From DSC:
I know Quentin Schultze from our years working together at Calvin College, in Grand Rapids, Michigan (USA). I have come to greatly appreciate Quin as a person of faith, as an innovative/entrepreneurial professor, as a mentor to his former students, and as an excellent communicator. 

Quin has written a very concise, wisdom-packed book that I would like to recommend to those people who are seeking to be better communicators, leaders, and servants. But I would especially like to recommend this book to the leadership at Google, Amazon, Apple, Microsoft, IBM, Facebook, Nvidia, the major companies developing robots, and other high-tech companies. Why do I list these organizations? Because given the exponential pace of technological change, these organizations — and their leaders — have an enormous responsibility to make sure that the technologies that they are developing result in positive changes for societies throughout the globe. They need wisdom, especially as they are working on emerging technologies such as Artificial Intelligence (AI), personal assistants and bots, algorithms, robotics, the Internet of Things, big data, blockchain and more. These technologies continue to exert an increasingly powerful influence on numerous societies throughout the globe today. And we haven’t seen anything yet! Just because we can develop and implement something, doesn’t mean that we should. Again, we need wisdom here.

But as Quin states, it’s not just about knowledge, the mind and our thoughts. It’s about our hearts as well. That is, we need leaders who care about others, who can listen well to others, who can serve others well while avoiding gimmicks, embracing diversity, building trust, fostering compromise and developing/exhibiting many of the other qualities that Quin writes about in his book. Our societies desperately need leaders who care about others and who seek to serve others well.

I highly recommend you pick up a copy of Quin’s book. There are few people who can communicate as much in as few words as Quin can. In fact, I wish that more writing on the web and more articles/research coming out of academia would be as concisely and powerfully written as Quin’s book, Communicate Like a True Leader: 30 Days of Life-Changing Wisdom.

 

 

To lead is to accept responsibility and act responsibly.
Quentin Schultze

 

 

 

100 Data and Analytics Predictions Through 2021 — from Gartner

From DSC:
I just wanted to include some excerpts (see below) from Gartner’s 100 Data and Analytics Predictions Through 2021 report. I do so to illustrate how technology’s impact continues to expand/grow in influence throughout many societies around the globe, as well as to say that if you want a sure thing job in the next 1-15 years, I would go into studying data science and/or artificial intelligence!

 



Excerpts:

As evidenced by its pervasiveness within our vast array of recently published Predicts 2017 research, it is clear that data and analytics are increasingly critical elements across most industries, business functions and IT disciplines. Most significantly, data and analytics are key to a successful digital business. This collection of more than 100 data-and-analytics-related Strategic Planning Assumptions (SPAs) or predictions through 2021, heralds several transformations and challenges ahead that CIOs and data and analytics leaders should embrace and include in their planning for successful strategies. Common themes across the discipline in general, and within particular business functions and industries, include:

  • Artificial intelligence (AI) is emerging as a core business and analytic competency. Beyond yesteryear’s hard-coded algorithms and manual data science activities, machine learning (ML) promises to transform business processes, reconfigure workforces, optimize infrastructure behavior and blend industries through rapidly improved decision making and process optimization.
  • Natural language is beginning to play a dual role in many organizations and applications as a source of input for analytic and other applications, and a variety of output, in addition to traditional analytic visualizations.
  • Information itself is being recognized as a corporate asset (albeit not yet a balance sheet asset), prompting organizations to become more disciplined about monetizing, managing and measuring it as they do with other assets. This includes “spending” it like cash, selling/licensing it to others, participating in emerging data marketplaces, applying asset management principles to improve its quality and availability, and quantifying its value and risks in a variety of ways.
  • Smart devices that both produce and consume Internet of Things (IoT) data will also move intelligent computing to the edge of business functions, enabling devices in almost every industry to operate and interact with humans and each other without a centralized command and control. The resulting opportunities for innovation are unbounded.
  • Trust becomes the watchword for businesses, devices and information, leading to the creation of digital ethics frameworks, accreditation and assessments. Most attempts at leveraging blockchain as a trust mechanism fail until technical limitations, particularly performance, are solved.

