How To Solve IoT's Big Data Challenge With Machine Learning

Tim Allen

Machine learning will come of age this year, moving from the research labs and proof-of-concept implementations to cutting-edge business solutions. Along the way, it will help power innovations such as autonomous vehicles, precision farming, therapeutic drug discovery, and advanced fraud detection for financial institutions.

Machine learning intersects with statistics, computer science, and artificial intelligence, focusing on the development of fast and efficient algorithms to enable real-time data processing. Rather than just follow explicitly programmed instructions, these machine learning algorithms learn from experience, making them a key component of artificial intelligence platforms.

Machine learning helps tackle IoT data flows

Machine learning may also help us with a challenge from one of last year’s most buzzed-about technology developments: the Internet of Things. The first generation of Big Data analytics grew up around the flow of information generated by social media, online shopping, online videos, web surfing, and other user-generated online behaviors, according to Vin Sharma, the director of machine learning solutions in Intel’s Data Center Group.

Analyzing these massive datasets required new technologies, flexible cloud computing, and virtualization software such as Apache Hadoop and Spark. It also needed more powerful, high-performance processors that provided the tools to uncover the insights in Big Data.

And today’s IoT-connected networks dwarf the data volume from this first era of Big Data. As devices and sensors continue proliferating, so will the volume of data they create.

For example, a single autonomous car will generate 4,000 GB of data per day. The new Airbus A380-1000 is equipped with 10,000 sensors in each wing. Legacy Big Data technology won’t be able to handle the data created by connected appliances in smart homes, traffic sensors in smart cities, and robotic systems in smart factories.

New and exciting system requirements

Machine learning is key to analyzing the enormous, repetitious volumes of data flowing from vast, always-on IoT networks. While machine learning may seem like science fiction to many, it is already in use and familiar to users of social media and online shopping (Facebook’s news feed relies on machine learning algorithms, and Amazon’s recommendation engine uses machine learning to suggest what book or movie you should enjoy next).

Machine learning systems recognize the normal flow patterns of data present on IoT networks and focus on the anomalies or patterns outside the norm. So from billions of data points, machine learning can separate the “signal from the noise” in vast data flows, helping organizations focus on what’s meaningful.

However, to be useful and effective for businesses, machine learning algorithms must run computations at enormous scale in a matter of milliseconds — on an ongoing basis. These ever more complex computations put pressure on traditional datacenter processors and computing platforms.

To operate at scale and in real time, machine learning systems require processors with multiple integrated cores, faster memory subsystems, and architectures that can parallelize processing for next generation analytical intelligence. These are platforms with built-in analytical processing engines as well as the capacity to run complex algorithms in-memory for real-time results and immediate application of insights.

Final prediction

Processors built for high-performance computing will be in high demand. Machine learning and artificial intelligence will need a lot more power as they begin to connect the dots between IoT data flows and customer engagement for improved sales and outreach.

These processors were traditionally the province of research laboratories and supercomputing challenges, such as modeling weather patterns and genome sequencing. But machine learning platforms will become more and more necessary as IoT networks become larger and more pervasive — and as businesses increasingly base their success on the insights found in machine-to-machine communication.

These processors deliver the performance required for the most demanding workloads, including machine learning and artificial intelligence algorithms. So they will no longer be confined to the rarified environments of supercomputing in research centers and universities, as they increasingly become a requirement for cutting-edge businesses.

For more on future tech, see 20 Technology Predictions To Keep Your Eye On In 2017.


Tim Allen

About Tim Allen

Tim Allen is the Global SAP Marketing Alliance Manager at Intel.

Answers To Two Burning Questions About Conversational AI

Warren Miller

Fire: one of civilization’s earliest and most groundbreaking technological advancements.

Two million years ago, when the first homo erectus shared his newfound discovery with his hominid peers, they likely ran for the hills. But once they realized everything they could achieve with fire—from seeing in the dark and keeping warm to cooking food and fashioning tools—they quickly came around.

People have always feared the unknown. Even today, innovative technology initially intimidates most people. But if a tool proves sound and benefits individuals in some tangible way, they’ll eagerly embrace it.

