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Amazon, AI and Medical Records: Do the Benefits Outweigh the Risks? - Knowledge Wharton
▻http://knowledge.wharton.upenn.edu/article/amazon-medical-records

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Last month, Amazon unveiled a service based on AI and machine-learning technology that could comb through patient medical records and extract valuable insights. It was seen as a game changer that could alleviate the administrative burden of doctors, introduce new treatments, empower patients and potentially lower health care costs. But it also carries risks to patient data privacy that calls for appropriate regulation, according to Wharton and other experts.

Branded Comprehend Medical, the Amazon Web Services offering aims “to understand and analyze the information that is often trapped in free-form, unstructured medical text, such as hospital admission notes or patient medical histories.” Essentially, it is a natural language processing service that pores through medical text for insights into disease conditions, medications and treatment outcomes from patient notes and other electronic health records.

The new service is Amazon’s latest foray into the health care sector. In June, the company paid $1 billion to buy online pharmacy PillPack, a Boston-based startup that specializes in packing monthly supplies of medicines to chronically ill patients. In January, Amazon teamed up with Berkshire Hathaway and JPMorgan Chase to form a health care alliance that aims to lower costs and improve the quality of medical care for their employees.

“Health care, like everything else, is becoming more of an information-based industry, and data is the gold standard — and Amazon knows as well as anyone how to handle and analyze data,” said Robert Field, Wharton lecturer in health care management who is also professor of health management and policy at Drexel University. “It’s a $3.5 trillion industry and 18% of our economy, so who wouldn’t want a piece of that?”

AI offers “enormous” promise when it comes to bringing in new and improved treatments for patient conditions, such as in the area of radiology, added Hempstead. Machine learning also potentially enables the continual improvement of treatment models, such as identifying people who could participate in clinical trials. Moreover, Amazon’s service could “empower a consumer to be more in charge of their own health and maybe be more active consumer of medical services that might be beneficial to their health,” she said.

On the flip side, it also could enable insurers to refuse to enroll patients that they might see as too risky, Hempstead said. Insurers are already accessing medical data and using technology in pricing their products for specific markets, and the Amazon service might make it easier for them to have access to such data, she noted.

#Santé_publique #Données_médicales #Amazon #Intelligence_artificielle

Why the Cambridge Analytica Scandal Is a Watershed Moment for Social Media - Knowledge Wharton
▻http://knowledge.wharton.upenn.edu/article/fallout-cambridge-analytica

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“We’re experiencing a watershed moment with regard to social media,” said Aral. “People are now beginning to realize that social media is not just either a fun plaything or a nuisance. It can have potentially real consequences in society.”

The Cambridge Analytica scandal underscores how little consumers know about the potential uses of their data, according to Berman. He recalled a scene in the film Minority Report where Tom Cruise enters a mall and sees holograms of personally targeted ads. “Online advertising today has reached about the same level of sophistication, in terms of targeting, and also some level of prediction,” he said. “It’s not only that the advertiser can tell what you bought in the past, but also what you may be looking to buy.”

Consumers are partially aware of that because they often see ads that show them products they have browsed, or websites they have visited, and these ads “chase them,” Berman said. “What consumers may be unaware of is how the advertiser determines what they’re looking to buy, and the Cambridge Analytica exposé shows a tiny part of this world.”

A research paper that Nave recently co-authored captures the potential impact of the kind of work Cambridge Analytica did for the Trump campaign. “On the one hand, this form of psychological mass persuasion could be used to help people make better decisions and lead healthier and happier lives,” it stated. “On the other hand, it could be used to covertly exploit weaknesses in their character and persuade them to take action against their own best interest, highlighting the potential need for policy interventions.”

Nave said the Cambridge Analytica scandal exposes exactly those types of risks, even as they existed before the internet era. “Propaganda is not a new invention, and neither is targeted messaging in marketing,” he said. “What this scandal demonstrates, however, is that our online behavior exposes a lot about our personality, fears and weaknesses – and that this information can be used for influencing our behavior.”

In Golbeck’s research projects involving the use of algorithms, she found that people “are really shocked that we’re able to get these insights like what your personality traits are, what your political preferences are, how influenced you can be, and how much of that data we’re able to harvest.”

Even more shocking, perhaps, is how easy it is to find the data. “Any app on Facebook can pull the kind of data that Cambridge Analytica did – they can [do so] for all of your data and the data of all your friends,” said Golbeck. “Even if you don’t install any apps, if your friends use apps, those apps can pull your data, and then once they have that [information] they can get these extremely deep, intimate insights using artificial intelligence, about how to influence you, how to change your behavior.” But she draws a line there: “It’s one thing if that’s to get you to buy a pair of shoes; it’s another thing if it’s to change the outcome of an election.”

