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Cloudera announces new machine learning products for data teams

 Cloudera announces new machine learning products for data teams

Software company Cloudera today announced a slew of machine learning product updates at the Strata Data London conference: Cloudera Data Science Workbench 1.4, Cloudera Altus Data Engineering on Microsoft Azure, and Cloudera Enterprise 6.0. All three are focused on facilitating collaboration among data teams, CEO Tom Reilly said.

“We believe data can make what is impossible today possible tomorrow. With enhanced capabilities in machine learning, analytics, and cloud, the new software products and cloud services we are announcing will enable our customers to more rapidly gain competitive advantages in the data economy,” Reilly said in a statement. “These enhancements demonstrate Cloudera’s commitment to market-leading innovations that empower enterprises to securely transform complex data into clear and actionable insights to propel their digital transformation.”

Cloudera Altus Data Engineering on Azure went live yesterday with support for Apache Spark, Apache Hive, Hive on Spark, and MapReduce 2. Cloud Enterprise 6.0 and Altus Analytic DB are available in beta today, while Data Science Workbench 1.4 is expected to launch this summer.

Data Science Workbench allows data science teams to build, run, train, compare, and implement machine learning models on a single platform. Version 1.4 features an improved toolkit for running and tracking experiments and a one-click tool that allows users to deploy models as Representational State Transfer (REST) APIs for networked applications.

Cloudera Atlas is a bit more cloud-centric; Cloudera claims it is the first “multi-cloud, multi-function” platform on a service. The products under its umbrella include Data Engineering for Azure, which grants processing jobs read and write access to Microsoft Azure Data Lake Store (ADLS), and Altus Analytic DB, a “data warehouse” service that delivers database analytics in SQL, Python, R, and other formats via Altus SDX. That is in addition to the Cloudera Altus software development kit (SDK), which allows programmatic access to Java and an automated workload performance monitor that flags potential problems.

Last, but not least, is Cloudera Enterprise, a platform for machine learning and analytics applications. The newest iteration (version 6.0) introduces GPU support and Apache Hive data warehouse optimizations that “significantly accelerate machine learning and data engineering applications” as compared to the previous release. It also offers Apache Solr 7.0 (with support for nested data types and JSON facets), Kafka 1.0, and Spark 2.2 as fully native components. Cloudera claims that even with as many as 2,500 nodes in a single Cloudera Manager 6.0 interface cluster, machine learning on the platform has the potential to be up to 10 times faster. Analytics workloads leveraging Apache Hive 2.0 can expect up to 80 percent better performance.

“We’re thrilled to be launching new capabilities in Cloudera Data Science Workbench that accelerate everyday workflows for data scientists, including experiment management and model deployment, with a seamless experience that also keeps data secure and under governance,” Hilary Mason, general manager of machine learning at Cloudera, said in a statement.

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Big Data – VentureBeat

Virtual Reality Redefines How Customers Experience Products

For years, Microsoft Dynamics customers have leveraged Powertrak CPQ software to ensure accuracy and efficiency in their sales quoting, ordering, and configuration processes. Five years ago, our product configurator customers had the option to transform their still images into interactive 2D drawings and 3D models. Today, they can take their configurable products and room layout designs from digital to virtual reality.

Our visual, 3D Product Configurator for Microsoft Dynamics CRM and ERP, enables sales, partners, and customers to see near-realistic product models and customize them by interactively dragging-and-dropping add-on parts. When the configuration is complete, users can invite stakeholders to validate the design by visualizing and exploring it in virtual reality (VR).

vr phone Virtual Reality Redefines How Customers Experience Products

Mobile Smartphone Virtual Reality

Why is VR an important piece to the configuration process?

While 3D product configuration software brings visualization, real-time pricing, and ordering to the customer’s fingertips, virtual reality takes the buying experience to a whole new level.

Transforming physical products into virtual is an effective way for your business to save money at trade shows while improving communication.

Here’s how…

Reduce Costs

Transporting tangible products to trade shows, conferences, experience centers is expensive. Take trade shows as an example. The drayage, shipping and labor costs are in the thousands of dollars for many companies.

If you’re presenting a large product or many products, tack on more money for upgrading the size of your booth space. How much are you spending on booth space and shipping at each event? Now multiple that number for each event over the course of the year. How many new clients is it going to take to produce a positive ROI on trade shows?

Here is where VR comes into play. Virtual Reality requires a minimal amount of space (typically 10’x10′). Plus, with the click of a button, you have access to hundreds of virtual products and layout designs. As a result, you are in line to save thousands on shipping, installation and dismantle charges.

Improve Communication

vr operateequipment Virtual Reality Redefines How Customers Experience Products

Interact with products in virtual reality

How do you present your products or services to your customers?

The traditional method of handing out a catalog, brochure or sheet of sales literature was an effective form of communication.

Today, we are seeing the sales cycle becoming longer as consumers demand more information before buying expensive products.

In the case of selling configurable products or room layout design, visual and virtual solutions truly help the sales professional inform, educate, and demonstrate the value of their products or services.

Visual configurators provide a near-realistic product model, so consumers can visualize and customize products and environments to their liking.

After the configuration is complete, users put on a virtual reality headset to fully experience the “virtual design.” Virtual Reality hand controllers are provided for navigation and product interaction. This immersive experience helps key stakeholders validate the design, observe product and object spacing, and determine if spacing affects walk flow.

For those interested in adding visual product configurations to Microsoft Dynamics 365 or virtual reality to their sales and marketing processes, please contact Axonom.

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CRM Software Blog | Dynamics 365

Wonders Of Wood: Transformation In The Forest Products Industry

When outspoken venture capitalist and Netscape co-founder Marc Andreessen wrote in The Wall Street Journal in 2011 that software is eating the world, he was only partly correct. In fact, business services based on software platforms are what’s eating the world.

Companies like Apple, which remade the mobile phone industry by offering app developers easy access to millions of iPhone owners through its iTunes App Store platform, are changing the economy. However, these world-eating companies are not just in the tech world. They are also emerging in industries that you might not expect: retailers, finance companies, transportation firms, and others outside of Silicon Valley are all at the forefront of the platform revolution.

These outsiders are taking platforms to the next level by building them around business services and data, not just apps. Companies are making business services such as logistics, 3D printing, and even roadside assistance for drivers available through a software connection that other companies can plug in to and consume or offer to their own customers.

SAP Q317 DigitalDoubles Feature1 Image2 Wonders Of Wood: Transformation In The Forest Products IndustryThere are two kinds of players in this business platform revolution: providers and participants. Providers create the platform and create incentives for developers to write apps for it. Developers, meanwhile, are participants; they can extend the reach of their apps by offering them through the platform’s virtual shelves.

Business platforms let companies outside of the technology world become powerful tech players, unleashing a torrent of innovation that they could never produce on their own. Good business platforms create millions in extra revenue for companies by enlisting external developers to innovate for them. It’s as if strangers are handing you entirely new revenue streams and business models on the street.

