NEXT LIST 2017 – 20 TECH VISIONARIES WHO ARE CREATING THE FUTURE

NEXT LIST 2017

20 TECH VISIONARIES WHO ARE CREATING THE FUTURE by WIRED staff

https://www.wired.com/2017/04/20-people-creating-future-next-list-2017/

MICROSOFT WILL BUILD computers even more sleek and beautiful than Apple’s. Robots will 3-D-print cool shoes that are personalized just for you. (And you’ll get them in just a few short days.) Neural networks will take over medical diagnostics, and Snapchat will try to take over the entire world. The women and men in these pages are the technical, creative, idealistic visionaries who are bringing the future to your doorstep. You might not recognize their names—they’re too busy working to court the spotlight—but you’ll soon hear about them a lot. They represent the best of what’s next.

Put Humans First, Code Second

Parisa Tabriz

Browser Boss | Google Chrome

As head of security for Google Chrome, Parisa Tabriz has spent four years focusing on a vulnerability so widespread, most engineers act as if it doesn’t exist: humanity. She has pushed her 52-person team to grapple with problems once written off as “user errors.” They’ve made key changes in how the browser communicates with people, rewriting Chrome’s warnings about insecure network connections at a sixth-grade reading level. Rather than depending on users to spot phishing schemes, the team is exploring machine-­learning tools to automatically detect them. And they’re starting to mark sites as “not secure” if they don’t use HTTPS encryption, pressuring the web to secure itself. “We’ve been accused of being paternalistic, but we’re in a position to protect people,” she says. “The goal isn’t to solve math problems. It’s to keep humans safe.” Tabriz, whose father is Iranian, has also made a point of hiring engineers from other countries—like Iran—where state internet surveillance is an oppressive, everyday concern. “You can’t keep people safe if you don’t understand those human challenges around the world.” —Andy Greenberg

Wall Street Can Run on Collaboration, Not Competition

Richard Craib

Founder | Numerai

Wall Street is capitalism at its fiercest. But Richard Craib believes it can also be a place for friendly collaborations. His hedge fund, San Francisco–based ­Numerai, relies on artificially intelligent algorithms to handle all trades. But the 29-year-old South African mathematician doesn’t build these algorithms himself. Instead, his fund crowdsources them from thousands of anonymous data scientists who vie for bitcoin rewards by building the most successful trading models. And that isn’t even the strangest part.

Ultimately, Craib doesn’t want these data scientists to get overly competitive. If only the best modelers win, they have little incentive to recruit fresh talent, which could dilute their rewards. Competitors’ self-­interest winds up at odds with getting the best minds, no matter who they are, working to improve the fund. To encourage cooperation, Craib developed Numer­aire, a kind of digital currency that rewards everyone when the fund does well. Data scientists bet Numer­aire on algorithms they think will succeed. When the models work, Numer­aire’s value goes up for everyone. “I don’t want to build a company or a startup or even a hedge fund,” Craib says. “I want to build a country—a place where everyone is working openly toward the same end.” —­Cade Metz

Microsoft Will Outdesign Apple

Kait Schoeck

Industrial Designer | Microsoft

Kait Schoeck wasn’t really supposed to end up at Micro­soft. She had enrolled at the Rhode Island School of Design in 2009 with plans to be a painter, or maybe an illustrator. “I didn’t know industrial design actually existed,” she says. That changed in school, where she switched majors and even­tually caught Microsoft’s attention. The company liked her unusual portfolio—there wasn’t much in it about computers. Now she’s one of the designers working on Microsoft’s Surface products, helping the com­pany achieve what for decades has seemed impossible: outdesigning Apple. Because Schoeck and her team aren’t bogged down by decades of PC-­design baggage, they freely break with convention. And because their desks are a few feet from a machine shop, they can build whatever they dream up. “Being able to hold the products we make—that’s when you really know what works,” Schoeck says. Early in her time at Microsoft, she co­invented the rolling hinge that makes the detachable Surface Book possible; her team has also found ways to make touchscreen laptops feel natural, to build tablets that really can replace your laptop, and to turn the old-school desktop PC into something more like a drawing table. Thanks to designers like Schoeck, Micro­soft’s machines aren’t just brainy anymore—they’re beautiful too. —David Pierce

Frugal Science Will Curb Disease

Manu Prakash

Founder | Foldscope Instruments

While visiting rabies clinics in India and Thailand, Manu Prakash made a damning realization: In remote villages, traditional microscopes are useless. Cumbersome to carry and expensive to maintain, the finely tuned machines are often relegated to a dusty lab corner while medical providers diagnose and treat patients in the field. So the Stanford bio­engineer set out to build what he calls “the pencil of micro­scopy”—a high-­performing tool that’s lightweight, ­durable, and cheap. In 2014 his lab unveiled the Foldscope, an origami-like paper microscope that magnifies objects up to 2,000 times but costs less than $1 to produce. “We quickly realized that writing scientific papers about it wasn’t good enough,” Prakash says. He turned his lab into a mini Foldscope factory, giving away microscopes to anyone who asked. Within a year, the lab had shipped 50,000 of them to users in 135 countries, from Mongolia to rural Montana; this year it aims to donate 1 million. An eager army of DIY scientists has used the tool to identify fake drugs, detect diseased crops, spot counter­feit currency, and more. Earlier this year, Prakash’s lab introduced the Paperfuge, a 20-cent centri­fuge inspired by an ancient spinning toy, which can be used to diagnose diseases like malaria. Prakash’s cheap, cleverly designed devices prove that when it comes to public health problems, the high tech (high-cost) solution isn’t always the best fix. Consider his lab’s latest achievement, a method of identifying mosquito species by recording their wing beats. The apparatus required? A flip phone. —Lauren Murrow

TV Ad Dollars Will Get Snapped Up

Jeff Lucas

VP and Global Head of Sales | Snap

In March, Snap’s public stock offering became the third-largest tech IPO of all time, raising $3.4 billion. Now it just needs to make money. As of January 2017, the six-year-old multi­media app had lost $1.2 billion, nearly half of that in 2016 alone. Its growth rate is slowing too: After averaging more than 15 million new daily users in each of the first three quarters of 2016, it added just 5 million in the fourth quarter. So last summer, the company poached media industry veteran Jeff Lucas, former head of sales at Viacom. In the wake of Snap’s IPO, he’s been tasked with backing up the brand’s billion-dollar hype with measurable profits. To do that, he’ll need to ward off copycat competitors like Insta­gram’s Stories and WhatsApp’s Status—direct descendants of Snapchat Stories, a series of snaps strung together chrono­logically—and lure ad spending away from Facebook and TV networks. He’s reportedly in talks with marketing agencies like Publicis Groupe, WPP, and Omnicom Group to land deals of $100 million to $200 million. In a crowded industry competing for advertising dollars, Lucas will be instrumental in getting those gatekeepers to open their coffers for Snap. —Davey Alba

SOURCE: EMARKETER

Encryption Alone Is Not Enough

John Brooks

Programmer | Ricochet

Thanks to messaging services like WhatsApp, Signal, and Apple’s iMessage, end-to-end encryption isn’t just for spies and cypherpunks anymore; it’s become nearly as standard as emoji. But sometimes an unbroken channel of encryption between sender and receiver isn’t enough. Sure, it hides the content of messages, but it doesn’t conceal the identities of who’s writing to whom—metadata that can reveal, say, the membership of an organization or a journalist’s web of sources. John Brooks, a 25-year-old middle school dropout, has created an app that may represent the next generation of secret-sharing tools: ones that promise to hide not just your words but also the social graph of your connections.

His chat app, called Ricochet, builds on a feature of the anonymity software Tor that’s rendered sites on the dark web untraceable and anonymous for years. But instead of cloaking web destinations, Ricochet applies those stealth features to your PC: It turns your computer into a piece of the darknet. And unlike almost all other messaging apps, Ricochet allows conversations to travel from the sender’s computer to the recipient’s without ever passing through a central server that can track the data or metadata of users’ communications. “There’s no record in the cloud somewhere that you ever used it,” Brooks says. “It’s all mixed in with everything else happening in Tor. You’re invisible among the crowd.” And when invisibility is an option, plain old encryption starts to feel awfully revealing. —­Andy Greenberg

Silicon Valley Can Spread the Wealth

Leslie Miley

President, West Coast | ­Venture for America

Silicon Valley generates astronomical levels of wealth. But you’d be hard-pressed to find the spoils of the tech industry extending far beyond the Bay Area, much less to ­Middle America. Leslie Miley wants to change that. Early this year he left his job as a director of engineering at Slack to launch an executive-­in-residence program at Venture for America. The project is designed to foster the building of tech businesses in emerging markets like Detroit and Baltimore. Starting this September, the residency will place Silicon Valley execs in yearlong stints in several of the program’s 18 innovation hubs, where they’ll advise area startups. The idea is that having well-connected leaders in such places may give local talent ties to Silicon Valley and inspire startups to set up shop in those cities. According to Miley, the program was fueled by industry-­wide anxiety following the 2016 election. “Tech enabled people to stay in their echo chambers,” Miley says. “We’re partially responsible.” Not just by building non-­inclusive platforms, he says, but by overlooking large swaths of the country in the hunt for talent. Davey Alba

