Artificial intelligence is coming, and it will change everything. The application of AI requires three basic components. First, deep learning and artificial neural networks require data for the learning process by which they train themselves to generate algorithms: so in a world of AI inflection, access to data — public, private, or proprietary — will become a key economic variable for company performance. Companies with data and the capacity to generate it, as well as companies with the political savvy to make use of externally generated public and private data, will benefit. The second necessary component is hardware. Today’s chip leaders will likely not be tomorrow’s. The key is the arrival of neuromorphic chips which discard the legacy chip architecture in favor of new architectures intrinsically suited for deep learning application — neuromorphic chips. Look not just for manufacturers, but for those companies that can harness network effects to win dominance in the adoption of their chips and their programming ecosystem. The final component is the training systems; look for companies able to implement fast and cheap training systems at scale.
In India, a government program has opened over 250 million bank accounts in the past four years, along with close to 1 billion biometric “Aadhaar” identification cards. The current government has moved toward reimbursing poor people on their purchases of basic goods with money paid directly into their bank accounts. With the banking infrastructure in place for electronic payments, the government of India in its most recent “Economic Survey” flirted with the idea of electronic UBI payments.
Ultimately, there’s a symbiotic relationship between electronic universal basic income payments and broader financial inclusion. Electronic UBI payments can bring unbanked adults into the formal financial sector; greater financial inclusion can lay the groundwork for the introduction of more efficient government payments.
So the question becomes, how do we reduce the risks of failure so that more people take more risks? Better yet, how do we increase the rate of failure? It may sound counter-intuitive, but failure is not something to avoid. It’s only through failure that we learn what doesn’t work and what might work instead. This is basically the scientific method in a nutshell. It’s designed to rule out what isn’t true, not to determine what is true. There is a very important difference between the two.
This is also how evolution works, through failure after failure. Nature isn’t determining the winner. Nature is simply determining all the losers, and those who don’t lose, win the game of evolution by default. So, the higher the rate of mutation, the more mutations can fail or not fail, and therefore the quicker an organism can adapt to a changing environment. In the same way, the higher the rate of failure in a market economy, the quicker the economy can evolve.
There’s also something else very important to understand about failure and success. One success can outweigh 100,000 failures. Venture capitalist Paul Graham of Y Combinator has described this as Black Swan Farming. When it comes to truly transformative ideas, they aren’t obviously great ideas, or else they’d already be more than just an idea, and when it comes to taking a risk on investing in a startup, the question is not so much if it will succeed, but if it will succeed BIG. What’s so interesting is that the biggest ideas tend to be seen as the least likely to succeed.
Now translate that to people themselves. What if the people most likely to massively change the world for the better, the Einsteins so to speak — the Black Swans, are oftentimes those least likely to be seen as deserving social investment? In that case, the smart approach would be to cast an extremely large net of social investment, in full recognition that even at such great cost, the ROI from the innovation of the Black Swans would far surpass the cost.
This happens to be exactly what Buckminster Fuller was thinking when he said, “We must do away with the absolutely specious notion that everybody has to earn a living. It is a fact today that one in ten thousand of us can make a technological breakthrough capable of supporting all the rest.” That is a fact, and it then begs the question, “How do we make sure we invest in every single one of those people such that all of society maximizes its collective ROI?”
“Animal intelligence may be the next big thing in artificial intelligence,” proclaims a video on the IEEE Spectrum site. “But first, scientists must digitize a rat brain.”
The video accompanied a 3,000-word article that explained that, currently, “there are some situations when a three-year-old can easily defeat the fanciest AI in the world.” Specifically, children excel at “one-shot learning” — the ability to recognize something after only seeing it once.
“Humans have an amazing ability to make inferences and generalize,” said Harvard University neuroscientist David Cox, who is also an expert on artificial intelligence. Or, as IEEE Spectrum puts it, AI researchers are “deeply envious of toddlers’ facility with it.”
So there’s now an ambitious $100 million project funded by the U.S. the Intelligence Advanced Research Projects Agency (IARPA, an intelligence community cross-agency office) to reverse engineer this kind of smarts. Its five-year mission: to identify a brain’s “strategy” for simple feats of identification, so it can be recreated with algorithms.
