Reality Check: Comparing HoloLens and Magic Leap

After trying demos of Magic Leap and HoloLens, it’s clear that commercializing augmented reality technology will be difficult.

A mockup shows HoloLens being used to provide remote help with home repairs.

I’ve seen two competing visions for a future in which virtual objects are merged seamlessly with the real world. Both were impressive in part, but they also made me wonder whether augmented reality will become a successful commercial reality anytime soon.

I’m the only person I know of to have tried both Microsoft’s HoloLens and the system being developed by a secretive startup called Magic Leap.

I got a peek at what Magic Leap is building back in December (see “10 Breakthrough Technologies 2015: Magic Leap”). In that demonstration, 3-D monsters and robots looked amazingly detailed and crisp, fitting in well with the surrounding world, though they were visible only with lenses attached to bulky hardware sitting on a cart, and no release date has yet been revealed.

I had my chance to see HoloLens during a recent visit to Microsoft’s headquarters in Redmond, Washington. HoloLens is a holographic system that the company plans to pack into a visor about the size of a pair of bulbous ski goggles. In January, Microsoft said HoloLens would be available “in the Windows 10 time frame,” and the company said this week that the new operating system will be released this summer.

I experienced three HoloLens demos. The first, HoloStudio, showed the possibilities for 3-D modeling and manipulation. The second let me explore the surface of Mars with a virtually present NASA scientist. And the third gave a sense of how I might use HoloLens in combination with a Skype video chat to get help with a real-world problem (in this case, installing a light switch).

Unlike with some stereoscopic virtual-reality 3-D technologies I’ve tried, such as Oculus Rift, HoloLens did not make me feel nauseous, which bodes well. Microsoft would not say how it works, but a bit of explanation in a Wired piece makes it sound as if it may be doing something somewhat similar to Magic Leap, which is using a tiny projector to shine light at your eyes that blends in very well with the light you get from the real world around you.

The final form Microsoft’s HoloLens will take.

But I was not blown away by what I saw in Redmond. The holograms looked great in a couple of instances, such as when I peered at the underside of a rock on a reconstruction of the surface of Mars, created with data from the Curiosity rover. More often, though, images appeared distractingly transparent and not nearly as crisp as the creatures Magic Leap showed me some months before. What’s more, the relatively narrow viewing area in front of my face meant the 3-D imagery seen through HoloLens was often interrupted by glimpses of the unenhanced world on the periphery. The headset also wasn’t closed off to the world around me, so I still had my natural peripheral vision of the unenhanced room. This was okay when looking at smaller or farther-away 3-D images, like an underwater scene I was shown during my first demo, or while moving around to inspect images close-up from different angles. The illusion got screwed up, though, when it came to looking at something larger than my field of view.

Microsoft is also still working on packing everything into the HoloLens form it has promised. Unlike the untethered headset that the company demonstrated in January, the device I tried was unwieldy and unfinished: it had see-through lenses attached to a heavy mass of electronics and plastic straps, tethered to a softly whirring rectangular box (Microsoft’s holographic processing unit) that I had to wear around my neck and to a nearby computer. I was instructed to touch only a plastic strap that fit over the top of my head; demo minders placed it on me and took it off at the end of each experience.

Even this level of limited mobility was more than I got at Magic Leap, but it’s clear the HoloLens team has a big task in getting the technology to fit into its smaller, consumer-ready design.

For instance, during the Mars demo, the room around me was blanketed with realistic images of the surface of the planet, and a detailed-looking rover sat in front of me, slightly to my right. But I could only see it one rectangle at a time; if my eyes strayed beyond that rectangle in front of me, I’d see bits of the room, but no hologram.

The issues extended to the opacity of the images, too. The demos were all held in rooms that had no windows, but lighting was kept at a normal level of brightness, and the rooms were decorated with furniture, knick-knacks, and other items on and near the walls—not unlike your average living room, and the kind of environment in which you’d be likely to use a HoloLens if you bought one for yourself. Yet I could often see bits of the room peeking through the images themselves in a way that interrupted, rather than worked with, the illusion.

The most impressive part of the HoloLens demos was the use of sensors to track where I was looking and gesturing, as well as what I was saying. My gaze was effectively a mouse, accurately highlighting what I was looking at. An up-and-down motion with my index finger—dubbed an “air tap” by the HoloLens crew—functioned as the mouse click to do things like paint a fish or place a flag in a certain spot on Mars. (I screwed this up a number of times; mostly because I wasn’t holding my finger up high enough.) Simple voice commands like “copy” and “rotate” worked well, too.

