The Enterprise Gets Smart
Companies are starting to leverage artificial intelligence and machine learning technologies to bolster customer experience, improve security and optimize operations.

Excerpt:

Assembling the right talent is another critical component of an AI initiative. While existing enterprise software platforms that add AI capabilities will make the technology accessible to mainstream business users, there will be a need to ramp up expertise in areas like data science, analytics and even nontraditional IT competencies, says Guarini.

“As we start to see the land grab for talent, there are some real gaps in emerging roles, and those that haven’t been as critical in the past,” Guarini  says, citing the need for people with expertise in disciplines like philosophy and linguistics, for example. “CIOs need to get in front of what they need in terms of capabilities and, in some cases, identify potential partners.”

 

 

 

Asilomar AI Principles

These principles were developed in conjunction with the 2017 Asilomar conference (videos here), through the process described here.

 

Artificial intelligence has already provided beneficial tools that are used every day by people around the world. Its continued development, guided by the following principles, will offer amazing opportunities to help and empower people in the decades and centuries ahead.

Research Issues

 

1) Research Goal: The goal of AI research should be to create not undirected intelligence, but beneficial intelligence.

2) Research Funding: Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:

  • How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked?
  • How can we grow our prosperity through automation while maintaining people’s resources and purpose?
  • How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI?
  • What set of values should AI be aligned with, and what legal and ethical status should it have?

3) Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.

4) Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI.

5) Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.

Ethics and Values

 

6) Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.

7) Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.

8) Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.

9) Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.

10) Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.

11) Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.

12) Personal Privacy: People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilize that data.

13) Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.

14) Shared Benefit: AI technologies should benefit and empower as many people as possible.

15) Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.

16) Human Control: Humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives.

17) Non-subversion: The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.

18) AI Arms Race: An arms race in lethal autonomous weapons should be avoided.

Longer-term Issues

 

19) Capability Caution: There being no consensus, we should avoid strong assumptions regarding upper limits on future AI capabilities.

20) Importance: Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources.

21) Risks: Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.

22) Recursive Self-Improvement: AI systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing quality or quantity must be subject to strict safety and control measures.

23) Common Good: Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organization.

 

 

 

Excerpts:
Creating human-level AI: Will it happen, and if so, when and how? What key remaining obstacles can be identified? How can we make future AI systems more robust than today’s, so that they do what we want without crashing, malfunctioning or getting hacked?

  • Talks:
    • Demis Hassabis (DeepMind)
    • Ray Kurzweil (Google) (video)
    • Yann LeCun (Facebook/NYU) (pdf) (video)
  • Panel with Anca Dragan (Berkeley), Demis Hassabis (DeepMind), Guru Banavar (IBM), Oren Etzioni (Allen Institute), Tom Gruber (Apple), Jürgen Schmidhuber (Swiss AI Lab), Yann LeCun (Facebook/NYU), Yoshua Bengio (Montreal) (video)
  • Superintelligence: Science or fiction? If human level general AI is developed, then what are likely outcomes? What can we do now to maximize the probability of a positive outcome? (video)
    • Talks:
      • Shane Legg (DeepMind)
      • Nick Bostrom (Oxford) (pdf) (video)
      • Jaan Tallinn (CSER/FLI) (pdf) (video)
    • Panel with Bart Selman (Cornell), David Chalmers (NYU), Elon Musk (Tesla, SpaceX), Jaan Tallinn (CSER/FLI), Nick Bostrom (FHI), Ray Kurzweil (Google), Stuart Russell (Berkeley), Sam Harris, Demis Hassabis (DeepMind): If we succeed in building human-level AGI, then what are likely outcomes? What would we like to happen?
    • Panel with Dario Amodei (OpenAI), Nate Soares (MIRI), Shane Legg (DeepMind), Richard Mallah (FLI), Stefano Ermon (Stanford), Viktoriya Krakovna (DeepMind/FLI): Technical research agenda: What can we do now to maximize the chances of a good outcome? (video)
  • Law, policy & ethics: How can we update legal systems, international treaties and algorithms to be more fair, ethical and efficient and to keep pace with AI?
    • Talks:
      • Matt Scherer (pdf) (video)
      • Heather Roff-Perkins (Oxford)
    • Panel with Martin Rees (CSER/Cambridge), Heather Roff-Perkins, Jason Matheny (IARPA), Steve Goose (HRW), Irakli Beridze (UNICRI), Rao Kambhampati (AAAI, ASU), Anthony Romero (ACLU): Policy & Governance (video)
    • Panel with Kate Crawford (Microsoft/MIT), Matt Scherer, Ryan Calo (U. Washington), Kent Walker (Google), Sam Altman (OpenAI): AI & Law (video)
    • Panel with Kay Firth-Butterfield (IEEE, Austin-AI), Wendell Wallach (Yale), Francesca Rossi (IBM/Padova), Huw Price (Cambridge, CFI), Margaret Boden (Sussex): AI & Ethics (video)

 

 

 

From DSC:
The following questions came to my mind today:

  • What are the future ramifications — for higher education — of an exponential population growth curve, especially in regards to providing access?
  • Are our current ways of providing an education going to hold up?
  • What about if the cost of obtaining a degree maintains its current trajectory?
  • What changes do we need to start planning for and/or begin making now?