Our Robots Are Powered by Poets and Musicians

Beth Holmes, Farah Houston, Michelle Riggen-­Ransom

HOLMES Knowledge Manager | Alexa Information team

HOUSTON Senior Manager | Alexa Personality team

RIGGEN-­RANSOM Managing Editor | Alexa Personality team

Behind your high tech digital assistant is a band of liberal arts majors. A trio of women shape the personality of Amazon’s Alexa, the AI-powered device used by tens of millions of consumers worldwide: Michelle Riggen-­Ransom, who has an MFA in creative writing, composes the bot’s raw responses; Farah Houston, a psychology grad specializing in personality science, ensures that those responses dovetail with customers’ expectations; and Beth Holmes, a mathematician with expertise in natural language processing, decides which current events are woven into Alexa’s vocabulary, from the Super Bowl to the Oscars. “The commonality is that most of us have been writers and have had to express humor in writing,” Houston says. Riggen-­Ransom oversees a group of playwrights, poets, fiction authors, and musicians who complete weekly writing exercises that are incorporated into Alexa’s persona. (The bot’s disposition is broadly defined in a “personality document,” which informs the group’s responses.) The content is then workshopped among the team; much of it ends up on the cutting room floor. Alexa’s temperament can swing from practical and direct to whimsical and jokey. The art is in striking the right balance, especially when it comes to addressing sensitive topics. “Our overall approach when talking to people about politics, sex, or religion has been to divert with humor,” Houston says. But thanks in part to her female-led team, the bot won’t stand for insults. “We work hard to always portray Alexa as confident and empowered,” Houston says. It takes a village to raise a fake lady. —­Davey Alba

Hard Data Can Improve Diversity

Laura I. Gómez

Founder | Atipica

Three years ago, Laura Gómez was participating in yet another diversity-in-tech panel, alongside representatives from Facebook and ­Google, when she snapped. “This is not a meritocracy, and we all know it,” the Latina entrepreneur announced. “This is cronyism. A Googler gets hired by Twitter, who gets hired by Facebook. Everyone is appointing their friends to positions of authority.” (As someone who has worked at Twitter, YouTube, and ­Google, she should know.) The breakthrough inspired Gómez to found Atipica, a recruiting software company that sorts job applicants solely by their skill set. That policy may seem obvious, but recruiters are prone to pattern-­matching in accordance with previous hires—giving preference to, say, Stanford-schooled ­Google engineers. Atipica isn’t designed to shame tech CEOs about their uber-white open offices; rather, it presents hard data, judgment-free. The company’s software—which draws on information from public, industry, and internal sources—reveals the type of person most likely to apply for a job, analyzes hiring patterns, and quantifies the likelihood that certain kinds of candidates will accept job offers. It also resurfaces diverse candidates for new job postings they’re qualified for, a strategy that has led thousands of applicants to be recontacted. Last fall, Atipica raised $2 million from True Ventures, Kapor Capital, Precursor Ventures, and others. For Gómez, a Mexican immigrant who was undocumented until the age of 18, the work is personal. “My mother was a nanny and a housekeeper for people in Silicon Valley,” she says. “My voice is the voice of immigrants.” Her company’s success shows that the struggle to diversify tech will be won not by indignant tweetstorms but by data. —Lauren Murrow

Music Will Leave the Studio Behind

Steve Lacy

Musician

Most musicians work in studios, with engineers and producers and dozens of contributors. Steve Lacy works in hotel rooms. Or in his car. One time at a barbershop. Anywhere inspiration strikes, really. And with every unconventional session, Lacy’s proving to the industry that good music doesn’t have to be sparkling and hyperproduced. He dropped his first official solo material in February, a series of songs (he won’t call it an album) made entirely in GarageBand. Lacy plugs his guitar into his iPhone’s Lightning port and sings right into the mic. The whole thing’s a bit shticky, sure, but the point is to show people that the tools you have don’t really matter. He’s no musical lightweight, though. Just 18, he’s already a sought-after producer, making beats with the likes of J. Cole and Kendrick Lamar. Lacy’s own style is a little bit pop, a little bit soul, and a little bit R&B. He calls it Plaid, because it’s a lot of funky patterns you can’t quite imagine together—but somehow it all works. Even he doesn’t always understand why, but he knows it does. Kendrick Lamar told him so. —David Pierce

SOURCE: RIAA

Microbiology Gets a Little Intelligent Design

Christina Agapakis

Creative Director | Ginkgo Bioworks

For a biologist, Christina Agapakis has an unusual role. At Ginkgo Bioworks, a Boston biotech firm that tweaks yeast and bacteria to create custom organisms for everything from fermentation to cosmetics, Agapakis is a bridge between the technical and creative sides of the business. She works with clients like food conglomerates to figure out how they can use engineered microbes to make their products better, cheaper, and more sustainable. Recently, French perfumer Robertet enlisted Ginkgo’s organism designers to create a custom yeast that could replicate the smell of rose oil. To do that, the designers inserted the scent-­producing genes from roses into yeast, which produced floral-­smelling compounds—no expensive rose petals necessary. Agapakis then worked with the company’s perfumers to develop new fragrances using this novel substance. “A lot of what I do is think about what this new technology can enable creatively,” she says. Biotech companies are learning that success requires more than good science—it takes imaginative thinking too. —Liz Stinson

Tech Workers, Not CEOs, Will Drive Real, Positive Change

Maciej Ceglowski

Founder | Pinboard

A tweet by @Pinboard reads, “Silicon Valley lemon­ade stand: 30 employees, $45 million in funding, sells $9 glasses of lemonade while illegally blocking sidewalk.” The account belongs to a bookmarking site founded by Polish-born web developer Maciej Ceglowski. Though he established the handle in 2009 intending to offer product support, Ceglowski now uses the account to gleefully skewer Silicon Valley to 38,700 Twitter followers. Since the presidential election, the developer’s criticism of his own industry has taken a more trenchant tone, energizing a new wave of tech activists. (On Facebook’s refusal to cut ties with Trump supporter Peter Thiel, he tweeted: “Facebook has a board member who heard credible accusations of sexual assault and threw $1.25M at the perpetrator. That requires comment.”) In December, thousands of tech employees signed an @Pinboard-championed pledge at Neveragain.tech, refusing to utilize their companies’ user data to build a Muslim registry. Last year, Ceglowski founded Tech Solidarity, a national group that meets to devise methods of organizing. The effort has become high-profile enough that even C-suite execs, like Facebook’s chief security officer, Alex Stamos, now attend. For all his trademark snark, Ceglowski maintains that his goal is to foster a more conscientious tech indus­try. He hopes that Tech Solidarity can develop an industry-wide code of ethics in the coming months—“move fast and break things” needs an update, he says—and eventually lead employees to unionize. He believes the best way to exert influence over powerful tech companies is from the inside out: by empowering their workers. —Davey Alba

China Will Lead the Tech Industry

Connie Chan

Partner | Andreessen Horowitz

Connie Chan has a master’s degree in engineering from Stanford, where her classmates were Facebook’s future first employees. She thought that she knew what tech’s leading edge looked like. Then she went to China and discovered she had no idea. On massively popular messaging apps like WeChat, people did way more than just talk. They got marriage licenses and birth certificates, paid utilities and traffic tickets, even had drugs delivered—all in-app. Tech companies in the US, she realized, could no longer take it for granted that they led while the world followed; the stereo­type that China’s tech companies are just copycats is obsolete. “If you study Chinese products, you can get inspiration,” Chan says. As a partner at Andreessen Horowitz, she now specializes in helping American startups understand just how much they have to learn as China’s tech industry races ahead of the US in everything from messaging to livestreaming (now a $5 billion market). No matter the protectionist rhetoric coming from the Trump administration, US tech firms see billions of dollars to be made in China, and vice versa. As these two financial giants play overseas footsie, Chan acts as a facilitator. “I spend so much time teaching people what they can’t see,” she says. It won’t stay invisible for long. —Marcus Wohlsen

SOURCES: RHODIUM GROUP; 2016 U.S. DATA: XINHUA NEWS AGENCY

Need Help Choosing a Wine? There’s a DNA-Based App for That.

James Lu

Senior VP of Applied Genomics | Helix

Advances in genetic sequencing mean that labs can now—quickly and cheaply—read millions of letters of DNA in a single gob of spit. Genomics researcher James Lu and his team at Helix (buoyed by $100 million in funding led by Illumina, the largest maker of DNA sequencers) are harnessing that information so you’ll be able to learn a lot more about yourself. How? There’s an app for that. First Helix will sequence and store your entire exome—every letter of the 22,000 genes that code for proteins in your body. (The technology uncovers much more data than genotyping, the process used by companies like 23andMe, which searches only for specific markers.) Then Helix partners will create apps that analyze everything from your cancer risk to, they say, your wine preferences, ranging from a few dollars to a few hundred dollars a pop. “Where one person may be interested in inherited diseases, someone else cares about fitness or nutrition,” Lu says. “We work with developers to provide better products and context for your genetic information.” Helix’s first partners include medical groups like the Mayo Clinic and New York’s Mount Sinai Hospital, which are developing genetic-education and health-­related apps, and National Geographic, which offers an app that uncovers your ancestors’ locations and migration patterns going back 200,000 years. Lu imagines future collaborations with, say, a travel service that plans your vacation itinerary based on your genealogy or a food delivery service that tailors menus to your metabolic profile. The project opens new markets for genetic research—and entirely new avenues of self-absorption for the selfie generation. —Lauren Murrow

SOURCE: NATIONAL HUMAN GENOME RESEARCH INSTITUTE

Techies Should Serve Their Country

Matt Cutts

Acting Administrator | United States Digital Service

Matt Cutts could easily have left his job at the US Digital Service after Inauguration Day—as many other Obama staffers did. His wife wasn’t in Washington, and neither was his main gig as Google’s chief spam fighter. But when the time came, he couldn’t walk away. “My heart says USDS,” he wrote to his wife, who eventually joined him in DC.

