More and more, it’s being used in what’s presented as the interest of public health. Australia recently expanded a program using facial recognition to enforce covid-19 safety precautions. People who are quarantining are subject to random check-ins, in which they’re required to send a selfie to confirm they are following rules. Location data is also collected, according to Reuters.
When it comes to essentials like emergency benefits to pay for housing and food, the first priority should be making sure everyone is able to access help, Greer says. Preventing fraud is a reasonable objective on the surface, she adds, but the most pressing goal must be to get people the benefits they need.
“Systems have to be built with human rights and with vulnerable people’s needs in mind from the start. Those can’t be afterthoughts,” Greer says. “They can’t be bug fixes after it already goes wrong.”
ID.me’s Hall says his company’s services are preferable to the existing methods of verifying identity and have helped states cut down on “massive” unemployment fraud since implementing face verification checks. He says unemployment claims have around a 91% true pass rate—either on their own or through a video call with an ID.me representative.
“[That] was our goal going in,” he says. “If we could automate away 91% of this, then the states that are just outgunned in terms of resources can use those resources to provide white-glove concierge service to the 9%.”
When users are not able to get through the face recognition process, ID.me emails them to follow up, according to Hall.
“Everything about this company is about helping people get access to things they’re eligible for,” he says.
Tech in the real world
The months that JB survived without income were difficult. The financial worry was enough to cause stress, and other troubles like a broken computer compounded the anxiety. Even their former employer couldn’t or wouldn’t help cut through the red tape.
“It’s very isolating to be like, ‘No one is helping me in any situation,’” JB says.
On the government side, experts say it makes sense that the pandemic brought new technology to the forefront, but cases like JB’s show that technology in itself is not the whole answer. Anne L. Washington, an assistant professor of data policy at New York University, says it’s tempting to consider a new government technology a success when it works most of the time during the research phase but fails 5% of the time in the real world. She compares the result to a game of musical chairs, where in a room of 100 people, five will always be left without a seat.
“The problem is that governments get some kind of technology and it works 95% of the time—they think it’s solved,” she says. Instead, human intervention becomes more important than ever. Says Washington: “They need a system to regularly handle the five people who are standing.”
There’s an additional layer of risk when a private company is involved. The biggest issue that arises in the rollout of a new kind of technology is where the data is kept, Washington says. Without a trusted entity that has the legal duty to protect people’s information, sensitive data could end up in the hands of others. How would we feel, for example, if the federal government had entrusted a private company with our Social Security numbers when they were created?
“The problem is that governments get some kind of technology and it works 95% of the time—they think it’s solved”
Anne L. Washington, New York University
Widespread and unchecked use of face recognition tools also has the potential to affect already marginalized groups more than others. Transgender people, for example, have detailed, frequent problems with tools like Google Photos, which may question whether pre- and post-transition photos show the same person. It means reckoning with the software over and over.
“[There’s] inaccuracy in technology’s ability to reflect the breadth of actual diversity and edge cases there are in the real world,” says Daly Barnett, a technologist at the Electronic Frontier Foundation. “We can’t rely on them to accurately classify and compute and reflect those beautiful edge cases.”
Worse than failure
Conversations about face recognition typically debate how the technology could fail or discriminate. But Barnett encourages people to think beyond whether the biometric tools work or not, or whether bias show up in the technology. She pushes back on the idea that we need them at all. Indeed, activists like Greer warn, the tools could be even more dangerous when they work perfectly. Face recognition has already been used to identify, punish, or stifle protesters, though people are fighting back. In Hong Kong, protesters wore masks and goggles to hide their faces from such police surveillance. In the US, federal prosecutors dropped charges against a protester identified using face recognition who had been accused of assaulting police officers.
The way forward: Merging IT and operations
“People in operations see a ton of opportunity,” says Irani-Famili, who has worked in the energy sector for the better part of a decade. For problems they encounter every day, OT dreams up potential fixes. For example, if there’s a power outage, relevant supervisors could automatically get notifications wherever they are. Or staff availability data could flow through company systems so supervisors and managers can more easily assign projects or shifts.
“And then they go and talk to IT, and IT’s response might be ‘Not possible. This could be breaking every security protocol,’” Irani-Famili says. Operations sees solutions to problems. IT sees cybersecurity, integration, and support risk. “But from the operations perspective, what they see is IT red tape, IT is not collaborating, or IT is not playing the game.”
It’s easy to describe IT and OT as different departments with different objectives and starkly different cultures. They are often managed independently in organizations and treated as isolated groups that cater to specific problems and employ their own protocols. But that results in inefficient, costly setups that fail to foster innovation and standardization.
As global economies gain steam after near collapse amid the 2020 coronavirus pandemic, the pressure is on to boost productivity, innovation, and agility. Companies need to increase the speed of business by digitizing processes and using the internet of things and artificial intelligence (AI) to extract actionable insight from large data sets.
To undergo such digital transformation in industries that rely heavily on physical assets—manufacturing, oil and gas, transportation, energy, and utilities—organizations must integrate IT and OT into one seamless organization that connects systems on both sides.
“IT/OT convergence is an inevitability,” says Fay Cranmer, senior managing director in Accenture’s natural resources practice and former chief information officer at mining company Rio Tinto. “It’s the only way to have a full digital transformation, especially in the heavy industry space.”