Education
Significant changes to the global education landscape have taken shape in 2016, and spotlight new and interesting trends for 2017 and beyond. “Predicts 2017: Education Gets Personal” is focused on several SPAs, each uniquely contributing to the foundation needed to create the digitalized education environments of the future. Organizations and institutions will require new strategies to leverage existing and new technologies to maximize benefits to the organization in fresh and
innovative ways.

  • By 2021, more than 30% of institutions will be forced to execute on a personalization strategy to maintain student enrollment.
  • By 2021, the top 100 higher education institutions will have to adopt AI technologies to stay competitive in research.

Artificial Intelligence
Business and IT leaders are stepping up to a broad range of opportunities enabled by AI, including autonomous vehicles, smart vision systems, virtual customer assistants, smart (personal) agents and natural-language processing. Gartner believes that this new general-purpose technology is just beginning a 75-year technology cycle that will have far-reaching implications for every industry. In “Predicts 2017: Artificial Intelligence,” we reflect on the near-term opportunities, and the potential burdens and risks that organizations face in exploiting AI. AI is changing the way in which organizations innovate and communicate their processes, products and services.

Practical strategies for employing AI and choosing the right vendors are available to data and analytics leaders right now.

  • By 2019, more than 10% of IT hires in customer service will mostly write scripts for bot interactions.
  • Through 2020, organizations using cognitive ergonomics and system design in new AI projects will achieve long-term success four times more often than others.
  • By 2020, 20% of companies will dedicate workers to monitor and guide neural networks.
  • By 2019, startups will overtake Amazon, Google, IBM and Microsoft in driving the AI economy with disruptive business solutions.
  • By 2019, AI platform services will cannibalize revenues for 30% of market-leading companies. “Predicts 2017: Drones”
  • By 2020, the top seven commercial drone manufacturers will all offer analytical software packages.
    “Predicts 2017: The Reinvention of Buying Behavior in Vertical-Industry Markets”
  • By 2021, 30% of net new revenue growth from industry-specific solutions will include AI technology.

Advanced Analytics and Data Science
Advanced analytics and data science are fast becoming mainstream solutions and competencies in most organizations, even supplanting traditional BI and analytics resources and budgets. They allow more types of knowledge and insights to be extracted from data. To become and remain competitive, enterprises must seek to adopt advanced analytics, and adapt their business models, establish specialist data science teams and rethink their overall strategies to keep pace with the competition. “Predicts 2017: Analytics Strategy and Technology” offers advice on overall strategy, approach and operational transformation to algorithmic business that leadership needs to build to reap the benefits.

  • By 2018, deep learning (deep neural networks [DNNs]) will be a standard component in 80% of data scientists’ tool boxes.
  • By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.
  • By 2019, natural-language generation will be a standard feature of 90% of modern BI and analytics platforms.
  • By 2019, 50% of analytics queries will be generated using search, natural-language query or voice, or will be autogenerated.
  • By 2019, citizen data scientists will surpass data scientists in the amount of advanced analysis
    produced.

 

 

By 2020, 95% of video/image content will never be viewed by humans; instead, it will be vetted by machines that provide some degree of automated analysis.

 

 

Through 2020, lack of data science professionals will inhibit 75% of organizations from achieving the full potential of IoT.

 

 

 

 

Future Forward: The Next Twenty Years of Higher Education — from Blackboard with a variety of contributors

Excerpts:

As you read their reflections you’ll find several themes emerge over and over:

  • Our current system is unsustainable and ill-suited for a globally connected world that is constantly changing.
  • Colleges and universities will have to change their current business model to continue to thrive, boost revenue and drive enrollment.
  • The “sage on the stage” and the “doc in the box” aren’t sustainable; new technologies will allow faculty to shift their focus on the application of learning rather than the acquisition of knowledge.
  • Data and the ability to transform that data into action will be the new lifeblood of the institution.
  • Finally, the heart and soul of any institution are its people. Adopting new technologies is only a small piece of the puzzle; institutions must also work with faculty and staff to change institutional culture.