As a member of the govern­ment’s tech task force, Cutts oversaw a team that worked on an online portal for veterans. Had he quit in January, he wouldn’t have seen two USDS initiatives—services for the Pentagon and the Army—through to completion. “The organization deserves to have someone who can help preserve its mission,” Cutts says. It also needs someone who can convince Silicon Valley types that managing the president’s Twitter feed isn’t the only tech job in government. Cutts, who avoids talking politics, has begun recruiting friends in the industry, telling them that no matter whom they voted for, “once you see the sorts of issues you can tackle here, it tends to be pretty addictive.” And you really can change the world (slowly). —Issie Lapowsky

Robots Will Make Fast Fashion Even Faster

Gerd Manz

VP of Future Team | Adidas

Cookie-cutter kicks aren’t good enough for Gen Z sneaker­heads. They want custom­ization, and they want it fast. “They get annoyed if it takes three seconds to download an app,” says industrial engineer Gerd Manz, who oversees technology innovation at Adidas. So he is heading up the company’s ambitious new manufacturing facilities—pointedly dubbed Speedfactories—staffed not by humans but by robots. The sportswear giant will start production in two Speedfactories this year, one in Ansbach, Germany, and another in Atlanta, each eventually capable of churning out 500,000 pairs of shoes a year, including one-of-a-kind designs. Thanks to tech like automated 3-D printing, robotic cutting, and computerized knitting, a shoe that today might spend 18 months in the development and manufacturing pipeline will soon be made from scratch in a matter of hours. And though the Speedfactories will initially be tasked with limited-edition runs, Manz, a sort of sneaker Willy Wonka, predicts that the complexes will ulti­mately produce fully customizable shoes. (You’ll even be able to watch a video of your own pair being made.) “It doesn’t matter to the Speedfactory manufacturing line if we make one or 1,000 of a product,” Manz says. The robot factories of the future will fulfill consumers’ desires: It’s hyper-­personalization at a breakneck pace. —Lauren Murrow

Artificial Intelligence Will Help Doctors Do Their Jobs Better

Lily Peng

Product Manager | Google Brain

In 2012, Google built an artificial intelligence system that could recognize cats in YouTube videos. The experiment may have seemed frivolous, but now Lily Peng is applying some of the same techniques to address a far more serious problem. She and her colleagues are using neural networks—complex mathematical systems for identifying patterns in data—to recognize diabetic retino­pathy, a leading cause of blindness among US adults.

Inside Google Brain, the company’s central AI lab, Peng is feeding thousands of retinal scans into neural networks and teaching them to “see” tiny hemorrhages and other lesions that are early warning signs of retinopathy. “This lets us identify the ­people who are at the highest risk and get them treatment soon rather than later,” says Peng, an MD herself who also has a PhD in bio­engineering.

She’s not out to replace doctors—the hope is that the system will eventually help overworked physicians in poorer parts of the world examine far more patients, far more quickly.

At hospitals in India, Peng is already running clinical trials in which her AI analyzes patients’ eye scans. In the future, doctors could work with AI to examine x-rays and MRIs to detect all sorts of ailments. “We want to increase access to care everywhere,” she says. By sharing the workload, machines can help make that possible. —Cade Metz

SOURCE: INTERNATIONAL FEDERATION OF ROBOTICS

Don’t wanna Cry? Use Linux 

Don’t wanna Cry? Use Linux. Life is too short to reboot. 

So far, over 213,000 computers across 99 countries around the world have been infected, and the infection is still rising even hours after the kill switch was triggered by the 22-years-old British security researcher behind the twitter handle ‚MalwareTech.‘

For those unaware, WannaCry is an insanely fast-spreading ransomware malware that leverages a Windows SMB exploit to remotely target a computer running on unpatched or unsupported versions of Windows.

So far, Criminals behind WannaCry Ransomware have received nearly 100 payments from victims, total 15 Bitcoins, equals to USD $26,090.


Once infected, WannaCry also scans for other vulnerable computers connected to the same network, as well scans random hosts on the wider Internet, to spread quickly.

The SMB exploit, currently being used by WannaCry, has been identified as EternalBlue, a collection of hacking tools allegedly created by the NSA and then subsequently dumped by a hacking group calling itself „The Shadow Brokers“ over a month ago.

„If NSA had privately disclosed the flaw used to attack hospitals when they *found* it, not when they lost it, this may not have happened,“ NSA whistleblower Edward Snowden says.

http://thehackernews.com/2017/05/wannacry-ransomware-cyber-attack.html

Delight Users with Animation

“Delight” is a word that we’re hearing and using more often to describe pleasurable moments in our products. Delight is the magic that makes us fall in love with a product. It’s a core element to strive for when designing. When it comes to providing pleasure or delight in our websites and apps, animations contribute a lot.

WHY DELIGHTFUL ANIMATION IS IMPORTANT

Digital design plays a crucial role in how customers experience a product. Modern design is highly focussed on usability, because usability allows people to easily accomplish their goals. However, designing for the user experience has a lot more to it than making a usable product. Good design is pleasurable and seductive. Good design is delightful. “At this point in experience design’s evolution, satisfaction ought to be the norm, and delight ought to be the goal,” says Stephen Anderson. Animation can help you achieve this goal.

WHEN TO USE DELIGHTFUL ANIMATION

Just like any other design element animation should contribute the user flow. Delightful animations are pleasurable for the user without detracting from the usability of the app. There are two cases when implementing delightful animation into your digital designs can strengthen UX:

  • Engaging and entertaining. Entertaining animation draws attention to our products by creating a strong first impression. It can make our products more memorable and more shareable.
  • Baking emotion in design. Showing the human side of your business or product can be a very powerful way for your audience to identify and empathize with you. The aim of emotional design is to create happiness. You want people to feel happy when they use your product.

Let’s look at a few ways animation can help create delightful moments:

1. KEEP USERS INTERESTED DURING LOADING

Loading time is an unavoidable situation for most digital products. But who says that loading should be boring? When we can’t shorten the line, we can certainly make the wait more pleasant. To ensure people don’t get bored while waiting for something to happen, you can offer them some distraction: this can be something fun or something unexpected. While animation won’t solve the problem, it definitely makes waiting less of a problem: fine animation can distract your users and make them ignore long loading times.

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Credits: Dribbble

2. MAKE A GREAT FIRST IMPRESSION

First impressions count: people judge things based on how they look. Good animation throughout the onboarding flow has a strong impact on how first-time users will engage with the app. A good first impression isn’t just about usability, it’s also about personality. If your first few app screens look a little different from similar products, you’ve shown the user that your entire product experience will likely be different too. For example, animating an illustration for a new feature can educate the user about the feature in a memorable way.

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Credits: Dribbble

3. MAKE YOUR INTERFACES FEEL MORE ALIVE

Creative animation can make your user experience truly delightful: they can transform familiar interactions into something much more enjoyable and have the power to encourage users to actually interact. Attention to fine movements can increase the level of usability and therefore desirability of the product.

4. INCORPORATE EMOTIONAL INTERACTIONS

Focusing on user emotions plays a huge role in UI interactions. As Aarron Walter said in his book Designing for Emotion: “Personality is the mysterious force that attracts us to certain people and repels us from others.” Using animation you can establish an emotional connection with your users, and remind them that there are real humans behind the design. An example of animation from ReadMe is full of emotions.

5. HELP USER RECOVER FROM UNEXPECTED ERRORS

‘Errors’ happen. They happen in our apps and they happen in our life. Sometimes they happen because we made mistakes. Sometimes because an app failed. Whatever the cause, these errors — and how they are handled — can have a huge impact on the way user experiences your app. A well-crafted error handling can turn a moment of failure into a moment of delight. When displaying an unexpected error, use it as an opportunity to delight with animation.

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Credits: Dribbble

6. MAKE A COMPLEX TASK FEEL EASIER

Animation is able to transform a complex task into an inviting experience.  Let’s take a MailChimp case for inspiration. What makes MailChimp awesome is its smooth functionality wrapped in cheeky humor and friendly animation. When you’re about to send out your first campaign, the accompanying animation shows how stressful it is. Mailchimp brings empathy to the design: by combining animated cartoons with tongue-in-cheek messages like “This is your moment of glory,” MailChimp softens the nervousness of sending your first emails.

7. BREATHE FUN INTO THE INTERACTIONS

People love to discover treats in interfaces just as they do in real life. The joy is more than the treat, it’s the discovery of the treat and the feeling that someone took the time to think of you.