Powering this movement are application programming interfaces (APIs) and software development kits (SDKs), which enable developers to easily plug their apps into a platform without having to know much about the complex software code that drives it. Developers get more time to focus on what they do best: writing great apps. Platform providers benefit because they can offer many innovative business services to end customers without having to create them themselves.

Any company can leverage APIs and SDKs to create new business models and products that might not, in fact, be its primary method of monetization. However, these platforms give companies new opportunities and let them outflank smaller, more nimble competitors.

Indeed, the platform economy can generate unbelievable revenue streams for companies. According to Platform Revolution authors Geoffrey G. Parker, Marshall W. Van Alstyne, and Sangeet Paul Choudary, travel site Expedia makes approximately 90% of its revenue by making business services available to other travel companies through its API.

In TechCrunch in May 2016, Matt Murphy and Steve Sloane wrote that “the number of SaaS applications has exploded and there is a rising wave of software innovation in APIs that provide critical connective tissue and increasingly important functionality.” ProgrammableWeb.com, an API resource and directory, offers searchable access to more than 15,000 different APIs.

According to Accenture Technology Vision 2016, 82% of executives believe that platforms will be the “glue that brings organizations together in the digital economy.” The top 15 platforms (which include companies built entirely on this software architecture, such as eBay and Priceline.com) have a combined market capitalization of US$ 2.6 trillion.

It’s time for all companies to join the revolution. Whether working in alliance with partners or launching entirely in-house, companies need to think about platforms now, because they will have a disruptive impact on every major industry.

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To the Barricades

Several factors converged to make monetizing a company’s business services easier. Many of the factors come from the rise of smartphones, specifically the rise of Bluetooth and 3G (and then 4G and LTE) connections. These connections turned smartphones into consumption hubs that weren’t feasible when high-speed mobile access was spottier.

One good example of this is PayPal’s rise. In the early 2000s, it functioned primarily as a standalone web site, but as mobile purchasing became more widespread, third-party merchants clamored to integrate PayPal’s payment processing service into their own sites and apps.

In Platform Revolution, Parker, Van Alstyne, and Choudary claim that “platforms are eating pipelines,” with pipelines being the old, direct-to-consumer business methods of the past. The first stage of this takeover involved much more efficient digital pipelines (think of Amazon in the retail space and Grubhub for food delivery) challenging their offline counterparts.

What Makes Great Business Platforms Run?

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The quality of the ecosystem that powers your platform is as important as the quality of experience you offer to customers. Here’s how to do it right.

Although the platform economy depends on them, application programming interfaces (APIs) and software development kits (SDKs) aren’t magic buttons. They’re tools that organizations can leverage to attract users and developers.

To succeed, organizations must ensure that APIs include extensive documentation and are easy for developers to add into their own products. Another part of platform success is building a general digital enterprise platform that includes both APIs and SDKs.

A good platform balances ease of use, developer support, security, data architecture (that is, will it play nice with a company’s existing systems?), edge processing (whether analytics are processed locally or in the cloud), and infrastructure (whether a platform provider operates its own data centers and cloud infrastructure or uses public cloud services). The exact formula for which elements to embrace, however, will vary according to the use case, the industry, the organization, and its customers.

In all cases, the platform should offer a value proposition that’s a cut above its competitors. That means a platform should offer a compelling business service that is difficult to duplicate.

By creating open standards and easy-to-work-with tools, organizations can greatly improve the platforms they offer. APIs and SDKs may sound complicated, but they’re just tools for talented people to do their jobs with. Enable these talented people, and your platform will take off.

In the second stage, platforms replace pipelines. Platform Revolution’s authors write: “The Internet no longer acts merely as a distribution channel (a pipeline). It also acts as a creation infrastructure and a coordination mechanism. Platforms are leveraging this new capability to create entirely new business models.” Good examples of second-stage companies include Airbnb, DoubleClick, Spotify, and Uber.

Allstate Takes Advantage of Its Hidden Jewels

Many companies taking advantage of platforms were around long before APIs, or even the internet, existed. Allstate, one of the largest insurers in the United States, has traditionally focused on insurance services. But recently, the company expanded into new markets—including the platform economy.

Allstate companies Allstate Roadside Services (ARS) and Arity, a technology company founded by Allstate in late 2016, have provided their parent company with new sources of revenue, thanks to new offerings. ARS launched Good Hands Rescue APIs, which allow third parties to leverage Allstate’s roadside assistance network in their own apps. Meanwhile, Arity offers a portfolio of APIs that let third parties leverage Allstate’s aggregate data on driver behavior and intellectual property related to risk prediction for uses spanning mobility, consumer, and insurance solutions.

SAP Q317 DigitalDoubles Feature1 Image4 Wonders Of Wood: Transformation In The Forest Products IndustryFor example, Verizon licenses an Allstate Good Hands Rescue API for its own roadside assistance app. And automakers GM and BMW also offer roadside assistance service through Allstate.

Potential customers for Arity’s API include insurance providers, shared mobility companies, automotive parts makers, telecoms, and others.

“Arity is an acknowledgement that we have to be digital first and think about the services we provide to customers and businesses,” says Chetan Phadnis, Arity’s head of product development. “Thinking about our intellectual property system and software products is a key part of our transformation. We think it will create new ways to make money in the vertical transportation ecosystem.”

One of Allstate’s major challenges is a change in auto ownership that threatens the traditional auto insurance model. No-car and one-car households are on the rise, ridesharing services such as Uber and Lyft work on very different insurance models than passenger cars or traditional taxi companies, and autonomous vehicles could disrupt the traditional auto insurance model entirely.

This means that companies like Allstate are smart to look for revenue streams beyond traditional insurance offerings. The intangible assets that Allstate has accumulated over the years—a massive aggregate collection of driver data, an extensive set of risk models and predictive algorithms, and a network of garages and mechanics to help stranded motorists—can also serve as a new revenue stream for the future.

By offering two distinct API services for the platform economy, Allstate is also able to see what customers might want in the future. While the Good Hands Rescue APIs let third-party users integrate a specific service (such as roadside assistance) into their software tools, Arity instead lets third-party developers leverage huge data sets as a piece of other, less narrowly defined projects, such as auto maintenance. As Arity gains insights into how customers use and respond to those offerings, it gets a preview into potential future directions for its own products and services.

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Farmers Harvest Cash from a Platform

Another example of innovation fueling the platform economy doesn’t come from a boldfaced tech name. Instead, it comes from a relatively small startup that has nimbly built its business model around data with an interesting twist: it turns its customers into entrepreneurs.

Farmobile is a Kansas City–based agriculture tech company whose smart device, the Passive Uplink Connection (PUC), can be plugged into tractors, combines, sprayers, and other farm equipment.

Farmobile uses the PUC to enable farmers to monetize data from their fields, which is one of the savviest routes to success with platforms—making your platform so irresistible to end consumers that they foment the revolution for you.

