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Imagine you’re on your daily commute to work, driving along a crowded highway while trying to resist looking at your phone. You’re already a little stressed out because you didn’t sleep well, woke up late, and have an important meeting in a couple hours, but you just don’t feel like your best self.
Suddenly another car cuts you off, coming way too close to your front bumper as it changes lanes. Your already-simmering emotions leap into overdrive, and you lay on the horn and shout curses no one can hear.
Except someone—or, rather, something—can hear: your car. Hearing your angry words, aggressive tone, and raised voice, and seeing your furrowed brow, the onboard computer goes into “soothe” mode, as it’s been programmed to do when it detects that you’re angry. It plays relaxing music at just the right volume, releases a puff of light lavender-scented essential oil, and maybe even says some meditative quotes to calm you down.
What do you think—creepy? Helpful? Awesome? Weird? Would you actually calm down, or get even more angry that a car is telling you what to do?
Scenarios like this (maybe without the lavender oil part) may not be imaginary for much longer, especially if companies working to integrate emotion-reading artificial intelligence into new cars have their way. And it wouldn’t just be a matter of your car soothing you when you’re upset—depending what sort of regulations are enacted, the car’s sensors, camera, and microphone could collect all kinds of data about you and sell it to third parties.
Computers and Feelings
Just as AI systems can be trained to tell the difference between a picture of a dog and one of a cat, they can learn to differentiate between an angry tone of voice or facial expression and a happy one. In fact, there’s a whole branch of machine intelligence devoted to creating systems that can recognize and react to human emotions; it’s called affective computing.
Emotion-reading AIs learn what different emotions look and sound like from large sets of labeled data; “smile = happy,” “tears = sad,” “shouting = angry,” and so on. The most sophisticated systems can likely even pick up on the micro-expressions that flash across our faces before we consciously have a chance to control them, as detailed by Daniel Goleman in his groundbreaking book Emotional Intelligence.
Affective computing company Affectiva, a spinoff from MIT Media Lab, says its algorithms are trained on 5,313,751 face videos (videos of people’s faces as they do an activity, have a conversation, or react to stimuli) representing about 2 billion facial frames. Fascinatingly, Affectiva claims its software can even account for cultural differences in emotional expression (for example, it’s more normalized in Western cultures to be very emotionally expressive, whereas Asian cultures tend to favor stoicism and politeness), as well as gender differences.
As reported in Motherboard, companies like Affectiva, Cerence, Xperi, and Eyeris have plans in the works to partner with automakers and install emotion-reading AI systems in new cars. Regulations passed last year in Europe and a bill just introduced this month in the US senate are helping make the idea of “driver monitoring” less weird, mainly by emphasizing the safety benefits of preemptive warning systems for tired or distracted drivers (remember that part in the beginning about sneaking glances at your phone? Yeah, that).
Drowsiness and distraction can’t really be called emotions, though—so why are they being lumped under an umbrella that has a lot of other implications, including what many may consider an eerily Big Brother-esque violation of privacy?
Our emotions, in fact, are among the most private things about us, since we are the only ones who know their true nature. We’ve developed the ability to hide and disguise our emotions, and this can be a useful skill at work, in relationships, and in scenarios that require negotiation or putting on a game face.
And I don’t know about you, but I’ve had more than one good cry in my car. It’s kind of the perfect place for it; private, secluded, soundproof.
Putting systems into cars that can recognize and collect data about our emotions under the guise of preventing accidents due to the state of mind of being distracted or the physical state of being sleepy, then, seems a bit like a bait and switch.
A Highway to Privacy Invasion?
European regulations will help keep driver data from being used for any purpose other than ensuring a safer ride. But the US is lagging behind on the privacy front, with car companies largely free from any enforceable laws that would keep them from using driver data as they please.
Affectiva lists the following as use cases for occupant monitoring in cars: personalizing content recommendations, providing alternate route recommendations, adapting environmental conditions like lighting and heating, and understanding user frustration with virtual assistants and designing those assistants to be emotion-aware so that they’re less frustrating.
Our phones already do the first two (though, granted, we’re not supposed to look at them while we drive—but most cars now let you use bluetooth to display your phone’s content on the dashboard), and the third is simply a matter of reaching a hand out to turn a dial or press a button. The last seems like a solution for a problem that wouldn’t exist without said… solution.
Despite how unnecessary and unsettling it may seem, though, emotion-reading AI isn’t going away, in cars or other products and services where it might provide value.
Besides automotive AI, Affectiva also makes software for clients in the advertising space. With consent, the built-in camera on users’ laptops records them while they watch ads, gauging their emotional response, what kind of marketing is most likely to engage them, and how likely they are to buy a given product. Emotion-recognition tech is also being used or considered for use in mental health applications, call centers, fraud monitoring, and education, among others.