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Credits: Dribbble

People will forget what you said, people will forget what you did, but people will never forget how you made them feel.—Maya Angelou

Never underestimate the power of delight to improve the user experience. The difference between products we love and those we simply tolerate is often the delight we have with them.

Of course, before your application can create an emotional connection with the user it must get the basics right.  Thus, make your product a joy to use by connecting feelings with features!

https://www.webdesignerdepot.com/2017/04/7-ways-to-delight-users-with-animation

How Banks Can Compete Against an Army of Fintech Startups

It’s been more than 25 years since Bill Gates dismissed retail banks as “dinosaurs,” but the statement may be as true today as it was then. Banking for small and medium-sized enterprises (SMEs) has been astonishingly unaffected by the rise of the Internet. To the extent that banks have digitized, they have focused on the most routine customer transactions, like online access to bank accounts and remote deposits. The marketing, underwriting, and servicing of SME loans have largely taken a backseat. Other sectors of retail lending have not fared much better. Recent analysis by Bain and SAP found that only 7% of bank credit products could be handled digitally from end to end.

The glacial pace at which banks have moved SME lending online has left them vulnerable. Gates’ original quote contended that the dinosaurs can be ”bypassed.” That hasn’t happened yet, but our research suggests the threat to retail banks from online lending is very real. If U.S. banks are going to survive the coming wave in financial technology (fintech), they’ll need to finally take digital transformation seriously. And our analysis suggests there are strategies that they can use to compete successfully online.

Lending to small and medium-sized businesses is ready to move online

Small businesses are starting to demand banking services that have engaging web and mobile user experiences, on par with the technologies they use in their personal lives. In a recent survey from Javelin Research, 56% of SMEs indicated a desire for better digital banking tools. In a separate, forthcoming survey conducted by Oliver Wyman and Fundera (where one of us works), over 60% of small business owners indicated that they would prefer to apply for loans entirely online.

In addition to improving the experience for business owners, digitization has the potential to substantially reduce the cost of lending at every stage of the process, making SME customers more profitable for lenders, and creating opportunities to serve a broader swath of SMEs. This is important because transaction costs in SME lending can be formidable and, as our research in a recent HBS Working Paper indicates, some small businesses are not being served. Transaction costs associated with making a $100,000 loan are roughly the same as making a $1,000,000 loan, but with less profit to the bank, which has led to banks prioritizing SMEs seeking higher loan amounts. The problem is that about 60% of small businesses want loans below $100,000. If digitization can decrease costs, it could help more of these small businesses get funded.

New digital entrants have spotted the market opportunity created by these dynamics, and the result is an explosion in online lending to SMEs from fintech startups. Last year, less than $10 billion in small-business loans was funded by online lenders, a fraction compared to the $300 billion in SME loans outstanding at U.S. banks. However, the current meager market share held by online lenders masks immense potential: Morgan Stanley estimates the total addressable market for online SME lenders is $280 billion and predicts the industry will grow at a 47% annualized rate through 2020. They estimate that online lenders will constitute nearly a fifth of the total SME lending market by then. This finding confirms what bankers fear: digitization upends business models, enabling greater competition that puts pressure on incumbents. Sometimes David can triumph over Goliath. As JPMorgan Chase’s CEO, Jamie Dimon, warned in a June 2015 letter to the bank’s shareholders, “Silicon Valley is coming.”

Can banks out-compete the disruptors?

Established banks have real advantages in serving the SME lending market, which should not be underestimated. Banks’ cost of capital is typically 50 basis points or less. These low-cost and reliable sources of funds are from taxpayer-insured deposits and the Federal Reserve’s discount window. By comparison, online lenders face capital costs that can be higher than 10%, sourced from potentially fickle institutional investors like hedge funds. Banks also have a built-in customer base, and access to proprietary data on depositors that can be used to find eligible borrowers who already have a relationship with the bank. Comparatively, online lenders have limited brand recognition, and acquiring small business customers online is expensive and competitive.

But banks’ ability to use these strengths to build real competitive advantage is not a forgone conclusion. The new online lenders have made the loan application process much more customer-friendly. Instead of walking into a branch on Main Street and spending hours filling out paperwork, borrowers can complete online applications with lenders like Lending Club and Kabbage in minutes and from their laptop or phone at any hour of the day. Approval times are cut to days or, in some cases, a few minutes, fueled by data-driven algorithms that quickly pre-qualify borrowers based on a handful of data points such as personal credit scores, Demand Deposit Account (DDA) data, tax returns, and three months of bank statements. Moreover, in instances where borrowers want to shop and compare myriad options in one place, they turn to online credit brokers like Fundera or Intuit’s QuickBooks Financing for a one-stop shopping experience. By contrast, banks — particularly regional and smaller banks — have traditionally relied on manual, paper-intensive underwriting processes, which draw out approval times to as much as 20 days.

The questions banks should ask themselves

We see four broad strategies that traditional banks could pursue to compete or collaborate with emerging online players—and in some cases do both simultaneously. The choice of strategy depends on how much investment of time and money the bank is willing to make to enter the new marketplace, and the level of integration the bank wants between the new digital activities and their traditional operations.

Two of the four options are low-integration strategies in which banks contract for new digital activities in arms-length agreements, or pursue long-term corporate investments in separate emerging companies. This amounts to putting a toe in the water, while keeping current operations relatively separate and pristine.

On the other end of the spectrum, banks choose higher-integration strategies, like investing in partnership arrangements, where the new technologies are integrated into the bank’s loan application and decision making apparatus, sometimes in the form of a “white label” arrangement. The recent partnership between OnDeck and JPMorgan Chase is such an example. Some large and even regional banks have made even more significant investment to build their own digital front ends (e.g. Eastern Bank). And as more of the new fintech companies become possible acquisition targets, banks may look to a “build or buy” strategy to gain these new digital capabilities.

For banks that choose to develop their own systems to compete head-on with new players, significant investment is required to automate routine aspects of underwriting, to better integrate their own proprietary account data, and to create a better customer experience through truly customer-friendly design. The design and user experience aspect is especially out of sync with bank culture, and many banks struggle with internal resistance.

Alternatively, banks can partner with online lenders in a range ways – from having an online lender power the bank’s online loan application, to using an online lender’s credit model to better underwrite and service bank loan applications. In these options, the critical question is whether the bank wants to keep its own underwriting criteria or use new algorithms developed by its digital partner. Though the new underwriting is fast and uses intriguing new data, such as current bank transaction and cash flows, it’s still early days for these new credit scoring methods, and they have largely not been tested through an economic downturn.

Another large downside of partnering with online lenders is the significant level of resources required for compliance with federal “third party” oversight, which makes banks responsible for the activities of their vendors and partners. In the U.S., at least three federal regulators have overlapping requirements in this area, creating a dampening effect that regulatory reform in Washington could serve to mitigate.

Banks that prefer a more “arm’s-length” arrangement have the option to buy loans originated on an alternative lender’s platform. This allows a bank to increase their exposure to SME loans and pick the credits they wish to hold, while freeing up capital for online lenders. This type of partnership is among the most prolific in the online small business lending world, with banks such as JPMorgan Chase, Bank of America, and SunTrust buying assets from leading online lenders.

The familiar David vs. Goliath script of the scrappy, internet-fueled startup vanquishing the clunky, brick-and-mortar-laden incumbent is repeated so often in startup circles that it is sometimes treated as inevitable. But in the real world, sometimes David wins, other times Goliath wins, and sometimes the right solution involves a combination of both. SME lending can remain a big business for banks, but only with deliberate choices about where to play and how to win. Banks must focus on areas where they can build a distinct competitive advantage, and find ways to partner with or learn from the new innovators.

https://hbr.org/2017/04/how-banks-can-compete-against-an-army-of-fintech-startups

The evidence is piling up — Silicon Valley is being destroyed

Silicon Valley is the story of overthrowing entrenched interests through innovation.

Children dream of becoming inventors, and scientists come to Silicon Valley from all over the world.

But something is wrong when Juicero and Theranos are in the headlines, and bad behavior from Uber executives overshadows actual innovation.

$120 million in venture funding from Google Ventures and Kleiner Perkins, for a juicer? And the founder, Doug Evans, calling himself himself Steve Jobs „in his pursuit of juicing perfection?“ And how is Theranos’s Elizabeth Holmes walking around freely?

Eventually, the rhetoric of innovation turns into …. a Google-backed punchline.

These stories are embarrassing, yes. But there’s something deeper going on here. Silicon Valley, an international treasure that birthed the technology of our age, is being destroyed.

Monopolies are now so powerful that they dictate the roll-out of new technology, and the only things left to invest in are the scraps that fall off the table.

Sometimes those scraps are Snapchat, which managed to keep alive, despite what Ben Thompson calls ‚theft‚ by Facebook.

Sometimes it’s Diapers.com, which was destroyed and bought out by Amazon through predatory pricing. And sometimes it’s Juicero and Theranos.

It’s not that Juicero and Theranos that are the problem. Mistakes — even really big, stupid ones — happen.

juicero 8Business Insider/Alyson Shontell

It’s that there is increasingly less good stuff to offset the bad. Pets.com was embarrassing in 2000, but that was also when Google was getting going. Today it’s all scraps.