Once installed, says CEO Jason Tatge, the PUC streams second-by-second data to farmers’ Farmobile accounts. This gives them finely detailed reports, called Electronic Field Records (EFRs), that they can use to improve their own business, share with trusted advisors, and sell to third parties.

The PUC gives farmers detailed records for tracking analytics on their crops, farms, and equipment and creates a marketplace where farmers can sell their data to third parties. Farmers benefit because they generate extra income; Farmobile benefits because it makes a commission on each purchase and builds a giant store of aggregated farming data.

This last bit is important if Farmobile is to successfully compete with traditional agricultural equipment manufacturers, which also gather data from farmers. Farmobile’s advantage (at least for now) is that the equipment makers limit their data gathering to their existing customer bases and sell it back to them in the form of services designed to improve crop yields and optimize equipment performance.

Farmobile, meanwhile, is trying to appeal to all farmers by sharing the wealth, which could help it leapfrog the giants that already have large customer bases. “The ability to bring data together easily is good for farmers, so we built API integrations to put data in one place,” says Tatge.

Farmers can resell their data on Farmobile’s Data Store to buyers such as reinsurance firm Guy Carpenter. To encourage farmers to opt in, says Tatge, “we told farmers that if they run our device over planting and harvest season, we can guarantee them $ 2 per acre for their EFRs.”

So far, Farmobile’s customers have sent the Data Store approximately 4,200 completed EFRs for both planting and harvest, which will serve as the backbone of the company’s data monetization efforts. Eventually, Farmobile hopes to expand the offerings on the Data Store to include records from at least 10 times as many different farm fields.

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Under Armour Binges on APIs

Another model for the emerging business platform world comes from Under Armour, the sports apparel giant. Alongside its very successful clothing and shoe lines, Under Armour has put its platform at the heart of its business model.

But rather than build a platform itself, Under Armour has used its growing revenues to create an industry-leading ecosystem. Over the past decade, it has purchased companies that already offer APIs, including MapMyFitness, Endomondo, and MyFitnessPal, and then linked them all together into a massive platform that serves 30 million consumers.

This strategy has made Under Armour an indispensable part of the sprawling mobile fitness economy. According to the company’s 2016 annual results, its business platform ecosystem, known as the Connected Fitness division, generated $ 80 million in revenue that year—a 51% increase over 2015.

SAP Q317 DigitalDoubles Feature1 Image7 Wonders Of Wood: Transformation In The Forest Products IndustryBy combining existing APIs from its different apps with original tools built in-house, extensive developer support, and a robust SDK, third-party developers have everything they need to build their own fitness app or web site.

Depending on their needs, third-party developers can sign up for several different payment plans with varying access to Under Armour’s APIs and SDKs. Indeed, the company’s tiered developer pricing plan for Connected Fitness, which is separated into Starter, Pro, and Premium levels, makes Under Armour seem more like a tech company than a sports apparel firm.

As a result, Under Armour’s APIs and SDKs are the underpinnings of a vast platform cooperative. Under Armour’s apps seamlessly integrate with popular services like Fitbit and Garmin (even though Under Armour has a fitness tracker of its own) and are licensed by corporations ranging from Microsoft to Coca-Cola to Purina. They’re even used by fitness app competitors like AthletePath and Lose It.

A large part of Under Armour’s success is the sheer amount of data its fitness apps collect and then make available to developers. MyFitnessPal, for instance, is an industry-leading calorie and food tracker used for weight loss, and Endomondo is an extremely popular running and biking record keeper and route-sharing platform.

One way of looking at the Connected Fitness platform is as a combination of traditional consumer purchasing data with insights gleaned from Under Armour’s suite of apps, as well as from the third-party apps that Under Armour’s products use.

Indeed, Under Armour gets a bonus from the platform economy: it helps the company understand its customers better, creating a virtuous cycle. As end users use different apps fueled by Under Armour’s services and data-sharing capabilities, Under Armour can then use that data to fuel customer engagement and attract additional third-party app developers to add new services to the ecosystem.

What Successful Platforms Have in Common

The most successful business platforms have three things in common: They’re easy to work with, they fulfill a market need, and they offer data that’s useful to customers.

For instance, Farmobile’s marketplace fulfills a valuable need in the market: it lets farmers monetize data and develop a new revenue stream that otherwise would not exist. Similarly, Allstate’s Arity experiment turns large volumes of data collected by Allstate over the years into a revenue stream that drives down costs for Arity’s clients by giving them more accurate data to integrate into their apps and software tools.

Meanwhile, Under Armour’s Connected Fitness platform and API suite encourage users to sign up for more apps in the company’s ecosystem. If you track your meals in MyFitnessPal, you’ll want to track your runs in Endomondo or MapMyRun. Similarly, if you’re an app developer in the health and fitness space, Under Armour has a readily available collection of tools that will make it easy for users to switch over to your app and cheaper for you to develop your app.

As the platform economy grows, all three of these approaches—Allstate’s leveraging of its legacy business data, Farmobile’s marketplace for users to become data entrepreneurs, and Under Armour’s one-stop fitness app ecosystem—are extremely useful examples of what happens next.

In the coming months and years, the platform economy will see other big changes. In 2016 for example, Apple, Microsoft, Facebook, and Google all released APIs for their AI-powered voice assistant platforms, the most famous of which is Apple’s Siri.

The introduction of APIs confirms that the AI technology behind these bots has matured significantly and that a new wave of AI-based platform innovation is nigh. (In fact, Digitalistpredicted last year that the emergence of an API for these AIs would open them up beyond conventional uses.) New voice-operated technologies such as Google Home and Amazon Alexa offer exciting opportunities for developers to create full-featured, immersive applications on top of existing platforms.

We will also see AI- and machine learning–based APIs emerge that will allow developers to quickly leverage unstructured data (such as social media posts or texts) for new applications and services. For instance, sentiment analysis APIs can help explore and better understand customers’ interests, emotions, and preferences in social media.

As large providers offer APIs and associated services for smaller organizations to leverage AI and machine learning, these companies can in turn create their own platforms for clients to use unstructured data—everything from insights from uploaded photographs to recognizing a user’s emotion based on facial expression or tone of voice—in their own apps and products. Meanwhile, the ever-increasing power of cloud platforms like Amazon Web Services and Microsoft Azure will give these computing-intensive app platforms the juice they need to become deeper and richer.

These business services will depend on easy ways to exchange and implement data for success. The good news is that finding easy ways to share data isn’t hard and the API and SDK offerings that fuel the platform economy will become increasingly robust. Thanks to the opportunities generated by these new platforms and the new opportunities offered to end users, developers, and platform businesses themselves, everyone stands to win—if they act soon. D!

About the Authors

Bernd Leukert is a member of the Executive Board, Products and Innovation, for SAP.

Björn Goerke is Chief Technology Officer and President, SAP Cloud Platform, for SAP.