“Facebook has tried to play both sides of [the issue],” said Golbeck. She recalled a study by scientists from Facebook and the University of California, San Diego, that claimed social media networks could have “a measurable if limited influence on voter turnout,” as The New York Times reported. “On one hand, they claim that they can have a big influence; on the other hand they want to say ‘No, no, we haven’t had any impact on this.’ So they are going to have a really tough act to play here, to actually justify what they’re claiming on both sides.”

Golbeck called for ways to codify how researchers could ethically go about their work using social media data, “and give people some of those rights in a broader space that they don’t have now.” Aral expected the solution to emerge in the form of “a middle ground where we learn to use these technologies ethically in order to enhance our society, our access to information, our ability to cooperate and coordinate with one another, and our ability to spread positive social change in the world.” At the same time, he advocated tightening use requirements for the data, and bringing back “the notion of informed consent and consent in a meaningful way, so that we can realize the promise of social media while avoiding the peril.”

Historically, marketers could collect individual data, but with social platforms, they can now also collect data about a user’s social contacts, said Berman. “These social contacts never gave permission explicitly for this information to be collected,” he added. “Consumers need to realize that by following someone or connecting to someone on social media, they also expose themselves to marketers who target the followed individual.”

In terms of safeguards, Berman said it is hard to know in advance what a company will do with the data it collects. “If they use it for normal advertising, say toothpaste, that may be legitimate, and if they use it for political advertising, as in elections, that may be illegitimate. But the data itself is the same data.”

According to Berman, most consumers, for example, don’t know that loyalty cards are used to track their behavior and that the data is sold to marketers. Would they stop using these cards if they knew? “I am not sure,” he said. “Research shows that people in surveys say they want to maintain their privacy rights, but when asked how much they’re willing to give up in customer experience – or to pay for it – the result is not too much. In other words, there’s a difference between how we care about privacy as an idea, and how much we’re willing to give up to maintain it.”

Golbeck said tools exist for users to limit the amount of data they let reside on social media platforms, including one called Facebook Timeline Cleaner, and a “tweet delete” feature on Twitter. _ “One way that you can make yourself less susceptible to some of this kind of targeting is to keep less data there, delete stuff more regularly, and treat it as an ephemeral platform, _ ” she said.

Mais est-ce crédible ? Les médias sociaux sont aussi des formes d’archives personnelles.

#Facebook #Cambridge_analytica

How Amazon Delivers on Its Core Product : Convenience - Knowledge Wharton
▻http://knowledge.wharton.upenn.edu/article/power-amazons-fulfillment-network

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Amazon sells more goods than any one person could count – but the e-commerce giant’s true “core product” is convenience, and how quickly it can get an order from customers’ virtual shopping carts to their real-life doorsteps.

Part of what makes it so easy for Amazon to offer two-day or even same-day shipping to customers is its vast network of distribution centers, which are located across the U.S. and store and ship products to their final destinations. New research from Wharton business economics and public policy professor Katja Seim takes a closer look at how significantly expanding that distribution center network over the past decade has been key to Amazon’s growth strategy.

Seim recently spoke to Knowledge@Wharton about her paper, “Economies of Density in E-Commerce: A Study of Amazon’s Fulfillment Center Network,” which was co-authored with Cornell’s Jean-Francois Houde and Penn State’s Peter Newberry.

Une étude intéressante sur la localisation des centres de distribution de Amazon (et l’importance du paiement des impôts et autres facilités fiscales).

#Amazon #Commerce_électronique

Seeing Red: Can a Brand Trademark a Signature Color? - Knowledge Wharton
▻http://knowledge.wharton.upenn.edu/article/louboutin-red-soles

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“Every time we think about trademarking, we start with the brand name and the logo. Those are the things that companies want to protect first,” Cesareo said. “But, especially in luxury, there are other things that consumers come to recognize as a symbol or a signature for the brand that need to be protected. For Christian Louboutin, the red sole is the signature of the shoe. It makes sense that he wants to protect it in every jurisdiction in the world. You need to understand that for a consumer, the red sole means something. It signals luxury, it signals quality, it’s a status symbol. It signals sexiness in general.”

Cesareo referenced the hit song “Bodak Yellow” by Cardi B., who raps about buying “red bottoms” as a benchmark of her own success.