HoloLens is also really good at having virtual objects follow the user around. As I chatted with a Microsoft employee over Skype, the simple diagram he drew about how to connect a light switch hovered in the air near the electrical box on the wall, while his video-chat window remained in my field of view, even as I moved about. This fits neatly with the idea that augmented reality could help employees in the field make repairs to things like air conditioners (see “Augmented Reality Gets to Work”).

A key difference compared to Magic Leap was that I was able to walk around some 3-D objects, such as an X-Wing fighter sitting in front of me; it looked fairly solid up close, though not intricately detailed. I was also able to modify 3-D objects, which was pretty cool. Using my gaze, gestures, and voice commands, I enlarged, copied, colored, and changed the angular position of a fish that was part of the ocean scene, for instance. And I could move objects from one spot to another, like a cartoonish pony I seated on a couch between the two HoloLens team members who were in the room with me for a mixed-reality photo.

Despite the issues yet to be overcome, Microsoft is making progress toward getting the technology behind HoloLens into a device you can actually wear. That’s not the same, though, as making an augmented reality device that is so useful and slickly packaged that millions of consumers will want to buy it. To do that, HoloLens, Magic Leap, and any other competitors must do much more.

It’s impossible to compare HoloLens and Magic Leap at this stage and declare one winner, at least given what I’ve seen in two very different demonstrations. What I can say is my experiences illustrate the enormous challenge of creating a truly engaging augmented reality experience in a practical, consumer-ready device.

It’s clearly incredibly hard to make this kind of stuff work in a convincing way on a headset—once you’ve figured out how to make good-looking virtual images, there’s the task of cramming all of the necessary computer hardware into a wearable device, making sure it looks good as the wearer is walking around, and figuring out a way to power it. This raises big questions about how good augmented reality can really get, and how useful it will be in the near future. If it doesn’t wow you, both in form and function, why would you buy it?

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Microsoft’s Wristband Would Like to Be Your Life Coach

Microsoft is working to combine biometric data collected by its new wristband with information from your calendar and contacts to make smarter observations.

The Microsoft Band uses a number of sensors to track activity and biometrics, and the company is working to draw insights from how such measurements relate to things like your meetings and contacts.

Microsoft’s first foray into wearable activity tracking will go beyond collecting and analyzing exercise and sleep patterns to, say, telling you how stressed out you get before an important meeting and offering breathing exercises to calm you down.

Released in October, the Microsoft Band costs $200 and houses a variety of sensors including a microphone, a GPS location sensor, motion sensors, an optical sensor that measures heart rate, a sensor that tracks skin conductance, which can reveal levels of stress, and even a UV sensor to calculate sun exposure, all encased in a black, rubbery bracelet with a rectangular touch screen. The band communicates with your smartphone via the Microsoft Health app, which itself communicates with Microsoft’s cloud-computing service to analyze the data you collect.

Many fitness bands and smart watches that can track your activities are already on the market, and devices like the Band and the soon-to-be-released Apple Watch—which also uses an optical sensor to determine heart rate—seem poised to deliver new ways to collect and use your biometric data. During a recent interview at Microsoft’s Redmond, Washington, headquarters, Matt Barlow, general manager of marketing for new devices, said the company is investigating the kinds of insights it can share with users by matching up biometric data with other sources of information like their calendar or contacts to show things like which events or people may stress them out.

In the coming months, the Microsoft Health app is poised to gain the ability to compare calendar or contact information with your physical state as measured by the band—your heart rate or skin conductance level, for instance—so the app could nudge you with detailed observations about how those things might relate. For instance, the app might send you an alert like, “I noticed you have a meeting with Susan tomorrow, and last time you met with her your heart rate went up 20 beats per minute and stayed elevated for an hour. How about trying this deep-breathing exercise that you can use with the Band?”

Initially, these kinds of scenarios are expected to become possible through an integration with Microsoft Office services, though over time it may branch out to include other services as well.

Emil Jovanov, an associate professor at the University of Alabama in Huntsville who directs a lab for real-time physiological monitoring and codirects a lab aimed at tracking mobile health and wellness, says that the types of sensors in emerging wearables are generally accurate enough to provide these kinds of insights.

He cautions, however, that trade-offs have to be made between accuracy, power consumption, weight, and size in order for these devices to be convenient to use. And he notes that optical sensors, such as the one used in the Microsoft Band, need good contact with the skin to work reliably, which is harder to achieve if you’ve got hairy arms or wear your wristband too loosely.