The Machine Intelligence from Cortical Networks (MICRONS) program is focusing on that area where the brain processes what it sees — the visual cortex. But it’s actually attempting to map every neuron in one cubic millimeter of brain tissue. 50,000 neurons — interconnected through a half a billion synapses. The ultimate big data set.
No one has ever attempted this before.
The article points out today’s neural networks are “loosely inspired by the brain’s structure,” but that human brains ultimately have 86 billion neurons — or, 1.7 million times more than appear in that cubic millimeter. Trillions of connections are possible. That’s 1,000,000,000,000. But apparently, the important thing seems to be identifying the mechanism that underlies it all.
“The big gap is understanding operations on a circuit level,” the IEEE quoted (now former) MICRONS program manager R. Jacob Vogelstein, “how thousands of neurons work together to process information.”
If they succeed, the article suggests, the payoff could be enormous: “The government’s big bet is that brainlike AI systems will be more adept than their predecessors at solving real-world problems.”
One obvious application would be identifying faces from security-camera footage — but the hope is to apply this to more than computer vision. The entirety of the cerebral context has a “suspiciously similar” structure, according to Cox — which suggests there’s one fundamental circuit that the brain uses to process information. Identifying it would be a major step toward human-like general intelligence.
Their research focuses on embedding state-of-the-art technological equipment on the brains of rats and mice. “It all starts with a rat in a cage learning to play a video game,” quipped the video that accompanies the article. The rat gets rewarded with a drop of sweet juice if they correctly identify one of two images flashed on a small display.
Using a two-photon excitation microscope, the researchers first scan a live rat’s brain with an infrared laser to record flashes from a fluorescent tag that indicates when a neuron is active. “The microscope makes movies of neural activities,” explained the video, while the article adds poetically that “The 3D video shows patterns that resemble green fireflies winking on and off in a summer night.”
“You can watch a rat having a thought,” Cox explained.
“It’s very similar to how you’d try to reverse engineer an integrated circuit,” Vogelstein added. “You could stare at the chip in extreme detail, but you won’t really know what it’s meant to do unless you see the circuit in operation.”
“It may be much easier to engineer the brain than to understand it.” — George Church, Wyss Institute for Biologically Inspired Engineering
But that’s just the beginning. The rat’s tissue is then FedEx’d from Massachusetts to Illinois, where the U.S. Department of Energy’s Argonne National Laboratory performs a sophisticated imaging using a particle accelerator to produce “extremely bright” X-rays. After the tissue is captured from different angles, the X-ray images can be combined into a three-dimensional image.
Then the brain returns to Massachusetts, where it’s sliced into 33,000 strips, each one just 30 nanometers wide, captured with tape strips and delivered to silicon wafers. The 33,000 slides then meet “the world’s fastest scanning electron microscopes,” which runs continuously, and creates an image of each one at a 4-nanometer resolution.
Now it’s possible to see the axons that connect the neurons, and — since there’s millions of them — there’s another piece of automation, a piece of software that can automatically trace the axons from one tissue slice to the next, along with its thousands of connections to other neurons. Or, as IEEE Spectrum puts it, it “reconstructs all the neural wiring within the cube of brain tissue.”
Ironically, the computer isn’t as good as a human scientist, but “there aren’t enough humans on earth to trace this much data,” said Cox, who the article describes as “obsessively focused on automating every step of the process.” It’s a problem that software engineers at Harvard and the Massachusetts Institute of Technology are already working on. But however the diagram gets made, researchers will then combine it with the fluorescent images of the rat brain’s activity, which according to the article should reveal the brain’s computational structures.
“It should show which neurons form a circuit that lights up when a rat sees an odd lumpy object, mentally flips it upside down, and decides that it’s a match for object A,” the article’s writer, Eliza Strickland, noted.
In an interesting twist, they also plan to train a neural network on the same image-recognition task, and compare the results.
Meanwhile, there’s also another MICRONS project taking an entirely different approach. Researchers at Harvard and Carnegie Mellon University have genetically engineered mice so there’s a unique sequence of molecules on both ends of each axon (a technique called “DNA barcoding.”) Then their software can generate a map by connecting each pair sharing the same axon-identifying code. One of the team’s leaders — George Church, a professor at Harvard’s Wyss Institute for Biologically Inspired Engineering — thinks the technique could ultimately map the whole brain of a mouse. That’s all 70 million neurons and 70 billion connections.