 

 

 

 

 

Links to sources:

 

 

The 2017 Top 10 IT Issues — a new report from Educause
It’s all about student success.

Excerpt:

Colleges and universities are concentrating on student success to address concerns about the costs, value, and outcomes of higher education. Student success initiatives are making use of every available resource and opportunity and involving every relevant stakeholder. Institutional technology is all three: resource, opportunity, and stakeholder.

The 2017 issues list identifies the four focus areas for higher education information technology:

  • Develop the IT foundations
  • Develop the data foundations
  • Ensure effective leadership
  • Enable successful students

These issues and focus areas are not just about today. Higher education information technology is very clearly building foundations for student success to last into the future.

 

 

 


 

Also see:

Educause Announces Top IT Issues, Trends and Tech Report for 2017 — from campustechnology.com by Dian Schaffhauser

Excerpt:

Expanding on the preview of its annual ranking of IT issues for higher education released last fall, Educause today announced its full report on the key issues, trends and technologies poised to impact higher ed in 2017. The prevailing themes across the board, according to the higher education technology association with a membership of 2,100 colleges, universities and other education organizations: information security, student success and data-informed decision-making.

The top 10 IT issues for 2017, reiterated in today’s report:

  1. Information security;
  2. Student success and completion;
  3. Data-informed decision-making;
  4. Strategic leadership;
  5. Sustainable funding;
  6. Data management and governance;
  7. Higher education affordability;
  8. Sustainable staffing;
  9. Next-generation enterprise IT; and
  10. Digital transformation of learning.

 

 

 

How will leadership change in the cognitive era? — from forbes.com by Chris Cancialosi

Excerpt:

Technological innovation is continuing to accelerate on a hockey stick growth curve. Companies like IBM, Microsoft, Facebook, and Amazon are bringing cognitive computing capability to the masses. And it’s only a matter of time until nearly every aspect of our work and personal lives are impacted.

These advances are still relatively new. Time will tell when and how they change things, but it will happen, and it will happen quickly. In a recent article, Steve Denning reminds us that a repeating pattern of massive transformation has occurred regularly over the last 250 years.

With massive change at our doorstep, now is the time to begin a collective discussion to help leaders navigate this new age.

 

Leadership behaviors that yielded success in the past may no longer be effective as the way we work changes over time.

 

 

From DSC:
First, some items regarding the enormous emphasis being put towards the use of robotics and automation:

  • $18.867 billion paid to acquire 50 robotics companies in 2016 — from robohub.org by Frank Tobe
    Excerpt:
    2016 was a banner year for acquisitions of companies involved in robotics and automation: 50 sold; 11 for amounts over $500 million; five were over a billion. 30 of the 50 companies disclosed transaction amounts which totaled up to a colossal $18.867 billion!
    .
  • 2017: The year people are forced to learn new skills… or join the Lost Generation — from enterpriseirregulars.com by Phil Fersht
    Excerpt (emphasis DSC):
    Let’s cut to the chase – there have never been times as uncertain as these in the world of business. There is no written rule-book to follow when it comes to career survival. The “Future of Work” is about making ourselves employable in a workforce where the priority of business leaders is to invest in automation and digital technology, more than training and developing their own workforces. As our soon-to-be-released State of Operations and Outsourcing 2017 study, conducted in conjunction with KPMG across 454 major enterprise buyers globally, shows a dramatic shift in priorities from senior managers (SVPs and above), where 43% are earmarking significant investment in robotic automation of processes, compared with only 28% placing a similar emphasis on training and change management. In fact, the same number of senior managers are as focused on cognitive computing as their own people… yes, folks, this is the singularity of enterprise operations, where cognitive computing now equals employees’ brains when it comes to investment!

    My deep-seated fear for today’s workforce is that we’re in danger of becoming this “Lost Generation” of workers if we persist in relying on what we already know, versus avoiding learning new skills that business leaders now need. We have to become students again, put our egos aside, and broaden our capabilities to avoid the quicksand of legacy executives no longer worth employing.