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Sethu M is President, Mobile Services, for SAP.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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Mining Big Data To Create Smart Products And Happy Customers

Last August, a woman arrived at a Reno, Nevada, hospital and told the attending doctors that she had recently returned from an extended trip to India, where she had broken her right thighbone two years ago. The woman, who was in her 70s, had subsequently developed an infection in her thigh and hip for which she was hospitalized in India several times. The Reno doctors recognized that the infection was serious—and the visit to India, where antibiotic-resistant bacteria runs rampant, raised red flags.

When none of the 14 antibiotics the physicians used to treat the woman worked, they sent a sample of the bacterium to the U.S. Centers for Disease Control (CDC) for testing. The CDC confirmed the doctors’ worst fears: the woman had a class of microbe called carbapenem-resistant Enterobacteriaceae (CRE). Carbapenems are a powerful class of antibiotics used as last-resort treatment for multidrug-resistant infections. The CDC further found that, in this patient’s case, the pathogen was impervious to all 26 antibiotics approved by the U.S. Food and Drug Administration (FDA).

In other words, there was no cure.

This is just the latest alarming development signaling the end of the road for antibiotics as we know them. In September, the woman died from septic shock, in which an infection takes over and shuts down the body’s systems, according to the CDC’s Morbidity and Mortality Weekly Report.

Other antibiotic options, had they been available, might have saved the Nevada woman. But the solution to the larger problem won’t be a new drug. It will have to be an entirely new approach to the diagnosis of infectious disease, to the use of antibiotics, and to the monitoring of antimicrobial resistance (AMR)—all enabled by new technology.

sap Q217 digital double feature2 images2 Mining Big Data To Create Smart Products And Happy CustomersBut that new technology is not being implemented fast enough to prevent what former CDC director Tom Frieden has nicknamed nightmare bacteria. And the nightmare is becoming scarier by the year. A 2014 British study calculated that 700,000 people die globally each year because of AMR. By 2050, the global cost of antibiotic resistance could grow to 10 million deaths and US$ 100 trillion a year, according to a 2014 estimate. And the rate of AMR is growing exponentially, thanks to the speed with which humans serving as hosts for these nasty bugs can move among healthcare facilities—or countries. In the United States, for example, CRE had been seen only in North Carolina in 2000; today it’s nationwide.

Abuse and overuse of antibiotics in healthcare and livestock production have enabled bacteria to both mutate and acquire resistant genes from other organisms, resulting in truly pan-drug resistant organisms. As ever-more powerful superbugs continue to proliferate, we are potentially facing the deadliest and most costly human-made catastrophe in modern times.

“Without urgent, coordinated action by many stakeholders, the world is headed for a post-antibiotic era, in which common infections and minor injuries which have been treatable for decades can once again kill,” said Dr. Keiji Fukuda, assistant director-general for health security for the World Health Organization (WHO).

Even if new antibiotics could solve the problem, there are obstacles to their development. For one thing, antibiotics have complex molecular structures, which slows the discovery process. Further, they aren’t terribly lucrative for pharmaceutical manufacturers: public health concerns call for new antimicrobials to be financially accessible to patients and used conservatively precisely because of the AMR issue, which reduces the financial incentives to create new compounds. The last entirely new class of antibiotic was introduced 30 year ago. Finally, bacteria will develop resistance to new antibiotics as well if we don’t adopt new approaches to using them.

Technology can play the lead role in heading off this disaster. Vast amounts of data from multiple sources are required for better decision making at all points in the process, from tracking or predicting antibiotic-resistant disease outbreaks to speeding the potential discovery of new antibiotic compounds. However, microbes will quickly adapt and resist new medications, too, if we don’t also employ systems that help doctors diagnose and treat infection in a more targeted and judicious way.

Indeed, digital tools can help in all four actions that the CDC recommends for combating AMR: preventing infections and their spread, tracking resistance patterns, improving antibiotic use, and developing new diagnostics and treatment.

Meanwhile, individuals who understand both the complexities of AMR and the value of technologies like machine learning, human-computer interaction (HCI), and mobile applications are working to develop and advocate for solutions that could save millions of lives.

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Keeping an Eye Out for Outbreaks

Like others who are leading the fight against AMR, Dr. Steven Solomon has no illusions about the difficulty of the challenge. “It is the single most complex problem in all of medicine and public health—far outpacing the complexity and the difficulty of any other problem that we face,” says Solomon, who is a global health consultant and former director of the CDC’s Office of Antimicrobial Resistance.

Solomon wants to take the battle against AMR beyond the laboratory. In his view, surveillance—tracking and analyzing various data on AMR—is critical, particularly given how quickly and widely it spreads. But surveillance efforts are currently fraught with shortcomings. The available data is fragmented and often not comparable. Hospitals fail to collect the representative samples necessary for surveillance analytics, collecting data only on those patients who experience resistance and not on those who get better. Laboratories use a wide variety of testing methods, and reporting is not always consistent or complete.

Surveillance can serve as an early warning system. But weaknesses in these systems have caused public health officials to consistently underestimate the impact of AMR in loss of lives and financial costs. That’s why improving surveillance must be a top priority, says Solomon, who previously served as chair of the U.S. Federal Interagency Task Force on AMR and has been tracking the advance of AMR since he joined the U.S. Public Health Service in 1981.

A Collaborative Diagnosis

Ineffective surveillance has also contributed to huge growth in the use of antibiotics when they aren’t warranted. Strong patient demand and financial incentives for prescribing physicians are blamed for antibiotics abuse in China. India has become the largest consumer of antibiotics on the planet, in part because they are prescribed or sold for diarrheal diseases and upper respiratory infections for which they have limited value. And many countries allow individuals to purchase antibiotics over the counter, exacerbating misuse and overuse.

In the United States, antibiotics are improperly prescribed 50% of the time, according to CDC estimates. One study of adult patients visiting U.S. doctors to treat respiratory problems found that more than two-thirds of antibiotics were prescribed for conditions that were not infections at all or for infections caused by viruses—for which an antibiotic would do nothing. That’s 27 million courses of antibiotics wasted a year—just for respiratory problems—in the United States alone.

And even in countries where there are national guidelines for prescribing antibiotics, those guidelines aren’t always followed. A study published in medical journal Family Practice showed that Swedish doctors, both those trained in Sweden and those trained abroad, inconsistently followed rules for prescribing antibiotics.

Solomon strongly believes that, worldwide, doctors need to expand their use of technology in their offices or at the bedside to guide them through a more rational approach to antibiotic use. Doctors have traditionally been reluctant to adopt digital technologies, but Solomon thinks that the AMR crisis could change that. New digital tools could help doctors and hospitals integrate guidelines for optimal antibiotic prescribing into their everyday treatment routines.

“Human-computer interactions are critical, as the amount of information available on antibiotic resistance far exceeds the ability of humans to process it,” says Solomon. “It offers the possibility of greatly enhancing the utility of computer-assisted physician order entry (CPOE), combined with clinical decision support.” Healthcare facilities could embed relevant information and protocols at the point of care, guiding the physician through diagnosis and prescription and, as a byproduct, facilitating the collection and reporting of antibiotic use.