Some quotes are listed below.

 

“What’s more, next-generation digital learning environments must bridge the divide between the faculty-directed instructivist model our colleges and universities have always favored and the learner-centric constructivist paradigm their students have come to expect and the economy now demands.”

It will be at least 10 years before systems such as this become the standard rather than the exception. Yet to achieve this timeline, we will have to begin fostering a very different campus culture that embraces technology for its experiential value rather than its transactional expediency, while viewing education as a lifelong pursuit rather than a degree-driven activity.

Susan Aldridge

 

 

 

Q: What are the biggest challenges facing higher education right now?

A: I think it is a difficult time for decisionmakers to know how to move boldly forward. It’s almost funny, nobody’s doing five-year strategic plans anymore. We used to do ten-year plans, but now it’s “What’s our guiding set of principles and then let’s sort of generally go towards that.” I think it’s really hard to move an entire institution, to know how to keep it sustainable and serving your core student population. Trying to figure out how to keep moving forward is not as simple as it used to be when you hired faculty and they showed up in the classroom. It’s time for a whole new leadership model. I’m not sure what that is, but we have to start reimagining our organizations and our institutions and even our leadership.

Marie Cini

 

 

 

One of the things that is frustrating to me is the argument that online learning is just another modality. Online learning is much more than that. It’s arguably the most transformative development since the G.I. Bill and, before that, the establishment of land-grant universities. 

I don’t think we should underestimate the profound impact online education has had and will continue to have on higher education. It’s not just another modality; it’s an entirely new industry.

Robert Hansen

 

 

From DSC:
And I would add (to Robert’s quote above) that not since the printing press was invented close to 500 years ago have we seen such an enormously powerful invention as the Internet. To bypass the Internet and the online-based learning opportunities that it can deliver is to move into a risky, potentially dangerous future. If your institution is doing that, your institution’s days could be numbered. As we move into the future — where numerous societies throughout the globe will be full of artificial intelligence, big data, robotics, algorithms, business’ digital transformations, and more — your institutions’ credibility could easily be at stake in a new, increasingly impactful way. Parents and students will want to know that there’s a solid ROI for them. They will want to know that a particular college or university has the foundational/core competencies and skills to prepare the learner for the future that the learner will encounter.

 

 

 

Q: What are the biggest challenges facing higher education right now?

A: I think the biggest challenge is the stubborn refusal of institutions to acknowledge that the 20th century university paradigm no longer works, or at least it doesn’t work anymore for the majority of our institutions. I’m not speaking on behalf of our members, but I think it’s fair to say that institutions are still almost entirely faculty-centered and not market-driven. Faculty, like so many university leaders today who come from faculty ranks, are so often ill-equipped to compete in the Wild West that we’re seeing today, and it’s not their fault. They’re trained to be biologists and historians and philosophers and musicians and English professors, and in the past there was very little need to be entrepreneurial. What’s required of university leadership now looks very much like what’s required in the fastpaced world of private industry.

If you are tuition dependent and you haven’t figured out how to serve the adult market yet, you’re in trouble.

Robert Hansen

 

 

 

It’s not just enough to put something online for autodidacts who already have the time, energy, and prior skills to be able to learn on their own. You really need to figure out how to embed all the supports that a student will need to be successful, and I don’t know if we’ve cracked that yet.

Amy Laitinen

 

 

 

The other company is Amazon. Their recent purchase of Whole Foods really surprised everybody. Now you have a massive digital retailer that has made billions staying in the online world going backwards into brick-and-mortar. I think if you look at what you can do on Amazon now, who’s to say in three years or five years, you won’t say, “You know what, I want to take this class. I want to purchase it through Amazon,” and it’s done through Amazon with their own LMS? Who’s to say they’re not already working on it?