Accuracy is still a challenge for companies making wearable devices for tracking biometric signals and activities—measurements tend to vary between devices, and even accurate readings may not always provide a valid measure of sleep patterns or stress. One way the Microsoft team is working on the accuracy of the data that its sensors record is by tracking people as they work out in a gym. It correlates data captured by its wristband there with data recorded by, for example, a machine that measures oxygen consumption.

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Facebook AI Software Learns and Answers Questions

Software able to read a synopsis of Lord of the Rings and answer questions about it could beef up Facebook search.

Facebook is working on artificial intelligence software that can process text and then answer questions about it. The effort could eventually lead to anything from better search on Facebook itself to more accurate and useful personal assistant software.

The social network’s chief technology officer, Mike Schroepfer, introduced the software, called Memory Network, in a talk at Facebook’s F8 developer conference in San Francisco on Thursday. He demonstrated how the software could acquire knowledge from text by showing how it was fed a super-simple synopsis of the book “Lord of the Rings”, in the form of phrases including “Bilbo travelled to the cave” and “Gollum dropped the ring there.” After that, the software could answer questions that required following the flow of events in the text, such as “Where is the ring?” and “Where is Frodo now?”

Extracting information from text and figuring out how to put it together into brand-new facts is a difficult task for computers to do–as Shroepfer noted in his demo, it requires the machine to understand the relationships between objects over time.

Facebook is making this work with a new twist on a recently-popular approach to machine learning called deep learning (see “10 Breakthrough Technologies 2014: Deep Learning”). That technique involves using networks of crude “neurons” to process data. Facebook added what Schroepfer described as a “multimillion-slot memory system” to such a network, which functions essentially as a short-term memory where facts can be stored and processed.

Facebook set up a research group dedicated to deep learning in 2013 (see “Facebook Launches Advanced AI Effort”). Like similar groups at Google and elsewhere, it has largely focused on using the technique to make software able to figure out what’s going on in images. In his talk, Schroepfer also showed results from a project in which his researchers taught deep learning software to classify 487 different sports from looking at video clips. He said it is good enough to differentiate between figure skating, speed skating, artistic roller skating, and ice hockey.

That such an apparently easy task is a major achievement for software is a reminder that even deep learning software is far from very intelligent. Such software could be valuable, though. If applied to the many videos uploaded to Facebook, it might make it possible to, say, show you ads that are closely targeted to what you’re watching.

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High-Resolution 3-D Scans Built from Drone Photos

A drone spent hours swarming around Rio’s iconic Christ statue to show a cheap way to capture highly accurate 3-D scans.

This drone flew around Rio’s Christ the Redeemer statue to capture photos later used to make an accurate 3-D model of the monument.

The 30-meter tall statue of Christ overlooking Rio de Janeiro from a nearby mountain was under construction for nine years before its opening in 1931. It took just hours to build the first detailed 3-D scan of the monument late last year, using more than 2,000 photos captured by a small drone that buzzed all around it with an ordinary digital camera. The statue’s digital double was unveiled last month, and is accurate to between two and five centimeters, enough to capture individual mosaic tiles.

The project was intended to help efforts to preserve the statue and to demonstrate how drones could lead to a dramatic increase in high-resolution 3-D replicas of buildings, terrain, and other objects. Being able to easily and frequently capture detailed 3-D imagery could have many uses, such as speeding up construction projects and helping Hollywood make better special effects, says Christoph Strecha, CEO and cofounder of Pix4D, the Swiss company that led the project. It collaborated with drone manufacturer Aeryon Labs and researchers at PUC University of Rio de Janeiro.

Mapping tools from companies including Google and Apple have made outdoor 3-D imagery commonplace in recent years. But they are mostly built with 3-D data from aircraft carrying expensive equipment. The 3-D shape of buildings and terrain is most often captured using a technique called lidar, which uses lasers. The photo-real 3-D models of cities in Apple’s “flyover” mode are made by processing images captured by a complex and expensive array of cameras (see “Ultrasharp 3-D Maps”). Both techniques rely on very accurate GPS technology.

Software stitches a high-resolution 3-D model by processing overlapping photos.

Drones don’t have very reliable GPS fixes by comparison, and can’t carry large sensors or cameras. But they are cheap, and Pix4D’s software can build highly accurate models by comparing many different overlapping photos, says Strecha. In fact, using lidar would have been impossible with the Rio statue, Strecha says, because of its size, shape, and location.