Church ultimately even suggests that one day it may be possible to ditch the silicon altogether, and engineer brains with special circuits that speed up their cognition — in effect, to build better biological brains: “I think we’ll soon have the ability to do synthetic neurobiology, to actually build brains that are variations on natural brains.”
In the article, he shares a near-heretical thought — that successfully recreating a brain with circuits and algorithms may not ultimately provide an answer. “I think understanding is a bit of a fetish among scientists,” he tells IEEE Spectrum, in a refreshing counterpoint to the prevailing belief that everything we humans experience can be reduced to patterns of electric pulses.
“It may be much easier to engineer the brain than to understand it.”
Feature image: IEEE video.
Other scientists have approached questions of human aging from a completely different direction, studying people who have achieved exceptionally long lives. One part of that research has pointed to genetic mutations that seem to be concentrated in centenarians, people who live beyond 100 years. Those gene variants are involved in some of the same processes that regulate metabolism and immunity, protecting cells in model animals.
These researchers are also finding that much of the human life span doesn’t just depend on characteristics people are born with, but rather on a host of other factors: lifestyle, relationships, environment, psychology. That might seem like common sense, but the centenarian studies give it scientific heft.
Research on so-called blue zones—a handful of regions that boast unusually high numbers of centenarians—has identified such hot spots as the interior mountains of Sardinia. There, the most salient local trait isn’t genetic, but rather a propensity for youthful exercise. It’s a region of shepherds, and many of the men who live to advanced ages spent their early lives walking long distances over steep terrain.
People in blue zones also tend to have common dietary habits. They eat smaller meals, have their last meal in the late afternoon or early evening, and consume lots of whole grains and vegetables—practices that are in line with modern dietary research. Social connections are crucial, too. In Okinawa’s blue zone, a cultural tradition called moais creates groups of five people obligated to be intimate, lifelong friends. That resonates with the idea that centenarians who have stronger social networks before reaching old age might fare better.
Psychology seems especially influential. Many centenarian studies point to the importance of mental resiliency and openness to new experiences. “Old people, especially centenarians, are confronted with key losses in all domains—health, mobility, loved ones—yet they seem happy and able to deal with those limitations,” says Daniela Jopp, a psychologist at the University of Lausanne who studied centenarians living in New York City. “Centenarians have quite a few psychological strengths that can buffer and protect them from difficulties.” Research on centenarians in the U.S. state of Georgia emphasized the importance of traits such as enthusiasm, warmth and self-discipline, with the single greatest predictor of health being their own perception of it. Optimism was literally healthy.
Cause and effect is tricky here. Someone who has managed to survive for a century might simply feel happy as a result. But it also may be that centenarians benefit from genetic traits that help them feel more upbeat—and that disposition, in turn, minimizes cellular damage. Leonard Poon, former director of the University of Georgia’s Institute of Gerontology, notes how people with a genetic variant linked to late-onset dementia and diabetes are more prone to stress than people who don’t have that mutation. It’s possible that their genetic makeup amplifies the effects of stress, which in turn accelerate the cellular breakdowns of aging. “Both psychology and biology would act synergistically,” says Poon. “The study of such interactions is the exciting new frontier.”
While lab-based gerontologists chafe at the notion that lifestyle alone will provide a longevity breakthrough, most also doubt that drugs by themselves will suffice. “I don’t believe that if you’re doing whatever you want, and then take one or two drugs, you’ll then live long and healthy,” says Fontana. “Social relationships, exercise, sleep, avoiding exposure to pollution—you can’t get all that with a pill.”
Finally, beyond the question of what will be required to make human lives longer and healthier is the matter of who will benefit. Massive health disparities already exist between those with different levels of wealth, and in the United States, the poorest 1% of Americans live an average of 12 fewer years than the richest 1%. If longevity breakthroughs carry a high price tag—such as that of everolimus, a cancer drug that could have antiaging effects and can cost more than $7,000 per month—the cost could exacerbate those differences.
“When we finally get the magic cocktail to make us live longer, will it be so crazily expensive that only a few people will have access to it and be able to benefit?” asks Raul Mostoslavsky, a molecular biologist at the Mass General Cancer Center and a faculty member at the Broad Institute.
Public health policy and social engineering aimed at prolonging lives might be as important—and as difficult—as finding the next aging breakthrough. “The question is,” says Fontana, “how can we change the system to give everyone the longest, best lives they can lead?”