Gerhardt, who is working on an article about color ownership, said color works so well in branding, yet few companies try to protect that commercial distinctiveness.

“Even though color is so expressive, 80% of the marks that have been registered in the last 25-year period are words; they’re text. Another 20% are designs,” she said. “If you try to make a pie chart of all the brands that claim color in terms of trying to register them as a trademark, it is .02% It is such a small sliver in my pie chart, you can barely see it.”

Copying a design is a form of piracy. In this case, Van Haren is trying to steal the status and appeal of the original, according to Cesareo. “Even though they weren’t knockoffs legally speaking, they were still pirating a design that Louboutin felt needed to be protected.”

Losing the case could be disastrous for Louboutin because it’s already one of the most counterfeited brands in the world, according to Cesareo. A few years ago, a shipment seized at the port in Los Angeles yielded more than 20,000 pairs of knock-off Louboutins, which would have been worth about $18 million if they were real.

“This could cause a real dilution of the brand,” she said. “Not just knock-offs of the brand itself, but the second that this becomes very widespread and everybody starts wearing red-soled shoes, they lose that scarcity, that status symbol. They’re going to lose their distinctive power. It doesn’t just mean that knock-off sales are going to go up, but sales of the original, authentic brand are going to go down.”

But Karol said the European court’s decision reflects a mindfulness of countervailing concerns. “You don’t want later designers to be worried about putting red on their shoes. We want red to be free for everybody to use. We want to keep that freedom and not chill these designers into worrying about how they are going to use it exactly.”

#Marques_déposées #Propriété_intellectuelle

Why Consumers Lose in the Big Pharma Wars - Knowledge Wharton
▻http://knowledge.wharton.upenn.edu/article/why-consumers-lose-in-the-big-pharma-wars

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Same Drug, Different Packaging

Drug companies make significant profits as long as their patents hold, preventing generics from entering the market. But they have found a clever way to extend patent expiration by “tweaking” the medications. Feldman cited a 10-year study of prescriptions sold in the U.S. that showed how companies extend the “protection cliff” by adding exclusivities, additional patents and other minor changes.

“Rather than creating new medicines, pharmaceutical companies are recycling and repurposing old ones,” she said. “Every year, at least 74% of the drugs associated with new patents were not new drugs coming on the market, but they were existing drugs. In other words, we are lavishing these rewards not on exciting new innovations but on tweaks to things that already exist.” She added that extending the protection cliff is particularly pronounced among blockbuster drugs. “Of the roughly 100 best-selling drugs, almost 80% extended their protection cliff at least once, and half extended the protection cliff more. We have lots of examples of what I like to call serial offenders.”

The Hatch-Waxman Act, enacted in 1984, was designed to allow rapid entry of generic drugs into the market as soon as patents ended. For a long time, the law was successful at bringing competitors into the fold, which brought down prices by up to 80%. The law also allows a drug company to challenge a patent. If the challenger wins, they are rewarded by being the only generic on the market for six months.

“That’s intended to help clear out weak and inappropriate patents,” Feldman said. “Unfortunately, the drug companies have taken this system and twisted it around.” She said patent holders are settling with challengers in what is known as “pay for delay” agreements. In other words, they pay that first generic to stay off the market.

“So, the branded drug stays on the market at a very high price. The generic gets paid a little of that monopoly rent to stay off the market. And when the generic comes to market, it still gets its six-month exclusivity,” she said. “It’s a win-win for the branded drug. It’s a win for the generic drug. Consumers and society lose. These things push out the horizon at which a generic drug would enter the market.”

Feldman believes the best way to fix the broken system is to simplify it. “Complexity breeds opportunity. And the drug industry is adept at exploiting that complexity. A simpler system makes it much more difficult to play games.” She advocates legal changes that would remove incentives and limit companies to one period of exclusivity on a chemical formulation with no allowance for extensions. She also calls for greater transparency in the process.

#Big_Pharma #Médicaments #Marché #Génériques

What Will Really Happen if the FCC Abandons Net Neutrality ?
▻http://knowledge.wharton.upenn.edu/article/net-neutrality-debate

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Article intéressant parce qu’il donne la parole aux opposants à la neutralité. Mais à trop vouloir jouer au centre, on finit par prendre le point de vue des dominants.