Other projects have also been weaving 3-D models from drone photos. Researchers led by Horst Bischof, a professor at the Technical University of Graz, Austria, are developing software that extracts information from such images. For example, for a company that restores old buildings, the researchers made a version of the software that calculates measurements necessary for producing custom-fit thermal insulation.

With the image processing more or less a solved problem, the ambitions of drone scanning will depend more on how well drones can be controlled or co?rdinated in challenging conditions like the winds around Rio’s Christ, or to cover larger areas, says Carl Salvaggio, a professor at Rochester Institute of Technology’s Center for Imaging Science. “Drones are good for small-scale projects but traditional aircraft offer the time in the air to collect whole cities,” he says. “Perhaps when there are ‘armies’ of drones in the air, we will see a different landscape emerge.”

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Seven Must-Read Stories (Week Ending March 28, 2015)

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Our Fear of Artificial Intelligence

Years ago I had coffee with a friend who ran a startup. He had just turned 40. His father was ill, his back was sore, and he found himself overwhelmed by life. “Don’t laugh at me,” he said, “but I was counting on the singularity.”

My friend worked in technology; he’d seen the changes that faster microprocessors and networks had wrought. It wasn’t that much of a step for him to believe that before he was beset by middle age, the intelligence of machines would exceed that of humans—a moment that futurists call the singularity. A benevolent superintelligence might analyze the human genetic code at great speed and unlock the secret to eternal youth. At the very least, it might know how to fix your back.

But what if it wasn’t so benevolent? Nick Bostrom, a philosopher who directs the Future of Humanity Institute at the University of Oxford, describes the following scenario in his book Superintelligence, which has prompted a great deal of debate about the future of artificial intelligence. Imagine a machine that we might call a “paper-clip maximizer”—that is, a machine programmed to make as many paper clips as possible. Now imagine that this machine somehow became incredibly intelligent. Given its goals, it might then decide to create new, more efficient paper-clip-manufacturing machines—until, King Midas style, it had converted essentially everything to paper clips.

No worries, you might say: you could just program it to make exactly a million paper clips and halt. But what if it makes the paper clips and then decides to check its work? Has it counted correctly? It needs to become smarter to be sure. The superintelligent machine manufactures some as-yet-uninvented raw-computing material (call it “computronium”) and uses that to check each doubt. But each new doubt yields further digital doubts, and so on, until the entire earth is converted to computronium. Except for the million paper clips.

Thing reviewed

“Superintelligence: Paths, Dangers, Strategies”
By Nick Bostrom
Oxford University Press, 2014

Bostrom does not believe that the paper-clip maximizer will come to be, exactly; it’s a thought experiment, one designed to show how even careful system design can fail to restrain extreme machine intelligence. But he does believe that superintelligence could emerge, and while it could be great, he thinks it could also decide it doesn’t need humans around. Or do any number of other things that destroy the world. The title of chapter 8 is: “Is the default outcome doom?”

If this sounds absurd to you, you’re not alone. Critics such as the robotics pioneer Rodney Brooks say that people who fear a runaway AI misunderstand what computers are doing when we say they’re thinking or getting smart. From this perspective, the putative superintelligence Bostrom describes is far in the future and perhaps impossible.

Yet a lot of smart, thoughtful people agree with Bostrom and are worried now. Why?

Volition

The question “Can a machine think?” has shadowed computer science from its beginnings. Alan Turing proposed in 1950 that a machine could be taught like a child; John McCarthy, inventor of the programming language LISP, coined the term “artificial intelligence” in 1955. As AI researchers in the 1960s and 1970s began to use computers to recognize images, translate between languages, and understand instructions in normal language and not just code, the idea that computers would eventually develop the ability to speak and think—and thus to do evil—bubbled into mainstream culture. Even beyond the oft-referenced HAL from 2001: A Space Odyssey, the 1970 movie Colossus: The Forbin Project featured a large blinking mainframe computer that brings the world to the brink of nuclear destruction; a similar theme was explored 13 years later in WarGames. The androids of 1973’s Westworld went crazy and started killing.

Extreme AI predictions are “comparable to seeing more efficient internal combustion engines… and jumping to the conclusion that the warp drives are just around the corner,” Rodney Brooks writes.