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Cincinnati Children’s Hospital’s antibiotic stewardship division has deployed a software program that gathers information from electronic medical records, order entries, computerized laboratory and pathology reports, and more. The system measures baseline antimicrobial use, dosing, duration, costs, and use patterns. It also analyzes bacteria and trends in their susceptibilities and helps with clinical decision making and prescription choices. The goal, says Dr. David Haslam, who heads the program, is to decrease the use of “big gun” super antibiotics in favor of more targeted treatment.

While this approach is not yet widespread, there is consensus that incorporating such clinical-decision support into electronic health records will help improve quality of care, contain costs, and reduce overtreatment in healthcare overall—not just in AMR. A 2013 randomized clinical trial finds that doctors who used decision-support tools were significantly less likely to order antibiotics than those in the control group and prescribed 50% fewer broad-spectrum antibiotics.

Putting mobile devices into doctors’ hands could also help them accept decision support, believes Solomon. Last summer, Scotland’s National Health Service developed an antimicrobial companion app to give practitioners nationwide mobile access to clinical guidance, as well as an audit tool to support boards in gathering data for local and national use.

“The immediacy and the consistency of the input to physicians at the time of ordering antibiotics may significantly help address the problem of overprescribing in ways that less-immediate interventions have failed to do,” Solomon says. In addition, handheld devices with so-called lab-on-a-chip  technology could be used to test clinical specimens at the bedside and transmit the data across cellular or satellite networks in areas where infrastructure is more limited.

Artificial intelligence (AI) and machine learning can also become invaluable technology collaborators to help doctors more precisely diagnose and treat infection. In such a system, “the physician and the AI program are really ‘co-prescribing,’” says Solomon. “The AI can handle so much more information than the physician and make recommendations that can incorporate more input on the type of infection, the patient’s physiologic status and history, and resistance patterns of recent isolates in that ward, in that hospital, and in the community.”

Speed Is Everything

Growing bacteria in a dish has never appealed to Dr. James Davis, a computational biologist with joint appointments at Argonne National Laboratory and the University of Chicago Computation Institute. The first of a growing breed of computational biologists, Davis chose a PhD advisor in 2004 who was steeped in bioinformatics technology “because you could see that things were starting to change,” he says. He was one of the first in his microbiology department to submit a completely “dry” dissertation—that is, one that was all digital with nothing grown in a lab.

Upon graduation, Davis wanted to see if it was possible to predict whether an organism would be susceptible or resistant to a given antibiotic, leading him to explore the potential of machine learning to predict AMR.

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As the availability of cheap computing power has gone up and the cost of genome sequencing has gone down, it has become possible to sequence a pathogen sample in order to detect its AMR resistance mechanisms. This could allow doctors to identify the nature of an infection in minutes instead of hours or days, says Davis.

Davis is part of a team creating a giant database of bacterial genomes with AMR metadata for the Pathosystems Resource Integration Center (PATRIC), funded by the U.S. National Institute of Allergy and Infectious Diseases to collect data on priority pathogens, such as tuberculosis and gonorrhea.

Because the current inability to identify microbes quickly is one of the biggest roadblocks to making an accurate diagnosis, the team’s work is critically important. The standard method for identifying drug resistance is to take a sample from a wound, blood, or urine and expose the resident bacteria to various antibiotics. If the bacterial colony continues to divide and thrive despite the presence of a normally effective drug, it indicates resistance. The process typically takes between 16 and 20 hours, itself an inordinate amount of time in matters of life and death. For certain strains of antibiotic-resistant tuberculosis, though, such testing can take a week. While physicians are waiting for test results, they often prescribe broad-spectrum antibiotics or make a best guess about what drug will work based on their knowledge of what’s happening in their hospital, “and in the meantime, you either get better,” says Davis, “or you don’t.”

At PATRIC, researchers are using machine-learning classifiers to identify regions of the genome involved in antibiotic resistance that could form the foundation for a “laboratory free” process for predicting resistance. Being able to identify the genetic mechanisms of AMR and predict the behavior of bacterial pathogens without petri dishes could inform clinical decision making and improve reaction time. Thus far, the researchers have developed machine-learning classifiers for identifying antibiotic resistance in Acinetobacter baumannii (a big player in hospital-acquired infection), methicillin-resistant Staphylococcus aureus (a.k.a. MRSA, a worldwide problem), and Streptococcus pneumoniae (a leading cause of bacterial meningitis), with accuracies ranging from 88% to 99%.

Houston Methodist Hospital, which uses the PATRIC database, is researching multidrug-resistant bacteria, specifically MRSA. Not only does resistance increase the cost of care, but people with MRSA are 64% more likely to die than people with a nonresistant form of the infection, according to WHO. Houston Methodist is investigating the molecular genetic causes of drug resistance in MRSA in order to identify new treatment approaches and help develop novel antimicrobial agents.

sap Q217 digital double feature2 images6 1024x572 Mining Big Data To Create Smart Products And Happy Customers

The Hunt for a New Class of Antibiotics

There are antibiotic-resistant bacteria, and then there’s Clostridium difficile—a.k.a. C. difficile—a bacterium that attacks the intestines even in young and healthy patients in hospitals after the use of antibiotics.

It is because of C. difficile that Dr. L. Clifford McDonald jumped into the AMR fight. The epidemiologist was finishing his work analyzing the spread of SARS in Toronto hospitals in 2004 when he turned his attention to C. difficile, convinced that the bacteria would become more common and more deadly. He was right, and today he’s at the forefront of treating the infection and preventing the spread of AMR as senior advisor for science and integrity in the CDC’s Division of Healthcare Quality Promotion. “[AMR] is an area that we’re funding heavily…insofar as the CDC budget can fund anything heavily,” says McDonald, whose group has awarded $ 14 million in contracts for innovative anti-AMR approaches.

Developing new antibiotics is a major part of the AMR battle. The majority of new antibiotics developed in recent years have been variations of existing drug classes. It’s been three decades since the last new class of antibiotics was introduced. Less than 5% of venture capital in pharmaceutical R&D is focused on antimicrobial development. A 2008 study found that less than 10% of the 167 antibiotics in development at the time had a new “mechanism of action” to deal with multidrug resistance. “The low-hanging fruit [of antibiotic development] has been picked,” noted a WHO report.

Researchers will have to dig much deeper to develop novel medicines. Machine learning could help drug developers sort through much larger data sets and go about the capital-intensive drug development process in a more prescriptive fashion, synthesizing those molecules most likely to have an impact.

McDonald believes that it will become easier to find new antibiotics if we gain a better understanding of the communities of bacteria living in each of us—as many as 1,000 different types of microbes live in our intestines, for example. Disruption to those microbial communities—our “microbiome”—can herald AMR. McDonald says that Big Data and machine learning will be needed to unlock our microbiomes, and that’s where much of the medical community’s investment is going.