Justin Louder

 

 

 

 

We are focused on four at Laureate. Probably in an increasing order of excitement to me are game-based learning (or gamification), adaptive learning, augmented and virtual reality, and cognitive tutoring.

Darrell Luzzo

 

 

 

 

I would wave my hand and have people lose their fear of change and recognize that you can innovate and do new things and still stay true to the core mission and values. My hope is that we harness our collective energy to help our students succeed and become fully engaged citizens.

Felice Nudelman

 

 

 

 

 

Top Trends in the Gartner Hype Cycle for Emerging Technologies, 2017 — from gartner.com by Kasey Panetta
Enterprises should explain the business potential of blockchain, artificial intelligence and augmented reality.

Excerpt (emphasis DSC):

…emerging technologies such as machine learning, blockchain, drones (commercial UAVs), software-defined security and brain-computer interfaces have moved significantly along the Hype Cycle since 2016.

The Gartner Hype Cycle for Emerging Technologies, 2017 focuses on three emerging technology mega-trends: Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms. Enterprise architects and technology innovation leaders should explore and ideate these three mega-trends to understand the future impacts to their business.

“Organizations will continue to be faced with rapidly accelerating technology innovation that will profoundly impact the way they deal with their workforces, customers and partners,” says Mike J. Walker, research director. “Our 2017 Hype Cycle reveals three distinct technology trends that profoundly create new experiences with unrivaled intelligence, and offer platforms that propel organizations to connect with new business ecosystems in order to become competitive over the next five to 10 years.”

 

 

 

 

 

 

 

Why Natural Language Processing is the Future of Business Intelligence — from dzone.com by Gur Tirosh
Until now, we have been interacting with computers in a way that they understand, rather than us. We have learned their language. But now, they’re learning ours.

Excerpt:

Every time you ask Siri for directions, a complex chain of cutting-edge code is activated. It allows “her” to understand your question, find the information you’re looking for, and respond to you in a language that you understand. This has only become possible in the last few years. Until now, we have been interacting with computers in a way that they understand, rather than us. We have learned their language.

But now, they’re learning ours.

The technology underpinning this revolution in human-computer relations is Natural Language Processing (NLP). And it’s already transforming BI, in ways that go far beyond simply making the interface easier. Before long, business transforming, life changing information will be discovered merely by talking with a chatbot.

This future is not far away. In some ways, it’s already here.

What Is Natural Language Processing?
NLP, otherwise known as computational linguistics, is the combination of Machine Learning, AI, and linguistics that allows us to talk to machines as if they were human.

 

 

But NLP aims to eventually render GUIs — even UIs — obsolete, so that interacting with a machine is as easy as talking to a human.

 

 

 

 

 

2017 Ed Tech Trends: The Halfway Point — from campustechnology.com by Rhea Kelly
Four higher ed IT leaders weigh in on the current state of education technology and what’s ahead.

This article includes some perspectives shared from the following 4 IT leaders:

  • Susan Aldridge, Senior Vice President for Online Learning, Drexel University (PA); President, Drexel University Online
  • Daniel Christian, Adjunct Faculty Member, Calvin College
  • Marci Powell, CEO/President, Marci Powell & Associates; Chair Emerita and Past President, United States Distance Learning Association
  • Phil Ventimiglia, Chief Innovation Officer, Georgia State University

 

 

Also see:

 

 

 

From DSC:
Reviewing the article below made me think of 2 potential additions to the Learning & Development Groups/Departments out there:

  1. Help people build their own learning ecosystems
  2. Design, develop, and implement workbots for self-service

 



 

Chatbots Poised to Revolutionize HR — from by Pratibha Nanduri

Excerpt:

Self-service is becoming an increasingly popular trend where people want to perform their tasks without needing help or input from anyone else. The increasing popularity of this trend is mainly attributed to the increasing use of computers and mobile devices to electronically manage all kinds of tasks.

