shockley Semiconductor, to start Fairchild Semiconductor in 1957 were called “the Traitorous Eight.”

WAYNE MILLER/MAGNUM PHOTOS

The venture business was expanding by the early 1970s, even though down markets made it a terrible time to raise money. But the firms founded and led by semiconductor veterans during this period became industry-defining ones. Gene Kleiner left Fairchild Semiconductor to cofound Kleiner Perkins, whose long list of hits included Genentech, Sun Microsystems, AOL, Google, and Amazon. Master intimidator Don Valentine founded Sequoia Capital, making early-stage investments in Atari and Apple, and later in Cisco, Google, Instagram, Airbnb, and many others.

Generations: “Pattern recognition”

Silicon Valley venture capitalists left their mark not only by choosing whom to invest in, but by advising and shaping the business sensibility of those they funded. They were more than bankers. They were mentors, professors, and father figures to young, inexperienced men who often knew a lot about technology and nothing about how to start and grow a business. 

“This model of one generation succeeding and then turning around to offer the next generation of entrepreneurs financial support and managerial expertise,” Silicon Valley historian Leslie Berlin writes, “is one of the most important and under-recognized secrets to Silicon Valley’s ongoing success.” Tech leaders agree with Berlin’s assessment. Apple cofounder Steve Jobs—who learned most of what he knew about business from the men of the semiconductor industry—likened it to passing a baton in a relay race.

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Predicting the climate bill’s effects is harder than you might think

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Predicting the climate bill’s effects is harder than you might think


Human decision-making can also cause models and reality to misalign. “People don’t necessarily always do what is, on paper, the most economic,” says Robbie Orvis, who leads the energy policy solutions program at Energy Innovation.

This is a common issue for consumer tax credits, like those for electric vehicles or home energy efficiency upgrades. Often people don’t have the information or funds needed to take advantage of tax credits.

Likewise, there are no assurances that credits in the power sectors will have the impact that modelers expect. Finding sites for new power projects and getting permits for them can be challenging, potentially derailing progress. Some of this friction is factored into the models, Orvis says. But there’s still potential for more challenges than modelers expect.

Not enough

Putting too much stock in results from models can be problematic, says James Bushnell, an economist at the University of California, Davis. For one thing, models could overestimate how much behavior change is because of tax credits. Some of the projects that are claiming tax credits would probably have been built anyway, Bushnell says, especially solar and wind installations, which are already becoming more widespread and cheaper to build.

Still, whether or not the bill meets the expectations of the modelers, it’s a step forward in providing climate-friendly incentives, since it replaces solar- and wind-specific credits with broader clean-energy credits that will be more flexible for developers in choosing which technologies to deploy.

Another positive of the legislation is all its long-term investments, whose potential impacts aren’t fully captured in the economic models. The bill includes money for research and development of new technologies like direct air capture and clean hydrogen, which are still unproven but could have major impacts on emissions in the coming decades if they prove to be efficient and practical. 

Whatever the effectiveness of the Inflation Reduction Act, however, it’s clear that more climate action is still needed to meet emissions goals in 2030 and beyond. Indeed, even if the predictions of the modelers are correct, the bill is still not sufficient for the US to meet its stated goals under the Paris agreement of cutting emissions to half of 2005 levels by 2030.

The path ahead for US climate action isn’t as certain as some might wish it were. But with the Inflation Reduction Act, the country has taken a big step. Exactly how big is still an open question. 

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