/2017

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

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