What Is Artificial Intelligence Called?!

Timo Elliott

I know it’s a strange question.

But as with any hot topic, there are many different terms thrown around that can mean slightly different things and lead to confusion.

Here is my attempt to clear things up.

The field of artificial intelligence (AI) research dates back to 1956, but the term has never referred to a specific type of technology. Rather, it’s a sociotechnical construct that refers to machine capabilities that can solve complex tasks that were until recently only possible by humans.

So AI is an umbrella term that encompasses a lot of different technologies. Most of the current interest in AI involves machine learning – algorithms that can learn on their own from data sets without having to be explicitly programmed.

The power of machine learning has leapt exponentially because of the increased availability of data, the plummeting cost of information storage, and the ability to access massive calculation power using cloud computing and more powerful processors.

Machine learning includes automated forms of the types of statistical algorithms you may have learned in school. These have long been used inside organizations, and have typically been referred to as data science, data mining, predictive analytics, or advanced analytics.

But because these terms have been in use for a long time, they are typically avoided by analysts and vendors trying to emphasize what is different about today’s AI opportunities.

 

One area where we’ve seen the most progress recently is in deep learning. It’s a specific type of machine learning in which several layers of neural networks mimic the way our own brains work. It’s the key technology behind recent breakthroughs in image tagging, voice transcription, automatic translation, and more.

Cognitive computing is another term that is typically used for the most sophisticated types of AI technology that try to mimic human reasoning. But its precise meaning isn’t clear, and many analysts and vendors avoid the term because it is so strongly associated with IBM.

The market seems to have settled into the following rule of thumb: If you’re talking about the business, consumer, or personal impact of these new technologies, you use the term artificial intelligence. But if you’re talking about the details of the technology to be implemented, you use the term machine learning.

Either way, the precise definitions should never get in the way of the primary goal of getting more value from your data. You have business needs, and there are technologies available now that can help. Whenever somebody tries to suck you into a nomenclature war and insists that they have the only “true” definition of one of these terms, try to bring the subject back to the concrete situation you are working on.

For more on how data can drive business strategy, see Why Machine Learning and Why Now?

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About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in articles such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

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

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

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.

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Robots Are Moving Into Our Human Resources Functions

Agnes Desplechin

“When we react it will be too late,” said Elon Musk, CEO of Tesla Motors (a pioneer in the connected car market), in July at the U.S. National Governors Association Summer Meeting. The businessman expressed his concern about the development of artificial intelligence and the delay in terms of regulation that would represent “a fundamental risk to the existence of human civilization.”

Today, the thought that humans can (in some activities) be substituted by robots no longer belongs solely to fictional works such as Frankenstein (1818) or current television shows Black Mirror and Westworld.

Concerns about potential failures caused by robots are very real and present today. Even before the most advanced prototypes of robots and the possibilities offered by artificial intelligence were considered, economist John Maynard Keynes prophesied the substitution of man by machines. In “Economic Possibilities for Our Grandchildren,” published in 1930, he questioned the effects of automation on jobs, well-being, and happiness, seeking to find solutions to the issue of “technological unemployment.”

A century later, the replacement of human workers by robots is anticipated across the job spectrum. According to Laurent Alexandre, technocrat, urological surgeon, and artificial intelligence (AI) advocate, all professions will, in the near future, be threatened by AI, which will soon be everywhere. Indeed, AI it is already in your pocket; Siri and Google Assistant are early chatbots, conversational robots that will replace salesmen, attorneys, journalists, and eventually, human resource assistants.

To better understand the stakes, we must understand what AI is. Consider a machine without AI, which makes decisions based on manually defined rules. When a machine facing a large data flow learns to analyze and make decisions, intelligence is born; this is machine learning. If you’re still confused about machine learning based on this description, let’s take the example of email that you define manually as “spam” within your mailbox. Once it learns the form, structure, sender, and other details that led you to mark a message as spam (i.e., the rules you defined, even subconsciously), the machine can make the decision that a message is spam. Unlike human intelligence, the machine can be caught off guard when there are exceptions.

How AI develops is of great interest in the context of HR functions. Some examples include using automated and intelligent filters for recruitment, using robots for interviews, or having chatbots act as human resource assistants in order to answer recurring questions from employees.

AI’s contribution is often measured in terms of time and cost savings, but it can also lead to more impartiality and efficiency. Even so, the human aspects and ethics must remain the core part of the HR role. As Elon Musk suggests, we must now ensure AI retains our standards, and, crucially for the HR profession, keeps the “human” in human resources.

Will intelligent machines and HR one day walk hand in hand? AI offers prospects that are very promising and prompt many questions that will shape the evolution of our profession.

AI’s ability to end bias hinges on teaching it to play fair and constantly questioning the results.
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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.

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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

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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!

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