When platform monopolies dictate the roll-out of technology, there is less and less innovation, fewer places to invest, less to invent. Eventually, the rhetoric of innovation turns into DISRUPT, a quickly canceled show on MSNBC, and Juicero, a Google-backed punchline.

This moment of stagnating innovation and productivity is happening because Silicon Valley has turned its back on its most important political friend: antitrust. Instead, it’s embraced what it should understand as the enemy of innovation: monopoly.

As Barry Lynn has shown, Silicon Valley was born of anti-monopoly.

Elizabeth Holmes TheranosElizabeth Holmes, CEO of Theranos.Larry Busacca/Getty

In 1956, a Republican administration and AT&T signed a consent decree forbidding AT&T from competing in any but common carrier communications services. The decree also forced AT&T to license its patents in a non-discriminatory manner to all comers.

One of those patents was for something called the transistor, which two small companies — Texas Instruments and Motorola — would commercialize.

In the 1960s and 1970s, an antitrust suit against IBM caused the company to unbundle its hardware and software, leading to the creation of the American software industry. It treated suppliers for its new personal computing business with kid gloves, including a small company called Micro-Soft. In the 1990s, a suit against Microsoft allowed another startup named Google to offer an innovative search engine

and ad business without fear that Microsoft would use its control of the browser to strangle it.

The great business historian Alfred Chandler, in his book on the electronic century, called antitrust regulators the „Gods“ of creation. Antitrust was originally understood as a uniquely American „charter of economic liberty“.

But there hasn’t been a Sherman Act Section 2 anti-monopolization case for 15 years. And the anti-merger Clayton Act is not being enforced. Neither Bush, nor Obama, nor Trump (so far), has seen fit to stop the monopolists from buying their way into dominance and blocking innovation.

Take Google.

Sergey BrinSergey Brin is the President of Alphabet, Google’s parent company.Robert Galbraith/Reuters

Yes, the company created an amazing search engine over fifteen years ago. Since then, the company bought YouTube, Doubleclick, Maps, and Admob; it buys a company a week at this point. And it often shuts down products that don’t reach 100M+ users, while investing in luxury juicing machines. Surely Google is creating cool technology. But is that technology really being deployed? Or is it locked away, as patents were in AT&T’s 1956 vault before the government stepped in?

What once were upstarts and innovators are now enthroned. For instance, the iPhone is ten years old. Innovation means waiting to see if Apple will offer a bigger screen.

Innovation means waiting to see if Apple will offer a bigger screen.

It’s almost as thrilling as seeing yet another press release about how self-driving cars are almost working. I’m on the edge of my seat.

This is a ridiculous situation. Silicon Valley helped created the personal computer! It commercialized the internet! Popularized email!

Its scientists and engineers change the world. We have such amazing technology, and such big problems. But our liberty to address those problems in the commercial world must be protected by a democracy in the form of antitrust rules and suits, or Silicon Valley will die.

American flag phone iphoneMark Wilson/Getty Images

Is that what Silicon Valley scientists and business leaders really want? To invest in and produce subpar juicers while everything cool waits on Jeff Bezos’s whim? Is that what they dreamed when they were young? Is that why they admired astronauts and entrepreneurs? Was their goal really to create „anti-competitive juice packet lock-in“?

That is where a lack of democracy has brought us, and Silicon Valley.

It is time for leaders in Silicon Valley to start demanding from our government the birthright of every American, which is an open market for commerce, innovation, and personal liberty.

It is time to demand antitrust, so that what once were innovative upstarts, and are now Kings, do not stop the next wave of innovation. Then there will be so much more to invest in, so much more to invent, and so much more to actually create.

Matt Stoller is a fellow at the Open Markets Program at New America. He first shared a version of this story on Twitter. The original tweets are below.

stoller 2Screenshot/Twitter

stollerScreenshot/Twitter

/end of story 🙂

http://www.businessinsider.de/the-evidence-is-piling-up-silicon-valley-is-being-destroyed-2017-4

TV May Actually Die Soon

FANG (Facebook, Amazon, Netflix, Google/YouTube) is about to take a huge bite out of traditional network TV (ABC, NBC, CBS and Fox), and the media business will never be the same.

To understand the profound implications of the recently announced NFL on Amazon Prime or YouTube TV, it may help to understand the economic engine that drives traditional commercial television.

The goal of the commercial TV business is to package a specific, targeted audience and sell it to the highest bidder. The more precise the targeting, the higher the fee; the bigger the targeted audience, the bigger the fee.

TV is data-poor

Because the broadcast television industry is data poor (it only offers metrics about itself), this model has never been a complete solution for brand or lifestyle advertisers. In practice, an advertiser needs to translate ratings and demographic information from Nielsen into knowledge and insights it can link to its key performance indicators (KPIs). Because content is distributed across so many non-TV platforms, this process gets more difficult every day. How effective was your broadcast TV buy? Was there an increase in sales that could be attributed to it? Could we have spent this portion of our advertising budget differently?

FANG is data-rich

There are four data sets that help define each of us: attention, consumption, passion and intention. While traditional broadcast TV tries to measure or attribute some of these to TV viewership, FANG has actionable data that drives KPIs.

Facebook knows what you are paying attention to. You post and share the things you care about, and your Facebook profile makes your attention actionable.

Amazon knows what you consume and what you’re thinking about consuming. If you’ve bought it or are planning to buy it, Amazon knows it and can act on that data.

Netflix knows your passions. You demonstrate how you can be reached on an emotional level every time you watch a video. Netflix knows more about the kind of entertainment that ignites your passions than you do. It continually acts on that data.

Google/YouTube knows your intentions. You never intend to go to Google and stay there; you search for what you intend to do. Your Google profile indicates, with a very high degree of accuracy, what you are likely to do in the near-term future. This is some of the clearest, most actionable data in the world.

We’ll still have four major networks, just not the familiar four

People often reminisce about the „good ol‘ days“ when there were four major networks: ABC, NBC, CBS and Fox. We are transitioning to a world where there will still be four networks, just not the four networks you’re used to. FANG is delivering actionable data to advertisers in ways that traditional broadcasters simply can’t.

The power of Amazon Prime to a fast-moving consumer goods company may be less significant than the power of Amazon Prime to a consumer electronics manufacturer, but Amazon is becoming a complete solution for all types of b-to-c — and many types of b-to-b — advertisers. Its size, scale and efficacy are truly stunning.

If YouTube TV and other over-the-top skinny bundles start to get traction, we are going to see a dramatic shift toward the data-rich, brand-safe, internet giants. (Yes, Facebook and Google will deal with their current content adjacency and brand safety problems, and you will forget they had them.) FANG will not be alone. Apple is going to get into this game, and there are international powerhouses like Alibaba and QQ that are already well on their way.

What does this really mean?

For today: Advertisers are spending, traditional networks are making money and all of this sounds like stuff you’ve heard before. But we’re only talking about timing. Traditional (linear) TV audiences are declining at a significant rate, and they are practically aged out of key demographics. Cable customers are also declining. So, the question is when this shift will make a difference, not if.

For consumers: More choice, more fun. Consumers don’t care about content transport mechanisms or broadcast business models, they just want their content.

For advertisers: Brands have never wanted to buy CPMs (cost per thousand impressions) or GRPs (gross rating points); they want to sell stuff. The data-rich FANG and other tech giants are offering data that can be turned directly into sales.

For networks: It’s just a matter of time before media without actionable data will be impossible to monetize. Can traditional TV catch up? Adapt or die!

http://adage.com/article/digitalnext/tv-die-stay-tuned/308618

Machine Learning – Basics – Einsatzgebiete – Technik

Machine Learning, Deep Learning, Cognitive Computing – Technologien der Künstlichen Intelligenz verbreiten sich rasant. Hintergrund ist, dass heute die Rechen- und Speicherkapazitäten zur Verfügung stehen, die KI-Szenarien möglich machen. Ein Überblick.
 
  • Machine Learning hilft, Muster in großen Datenbeständen zu erkennen und daraus Erkenntnisse zu gewinnen
  • Die Einsatzszenarien reichen von der Spamanalyse über Stauprognosen bis hin zur medizinischen Diagnostik
  • Technische Grundlage ist eine Cloud-basierte Digital Infrastructure Platform

http://www.computerwoche.de/a/machine-learning-darum-geht-s,3330413
http://www.computerwoche.de/a/machine-learning-das-haben-deutsche-unternehmen-vor,3330418
http://www.computerwoche.de/a/machine-learning-die-technik,3330420

Künstliche Intelligenz und Machine Learning (ML) sind keine neuen Technologien, doch im praktischen Einsatz spielen sie erst jetzt eine wichtige Rolle. Woran liegt das? Wichtigste Voraussetzung für lernende Systeme und entsprechende Algorithmen sind ausreichende Rechenkapazitäten und der Zugriff auf riesige Datenmengen – egal ob es sich um Kunden-, Log- oder Sensordaten handelt. Sie sind für das Training der Algorithmen und die Modellbildung unverzichtbar – und sie stehen mit Public- und Private-Cloud-Infrastrukturen zur Verfügung.