1. Deep Learning Could Be Worth 35 Amazons
Deep learning is a subcategory of machine learning which is itself a subcategory of artificial intelligence. Deep learning is the source of much of the hype surrounding AI today. (You know you may be in a hype bubble when ads tout AI on Sunday golf commercial breaks.)
Behind the hype, however, big tech companies are pursuing deep learning to do very practical things. And whereas the internet, which unleashed trillions in market value, transformed several industries—news, entertainment, advertising, etc.—deep learning will work its way into even more, Wood said.
As deep learning advances, it should automate and improve technology, transportation, manufacturing, healthcare, finance, and more. And as is often the case with emerging technologies, it may form entirely new businesses we have yet to imagine.
“Bill Gates has said a breakthrough in machine learning would be worth 10 Microsofts. Microsoft is $550 to $600 billion,” Wood said. “We think deep learning is going to be twice that. We think [it] could approach $17 trillion in market cap—which would be 35 Amazons.”
2. Fleets of Autonomous Taxis to Overtake Automakers
Wood didn’t mince words about a future when cars drive themselves.
“This is the biggest change that the automotive industry has ever faced,” she said.
Today’s automakers have a global market capitalization of a trillion dollars. Meanwhile, mobility-as-a-service companies as a whole (think ridesharing) are valued around $115 billion. If this number took into account expectations of a driverless future, it’d be higher.
The mobility-as-a-service market, which will slash the cost of “point-to-point” travel, could be worth more than today’s automakers combined, Wood said. Twice as much, in fact. As gross sales grow to something like $10 trillion in the early 2030s, her firm thinks some 20% of that will go to platform providers. It could be a $2 trillion opportunity.
Wood said a handful of companies will dominate the market, and Tesla is well positioned to be one of those companies. They are developing both the hardware, electric cars, and the software, self-driving algorithms. And although analysts tend to look at them as a just an automaker right now, that’s not all they’ll be down the road.
“We think if [Tesla] got even 5% of this global market for autonomous taxi networks, it should be worth another $100 billion today,” Wood said.
3. 3D Printing Goes Big With Finished Products at Scale
3D printing has become part of mainstream consciousness thanks, mostly, to the prospect of desktop printers for consumer prices. But these are imperfect, and the dream of an at-home replicator still eludes us. The manufacturing industry, however, is much closer to using 3D printers at scale.
Not long ago, we wrote about Carbon’s partnership with Adidas to mass-produce shoe midsoles. This is significant because, whereas industrial 3D printing has focused on prototyping to date, improving cost, quality, and speed are making it viable for finished products.
According to ARK, 3D printing may grow into a $41 billion market by 2020, and Wood noted a McKinsey forecast of as much as $490 billion by 2025. “McKinsey will be right if 3D printing actually becomes a part of the industrial production process, so end-use parts,” Wood said.
4. CRISPR Starts With Genetic Therapy, But It Doesn’t End There
According to ARK, the cost of genome editing has fallen 28x to 52x (depending on reagents) in the last four years. CRISPR is the technique leading the genome editing revolution, dramatically cutting time and cost while maintaining editing efficiency. Despite its potential, Wood said she isn’t hearing enough about it from investors yet.
“There are roughly 10,000 monogenic or single-gene diseases. Only 5% are treatable today,” she said. ARK believes treating these diseases is worth an annual $70 billion globally. Other areas of interest include stem cell therapy research, personalized medicine, drug development, agriculture, biofuels, and more.
Still, the big names in this area—Intellia, Editas, and CRISPR—aren’t on the radar.
“You can see if a company in this space has a strong IP position, as Genentech did in 1980, then the growth rates can be enormous,” Wood said. “Again, you don’t hear these names, and that’s quite interesting to me. We think there are very low expectations in that space.”
5. Mobile Transactions Could Grow 15x by 2020
By 2020, 75% of the world will own a smartphone, according to ARK. Amid smartphones’ many uses, mobile payments will be one of the most impactful. Coupled with better security (biometrics) and wider acceptance (NFC and point-of-sale), ARK thinks mobile transactions could grow 15x, from $1 trillion today to upwards of $15 trillion by 2020.
In addition, to making sharing economy transactions more frictionless, they are generally key to financial inclusion in emerging and developed markets, ARK says. And big emerging markets, such as India and China, are at the forefront, thanks to favorable regulations.