Supporters often link net neutrality to free speech and unfettered, equal access to the internet. They also want stricter rules to curb the conduct of ISPs. “Removal of the net neutrality rules could entirely take down the internet as a free and open source of information,” said Jennifer Golbeck, a professor at the University of Maryland, on the Knowledge@Wharton show on SiriusXM channel 111. “It’s going to be more corporate control over the content we see … potentially not just favoring things that benefit [ISPs] financially but favoring them politically.”

But critics say that too much regulation dampens innovation and investments in the internet, which has thrived for decades without formal net neutrality rules. For example, net neutrality would tamp down on innovations such as T-Mobile’s “Binge On” service, which lets customers stream video from Netflix, YouTube, Hulu and other sites without counting it against their data buckets, said Christopher Yoo, professor of law, communication and computer and information science at the University of Pennsylvania, on the radio show. Moreover, the order brings back the FTC as the antitrust enforcer of ISP behavior, protecting consumer interests and banning deceptive business practices. (Listen to a podcast of the radio show featuring Yoo and Golbeck using the player above.)

As providers of information services, ISPs were much more lightly regulated than telecommunications services — such as the old Ma Bell. However, the FCC did adopt policies to preserve free internet access and usage and curb abuses. In 2004, FCC Chairman Michael Powell under President George W. Bush set out four principles of internet freedom: the freedom to access lawful content, use applications, attach personal devices to the network and obtain service plan information.

In 2010, under Obama’s first FCC chairman, Julius Genachowski, the agency’s Open Internet Order adopted anti-blocking and anti-discrimination rules after finding out that Comcast throttled BitTorrent, a bandwidth-intensive, peer-to-peer site where users shared files of TV shows, movies or other content. Faulhaber says Comcast made the mistake of “targeting a particular upstream company. That you can’t do. If you want to control traffic, you have to do it in a much less discriminatory way.”

But the 2010 order, which also required ISPs to disclose their network management practices, performance and commercial terms, was vacated by a federal court in 2014 after Verizon sued the FCC. The court said the FCC did not have the authority to act because ISPs are not regulated like common telephone carriers.

This ruling led to the 2015 order by Wheeler that reclassified ISPs like landline phone companies, giving the agency the power to regulate many things, including prices set by broadband providers, although this was set aside. The order also specified the no-blocking and no-discrimination of traffic, and banned paid prioritization, which would give faster internet lanes to companies that pay for it. And it crafted internet conduct standards that ISPs must follow. Last year, an appellate court upheld this order.

The current proposal by Pai rolls back Wheeler’s order, and more. It classifies ISPs back under information services. It allows paid prioritization. It also punts the policing of any ISP blocking and discriminatory behavior to the FTC to be investigated on a case-by-case basis. It dismantles Wheeler’s internet conduct standards because they are “vague and expansive.” But the proposed order does adopt transparency rules, requiring ISPs to disclose information about their practices to the FCC and the public.

For ISPs, the issue is not so much net neutrality as it is about Title II. “All of the major ISPs like Comcast and AT&T are on the record saying that they support the idea of net neutrality, but they just oppose the legal classification of broadband as a regulated telecommunications service,” Werbach says. “I wouldn’t expect to see any dramatic changes in the companies’ practices near term. They’re going to wait and see how this all plays out, and they’re also not going to do something that will provoke significant backlash and pressure for more regulation.”

During her radio show appearance, Golbeck noted that the danger of fast lanes is that smaller websites that cannot afford to pay the ISP could be left behind. Research shows that “even delays of less than a second in serving up content [will make people] bail from your site and go someplace else.” Conversely, she said, if ISPs speed up access to popular sites like Amazon and Netflix because they pay, “it inhibits the ability for other new startup sites to compete.”

#Neutralité_internet

So Long, Selfies : Why Candid Photos Make a Better Impression - Knowledge Wharton
▻http://knowledge.wharton.upenn.edu/article/power-candid-photos

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In our increasingly digital society, a friend or colleague’s first impression of you is just as likely to come from a profile photo on a social media site as it is from an in-person meeting. While it’s tempting to display only images where every hair is in place, new research from Wharton marketing professor Jonah Berger finds that people are more attracted to authenticity than perfection. In, “A Candid Advantage? The Social Benefits of Candid Photos,” Berger and co-author Alixandra Barasch of New York University compare audience reactions to posed vs. candid photos in online profiles. When observers viewed profiles that displayed unvarnished images — or those that seemed to be unvarnished — they reported feeling more connected to those people and more interested in getting to know them. Berger recently spoke to Knowledge@Wharton about the research and its implications for how individuals and companies present themselves.