When AI research fell far short of its lofty goals, funding dried up to a trickle, beginning long “AI winters.” Even so, the torch of the intelligent machine was carried forth in the 1980s and ’90s by sci-fi authors like Vernor Vinge, who popularized the concept of the singularity; researchers like the roboticist Hans Moravec, an expert in computer vision; and the engineer/entrepreneur Ray Kurzweil, author of the 1999 book The Age of Spiritual Machines. Whereas Turing had posited a humanlike intelligence, Vinge, Moravec, and Kurzweil were thinking bigger: when a computer became capable of independently devising ways to achieve goals, it would very likely be capable of introspection—and thus able to modify its software and make itself more intelligent. In short order, such a computer would be able to design its own hardware.

As Kurzweil described it, this would begin a beautiful new era. Such machines would have the insight and patience (measured in picoseconds) to solve the outstanding problems of nanotechnology and spaceflight; they would improve the human condition and let us upload our consciousness into an immortal digital form. Intelligence would spread throughout the cosmos.

You can also find the exact opposite of such sunny optimism. Stephen Hawking has warned that because people would be unable to compete with an advanced AI, it “could spell the end of the human race.” Upon reading Superintelligence, the entrepreneur Elon Musk tweeted: “Hope we’re not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable.” Musk then followed with a $10 million grant to the Future of Life Institute. Not to be confused with Bostrom’s center, this is an organization that says it is “working to mitigate existential risks facing humanity,” the ones that could arise “from the development of human-level artificial intelligence.”

No one is suggesting that anything like superintelligence exists now. In fact, we still have nothing approaching a general-purpose artificial intelligence or even a clear path to how it could be achieved. Recent advances in AI, from automated assistants such as Apple’s Siri to Google’s driverless cars, also reveal the technology’s severe limitations; both can be thrown off by situations that they haven’t encountered before. Artificial neural networks can learn for themselves to recognize cats in photos. But they must be shown hundreds of thousands of examples and still end up much less accurate at spotting cats than a child.

This is where skeptics such as Brooks, a founder of iRobot and Rethink Robotics, come in. Even if it’s impressive—relative to what earlier computers could manage—for a computer to recognize a picture of a cat, the machine has no volition, no sense of what cat-ness is or what else is happening in the picture, and none of the countless other insights that humans have. In this view, AI could possibly lead to intelligent machines, but it would take much more work than people like Bostrom imagine. And even if it could happen, intelligence will not necessarily lead to sentience. Extrapolating from the state of AI today to suggest that superintelligence is looming is “comparable to seeing more efficient internal combustion engines appearing and jumping to the conclusion that warp drives are just around the corner,” Brooks wrote recently on Edge.org. “Malevolent AI” is nothing to worry about, he says, for a few hundred years at least.

Insurance policy

Even if the odds of a superintelligence arising are very long, perhaps it’s irresponsible to take the chance. One person who shares Bostrom’s concerns is Stuart J. Russell, a professor of computer science at the University of California, Berkeley. Russell is the author, with Peter Norvig (a peer of Kurzweil’s at Google), of Artificial Intelligence: A Modern Approach, which has been the standard AI textbook for two decades.

“There are a lot of supposedly smart public intellectuals who just haven’t a clue,” Russell told me. He pointed out that AI has advanced tremendously in the last decade, and that while the public might understand progress in terms of Moore’s Law (faster computers are doing more), in fact recent AI work has been fundamental, with techniques like deep learning laying the groundwork for computers that can automatically increase their understanding of the world around them.

Bostrom’s book proposes ways to align computers with human needs. We’re basically telling a god how we’d like to be treated.

Because Google, Facebook, and other companies are actively looking to create an intelligent, “learning” machine, he reasons, “I would say that one of the things we ought not to do is to press full steam ahead on building superintelligence without giving thought to the potential risks. It just seems a bit daft.” Russell made an analogy: “It’s like fusion research. If you ask a fusion researcher what they do, they say they work on containment. If you want unlimited energy you’d better contain the fusion reaction.” Similarly, he says, if you want unlimited intelligence, you’d better figure out how to align computers with human needs.

Bostrom’s book is a research proposal for doing so. A superintelligence would be godlike, but would it be animated by wrath or by love? It’s up to us (that is, the engineers). Like any parent, we must give our child a set of values. And not just any values, but those that are in the best interest of humanity. We’re basically telling a god how we’d like to be treated. How to proceed?

Bostrom draws heavily on an idea from a thinker named Eliezer Yudkowsky, who talks about “coherent extrapolated volition”—the consensus-derived “best self” of all people. AI would, we hope, wish to give us rich, happy, fulfilling lives: fix our sore backs and show us how to get to Mars. And since humans will never fully agree on anything, we’ll sometimes need it to decide for us—to make the best decisions for humanity as a whole. How, then, do we program those values into our (potential) superintelligences? What sort of mathematics can define them? These are the problems, Bostrom believes, that researchers should be solving now. Bostrom says it is “the essential task of our age.”