As employee tolerance for downtime reduces and preferences for mobility increases, the bureaucracy which exists in managing everyday HR related tasks in the workplace will also have to be replaced. A large number of companies have still not automated even their basic HR services such as handling inquiries about holidays and leaves. Employees in such organizations still have to send their query and then wait for HR to respond.

As the number of employees goes up in an organization, the time taken by HR managers to respond to mundane admin tasks also increases. This leaves very little time for the HR manager to focus on strategic HR initiatives.

Chatbots that are powered by AI and machine learning are increasingly being used to automate mundane and repetitive tasks. They can also be leveraged in HR to simulate intelligent SMS-based conversations between employees and HR team members to automate basic HR tasks.

 



 

 

 

The Business of Artificial Intelligence — from hbr.org by Erik Brynjolfsson & Andrew McAfee

Excerpts (emphasis DSC):

The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own.

Why is this such a big deal? Two reasons. First, we humans know more than we can tell: We can’t explain exactly how we’re able to do a lot of things — from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn’t automate many tasks. Now we can.

Second, ML systems are often excellent learners. They can achieve superhuman performance in a wide range of activities, including detecting fraud and diagnosing disease. Excellent digital learners are being deployed across the economy, and their impact will be profound.

In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination.

The machine learns from examples, rather than being explicitly programmed for a particular outcome.

 

Let’s start by exploring what AI is already doing and how quickly it is improving. The biggest advances have been in two broad areas: perception and cognition. …For instance, Aptonomy and Sanbot, makers respectively of drones and robots, are using improved vision systems to automate much of the work of security guards. 

 

 

Machine learning is driving changes at three levels: tasks and occupations, business processes, and business models. 

 

 

You may have noticed that Facebook and other apps now recognize many of your friends’ faces in posted photos and prompt you to tag them with their names.

 

 

 

AI is making it extremely easy for students to cheat — from wired.com by Pippa Biddle

Excerpt (emphasis DSC):

For years, students have turned to CliffsNotes for speedy reads of books, SparkNotes to whip up talking points for class discussions, and Wikipedia to pad their papers with historical tidbits. But today’s students have smarter tools at their disposal—namely, Wolfram|Alpha, a program that uses artificial intelligence to perfectly and untraceably solve equations. Wolfram|Alpha uses natural language processing technology, part of the AI family, to provide students with an academic shortcut that is faster than a tutor, more reliable than copying off of friends, and much easier than figuring out a solution yourself.

 

Use of Wolfram|Alpha is difficult to trace, and in the hands of ambitious students, its perfect solutions are having unexpected consequences.

 

 

 

 

Video: 4 FAQs about Watson as tutor — from er.educause.edu by Satya Nitta

Excerpt:

How is IBM using Watson’s intelligent tutoring system? So we are attempting to mimic the best practices of human tutoring. The gold standard will always remain one on one human to human tutoring. The whole idea here is an intelligent tutoring system as a computing system that works autonomously with learners, so there is no human intervention. It’s basically pretending to be the teacher itself and it’s working with the learner. What we’re attempting to do is we’re attempting to basically put conversational systems, systems that understand human conversation and dialogue, and we’re trying to build a system that, in a very natural way, interacts with people through conversation. The system basically has the ability to ask questions, to answer questions, to know who you are and where you are in your learning journey, what you’re struggling with, what you’re strong on and it will personalize its pedagogy to you.

There’s a natural language understanding system and a machine learning system that’s trying to figure out where you are in your learning journey and what the appropriate intervention is for you. The natural language system enables this interaction that’s very rich and conversation-based, where you can basically have a human-like conversation with it and, to a large extent, it will try to understand and to retrieve the right things for you. Again the most important thing is that we will set the expectations appropriately and we have appropriate exit criteria for when the system doesn’t actually understand what you’re trying to do.

 

 

 
© 2017 | Daniel Christian