What’s interesting is that would suggest that that photo makes you look the best; that by sharing those posed photos, you’re not only looking good, but you’re helping others get to know you and making them want to interact with you. But we found something that wasn’t entirely in line with that. If you ask posters which photo they would choose, which one they would post, which one they think other people would like more, people have this intuition that posed photos are better. And that is because as a photo taker, you think a lot about how you come off to others. You think by controlling the lighting and your smile, that you’re presenting your best self.

But as an observer, someone who’s looking at those photos, what we found was quite surprising. Candid photos, where someone isn’t looking directly at the camera or looks like they’re not posing, actually lead to better impressions. People are more interested in getting to know someone, more interested in dating them and potentially more interested in being friends with them if that person has a candid rather than posed photo. The reason why is somewhat surprising, but simple once you hear it. It’s all about authenticity or whether someone is genuine. We think that by posting posed photos, people are getting the best version of us. But what we don’t realize is that when people see that best version, they don’t really have a good sense of who we are. Sure, there are a lot of photos online of people looking perfect and smiling. But that doesn’t really tell us much about them because they all look the same. It’s everyone presenting their best self, not their real self.

As a side note, there was a great piece of research recently looking at how stock images have changed over time, particularly of women. The most popular stock image of women, say, 10 years ago was a woman at a spa. Now, it’s a woman mountain climbing. The way these stock images are used really change our perceptions of the world.

#Images #Selfies #Médias_sociaux #Présentation_de_soi

The Biggest Misconceptions about Artificial Intelligence
▻http://knowledge.wharton.upenn.edu/article/whats-behind-the-hype-about-artificial-intelligence-separat

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Knowledge@Wharton: Interest in artificial intelligence has picked up dramatically in recent times. What is driving this hype? What are some of the biggest prevailing misconceptions about AI and how would you separate the hype from reality?

Apoorv Saxena: There are multiple factors driving strong interest in AI recently. First is significant gains in dealing with long-standing problems in AI. These are mostly problems of image and speech understanding. For example, now computers are able to transcribe human speech better than humans. Understanding speech has been worked on for almost 20 to 30 years, and only recently have we seen significant gains in that area. The same thing is true of image understanding, and also of specific parts of human language understanding such as translation.

Such progress has been made possible by applying an old technique called deep learning and running it on highly distributed and scalable computing infrastructure. This combined with availability of large amounts of data to train these algorithms and easy-to-use tools to build AI models, are the major factors driving interest in AI.

It is natural for people to project the recent successes in specific domains into the future. Some are even projecting the present into domains where deep learning has not been very effective, and that creates a lot of misconception and also hype. AI is still pretty bad in how it learns new concepts and extending that learning to new contexts.

For example, AI systems still require a tremendous amount of data to train. Humans do not need to look at 40,000 images of cats to identify a cat. A human child can look at two cats and figure out what a cat and a dog is — and to distinguish between them. So today’s AI systems are nowhere close to replicating how the human mind learns. That will be a challenge for the foreseeable future.

Alors que tout est clean, la dernière phrase est impressionnante : « That will be a challenge for the foreseeable future ». Il ne s’agit pas de renoncer à la compréhension/création de concepts par les ordinateurs, mais de se donner le temps de le faire demain. Dans World without mind , Franklin Foer parle longuement de cette volonté des dirigeants de Google de construire un ordinateur qui serait un cerveau humain amélioré. Mais quid des émotions, des sentiments, de la relation physique au monde ?

As I mentioned in narrow domains such as speech recognition AI is now more sophisticated than the best humans while in more general domains that require reasoning, context understanding and goal seeking, AI can’t even compete with a five-year old child. I think AI systems have still not figured out to do unsupervised learning well, or learned how to train on a very limited amount of data, or train without a lot of human intervention. That is going to be the main thing that continues to remain difficult . None of the recent research have shown a lot of progress here.

Knowledge@Wharton: In addition to machine learning, you also referred a couple of times to deep learning. For many of our readers who are not experts in AI, could you explain how deep learning differs from machine learning? What are some of the biggest breakthroughs in deep learning?

Saxena: Machine learning is much broader than deep learning. Machine learning is essentially a computer learning patterns from data and using the learned patterns to make predictions on new data. Deep learning is a specific machine learning technique.

Deep learning is modeled on how human brains supposedly learn and use neural networks — a layered network of neurons to learn patterns from data and make predictions. So just as humans use different levels of conceptualization to understand a complex problem, each layer of neurons abstracts out a specific feature or concept in an hierarchical way to understand complex patterns. And the beauty of deep learning is that unlike other machine learning techniques whose prediction performance plateaus when you feed in more training data, deep learning performance continues to improve with more data. Also deep learning has been applied to solve very different sets of problems and shown good performance, which is typically not possible with other techniques. All these makes deep learning special, especially for problems where you could throw in more data and computing power easily.