But there are significant challenges to overcome. Many industrial environments are characterized by legacy equipment, time-honored, manual processes, and resistance to change—from both sections of the business, OT and IT. Often the attitude is, OT alone knows how to generate the products and services that produce revenue for the company.
Conversely, IT folks often think only they know how to help modernize OT departments, by enabling the systems that allow the benefits of AI, the internet of things, and other digital technologies. True collaboration is a must, but the complexity of new technology and infrastructure merging with legacy machines prompts questions concerning investment, leadership, and governance.
Bala Arunachalam, an executive in oil and gas for more than 30 years, says specific industry characteristics are a big factor. “This industry is a legacy industry. For them to move onto the technology space, to capitalize on the opportunity that is in front of them, is a struggle.”
As physical assets, whether in the factory or out in the field, become digitized through internet-of-things technology; as applications, data storage, and data processing move to the cloud; and as employees stick to their home offices more than a year into the pandemic, any perceived boundaries between OT and rest of the business are crumbling. “The challenge is that we need to bring data together across all those boundaries,” says Cranmer. The biggest hurdles, she says, are organizational and cultural. “The technological side is much more easily overcome than the human side.”
The good news is there are guidelines that organizations can follow to achieve the IT/OT integration that’s so critical for successful digital transformation initiatives.
Download the full report.
Forget dating apps: Here’s how the net’s newest matchmakers help you find love
The thread took off. Morgan basked in the feel-good vibes of seeing people find each other—“I love love!”—and reveled in the real-life connections she was able to mastermind: multiple dates in her hometown of Portland, Oregon; someone who was thinking of flying to meet somebody in New York because of the thread; even a short relationship. Even today, people continue to add their pictures to the thread, seeking love all across the United States.
If this feels a bit like old-fashioned matchmaking, it is. But it’s a long way from gossipy neighborhood grandmas setting up dates. These operations are often ad hoc, based on platforms like Twitter and TikTok, and—unlike the dating apps, with their endless menu of eligible suitors—hyperfocused on one person at a time.
Play by mail
Randa Sakallah launched Hot Singles in December 2020 to solve her own dating blues. She’d just moved to New York to work in tech and was “sick of swiping.” So she created an email newsletter using the platform Substack that had a seemingly simple premise: apply via Google Form to be featured, and if you are, your profile—and yours only—is sent to an audience of thousands.
Yes, each profile features the requisite information: name, sexual orientation, interests, and some photos. But crucially, it has a wry editorial slant that comes from Sakallah’s questions and the email presentation. This week’s single, for example, is asked what animal she would be; the answer is somewhere between a peacock and a sea otter. (“My main goals in life are to snack, hold hands, and maybe splash around a bit,” she writes.)
Sakallah says part of the appeal of Hot Singles is that only one person’s profile is delivered via email on Friday. It’s not a stream of potential faces available on demand, she says, which makes it possible to really savor getting to know a single person as a human being and not an algorithmically offered statistic.
“I try to tell a story and give them a voice,” says Sakallah. “You really want to think about the whole person.”
Dating apps may be quick and easy to use, but critics say their design and their focus on images reduces people to caricatures. Morgan, who started the long-running Twitter thread, is a black woman who says that the dating-app experience can be exhausting because of her race.
“I’ve had friends just put their photo and an emoji up, and they would get someone asking them to coffee so fast,” she said. Meanwhile, “I’d have to put more work into my profile and write paragraphs.” The results of her effort either didn’t get read or attracted a slew of uncomfortable, racist comments. “It was frustrating,” she says.
Scratching a different itch
Dating-app fatigue has a number of sources. There’s the paradox of choice: you want to be able to select from a wide variety of people, but that variety can be debilitatingly overwhelming. Plus, the geographic parameters typically set on such apps often actually make the dating pool worse.
Alexis Germany, a professional matchmaker, decided to try TikTok videos during the pandemic to showcase people and has found them immensely popular—particularly among people who don’t live in the same place.
“What makes you think your person is in your city?” Germany says. “If they’re a car ride away or a short plane ride away, it could work.”
These weird virtual creatures evolve their bodies to solve problems
“It’s already known that certain bodies accelerate learning,” says Bongard. “This work shows that AI that can search for such bodies.” Bongard’s lab has developed robot bodies that are adapted to particular tasks, such as giving callus-like coatings to feet to reduce wear and tear. Gupta and his colleagues extend this idea, says Bongard. “They show that the right body can also speed up changes in the robot’s brain.”
Ultimately, this technique could reverse the way we think of building physical robots, says Gupta. Instead of starting with a fixed body configuration and then training the robot to do a particular task, you could use DERL to let the optimal body plan for that task evolve and then build that.
Gupta’s unimals are part of a broad shift in how researchers are thinking about AI. Instead of training AIs on specific tasks, such as playing Go or analyzing a medical scan, researchers are starting to drop bots into virtual sandboxes—such as POET, OpenAI’s virtual hide-and-seek arena, and DeepMind’s virtual playground XLand—and getting them to learn how to solve multiple tasks in ever-changing, open-ended training dojos. Instead of mastering a single challenge, AIs trained in this way learn general skills.
For Gupta, free-form exploration will be key for the next generation of AIs. “We need truly open-ended environments to create intelligent agents,” he says.