“Asia is leading the charge here,” Wood said. “You look at companies like Tencent and Alipay. They are really moving very quickly towards mobile and actually showing us the way.”
6. Robotics and Automation to Liberate $12 Trillion by 2035
Robots aren’t just for auto manufacturers anymore. Driven by continued cost declines and easier programming, more businesses are adopting robots. Amazon’s robot workforce in warehouses has grown from 1,000 to nearly 50,000 since 2014. “And they have never laid off anyone, other than for performance reasons, in their distribution centers,” Wood said.
But she understands fears over lost jobs.
This is only the beginning of a big round of automation driven by cheaper, smarter, safer, and more flexible robots. She agrees there will be a lot of displacement. Still, some commentators overlook associated productivity gains. By 2035, Wood said US GDP could be $12 trillion more than it would have been without robotics and automation—that’s a $40 trillion economy instead of a $28 trillion economy.
“This is the history of technology. Productivity. New products and services. It is our job as investors to figure out where that $12 trillion is,” Wood said. “We can’t even imagine it right now. We couldn’t imagine what the internet was going to do with us in the early ’90s.”
7. Blockchain and Cryptoassets: Speculatively Spectacular
Blockchain-enabled cryptoassets, such as Bitcoin, Ethereum, and Steem, have caused more than a stir in recent years. In addition to Bitcoin, there are now some 700 cryptoassets of various shapes and hues. Bitcoin still rules the roost with a market value of nearly $40 billion, up from just $3 billion two years ago, according to ARK. But it’s only half the total.
“This market is nascent. There are a lot of growing pains taking place right now in the crypto world, but the promise is there,” Wood said. “It’s a very hot space.”
Like all young markets, ARK says, cryptoasset markets are “characterized by enthusiasm, uncertainty, and speculation.” The firm’s blockchain products lead, Chris Burniske, uses Twitter—which is where he says the community congregates—to take the temperature. In a recent Twitter poll, 62% of respondents said they believed the market’s total value would exceed a trillion dollars in 10 years. In a followup, more focused on the trillion-plus crowd, 35% favored $1–$5 trillion, 17% guessed $5–$10 trillion, and 34% chose $10+ trillion.
Looking past the speculation, Wood believes there’s at least one big area blockchain and cryptoassets are poised to break into: the $500-billion, fee-based business of sending money across borders known as remittances.
“If you look at the Philippines-to-South Korean corridor, what you’re seeing already is that Bitcoin is 20% of the remittances market,” Wood said. “The migrant workers who are transmitting currency, they don’t know that Bitcoin is what’s enabling such a low-fee transaction. It’s the rails, effectively. They just see the fiat transfer. We think that that’s going to be a very exciting market.”
Industrialisation meant that new sources of power emerged to challenge the old aristocratic elites—industrialists and factory workers. Workers were able to use their muscle to demand more rights and, increasingly, the vote. Elites turned to nationalism as a way of distracting voters from economic issues and shoring up their support. This nationalism led to clashes with the other great powers where their interests diverged; between Britain and Russia in Asia; Russia and Austria in the Balkans; Germany and France in north Africa.
So let us move to the current era of globalisation, during which the export share of global GDP has more than doubled since the 1960s. New economic powers have emerged to challenge American dominance; first, Japan, and now China and potentially India. Imperial overstretch threatens America as it did Edwardian Britain. The ability and, more recently, willingness of America to act as global policeman has been eroded. Indeed, unlike Britain in 1914, America is a net debtor not a creditor. Can it hold China’s ambitions in check in the Pacific, counterbalance Iran and the terrorists of Islamic State in the middle east and deal with a nationalist Russian regime? It seems clear that other powers think it can’t. They are pushing to see whether America will react.
Within the economy two big changes have occurred. Manufacturing capacity has moved from the developed world to Asia. Technology has rewarded skilled workers and widened pay gaps. Voters have rebelled by turning to parties that reject globalisation. This didn’t happen in France but generally it has made life more difficult for centre-left parties and turned centre-right parties more nativist. America’s Republicans used to be enthusiasts for free trade. Now they have elected Donald Trump.
Just as in the first era, globalisation has disrupted international and domestic power structures. Thankfully this does not mean that another world war is inevitable. But it is easy to imagine regional conflicts: Iran against Saudi Arabia, or an American attack on North Korea that provokes a Chinese reaction.