Bildanalyse und -erkennung ist das wichtigste Machine-Learning-Thema, doch die Spracherkennung und -verarbeitung ist schwer im Kommen.
Bildanalyse und -erkennung ist das wichtigste Machine-Learning-Thema, doch die Spracherkennung und -verarbeitung ist schwer im Kommen.
Foto: Crisp Research, Kassel

 

Die Analysten von Crisp Research sind im Rahmen einer umfassenden Studie gemeinsam mit The unbelievable Machine Company und Hewlett-Packard Enterprise (HPE) der Frage nachgegangen, welche Rolle Machine Learning heute und in Zukunft im Unternehmenseinsatz spielen wird. Dabei zeigt sich, dass deutsche Unternehmen hier schon recht weit fortgeschritten sind. Bereits ein Fünftel setzt ML-Technologien aktiv ein, 64 Prozent beschäftigen sich intensiv mit dem Thema und vier von fünf Befragten sagen sogar, ML werde irgendwann eine der Kerntechnologien des vollständig digitalisierten Unternehmens sein.

Muster erkennen und Vorhersagen treffen

ML-Algorithmen helfen den Menschen, Muster in vorhandenen Datenbeständen zu erkennen, Vorhersagen zu treffen oder Daten zu klassifizieren. Mit mathematischen Modellen können neue Erkenntnisse auf Grundlage dieser Muster gewonnen werden. Das gilt für viele Lebens- und Geschäftsbereiche. Oftmals profitieren Internet-Nutzer längst davon, ohne über die Technologie im Hintergrund nachzudenken.

Das Spektrum der Anwendungen reicht von Musik- und Filmempfehlungen im privaten Umfeld bis hin zur Verbesserung von Marketing-Kampagnen, Kundenservices oder auch Logistikrouten im geschäftlichen Bereich. Dafür steht ein breites Spektrum an ML-Verfahren zur Verfügung, darunter Lineare Regression, Instanzenbasiertes Lernen, Entscheidungs-Baum-Algorithmen, Bayesche Statistik, Clusteranalyse, Neuronale Netzwerke, Deep Learning und Verfahren zur Dimensionsreduktion.

Die Anwendungsbereiche sind vielfältig und teilweise bekannt. Man denke etwa an Spam-Erkennung, die Personalisierung von Inhalten, das Klassifizieren von Dokumenten, Sentiment-Analysen, Prognosen der Kundenabwanderung, E-Mail-Klassifizierung, Analyse von Upselling-Möglichkeiten, Stauprognosen, Genomanalysen, medizinische Diagnostik, Chatbots und vieles mehr. Für nahezu alle Branchen und Unternehmenstypen ergeben sich also Gelegenheiten.

Moderne IT-Plattformen unterstützen KI

Machine Learning ist laut Crisp Research idealerweise Bestandteil einer modernen, skalierungsfähigen und flexiblen IT-Infrastruktur – einer „Digital Infrastructure Platform“. Diese zeichnet sich durch Elastizität, Automatisierung, eine API-basierte Architektur und Agilität aus. Eine solche Plattform ist in der Regel Cloud-basiert aufgesetzt und dient als Grundlage für die Entwicklung und den Betrieb neuer digitaler Anwendungen und Prozesse. Sie bietet eine offene Architektur, Programmierschnittstellen (APIs), um externe Services zu integrieren, die Unterstützung von DevOps-Konzepten sowie moderne Methoden für kurze Release- und Innovationszyklen.

Die Verarbeitung und Analyse großer Datenmengen ist eine Kernaufgabe einer solchen Digital Infrastructure Platform. Deshalb müssen die IT-Verantwortlichen Sorge tragen, dass ihre IT mit unterschiedlichen Verfahren der Künstlichen Intelligenz umgehen kann. Server-, Storage- und Netzwerk-Infrastrukturen müssen auf neue ML-basierte Workloads ausgelegt sein. Auch das Daten-Management muss vorbereitet sein, damit ML-as-a-Service-Angebote in der Cloud genutzt werden können.

Im Kontext von ML haben sich in den vergangenen Monaten auch alternative Hardwarekomponenten durchgesetzt, etwa GPU-basierte Cluster von Nvidia, Googles Tensor Processing Unit (TPU) oder IBMs TrueNorth-Prozessor. Unternehmen müssen sich entscheiden, ob sie hier selbst investieren oder die Angebote entsprechender Cloud-Provider nutzen wollen.

Einer der großen Anwendungsbereiche für ML ist die Spracherkennung und -verarbeitung. Amazons Alexa zieht gerade in die Haushalte ein, Microsoft, Google, Facebook und IBM haben hier einen Großteil ihrer Forschungs- und Entwicklungsgelder investiert sowie spezialisierte Firmen zugekauft. Es lässt sich absehen, dass natürlichsprachige Kommunikation an der Kundenschnittstelle selbstverständlicher wird. Auch die Bedienung von digitalen Produkten und Enterprise-IT-Lösungen wird via Sprachbefehl möglich sein. Das hat sowohl Auswirkungen auf das Customer-Frontend als auch auf das IT-Backend.

Niedrige Einstiegshürden in Machine Learning

Da die großen Cloud-Anbieter ML-Services und -Produkte in ihr Leistungsportfolio aufgenommen haben, ist es für Anwender relativ einfach, einen Einstieg zu finden. Amazon Machine Learning, Microsoft Azure Machine Learning, IBM Bluemix und Google Machine Learning erlauben einen kostengünstigen Zugang zu entsprechenden Diensten über die Public Cloud. Anwender brauchen also keinen eigenen Supercomputer, kein Team von Statistikexperten und kein dediziertes Infrastruktur-Management mehr. Mit ein paar Kommandos über die APIs der großen Public-Cloud-Provider können sie loslegen.

Anwender brauchen vor allem Hilfe bei der Datenexploration.
Anwender brauchen vor allem Hilfe bei der Datenexploration.
Foto: Crisp Research, Kassel

 

Sie finden dort unterschiedliche Machine-Learning-Verfahren sowie Dienste und Tools wie etwa grafische Programmiermodelle und Storage-Dienste vor. Je mehr sie sich darauf einlassen, desto größer wird allerdings das Risiko eines Vendor-Lock-ins. Deshalb sollten sich Anwender vor dem Start Gedanken über ihre Strategie machen. IT-Dienstleister und Managed-Service-Provider können ebenso ML-Systeme und Infrastrukturen bereitstellen und betreiben, so dass Unabhängigkeit von den Public-Cloud-Providern und ihren SLAs ebenso möglich ist.

Verschiedene Spielarten der KI

Machine Learning, Deep Learning, Cognitive Computing – derzeit kursieren eine Reihe von KI-Begriffen, deren Abgrenzung voneinander nicht ganz einfach ist. Crisp Research wählt dafür die Dimensionen „Clarity of Purpose“ (Orientierung am Einsatzweck) und „Degree of Autonomy“ (Grad der Autonomie). ML-Systeme sind derzeit größtenteils auf Einsatzzwecke hin entwickelt und trainiert. Sie erkennen beispielsweise im Fertigungsprozess fehlerhafte Produkte im Rahmen einer Qualitätskontrolle. Ihre Aufgabe ist klar umrissen, es gibt keine Autonomie.

Deep-Learning-Systeme hingegen sind in der Lage, mittels Neuronaler Netze eigenständig zu lernen. Simulierte Neuronen werden in vielen Schichten übereinander modelliert und angeordnet. Jede Ebene des Netzwerks erfüllt dabei eigenständig bestimmte Aufgaben, etwa das Erkennen von Kanten. Diese Information wird eigenständig an die nächste Ebene weitergegeben und fließt dort in die Verarbeitung ein. Im Zusammenspiel mit großen Mengen an Trainingsdaten lernen solche Netzwerke, bestimmte Aufgaben zu erledigen – etwa das Identifizieren von Krebszellen in medizinischen Bildern.

Deep-Learning-Systeme arbeiten autonomer

Deep-Learning-Systeme arbeiten also deutlich autonomer als ML-Systeme, da die Neuronalen Netzwerke darauf trainiert werden, selbständig zu lernen und Entscheidungen zu treffen, die von außen nicht unbedingt nachvollziehbar sind.

Als dritte Spielart der KI gilt das Cognitive Computing, das insbesondere von IBM mit seiner Watson-Technologie propagiert wird. Solche Systeme zeichnen sich dadurch aus, dass sie in einer Assistenzfunktion oder gar als Ersatz des Menschen Aufgaben übernehmen und Entscheidungen treffen und dabei mit Ambiguität und Unschärfe umgehen können. Als Beispiele können das Schadensfall-Management in einer Versicherung dienen, eine Service-Hotline oder die Diagnostik im Krankenhaus.

Auch wenn hier bereits ein hohes Maß an Autonomie erreicht werden kann, ist der Weg zu echter Künstlicher Intelligenz mit autonomen kognitiven Fähigkeiten noch weit. Die Wissenschaft beschäftigt sich aber intensiv damit und streitet darüber, ob und wann dieses Ziel erreicht werden kann. Derweil sind Unternehmen gut beraten, sich mit den machbaren Use Cases zu beschäftigen, von denen es bereits eine Menge gibt.