 

 

 

Below are some other resources along these lines:

 

From DSC:
Given that these trends continue (i.e., to outsource work to software and to robots), what will the ramifications be for:

  • Society at large? Will enough people have enough income to purchase the products/services made by the robots and the software?
  • Will there be major civil unrest / instability? Will crime rates shoot through the roof as peoples’ desperation and frustration escalate?
  • How we should change our curricula within K-12?
  • How should we change our curricular within higher education?
  • How should corporate training & development departments/groups respond to these trends?
  • Is there some new criteria that we need to use (or increase the usage of) in selecting C-level executives?

People don’t want to hear about it. But if the only thing that the C-level suites out there care about is maximizing profits and minimizing costs — REGARDLESS of what happens to humankind — then we are likely going to be creating a very dangerous future. Capitalism will have gone awry. (By the way, the C-level suite is probably making their decisions based upon how their performance is judged by Wall Street and by shareholders. So I can’t really put all the blame on them. Perhaps the enemy is ourselves…?) 

Bottom line: We need to be careful which technologies we implement — and how they are implemented. We need to create a dream in our futures, not a nightmare. We need people at the helms who care about their fellow humankind, and who use the power of these technologies responsibly.

 

 

5 Online Education Trends to Watch in 2017 — from usnews.com by Jordan Friedman
Experts predict more online programs will offer alternative credentials and degrees in specialized fields.

Excerpts:

  1. Greater emphasis on nontraditional credentials
  2. Increased use of big data to measure student performance
  3. Greater incorporation of artificial intelligence into classes
  4. Growth of nonprofit online programs
  5. Online degrees in surprising and specialized disciplines

 

 

The Future of Online Learning Is Offline: What Strava Can Teach Digital Course Designers — from edsurge.com by Amy Ahearn

Excerpt:

I became a Strava user in 2013, around the same time I became an online course designer. Quickly I found that even as I logged runs on Strava daily, I struggled to find the time to log into platforms like Coursera, Udemy or Udacity to finish courses produced by my fellow instructional designers. What was happening? Why was the fitness app so “sticky” as opposed to the online learning platforms?

As a thought experiment, I tried to recast my Strava experience in pedagogical terms. I realized that I was recording hours of deliberate practice (my early morning runs), formative assessments (the occasional speed workout on the track) and even a few summative assessments (races) on the app. Strava was motivating my consistent use by overlaying a digital grid on my existing offline activities. It let me reconnect with college teammates who could keep me motivated. It enabled me to analyze the results of my efforts and compare them to others. I didn’t have to be trapped behind a computer to benefit from this form of digital engagement—yet it was giving me personalized feedback and results. How could we apply the same practices to learning?

I’ve come to believe that one of the biggest misunderstandings about online learning is that it has to be limited to things that can be done in front of a computer screen. Instead, we need to reimagine online courses as something that can enable the interplay between offline activities and digital augmentation.

A few companies are heading that way. Edthena enables teachers to record videos of themselves teaching and then upload these to the platform to get feedback from mentors.

 

 

DIY’s JAM online courses let kids complete hands-on activities like drawing or building with LEGOs and then has them upload pictures of their work to earn badges and share their projects.

 

 

My team at +Acumen has built online courses that let teams complete projects together offline and then upload their prototypes to the NovoEd platform to receive feedback from peers. University campuses are integrating Kaltura into their LMS platforms to enable students to capture and upload videos.

 

 

We need to focus less on building multiple choice quizzes or slick lecture videos and more on finding ways to robustly capture evidence of offline learning that can be validated and critiqued at scale by peers and experts online.

 

 

 

 

 

 

If you doubt that we are on an exponential pace of change, you need to check these articles out! [Christian]

exponentialpaceofchange-danielchristiansep2016

 

From DSC:
The articles listed in
this PDF document demonstrate the exponential pace of technological change that many nations across the globe are currently experiencing and will likely be experiencing for the foreseeable future. As we are no longer on a linear trajectory, we need to consider what this new trajectory means for how we:

  • Educate and prepare our youth in K-12
  • Educate and prepare our young men and women studying within higher education
  • Restructure/re-envision our corporate training/L&D departments
  • Equip our freelancers and others to find work
  • Help people in the workforce remain relevant/marketable/properly skilled
  • Encourage and better enable lifelong learning
  • Attempt to keep up w/ this pace of change — legally, ethically, morally, and psychologically

 

PDF file here

 

One thought that comes to mind…when we’re moving this fast, we need to be looking upwards and outwards into the horizons — constantly pulse-checking the landscapes. We can’t be looking down or be so buried in our current positions/tasks that we aren’t noticing the changes that are happening around us.