Knowledge@Wharton: The other area of AI that gets a lot of attention is natural language processing, often involving intelligent assistants, like Siri from Apple, Alexa from Amazon, or Cortana from Microsoft. How are chatbots evolving, and what is the future of the chatbot?

Saxena: This is a huge area of investment for all of the big players, as you mentioned. This is generating a lot of interest, for two reasons. It is the most natural way for people to interact with machines, by just talking to them and the machines understanding. This has led to a fundamental shift in how computers and humans interact. Almost everybody believes this will be the next big thing.

Still, early versions of this technology have been very disappointing. The reason is that natural language understanding or processing is extremely tough. You can’t use just one technique or deep learning model, for example, as you can for image understanding or speech understanding and solve everything. Natural language understanding inherently is different. Understanding natural language or conversation requires huge amounts of human knowledge and background knowledge. Because there’s so much context associated with language, unless you teach your agent all of the human knowledge, it falls short in understanding even basic stuff.

De la compétition à l’heure du vectorialisme :

Knowledge@Wharton: That sounds incredible. Now, a number of big companies are active in AI — especially Google, Microsoft, Amazon, Apple in the U.S., or in China you have Baidu, Alibaba and Tencent. What opportunities exist in AI for startups and smaller companies? How can they add value? How do you see them fitting into the broader AI ecosystem?

Saxena: I see value for both big and small companies. A lot of the investments by the big players in this space are in building platforms where others can build AI applications. Almost every player in the AI space, including Google, has created platforms on which others can build applications. This is similar to what they did for Android or mobile platforms. Once the platform is built, others can build applications. So clearly that is where the focus is. Clearly there is a big opportunity for startups to build applications using some of the open source tools created by these big players.

The second area where startups will continue to play is with what we call vertical domains. So a big part of the advances in AI will come through a combination of good algorithms with proprietary data. Even though the Googles of the world and other big players have some of the best engineering talent and also the algorithms, they don’t have data. So for example, a company that has proprietary health care data can build a health care AI startup and compete with the big players. The same thing is true of industries such as finance or retail.

#Intelligence_artificielle #vectorialisme #deep_learning #Google

Why China Is Leading the Fintech Race - Knowledge Wharton
▻http://knowledge.wharton.upenn.edu/article/why-china-leads-the-fintech-race

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But there are actually seven tech firms in the global top 10 now — the other two are Chinese: Alibaba and Tencent. In China, everyone knows “BAT” (Baidu, the Chinese Google, along with Alibaba and Tencent). There is much less BAT buzz outside China, for two reasons.

First, the Chinese government’s “great firewall” around the internet not only restricts the flow of information in China, it also helps protect Chinese firms from international competition. Second, the Chinese tech companies have tended to be rapid adopters and adapters of innovations generated elsewhere rather than breakthrough inventors themselves.

The first point continues to hold: “techno-nationalism” is a new term of concern in the West when it comes to China’s aspirations to chart its own course to global prominence in technology, by protecting the domestic market and always with a close eye on the IP of others.
Knowledge@Wharton High School

But the second reason for discounting Chinese tech — that they are incapable of creating true innovation — is rapidly receding as a viable criticism. Anyone who ignores Alibaba and Tencent does so at their own peril because of real innovations they are implementing in China, and what they hope to do globally tomorrow.

When it comes to Alibaba, think less eBay meets Wal-Mart and Amazon, and more fintech. For Tencent, think less social media and e-sports and more fintech.

Four hundred and sixty-nine million people made online payments in China in 2016. A larger number used phones to pay in offline retail stores. For comparison, the user base of Apple Pay, by far the dominant American player, was 12 million in the U.S. last year. I am now used to seeing people in China paying for everything from taxis and coffee to clothes and meals with either WeChat (Weixin) Pay or Alipay (Zhifubao) — another world from the China of the early 2000s when you had to pay hotel bills with a series of 100 RMB notes.

No one knows who will win this global competition, but the recent history of digital payments underlines a key fact. The extraordinary innovativeness of the U.S. tech sector is justly acclaimed. However, it is no longer immune to the forces of globalization and global competition that have disrupted so many other industries in the past few decades.

#GAFA #TAB #Chine #Fintech #Alibaba #Tencent #Baidu