Im Zuge des Digitalisierungstrends kommt in vielen Unternehmen Analytics auf die Tagesordnung – und damit auch Machine Learning und Deep Learning. Jetzt geht es darum, den Datenschatz zu heben.
  • Viele Unternehmen haben Data Lakes mit strukturierten und unstrukturierten Daten aufgebaut. Jetzt gilt es, etwas daraus zu machen
  • Einsatzgebiete für Machine Learning sind etwa Prozessverbesserungen sowie eine bessere Kundenansprache und ein möglichst effizienter Support
  • In vielen Branchen ist der Abstand zwischen Vorreitern und Nachzüglern riesig

Die Phantasien und Visionen rund um die digitale Zukunft kennen derzeit keine Grenzen. Vollautomatisierte Produktionsstraßen, autonome Verkehrssysteme, intelligente digitale Assistenten – es vergeht kaum ein Tag, an dem nicht neue Szenarien diskutiert werden. Dadurch fühlen sich viele Firmen unter Druck gesetzt. Sie arbeiten am „digitalen Unternehmen“ und entdecken ihre Daten als Grundlagen für neue Geschäftsmodelle und Services. So gewinnt Analytics an Bedeutung – und mit der Analytics-Strategie kommen KI und Machine Learning (ML) auf die Tagesordnung.

Aus diesen Gründen beschäftigen sich Anwender mit Machine Learning.
Aus diesen Gründen beschäftigen sich Anwender mit Machine Learning.
Foto: Crisp Research

 

IT- und Digitalisierungsentscheider vermuten ein enormes Potenzial hinter dem Thema Machine Learning. Eine Umfrage, die das Analystenhaus Crisp Research unterstützt von The unbelievable Machine Company und Hewlett-Packard Enterprise (HPE) auf den Weg gebracht hat, zeigt, dass nur drei Prozent der knapp 250 Befragten ML für einen Marketing-Hype halten. Ein Drittel bezeichnet ML-Verfahren in begrenzten Einsatzbereichen als sinnvoll, sogar 43 Prozent sind überzeugt davon, dass ML ein wichtiger Aspekt künftiger Big-Data- und Analytics-Strategien wird.

Wie die Initiatoren der Studie feststellen, ist das kein überraschendes Ergebnis. Die meisten Unternehmen haben im großen Stil in Big-Data-Infrastrukturen und eigene Data Lakes investiert, um ihre Unternehmensdaten zusammenzuführen und auswertbar zu machen. ML ermöglicht einen hohen Automationsgrad in der Datenanalyse und hilft somit, den verborgenen Schatz zu heben. Daten gelten als großes Asset, doch den Beweis dafür haben viele Firmen noch nicht gebracht. Technologien und Use Cases rund um Machine Learning versprechen Abhilfe.

Immenses Innovationspotenzial

Immerhin 16 Prozent der befragten sehen ML sogar als neue „Kerntechnologie eines vollständig digitalen Unternehmens“. Das Innovations- und Gestaltungspotenzial scheint also immens, wenngleich viele Probleme rund um Datenqualität, Governance, API-Management, Infrastruktur und vor allem Personal den Trend noch bremsen.

Rund 34 Prozent der Befragten beschäftigen sich mit ML, weil sie ihre internen Prozesse in der Produktion, Logistik oder im Qualitätsmanagement verbessern wollen. Sie erheben beispielsweise Daten im Produktionsablauf, um ihre Fertigung optimieren zu können. Fast ebenso viele wollen Initiativen rund um die Customer Experience vorantreiben – etwa in E-Commerce, Marketing oder im Bereich der Portale und Apps. Sie versprechen sich davon beispielsweise eine personalisierte Kundenansprache, um Produkte oder Dienste zielgerichteter an den Konsumenten bringen zu können. Mit 19 Prozent ist die Gruppe derer, die Wartungs- und Supportleistungen optimieren wollen (Predictive Maintenance), etwas kleiner. Hinzu kommen Betriebe, die sich grundsätzlich mit neuen Technologien beschäftigen (28 Prozent) oder durch Berater und Analysten auf das Thema aufmerksam geworden sind (27 Prozent).

Elementar für selbstfahrende Autos

Das Nutzungsverhalten von ML ist nicht nur zwischen, sondern auch innerhalb der Branchen sehr unterschiedlich ausgeprägt. In der Automobilbranche etwa gibt es große Abstände zwischen den Vorreitern und den Nachzüglern. Für die Entwicklung und Produktion selbstfahrender Autos sind Bild- und Videoanalyse in Echtzeit sowie statistische Verfahren und mathematische Modelle aus Machine Learning und Deep Learning weit verbreitet. Einige Verfahren werden auch dazu verwendet, Fabrikationsfehler in der Fertigung zu erkennen.

Der Anteil der Innovatoren, die ML bereits in weiten Teilen einsetzen, ist in der Automobilbranche mit rund 20 Prozent am größten. Demgegenüber stehen allerdings 60 Prozent, die sich zwar mit ML beschäftigen, aber noch in der Evaluierungs- und Planungsphase stecken. So zeigt sich, dass in der Autobranche einige Leuchttürme das Bild prägen, von einer flächendeckenden Adaption aber nicht die Rede sein kann.

Status der Branchen bei der Einführung von Machine-Learning-Technologien
Status der Branchen bei der Einführung von Machine-Learning-Technologien
Foto: Crisp Research

 

Auch die Maschinen- und Anlagenbauer stecken noch zur Hälfte (53 Prozent) in der Evaluierungs- und Planungsphase. Ein knappe Drittel nutzt ML in ausgewählten Anwendungsbereichen produktiv und 18 Prozent bauen derzeit Prototypen. Weiter sind die Handels- und Konsumgüterfirmen, die zu 44 Prozent dabei sind, ML in ersten Projekten und Prototypen zu erproben. Das überrascht insofern nicht, als diese Firmen in der Regel gute gepflegte Datenbestände haben und viel Erfahrung mit Business Intelligence und Data Warehouses besitzen. Gelingt es ihnen, Preisstrategien, Warenverfügbarkeiten oder Marketing-Kampagnen messbar zu verbessern, wird ML als willkommenes Innovationsinstrument bestehender Big-Data-Strategien gesehen.

Gleiches gilt für die IT-, TK- und Medienbranche: Dort kommen ML-Verfahren etwa zum Ausspielen von Online-Werbung, Berechnen von Kaufwahrscheinlichkeiten (Conversion Rates) oder dem Personalisieren von Webinhalten und Einkaufsempfehlungen längst zum Einsatz. Bei den professionellen Dienstleistern spielen das Messen und Verbessern der Kundenbindung, der Dienstleistungsqualität und der Termintreue eine wichtige Rolle, sind das doch die wettbewerbsdifferenzierenden Faktoren.

IT-Abteilungen sind zuständig

Knapp 60 Prozent der befragten Entscheider gaben an, ihre IT-Abteilung sei federführend zuständig, wenn es um ML-Projekte gehe. Den Studienautoren von Crisp zufolge liegt das an der hohen technologischen Komplexität des Themas. Neben mathematischen und statistischen Skills ist demnach auch eine große Bandbreite an Fertigkeiten im Bereich der IT-Operations gefragt. Hinzu kommen die BI- und Analytics-Fähigkeiten, die hier oftmals angesiedelt sind.

Doch auch Fachabteilungen wie Logistik und Produktion sind mit im Boot, weil sie in der Regel die Prozessverbesserungs- und IoT-Szenarien vorantreiben. Die großen Mengen an Maschinen-, Produktions-, Logistik- sowie sonstigen Sensor- und Log-Daten müssen auf Muster und Korrelationen hin abgefragt werden – eine Aufgabe für Fertigung und Logistik.

Und schließlich sind auch Kundenservice und -support führende Instanzen, wenn es um die Einführung von ML geht. Sie wollen die personalisierte Kundeninteraktion vorantreiben und sammeln in ihren Bereichen die Text-, Bild- und Audiodaten, die das Potenzial für Analysen bieten. Interessant an der Umfrage ist indes, dass Marketing und Kommunikation von ML oft nichts wissen wollen, obwohl sie reichlich Einsatzszenarien hätten. Sie könnten etwa Kundenbeziehungen auswerten und die Kundenbindung verbessern, automatisiertes Medien-Monitoring vorantreiben oder das Social Web mit Sentiment-Analysen bearbeiten. All das findet aber relativ selten statt, was Crisp Research mit der traditionell „passiven, technologieagnostischen Rolle“ dieser Abteilungen begründet. Marketing- und Kommunikationsabteilungen treten demnach meist als „Anforderer“ und interne Kunden auf, nicht als diejenigen, die tiefer in Technologien einsteigen.

Welche Machine-Learning-Funktionen benötigen Unternehmen wofür? Und wann kommen welche Lernstile, Frameworks, Programmiersprachen und Algorithmen zum Einsatz? Meistens beginnen Firmen mit Bildanalyse und -erkennung.
 
  • Bild- und Spracherkennung sind die wichtigsten Anwendungen im Bereich Machine Learning
  • Geht es um die Plattformauswahl, wird die Public Cloud zunehmend wichtig
  • Grafikprozessoren setzen sich im Bereich Deep Learning durch

Wie die Analysten von Crisp Research im Rahmen einer umfassenden Studie gemeinsam mit The unbelievable Machine Company und Hewlett-Packard Enterprise (HPE) schreiben, gibt die Mehrheit der rund 250 befragten IT-Entscheider an, mit der Bildanalyse und -erkennung in das komplexe Thema Machine Learning (ML) einzusteigen. So werden beispielsweise in Industrieunternehmen Fremdkörper auf Förderbändern identifiziert, fehlerhafte Einfärbungen von Produkten entdeckt oder von autonomen Fahrzeugen Straßenschilder erkannt.