One technology that people are currently on the fence about—particularly in the enterprise space—is conversational artificial intelligence (AI).

While voice-activated digital assistants powered by conversational AI have been a mainstay in the home for the past several years, organizations are just beginning to bring them into the workplace. Many companies remain skeptical, however. They wonder whether these digital assistants can truly help them simplify the lives of their customers and employees. They wonder if they can leverage the technology to save time, cut costs, and increase productivity.

But most of all, they wonder if they can rely on these digital assistants to support people around the globe who speak different languages, and if this technology can securely protect their most sensitive data and proprietary information.

Here are two burning questions companies have about adopting conversational AI tools—and reasons they can finally put their reservations to rest.

1. Does conversational AI support my native language?

Multilingualism is an impressive characteristic, and the ability to fluently speak multiple languages opens up whole new worlds.

What if you could speak 21 different languages? Imagine what you could achieve. Imagine how much you could help others.

Apple’s Siri can do just that. In fact, the company’s digital assistant is way ahead of its conversational AI competitors when it comes to the number of languages it supports. Comparatively, Microsoft’s Cortana supports eight languages, Google Assistant supports four, and Amazon Alexa supports two.

The important thing to note here, however, is that because conversational AI is a branch of machine learning, it has the ability to support any native tongue—eventually.

First, your digital assistant needs a strong knowledge base in each language, be it one widely spoken around the globe, like English, or one used in a specific area of the world, like Shanghainese.

It then requires deep learning algorithms that help your device process structured and unstructured language data in the form of e-mails, online chat logs, or phone transcripts. By studying this data, digital assistants can iron out complex communication issues and improve how they interact with users, no matter the language.

Rather than using a linguistic, rules-based approach—where the device would have to identify nouns, verbs, and adjectives—machine learning is a more scalable solution that enables digital assistants to figure out how words are connected and what phrases do or don’t make sense. In other words, no one has to continue defining specific syntactical rules for the device. It learns them on its own.

If a voice-activated digital assistant doesn’t currently support your native language, rest assured—it can and it will.

2. Will digital assistants threaten the security of my company’s data and proprietary information?

Security is a major concern for companies in today’s digital age—and understandably so.

Cybercrime affected nearly one-third of all organizations in 2016, according to a PwC survey. And Vanson Bourne research found that 87% of CIOs believe their companies lack the security controls necessary to adequately protect their businesses in the future.

While security breaches are certainly something to worry about, sharing your data with digital assistants is more helpful than harmful. And the more data your digital assistants collect, the more their conversational AI capabilities improve, and the better they can assist you.

So, the solution to protecting your data isn’t to stop communicating and sharing your data with digital assistants. Instead, your security depends on taking the proper safety precautions. These include:

  • Muting your device: Although digital assistants wait to hear a trigger word or phrase before helping you, their microphones are always listening—unless you mute them, of course. Find the mute button on your device, and only unmute your digital assistant when you’re actively using it.
  • Sharing only what’s necessary: Giving your digital assistant access to your calendar is one thing. Sharing confidential financial information is something else altogether. Exercise caution in what details you provide your digital assistant.
  • Deleting old recordings: Digital assistants retain audio files of the questions you ask them for months or even years. You do, however, have the option to erase these recordings, and you don’t need to be a magician to make these files disappear; simply visit a website and hit the delete button.

There are also steps that technology companies and developers can take to protect your data. For example, they can ensure that your information is inaccessible to unauthorized users. Today, digital assistants cannot tell the difference between voices. But in the future, with greater conversational AI capabilities, these devices will come equipped with biometric-based authentication such as voice recognition technology—so if your digital assistant is hacked or stolen, an unauthorized user will be unable to control your device and access your data.

Technology companies could also impose severe restrictions around the use and sharing of your company’s proprietary information. If an organization develops a digital assistant for both you and an industry competitor, it can keep your trade secrets private. Your knowledge base will be reserved for your business only. That means no other company can benefit from the questions your employees ask your digital assistant.

By taking the proper precautions with your device—and trusting that the enterprises developing them will do the same—you can rest easy that your company’s data and propriety information will remain safe.