For the civilian, there’s no reason to lose sleep over scary robots. We have no technology that is remotely close to superintelligence. Then again, many of the largest corporations in the world are deeply invested in making their computers more intelligent; a true AI would give any one of these companies an unbelievable advantage. They also should be attuned to its potential downsides and figuring out how to avoid them.

This somewhat more nuanced suggestion—without any claims of a looming AI-mageddon—is the basis of an open letter on the website of the Future of Life Institute, the group that got Musk’s donation. Rather than warning of existential disaster, the letter calls for more research into reaping the benefits of AI “while avoiding potential pitfalls.” This letter is signed not just by AI outsiders such as Hawking, Musk, and Bostrom but also by prominent computer scientists (including Demis Hassabis, a top AI researcher). You can see where they’re coming from. After all, if they develop an artificial intelligence that doesn’t share the best human values, it will mean they weren’t smart enough to control their own creations.

Paul Ford, a freelance writer in New York, wrote about Bitcoin in March/April 2014.

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Ripple, a Cryptocurrency Company, Wants to Rewire Bank Authentication

A digital-currency company thinks it can protect the personal information used to perform identity checks in the financial industry.

Companies built around Bitcoin and other digital currencies mostly focus on storing and transferring money. But at least one company is trying to prove that some of the underlying technology can have a much wider impact on the financial industry.

That startup, Ripple Labs, has already had some success persuading banks to use its Bitcoin-inspired protocol to speed up money transfers made in any currency, especially across borders (see “50 Smartest Companies 2014: Ripple Labs”). Now it is building a system that uses some similar cryptographic tricks to improve the way financial companies check the identity of their customers. The system could also provide a more secure way to log in to other online services.

Verifying identity is a constant, expensive headache for financial institutions, which are bound by strict regulations designed to curtail money laundering and support for blacklisted organizations such as terrorist groups. Most banks turn to one of a handful of large data brokers, such as Experian or Acxiom, to power their ID checks. When you open a new account, a bank gathers key personal information and sends it to its broker to verify your identity, and to confirm that you aren’t on any block list.

Under Ripple’s system, the same basic process would take place. However, your personal information would be used to generate a unique cryptographic token. A bank could send the token to a data broker that has its own token, made using your personal information at an earlier time. The math underpinning Ripple’s system would allow the broker to confirm that the data you had given the bank was correct, without either the bank or the broker ever revealing the data itself.

Apple’s mobile payment technology uses similar technology to protect credit card numbers (see “10 Breakthrough Technologies 2015: Apple Pay”). When you use Apple Pay, only a cryptographic token representing your credit card number is transferred to the merchant. That token can be used to charge your card, but it won’t reveal anything to anyone who manages to steal it, and it cannot be reused.

Stefan Thomas, chief technology officer of Ripple, says its ID verification system should reduce the risk that personal data will be stolen or accidentally leaked and should also be faster than the systems used today, which have developed gradually over decades and still use outdated technology. He says Ripple decided to develop the technology after it became clear that the financial system needed more than just new ways to transfer money.

Thomas adds that by cutting costs and security risks, Ripple’s system might allow cheaper data brokers to emerge. It could also make it easier for banks to operate in poorer parts of the world, where verification systems can be particularly expensive to operate, even for U.S. banks, he says. And Ripple’s engineers are also working on ways their protocol can be used to log in to online services.

Sarah Jane Hughes, a law professor at Indiana University who specializes in payment systems, says Ripple has identified a legitimate opportunity. Companies spend a lot on complying with identity verification rules, and mistakes are expensive, she says. For example, PayPal agreed to pay $7.7 million to the U.S. Treasury last week for failing to block just under 500 transactions involving people subject to U.S. sanctions. “If you could do verification more rapidly and with a greater degree of certainty, it would be hugely valuable,” says Hughes.

However, Hughes says, switching to a new system would not be easy for most financial institutions. They would probably have to retain the old system for some time for compatibility reasons. That means Ripple’s idea would have to deliver significant benefits to gain traction.

Ripple has a basic version of its system working today. It is testing it internally and with partners such as Shift, a startup working on a debit card that can be used to spend digital currencies. Later this year, Thomas hopes start talking with banks that already use Ripple about testing the system.

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