Diese Machine-Learning-Funktionen nutzen die Anwender.
Diese Machine-Learning-Funktionen nutzen die Anwender.
Foto: Crisp Research, Kassel

 

Wichtig sind ML-Verfahren auch zur Sprachsteuerung und -erkennung (42 Prozent). Eng damit verbunden sind Natural Language Processing und Textanalyse – also das semantische Erfassen von Sprachinhalten und Texten. Heute beschäftigen sich 35 Prozent der Unternehmen damit, Tendenz steigend. Hintergrund ist, dass konversationsbasierte Benutzerschnittstellen derzeit einen Aufschwung erleben.

Chatbots, Gesichtserkennung, Sentiment-Analyse und mehr

Machine Learning kommt außerdem bei rund einem Drittel der Befragten im Zusammenhang mit der Entwicklung digitaler Assistenten, sogenannter Bots zum Einsatz. Weitere Einsatzgebiete sind Gesichtserkennung, die Sentiment-Analyse und besondere Verfahren der Mustererkennung – oft in einem unternehmens- oder branchenspezifischen Kontext. Die Spracherkennung ist vor allem für Marketingentscheider interessant, da digitale Assistenten für die Automatisierung von Call-Center-Abläufen oder die Echtzeit-Kommunikation mit dem Kunden an Bedeutung gewinnen. Auch die Personalisierung von Produktempfehlungen ist ein wichtiger Use-Case.

Ein Blick auf die Nutzungsszenarien von ML-Technologien zeigt, dass Bildanalyse und -erkennung heute weit vorne rangieren, doch die Zukunft gehört eher der Sprachsteuerung und – erkennung, ebenso der Textanalyse und Natural Language Processing (NLP). Insgesamt werden ML-Technologien auf breiter Front an Bedeutung gewinne, auch etwa im Bereich der Videoanalyse, der Sentiment-Analyse, der Gesichtserkennung sowie beim Einsatz intelligenter Bots.

Schaut man auf die einzelnen Unternehmensbereiche, so wird deutlich, dass sich die für Customer Experience Management zuständigen Einheiten ML-Technologien vor allem im Bereich der Kundensegmentierung, der personalisierten Produktempfehlung, der Spracherkennung und teilweise auch der Gesichtserkennung bedienen. IT-Abteilungen treiben damit E-Mail-Klassifizierung, Spam-Erkennung, Diagnosesysteme und das Klassifizieren von Dokumenten voran. Die Produktion ist vor allem auf Prozessverbesserungen aus, während Kundendienst und Support ihre Diagnoseysteme vorantreiben und an automatisierten Lösungsempfehlungen arbeiten. Auch Call-Center-Gespräche werden bereits analysiert, teilweise auch mit der Absicht, positive und negative Äußerungen der Kunden zu erkennen (Sentiment-Analyse).

Auch die Bereiche Finance und Human Resources sowie das Management generell nutzen vermehrt ML-Technologien. Wichtigstes Einsatzgebiet sind hier das Risiko-Management sowie Forecasting und Prognosen. Im HR-Bereich werden auch Trainingsempfehlungen automatisiert erstellt, Lebensläufe überprüft und das Talent-Management vorangetrieben. Im zentralen Einkauf und dem Management der Lieferanten ist die Digital Supply-Chain-Verbesserung das Kernaufgabengebiet von ML-technologie. Vermehrt werden hier auch Demand Forecastings ermittelt, Risiken im Zusammenhang mit bestimmten Lieferanten analysiert und generell Entscheidungsprozesse digital unterstützt.

Machine-Learning-Plattformen und -Produkte

Geht es um die Auswahl von Plattformen und -Produkten, spielen Lösungen aus der Public Cloud eine zunehmend wichtige Rolle (Machine Learning as a Service). Um Komplexität aus dem Wege zu gehen und weil die großen Cloud-Provider auch die maßgeblichen Innovatoren auf diesem Gebiet sind, entscheiden sich viele Anwender für diese Cloud-Lösungen. Während 38,1 der Befragten Lösungen aus der Public-Cloud bevorzugen, wählen 19,1 Prozent proprietäre Lösungen ausgesuchter Anbieter und 18,5 Prozent Open-Source-Alternativen. Der Rest verfolgt entweder eine hybride Strategie (15,5 Prozent) oder hat sich noch keine Meinung dazu gebildet (8,8 Prozent).

Welche Cloud-Angebote zu Machine Learning sind im Einsatz?
Welche Cloud-Angebote zu Machine Learning sind im Einsatz?
Foto: Crisp Research

 

Unter den Cloud-basierten Lösungen hat AWS den höchsten Bekanntheitsgrad: 71 Prozent der Entscheider geben an, dass ihnen Amazon in diesem Kontext bekannt sei. Auch Microsoft, Google und IBM sind den Umfrageteilnehmern zu mehr als zwei Drittel im ML-Umfeld ein Begriff. Interessanterweise nutzen aber nur 17 Prozent der befragten die AWS-Cloud-Dienste im Kontext der Evaluierung, Projektierung sowie im produktiven Betrieb für ML. Jeweils rund ein Drittel der Befragten beschäftigt sich indes mit IBM Watson, Microsoft Azure oder der Google Cloud Machine Learning Plattform.

Die Analysten nehmen an, dass dies viel mit den Marketing-Anstrengungen der Hersteller zu tun hat. IBM und Microsoft investieren demnach massiv in ihre Cognitive- beziehungsweise KI-Strategie. Beide haben einen starken Mittelstands- und Großkundenvertrieb und ein großes Partnernetzwerk. Google indes verdanke seine Position dem Image als gewaltige daten- und Analytics-Maschine, die den Markt durch viele Innovationen treibe – etwa Tensorflow, viele ML-APIs und auch eigene Hardware. Schließlich zähle aber auch HP Enterprise mit „Haven on Demand“ zu den relevanten ML-Playern und werde von 14 Prozent der Befragten genutzt.

Deep Learning ist schwieriger

Bereits in den 40er Jahren des vergangenen Jahrhunderts wurden die ersten neuronalen Lernregeln beschrieben. Die wissenschaftlichen Erkenntnisse wuchsen rasch, die Anzahl der Algorithmen ebenfalls – doch es fehlte an der notwendigen Rechenleistung, um „Rückgekoppelte Neuronale Netzwerke“ in der Fläche zu nutzen. Heute sind diese unter dem Begriff Deep Learning in aller Munde, sie könnten Bereiche wie Handschriftenerkennung, Spracherkennung, maschinelles Übersetzen oder auch automatische Bildbeschreibungen revolutionieren.

Hintergrund ist, dass eine Präzision erreicht werden kann, die menschliche Fähigkeiten im jeweiligen Zusammenhang weit übertrifft. Dabei spannen neuronale Netze Ebenen von unterschiedlicher Komplexität auf. Je mehr Daten so einem neuronalen Netz zum Trainieren zur Verfügung stehen, desto besser werden die Ergebnisse beziehungsweise die trainierte Künstliche Intelligenz. So lernt ein System beispielsweise, wie anhand einer Computer-Tomografie Krebsgeschwüre diagnostiziert werden können, die das menschliche Auge nicht so einfach sieht.

Grafikprozessoren bieten die nötige Performance

Im Bereich des Deep Learning haben sich hardwareseitig Grafikprozessoren (GPUs) wegen ihre hohen Performance als besonders geeignet erwiesen. Förderlich waren außerdem die schier unbegrenzte Rechenpower, die sich aus den Public-Cloud-Ressourcen ergibt, sowie die Verfügbarkeit großer Mengen von Daten aus den verschiedensten Anwendungsgebieten. Unternehmen nutzen bereits Deep-learning-Algorithmen, im bestimmte Merkmal in Bildern aufzuspüren, Videoanalysen vorzunehmen, Umweltparameter beim autonomen Fahren zu verarbeiten oder automatische Sprachverarbeitung voranzutreiben.

In der Crisp-Umfrage geben 48 Prozent der Teilnehmer an, von Deep Learning zumindest gehört oder gelesen zu haben. Weitere 21 Prozent sind bereits in einer konkreten Evaluationsphase. Sie haben Erkenntnisse gesammelt und arbeiten nun an konkreten Prototypen, um ihr gewünschtes Einsatzszenario zu validieren. Weitere fünf Prozent sind sogar noch einen Schritt weiter und haben bereits Deep Learning im Einsatz. Vor allem Startups und Konzerne – auch hier wieder vor allem aus dem Automotive-Sektor – haben hier die Nase vorn.

Unter den Frameworks und Bibliotheken, die für das Implementieren von Deep-Learning-Algorithmen eine Rolle spielen, spielen unter anderem Microsofts „Computational Network Toolkit“ (CNTK) sowie jede Menge Public-Cloud- und Open-Source-Lösungen eine Rolle (eine Übersicht gibt es hier http://deeplearning.net/software_links/).