Hesitate to adopt a digital assistant and you’re playing with fire

Two million years ago, our ancestors took a revolutionary step when they discovered how to control fire. But they also learned that if you play with fire, you’re bound to get burned.

Today, if you fear emerging technologies like conversational AI and hesitate to adopt a digital assistant in the workplace, you risk the painful sting of missed opportunities.

Interested in learning more about conversational AI and how digital assistants can empower your enterprise to thrive? Join Juergen Mueller’s strategy talk at SAP TechEd in Las Vegas, September 25-29, or sign up to attend an upcoming SAP TechEd event in Bangalore on October 25-27, or Barcelona on November 14–16 to hear from inspiring industry thought leaders and see innovative technology solutions in action.


AI, Blockchain, And Cloud Fuel Banking’s Evolution

John Bertrand

Artificial intelligence (AI), blockchain, and cloud technologies are increasingly appearing on the horizon. This could be exactly what the banker ordered, given the legal mandates for open banking and General Data Protection Regulation for 2018. These three key technologies can fuel the financial services industry’s evolution into the digital age.

Artificial intelligence

AI is a collection of machine learning, natural language processing, and cognitive computing designed for scale. It is this scalability that is exciting, as it can create exponential growth and deliver today’s required personalized communications. For example, in July 2017 UK payments processed 21 million payments per business day. If 0.5% of the daily volume needed additional review, 105,000 items would need to be checked, often manually and with rules-based, tick-the-box solutions. AI would significantly increase productivity by matching payment behavior and pattern recognition, and simply asking the question, “does this look right?” AI-powered chatbots could help business users and consumers answer inquiries and enhance the customer experience.


Blockchain is also a mix of technologies that enables us to trust someone we do not know and protects us from cybercriminals. The block contains vital information about a party, and the chain is the sequence of third-party, verified events that have taken place over the history of the transaction. Blockchain is fully encrypted and can be permissioned for private and public groups. Given the manual, paper-based state of the supply chain, it is not surprising that we’re seeing many new proofs of concepts and pilots using blockchain.


Cloud computing gives improved security, scale, and agility to respond to market demands and can decrease banks’ cost bases. The advances in cloud technologies permit software applications to move seamlessly between legacy, private cloud, and public cloud solutions. One such technology, containers, allows the applications to flow safely across the end-to-end processes regardless of the underlying technologies, much like how shipping containers transformed the inefficient, non-scalable 20th century transportation industry to the one today.

Finance’s digital evolution

These technologies are could be the savior of financial services industry. Financial services are rapidly becoming a technology-driven sector, evidenced by the increasing amount of money being spent in this area.

  • Financial services is now one of the largest buyers of software
  • IDC expects this figure to grow more than five percent over 2016’s spending
  • The forecast of $2.7 trillion in worldwide IT spending by 2020 is led by the financial services industry

Legacy banks and financial services firms can either build the technology themselves or work with fintechs to do so; either way it has to be done. Eminent evolutionary biologist Charles Darwin could have been discussing this new banking environment when he noted:

It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change. 

Potential impacts of financial services’ digital evolution include:

  • Low-cost centers using AI to increase straight-through processing (STP) to 100%, thus removing cost, increasing customer satisfaction, and reducing liabilities from errors
  • Administration of trade finance through blockchain to reduce costs and increase certainty of ownership at any point in time
  • Spare computer capacity created by using the cloud, enabling banks to meet peak-day requirements and increase cybersecurity

Security is now a bottom-line concern. See The Future of Cybersecurity: Trust as Competitive Advantage.

This article was originally published on Finextra.


Diving Deep Into Digital Experiences

Kai Goerlich


Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.

What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”

Download the executive brief Diving Deep Into Digital Experiences.

Read the full article Swimming in the Immersive Digital Experience.


Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu


Jenny Dearborn: Soft Skills Will Be Essential for Future Careers

Jenny Dearborn

The Japanese culture has always shown a special reverence for its elderly. That’s why, in 1963, the government began a tradition of giving a silver dish, called a sakazuki, to each citizen who reached the age of 100 by Keiro no Hi (Respect for the Elders Day), which is celebrated on the third Monday of each September.