In a 2015 TED talk, Affectiva co-founder Rana El-Kaliouby told her audience that we’re living in a world increasingly devoid of emotion, and her goal was to bring emotions back into our digital experiences. Soon they’ll be in our cars, too; whether the benefits will outweigh the costs remains to be seen.
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Long before coronavirus appeared and shattered our pre-existing “normal,” the future of work was a widely discussed and debated topic. We’ve watched automation slowly but surely expand its capabilities and take over more jobs, and we’ve wondered what artificial intelligence will eventually be capable of.
The pandemic swiftly turned the working world on its head, putting millions of people out of a job and forcing millions more to work remotely. But essential questions remain largely unchanged: we still want to make sure we’re not replaced, we want to add value, and we want an equitable society where different types of work are valued fairly.
To address these issues—as well as how the pandemic has impacted them—this week Singularity University held a digital summit on the future of work. Forty-three speakers from multiple backgrounds, countries, and sectors of the economy shared their expertise on everything from work in developing markets to why we shouldn’t want to go back to the old normal.
Gary Bolles, SU’s chair for the Future of Work, kicked off the discussion with his thoughts on a future of work that’s human-centric, including why it matters and how to build it.
What Is Work?
“Work” seems like a straightforward concept to define, but since it’s constantly shifting shape over time, let’s make sure we’re on the same page. Bolles defined work, very basically, as human skills applied to problems.
“It doesn’t matter if it’s a dirty floor or a complex market entry strategy or a major challenge in the world,” he said. “We as humans create value by applying our skills to solve problems in the world.” You can think of the problems that need solving as the demand and human skills as the supply, and the two are in constant oscillation, including, every few decades or centuries, a massive shift.
We’re in the midst of one of those shifts right now (and we already were, long before the pandemic). Skills that have long been in demand are declining. The World Economic Forum’s 2018 Future of Jobs report listed things like manual dexterity, management of financial and material resources, and quality control and safety awareness as declining skills. Meanwhile, skills the next generation will need include analytical thinking and innovation, emotional intelligence, creativity, and systems analysis.
Along Came a Pandemic
With the outbreak of coronavirus and its spread around the world, the demand side of work shrunk; all the problems that needed solving gave way to the much bigger, more immediate problem of keeping people alive. But as a result, tens of millions of people around the world are out of work—and those are just the ones that are being counted, and they’re a fraction of the true total. There are additional millions in seasonal or gig jobs or who work in informal economies now without work, too.
“This is our opportunity to focus,” Bolles said. “How do we help people re-engage with work? And make it better work, a better economy, and a better set of design heuristics for a world that we all want?”
Bolles posed five key questions—some spurred by impact of the pandemic—on which future of work conversations should focus to make sure it’s a human-centric future.
1. What does an inclusive world of work look like? Rather than seeing our current systems of work as immutable, we need to actually understand those systems and how we want to change them.
2. How can we increase the value of human work? We know that robots and software are going to be fine in the future—but for humans to be fine, we need to design for that very intentionally.
3. How can entrepreneurship help create a better world of work? In many economies the new value that’s created often comes from younger companies; how do we nurture entrepreneurship?
4. What will the intersection of workplace and geography look like? A large percentage of the global workforce is now working from home; what could some of the outcomes of that be? How does gig work fit in?
5. How can we ensure a healthy evolution of work and life? The health and the protection of those at risk is why we shut down our economies, but we need to find a balance that allows people to work while keeping them safe.
Problem-Solving Doesn’t End
The end result these questions are driving towards, and our overarching goal, is maximizing human potential. “If we come up with ways we can continue to do that, we’ll have a much more beneficial future of work,” Bolles said. “We should all be talking about where we can have an impact.”
One small silver lining? We had plenty of problems to solve in the world before ever hearing about coronavirus, and now we have even more. Is the pace of automation accelerating due to the virus? Yes. Are companies finding more ways to automate their processes in order to keep people from getting sick? They are.
But we have a slew of new problems on our hands, and we’re not going to stop needing human skills to solve them (not to mention the new problems that will surely emerge as second- and third-order effects of the shutdowns). If Bolles’ definition of work holds up, we’ve got ours cut out for us.
In an article from April titled The Great Reset, Bolles outlined three phases of the unemployment slump (we’re currently still in the first phase) and what we should be doing to minimize the damage. “The evolution of work is not about what will happen 10 to 20 years from now,” he said. “It’s about what we could be doing differently today.”
Watch Bolles’ talk and those of dozens of other experts for more insights into building a human-centric future of work here.
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