He predicts that within five years, hospitals will take fecal samples or skin swabs and sequence the microorganisms in them as a kind of pulse check on antibiotic resistance. “Just doing the bioinformatics to sort out what’s there and the types of antibiotic resistance that might be in that microbiome is a Big Data challenge,” McDonald says. “The only way to make sense of it, going forward, will be advanced analytic techniques, which will no doubt include machine learning.”

Reducing Resistance on the Farm

Bringing information closer to where it’s needed could also help reduce agriculture’s contribution to the antibiotic resistance problem. Antibiotics are widely given to livestock to promote growth or prevent disease. In the United States, more kilograms of antibiotics are administered to animals than to people, according to data from the FDA.

One company has developed a rapid, on-farm diagnostics tool to provide livestock producers with more accurate disease detection to make more informed management and treatment decisions, which it says has demonstrated a 47% to 59% reduction in antibiotic usage. Such systems, combined with pressure or regulations to reduce antibiotic use in meat production, could also help turn the AMR tide.

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Breaking Down Data Silos Is the First Step

Adding to the complexity of the fight against AMR is the structure and culture of the global healthcare system itself. Historically, healthcare has been a siloed industry, notorious for its scattered approach focused on transactions rather than healthy outcomes or the true value of treatment. There’s no definitive data on the impact of AMR worldwide; the best we can do is infer estimates from the information that does exist.

The biggest issue is the availability of good data to share through mobile solutions, to drive HCI clinical-decision support tools, and to feed supercomputers and machine-learning platforms. “We have a fragmented healthcare delivery system and therefore we have fragmented information. Getting these sources of data all into one place and then enabling them all to talk to each other has been problematic,” McDonald says.

Collecting, integrating, and sharing AMR-related data on a national and ultimately global scale will be necessary to better understand the issue. HCI and mobile tools can help doctors, hospitals, and public health authorities collect more information while advanced analytics, machine learning, and in-memory computing can enable them to analyze that data in close to real time. As a result, we’ll better understand patterns of resistance from the bedside to the community and up to national and international levels, says Solomon. The good news is that new technology capabilities like AI and new potential streams of data are coming online as an era of data sharing in healthcare is beginning to dawn, adds McDonald.

The ideal goal is a digitally enabled virtuous cycle of information and treatment that could save millions of dollars, lives, and perhaps even civilization if we can get there. D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

About the Authors:

Dr. David Delaney is Chief Medical Officer for SAP.

Joseph Miles is Global Vice President, Life Sciences, for SAP.

Walt Ellenberger is Senior Director Business Development, Healthcare Transformation and Innovation, for SAP.

Saravana Chandran is Senior Director, Advanced Analytics, for SAP.

Stephanie Overby is an independent writer and editor focused on the intersection of business and technology.


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Digitalist Magazine

Three Big Trends To Help You Better Personalize Your Products

280461 GettyImages 544365884 high e1504147593397 Three Big Trends To Help You Better Personalize Your Products

Also, while WhatsApp has a large following, China’s 768 million WeChat users have access to far more advanced features, such as electronic payments and online gaming. It seems as if WeChat payments are ubiquitous in China and will soon replace both cash and credit card payments.

The bottom line

In a globally connected world, automotive companies are reinventing themselves. China is a great example of jumping technology stages in an effort to become the leader in electric mobility.

Technology companies recognize the gravitas of China becoming a leader in the automotive world. Recognizing also means listening to the specifics of the market, the tech infrastructure ecosystem, and consumer preferences, and providing breakthrough cloud-based technologies that enable fast-moving players to stay ahead of the game.

This story also appeared on the SAP Community. Follow me @ulimuench.

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How to list the pairwise matrix products of two lists of matrices

 How to list the pairwise matrix products of two lists of matrices

I have two lists of 3×3 matrices, of equal length. I would like to make a list of their pairwise matrix products. Is there a more elegant way to do it than this ?

rotationmatrices={{{1, 0, 0}, {0, 1, 0}, {0, 0, 1}}, {{0, 1, 0}, {-1, 0, 0}, {0, 0, 1}}, {{1, 0, 0}, {0, 0, -1}, {0, 1, 0}}, {{0, 1, 0}, {-1, 0, 0}, {0, 0, 1}}};
scalematrices={{{1, 0, 0}, {0, 1, 0}, {0, 0, 1}}, {{1, 0, 0}, {0, 1, 0}, {0, 0, 1}}, {{1, 0, 0}, {0, 2, 0}, {0, 0, 1}}, {{2, 0, 0}, {0, 1, 0}, {0, 0, 2}}};
Map[Apply[Dot, Transpose[{rotationmatrices, scalematrices}][[#]]] &, Range[Length[rotationmatrices]]]

I also tried

Inner[Dot, rotationmatrices, scalematrices, List]


Inner[Dot[#1, #2] &, rotationmatrices, scalematrices, List]

but neither of these worked.

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Recent Questions – Mathematica Stack Exchange

Build Data Products to Be “Over-the-Counter”

otc Build Data Products to Be “Over the Counter”

Dr. John Snow (the Soho doctor, not the Game of Thrones Northman) saved London from cholera when he selected the best format to visualize deaths on a map. Florence Nightingale saved thousands of Crimean War soldiers when she clearly annotated deaths by preventable disease in her argument for a new hospital. Data can be life-changing when it is properly communicated: when building interfaces to display data, product builders can follow a set of standards that can improve data UX by up to 4x.

What does it mean to be “over-the-counter”?

Fortunately, we have a succinct list of best practices to follow when building a product through which data is displayed. These research-based best practices are summarized as Over-the-Counter Data Standards. The standards received this name because “over-the-counter” means an item is easy to understand and use without the help of an expert. This ease-of-use is exactly what we want to give to our products’ users. Thus, Over-the-Counter Data Standards tell those of us building products and data displays exactly how to design an environment where data can be viewed and understood with ease.

Building products that follow the “over-the-counter” research-based best practices can improve your user’s understanding of data by up to 4x.

Understanding how over-the-counter elements are used in medicine helps to underscore their value in data products. When my young daughter got sick with the flu, I could easily find and use over-the-counter medicine to alleviate her symptoms. The packaging/display featured a child’s photo to indicate the medicine was safe for kids. The bottle’s label told me how to use the medicine (how much to give my daughter and when) and possible dangers. Supplemental documentation was tucked inside the packaging to offer information too extensive to fit on the label. The medicine’s content was comprised of key, unexpired ingredients that would meet our needs. If all this was not enough, I could always search an online help system for information on related topics.

The point is, key components are in place so someone using over-the-counter medicine is likely to use the product appropriately and successfully without an expert present. This is exactly what we need for products that display data. We need five over-the-counter components (packaging/display, label, supplemental documentation, content, help system) in place and well-designed so someone using a data display is likely to use the data appropriately and understand its implications. Over-the-Counter Data Standards summarize best data reporting practices within these five components, as informed by extensive research (see Rankin, 2016b), so those viewing data will easily understand it.