That first year, there were 153 recipients, according to The Japan Times. By 2016, the number had swelled to more than 65,000, and the dishes cost the already cash-strapped government more than US$2 million, Business Insider reports. Despite the country’s continued devotion to its seniors, the article continues, the government felt obliged to downgrade the finish of the dishes to silver plating to save money.

What tends to get lost in discussions about automation taking over jobs and Millennials taking over the workplace is the impact of increased longevity. In the future, people will need to be in the workforce much longer than they are today. Half of the people born in Japan today, for example, are predicted to live to 107, making their ancestors seem fragile, according to Lynda Gratton and Andrew Scott, professors at the London Business School and authors of The 100-Year Life: Living and Working in an Age of Longevity.

The End of the Three-Stage Career

Assuming that advances in healthcare continue, future generations in wealthier societies could be looking at careers lasting 65 or more years, rather than at the roughly 40 years for today’s 70-year-olds, write Gratton and Scott. The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

It will be replaced by a new model in which people continually learn new skills and shed old ones. Consider that today’s most in-demand occupations and specialties did not exist 10 years ago, according to The Future of Jobs, a report from the World Economic Forum.

And the pace of change is only going to accelerate. Sixty-five percent of children entering primary school today will ultimately end up working in jobs that don’t yet exist, the report notes.

Our current educational systems are not equipped to cope with this degree of change. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is outdated by the time students graduate, the report continues.

Skills That Transcend the Job Market

Instead of treating post-secondary education as a jumping-off point for a specific career path, we may see a switch to a shorter school career that focuses more on skills that transcend a constantly shifting job market. Today, some of these skills, such as complex problem solving and critical thinking, are taught mostly in the context of broader disciplines, such as math or the humanities.

Other competencies that will become critically important in the future are currently treated as if they come naturally or over time with maturity or experience. We receive little, if any, formal training, for example, in creativity and innovation, empathy, emotional intelligence, cross-cultural awareness, persuasion, active listening, and acceptance of change. (No wonder the self-help marketplace continues to thrive!)

The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

These skills, which today are heaped together under the dismissive “soft” rubric, are going to harden up to become indispensable. They will become more important, thanks to artificial intelligence and machine learning, which will usher in an era of infinite information, rendering the concept of an expert in most of today’s job disciplines a quaint relic. As our ability to know more than those around us decreases, our need to be able to collaborate well (with both humans and machines) will help define our success in the future.

Individuals and organizations alike will have to learn how to become more flexible and ready to give up set-in-stone ideas about how businesses and careers are supposed to operate. Given the rapid advances in knowledge and attendant skills that the future will bring, we must be willing to say, repeatedly, that whatever we’ve learned to that point doesn’t apply anymore.

Careers will become more like life itself: a series of unpredictable, fluid experiences rather than a tightly scripted narrative. We need to think about the way forward and be more willing to accept change at the individual and organizational levels.

Rethink Employee Training

One way that organizations can help employees manage this shift is by rethinking training. Today, overworked and overwhelmed employees devote just 1% of their workweek to learning, according to a study by consultancy Bersin by Deloitte. Meanwhile, top business leaders such as Bill Gates and Nike founder Phil Knight spend about five hours a week reading, thinking, and experimenting, according to an article in Inc. magazine.

If organizations are to avoid high turnover costs in a world where the need for new skills is shifting constantly, they must give employees more time for learning and make training courses more relevant to the future needs of organizations and individuals, not just to their current needs.

The amount of learning required will vary by role. That’s why at SAP we’re creating learning personas for specific roles in the company and determining how many hours will be required for each. We’re also dividing up training hours into distinct topics:

  • Law: 10%. This is training required by law, such as training to prevent sexual harassment in the workplace.

  • Company: 20%. Company training includes internal policies and systems.

  • Business: 30%. Employees learn skills required for their current roles in their business units.

  • Future: 40%. This is internal, external, and employee-driven training to close critical skill gaps for jobs of the future.

In the future, we will always need to learn, grow, read, seek out knowledge and truth, and better ourselves with new skills. With the support of employers and educators, we will transform our hardwired fear of change into excitement for change.

We must be able to say to ourselves, “I’m excited to learn something new that I never thought I could do or that never seemed possible before.” D!