 

 

 

Tech breakthroughs megatrend— from pwc.com by Vicki Huff Eckert, Sahil Bhardwaj, and Chris Curran; with thanks to Woontack Woo for this resource 

Excerpt:

Given the sheer pace and acceleration of technological advances in recent years, business leaders can be forgiven for feeling dazed and perhaps a little frustrated. When we talked to CEOs as part of our annual Global CEO Survey, 61% of them told us they were concerned about the speed of technological change in their industries. Sure, more and more C-suite executives are genuinely tech-savvy – increasingly effective champions for their companies’ IT vision – and more and more of them know that digital disruption can be friend as well as enemy. But it’s fair to say that most struggle to find the time and energy necessary to keep up with the technologies driving transformation across every industry and in every part of the world.

Not one catalyst, but several
History is littered with companies that have waited out the Next New Thing in the belief that it’s a technology trend that won’t amount to much, or that won’t affect their industries for decades. Yet disruption happens. It’s safe to say that the history of humankind is a history of disruption – a stream of innovations that have tipped the balance in favour of the innovators. In that sense, technological breakthroughs are the original megatrend. What’s unique in the 21st century, though, is the ubiquity of technology, together with its accessibility, reach, depth, and impact.

Business leaders worldwide acknowledge these changes, and have a clear sense of their significance. CEOs don’t single out any particular catalyst that leads them to that conclusion. But we maintain that technological advancements are appearing, rapidly and simultaneously, in fields as disparate as healthcare and industrial manufacturing, because of the following concurrent factors…

 

pwc-global-megatrends-july2016

 

From DSC:
For those of us working in K-20 as well as in the corporate training/L&D space, how are we doing in getting people trained and ready to deal these developments?

 

 

 

 

DanielChristian-NeedForMoreTrimTabGroupsHE-July2016

 

The need for more “Trim Tab Groups” in higher education — from evolllution.com by Daniel Christian

Excerpt:

So to apply this concept to the world of higher education: what higher education institutions need to develop these days are those smaller, nimbler groups that can innovate and experiment with a variety of things. Those smaller groups can then hand over to the larger organization—or to a brand new branch of the existing organization—what is successful and is showing promise. Then the smaller, nimbler group can move onto something else.

By forming Trim Tab Groups throughout higher education, we gain the capacity to experiment with relatively small projects that will ultimately have much larger impacts on the institutions and the learners that those institutions serve.

 

trim-tab

 

 

As such, many segments of higher education must adapt and change—or risk servicing far fewer learners over the next two to three decades, as they watch their customers head elsewhere. And then it’s a costly game of musical chairs for faculty and staff, as the larger organizations downsize.

— Daniel Christian

 

 

 

 

Accenture-TechVision2016

 

Example slides from their
SlideShare presentation:

 

Accenture-TechVision2016-2

Accenture-TechVision2016-3

Accenture-TechVision2016-4

Accenture-TechVision2016-5-Abilityto-learn

and from the PDF:

Accenture-TechVision2016-6-PaceOfChange

 

accenture: Technology Vision 2016 | People First: The Remedy to Digital Culture Shock — from accenture.com

Excerpt:

Winners in the digital age do much more than complete a technology checklist. They know their success hinges on people. Understanding changing customer needs and behaviors is, of course, hugely important. But the real deciding factor in the digital era will be the ability to evolve corporate culture. That means not simply taking advantage of emerging technologies but, critically, embracing the new business strategies that those technologies drive.

You can’t solve this challenge just by consuming more and more technology. Nor, as some fear, by replacing humans with machines. Instead, enterprises must focus on enabling people – consumers, employees and ecosystem partners – to do more with technology. That demands a digital corporate culture enabling people to continuously adapt, learn, create new solutions, drive relentless change, and disrupt the status quo. In an age where tech is grabbing the limelight, true leaders will, in fact, put people first.

 

 

But the real deciding factor in the era of intelligence will be a company’s ability to evolve its corporate culture to not only take advantage of emerging technologies, but also, critically, embrace the new business strategies that those technologies drive.

 

 

From DSC:
Are we preparing our students to be ready for — and successful in — this changing workplace?  Are adults ready for this changing workplace? It appears that some are, and some are left reeling by the pace of change.

What is our role as educators in K-12? In higher ed?

What are the roles of trainers and/or mentors in the marketplace?

How does one help another person to learn quickly?

 

 

 

 

——–

Addendum:

 
© 2016 Learning Ecosystems