What are these research-based standards?

I first implemented Over-the-Counter Data Standards using TIBCO Jaspersoft’s embeddable BI suite when building data reports to be housed within Illuminate Education software products. Jaspersoft gave me the flexibility to add varied annotations, condition-based color, links to resources, and much more to adhere to all of the standards for effective data reporting. Extensive research findings indicated these standards were necessary to ensure data would be understood by our products’ users.

Anyone building products can offer the same increased odds for their data’s usability, as the standards are easy to access and follow. The Over-the-Counter Data Standards are available for free. Though a book (see Rankin, 2016a) provides steps and illustrations for following these data reporting standards, the data reporting standards, templates, and other resources are all available for free at overthecounterdata.com and are easy to use. Additionally, the standards were based on more than 300 studies and other expert sources from varied fields concerning best practices when reporting data (such as most effective data visualization techniques); these sources were reviewed and compiled in the Rankin (2016b) study, which was published as another book.

Data can impact lives, just as it did in Dr. Snow and Florence Nightingale’s time. If you are a product builder and will be sharing data with product users, such as building a data system or an interface in which data is displayed, do you want people to understand the data? Do you want users to do so quickly and easily? Do you want users of all backgrounds and intellects to be able to use the product and its data successfully without the help of an expert? If your answer to any of these questions is yes, you will want to follow research-based best practices so your data can be over-the-counter, too.

Want to learn more? Join Jenny Rankin, Ph.D. and Ernesto Ongaro in a Webinar titled “Make your Reports over the Counter” on May 16th. Learn more about TIBCO Jaspersoft business intelligence software and try it out for free.


Rankin, J. G. (2016a). Designing Data Reports that Work: A Guide for Creating Data Systems in Schools and Districts. New York, NY: Routledge. ISBN 978-1-315-66584-9

Rankin, J. G. (2016b). Standards for reporting data to educators: What educational leaders should know and demand. New York, NY: Routledge/Taylor & Francis.

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The TIBCO Blog

Selected Products Share of Relevant Category

I am constantly amazed at how much you can achieve in Power Pivot with a relatively small amount of DAX knowledge.  I was working with a client recently and he wanted me to produce a report to see the following:

1.  The sales of selected products (he would select the ones he wanted to see from a list)
2.  To see what the percentage those selected product sales made up of the relevant categories.
3.   If he selected more than 1 product from different product categories then the share of relevant categories should be across all categories the selected products came from (the aggregate).

At first glance, this seems like a hard thing to do (detect the relevant categories related to the selected products) however DAX is an amazing language.  I have built a demo using Adventure Works to show how easily this can be done.

I set up a Pivot Table pointing to a simplified star schema version of Adventure Works data model that has Product Category and Sub Category as columns in the product table.

model2 Selected Products Share of Relevant Category

I added a slicer on the product name and selected 2 products “Classic Vest, L” and “Bike Wash – Dissolver”. image thumb 8 Selected Products Share of Relevant Category

The sales for these products are shown in the pivot table above along with the relevant sub category name, sub category sales and the % of total sub category. The simple trick to all of these calculations is to be able to determine which sub category the selected product belongs to.

Values is a great little function.  It does many things (as explained by Scott from Tiny Lizard here).  I used VALUES as the main function to solve this problem.  Firstly I wrote the following measure so I could “see” what the sub categories were.

Relevant Sub Category =IF(HASONEVALUE(Products[SubCategory]),VALUES(Products[SubCategory]),BLANK())

If the result of VALUES is a single row (ie only 1 value), then it is quite permissible to place that value into a pivot table.  Power Pivot will convert the table (a single row table in this case) to a scalar value for you automatically.  However you must protect the pivot table to prevent the accidental placement of a table with more than 1 row into the pivot – this is not allowed.  To do this you must wrap your formula inside an IF(HASONEVALUE()) construct as you can see in the formula I used above.  The BLANK() alternate result of the IF statement you can see above is optional (and the default), so this parameter can be omitted and it will return the same outcome.

If you are using Excel 2016 or Power BI Desktop, then there is a new function (not available in earlier versions of Power Pivot) that allows you to take a table of values and concatenate those values into a single scalar value.   In the Grand Total Row of Column E in the Pivot Table above, the VALUES(Product[SubCategory]) has 2 values in the current filter context, hence VALUES will return a single column table with 2 rows (one for Vests and one for Cleaners).  A Pivot Table cannot render a 2 row table into a cell, but it is possible to concatenate the values from each row into a single scalar value (with Excel 2016 and Power BI).

Modifying my original formula, I added the following extra CONCATENATEX code into the formula.

CONCATENATEX(VALUES(Products[SubCategory]),Products[SubCategory],”, “))

The final formula for “Relevant Sub Category” is therefore

CONCATENATEX(VALUES(Products[SubCategory]),Products[SubCategory],”, “)

image thumb 9 Selected Products Share of Relevant Category

As you can see above, now when there is more than 1 value in the current filter context, the formula returns all of those values into a single scalar string that can be inserted into a pivot table.

The great thing about the measure “Relevant Sub Category” above is that you can “See” that what is happening behind the scenes with the VALUES function.  I find this one of the hardest things for Excel folk to learn is how to “visualise” table functions.  I find that writing measures to help you “see” the contents of table functions really helps Excel folk move forward.

I then wrote the measure Sub Category Total Sales using the VALUES formula but this time I passed the table as a filter inside CALCULATE as follows.

Total Sub Category Sales = CALCULATE([Total Sales],ALL(Products),VALUES(Products[SubCategory]))

The above CALCULATE formula has 2 table filters; an ALL(Products) table and the VALUES(Products[SubCategory]) table.

ALL(Products) is required to remove the current filter context from the selected products in the pivot table.  Without this ALL function, the total would simply be the total sales for the selected products.

The second table function VALUES(Products[SubCategory]) (as we already know from above) produces a table of Sub Categories that is relevant to the products selected in the current filter context.  When there is a single row in the pivot table there is only 1 relevant sub category.  But on the grand total row in the pivot table, there are currently 2 relevant sub categories.  This table of relevant sub categories is passed to CALCULATE and CALCULATE applies this table as a new filter to the data model before propagating the filter from the product table to the sales table before finally adding up the sales for those relevant sub categories.

The final formula is now simple to create as follows:

Products % of Sub Category Sales = DIVIDE([Total Sales],[Total Sub Category Sales])

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Amazon kills dodgy review system linked to free, discount products

credcnetamazon shipping box Amazon kills dodgy review system linked to free, discount productsCNET

Amazon has taken a stand against less-than-honest views with revisions to the firm’s terms of service in an attempt to keep review quality under control.

Amazon has become one of the largest and most well-known e-commerce giants online. The company operates worldwide and not only hosts everything from paint to clothing but has also expanded into groceries, fresh food and most recently, restaurants (that’s not to mention experiments with same-day delivery via drone and connected home technology).