The real danger is that this a zero-sum game. Governments will appear to grab a larger share of global trade for their own countries. In doing so, they will cause trade to shrink. That might make voters even angrier. From the early 1980s to 2008, most companies could count on a business-friendly political environment in the developed world. But it looks as if that era has ended with the financial crisis. Globalisation has caused another counter-reaction.
The best hope is that technology can deliver the economic growth and rising prosperity voters want. If that happens, these threats will not disappear but they will be much reduced. But for all the hype about new technology, productivity has been sluggish. The omens are not great.
Big ideas, big consequences… worth trying to understand… value contribution
Economies all over the world are in the midst of many great changes and uncertainty.
Most importantly, digitalisation, globalisation and an ageing population will break the traditional connection between growth, productivity and well-being in an unforeseen manner. Political leaders all over the world struggle to grow the economy, increase exports and create new jobs. A higher employment rate in paid employment is a generally accepted goal. In 2017, politics is still founded on the idea that increasing labour in the market results in growth.
In this paper we argue differently. Digitalisation, globalisation and ageing seem to be breaking the connection between growth and well-being.
This article was triggered by the contemporary experiences of the authors. We were tired of hearing economists talk about how necessary growth and productivity are for well-being. We don’t believe in this story any more.
So… Seth Miller doesn’t have to be 100% right… he can be 50% right and still rock your investing world.
Big Oil believes it is different. I am less optimistic for them.
An autonomous vehicle will cost about $0.13 per mile to operate, and even less as battery life improves. By comparison, your 20 miles per gallon automobile costs $0.10 per mile to refuel if gasoline is $2/gallon, and that is before paying for insurance, repairs, or parking. Add those, and the price of operating a vehicle you have already paid off shoots to $0.20 per mile, or more.
And this is what will kill oil: It will cost less to hail an autonomous electric vehicle than to drive the car that you already own.
If you think this reasoning is too coarse, consider the recent analysis from the consulting company RethinkX (run by the aforementioned Tony Seba), which built a much more detailed, sophisticated model to explicitly analyze the future costs of autonomous vehicles. Here is a sampling of what they predict:
Self-driving cars will launch around 2021
A private ride will be priced at 16¢ per mile, falling to 10¢ over time.
A shared ride will be priced at 5¢ per mile, falling to 3¢ over time.
By 2022, oil use will have peaked
By 2023, used car prices will crash as people give up their vehicles. New car sales for individuals will drop to nearly zero.
By 2030, gasoline use for cars will have dropped to near zero, and total crude oil use will have dropped by 30% compared to today.
The driver behind all this is simple: Given a choice, people will select the cheaper option.
Your initial reaction may be to believe that cars are somehow different — they are built into the fabric of our culture. But consider how people have proven more than happy to sell seemingly unyielding parts of their culture for far less money. Think about how long a beloved mom and pop store lasts after Walmart moves into town, or how hard we try to “Buy American” when a cheaper option from China emerges.
And autonomous vehicles will not only be cheaper, but more convenient as well — there is no need to focus on driving, there will be fewer accidents, and no need to circle the lot for parking. And your garage suddenly becomes a sunroom.
For the moment, let’s make the assumption that the RethinkX team has their analysis right (and I broadly agree): Self-driving EVs will be approved worldwide starting around 2021, and adoption will occur in less than a decade.
A QR code is a two-dimensional barcode with a random pattern of tiny black squares against a white background, capable of holding 300 times more data than a traditional one-dimensional code. According to internet consulting firm iResearch, payments made via mobile devices by Chinese consumers last year reached 38 trillion yuan (US$5.5 trillion, HK$43 trillion), more than half the nation’s GDP.
QR code scams rise in China, putting e-payment security in spotlight
Thanks to QR code’s rapidly increasing usage at off-line shops, the amount of mobile payments on the mainland is now 50 times greater than that of the US. Mobile payments in the US totalled US$112 billion in 2016, according to Forrester Research.
To consumer behaviour researcher Chen Yiwen, we are witnessing the dawn of “codeconomy”.
“China has started the transition to a cash-free economy faster than anyone could have imagined, largely because of the viral spread of two-dimensional barcode,” said Chen, a professor and researcher with the Institute of Psychology, Chinese Academy of Sciences in Beijing. “It creates a new economy based on scannable codes.”