See also: Amazon, Morrisons strike online UK groceries deal

However, with size often comes corruption. There are a thousand ways for scammers to quietly make a fortune off Amazon-hosted e-books by fiddling with reviews, and now, Amazon wants to tackle the problem of dishonest reviews as a whole.

In an update to community guidelines this week, Chee Chew, VP of Customer Experience at Amazon revealed that with the exception of paying people outright for reviews, if they received a free or discounted product, “incentivized” reviews were once acceptable.

However, in reality, if someone is given a free or heavily discounted item, the likelihood is they will want to keep that vendor sweet and not burn a bridge — and so the review may not be as honest as you would like — and the only stipulation the user has to adhere to is to mention the link within their review.

How Harley-Davidson and Other Companies Deliver Individualized Products

As robotic technologies continue to advance, along with related technologies such as speech and image recognition, memory and analytics, and virtual and augmented reality, better, faster, and cheaper robots will emerge. These machines – sophisticated, discerning, and increasingly autonomous – are certain to have an impact on business and society. But will they bring job displacement and danger or create new categories of employment and protect humankind?

We talked to SAP’s Kai Goerlich, along with Doug Stephen of the Institute for Human and Machine Cognition and Brett Kennedy from NASA’s Jet Propulsion Laboratory, about the advances we can expect in robotics, robots’ limitations, and their likely impact on the world.

SAP Robotics QA images2400x16002 1 1024x683 How Harley Davidson and Other Companies Deliver Individualized Products

qa q How Harley Davidson and Other Companies Deliver Individualized ProductsWhat are the biggest drivers of the robot future?

Kai Goerlich: Several trends will come together to drive the robotics market in the next 15 to 20 years. The number of connected things and sensors will grow to the billions and the data universe will likewise explode. We think the speed of analytics will increase, with queries answered in milliseconds. Image and voice recognition – already quite good – will surpass human capabilities. And the virtual and augmented reality businesses will take off. These technologies are all building blocks for a new form of robotics that will vastly expand today’s capabilities in a diversity of forms and applications.

Brett Kennedy: When I was getting out of school, there weren’t that many people working in robotics. Now kids in grade school are exposed to a lot of things that I had to learn on the job, so they come into the workplace with a lot more knowledge and fewer preconceptions about what robots can or can’t do based on their experiences in different industries. That results in a much better-trained workforce in robotics, which I think is the most important thing.

In addition, many of the parts that we need for more sophisticated robots are coming out of other fields. We could never create enough critical mass to develop these technologies specifically for robotics. But we’re getting them from other places. Improvements in battery technology, which enable a robot to function without being plugged in, are being driven by industries such as mobile electronics and automotive, for example. Our RoboSimian has a battery drive originally designed for an electric motorcycle.

qa q How Harley Davidson and Other Companies Deliver Individualized ProductsDo you anticipate a limit to the tasks robots will be able to master as these core technologies evolve?

Goerlich: Robots will take over more and more complex functions, but I think the ultimate result will be that new forms of human-machine interactions will emerge. Robots have advantages in crunching numbers, lifting heavy objects, working in dangerous environments, moving with precision, and performing repetitive tasks. However, humans still have advantages in areas such as abstraction, curiosity, creativity, dexterity, fast and multidimensional feedback, self-motivation, goal setting, and empathy. We’re also comparatively lightweight and efficient.

Doug Stephen: We’re moving toward a human-machine collaboration approach, which I think will become the norm for more complex tasks for a very long time. Even when we get to the point of creating more-complex and general-purpose robots, they won’t be autonomous. They’ll have a great deal of interaction with some sort of human teammate or operator.

qa q How Harley Davidson and Other Companies Deliver Individualized ProductsHow about the Mars Rover? It’s relatively autonomous already.

Kennedy: The Mars Rover is autonomous to a certain degree. It is capable of supervised autonomy because there’s no way to control it at that distance with a joystick. But it’s really just executing the intent of the operator here on the ground.

In 2010, DARPA launched its four-year Autonomous Robotic Manipulator Challenge to create machines capable of carrying out complex tasks with only high-level human involvement. Some robots completed the challenge, but they were incredibly slow. We may get to a point where robots can do these sorts of things on their own. But they’re just not as good as people at this point. I don’t think we’re all going to be coming home to robot butlers anytime soon.

Stephen: It’s extremely difficult to program robots to behave as humans do. When we trip over something, we can recover quickly, but a robot will topple over and damage itself. The problem is that our understanding of our human abilities is limited. We have to figure out how to formally define the processes that human beings or any legged animals use to maintain balance or to walk and then tell a robot how to do it.

You have to be really explicit in the instructions that you give to these machines. Amazon has been working on these problems for a while with its “picking challenge”: How do you teach a robot to pick and pack boxes the way a human does? Right now, it’s a challenge for robots to identify what each item is.

qa q How Harley Davidson and Other Companies Deliver Individualized ProductsSo if I’m not coming home to a robot butler in 20 years, what am I coming home to?

Goerlich: We naturally tend to imagine humanoid robots, but I think the emphasis will be on human-controlled robots, not necessarily humanshaped units. Independent robots will make sense in some niches, but they are more complex and expensive. The symbiosis of human and machine is more logical. It will be the most efficient way forward. Robotic suits, exoskeletons, and robotic limbs with all kinds of human support functions will be the norm. The future will be more Iron Man than Terminator.

qa q How Harley Davidson and Other Companies Deliver Individualized ProductsWhat will be the impact on the job market as robots become more advanced?

SAP Robotics QA images2400x16004 1 1024x683 How Harley Davidson and Other Companies Deliver Individualized ProductsGoerlich: The default fear is of a labor-light economy where robots do most of the work and humans take what’s left over. But that’s lastcentury thinking. Robots won’t simply replace workers on the assembly line. In fact, we may not have centralized factories anymore; 3D printing and the maker movement could change all that. And it is probably not the Terminator scenario either, where humanoid robots take over the world and threaten humankind. The indicators instead point to human-machine coevolution.

There’s no denying that advances in robotics and artificial intelligence will displace some jobs performed by humans today. But for every repetitive job that is lost to automation, it’s possible that a more interesting, creative job will take its place. This will require humans to focus on the skills that robots can’t replicate – and, of course, rethink how we do things and how the economy works.

qa q How Harley Davidson and Other Companies Deliver Individualized ProductsWhat can businesses do today to embrace the projected benefits of advanced robotics?

Kennedy: Experiment. The very best things that we’ve been able to produce have come from people having the tools an d then figuring out how they can be used. I don’t think we understand the future well enough to be able to predict exactly how robots are going to be used, but I think we can say that they certainly will be used. Stephanie Overby is an independent writer and editor focused on the intersection of business and technology.

Stephanie Overby  is an independent writer and editor focused on the intersection of business and technology

To learn more about how humans and robots will co-evolve, read the in-depth report Bring Your Robot to Work.

Download the PDF

Future of Business 728x180 How Harley Davidson and Other Companies Deliver Individualized Products


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