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)

 

 

 

Blockchain: Letting students own their credentials — from campustechnology.com by Dian Schaffhauser
Very soon this nascent technology could securely enable registrars to help students verify credentials without the hassle of ordering copies of transcripts.

Excerpt:

While truth may seem evasive on many fronts, a joint academic and industry effort is underway to codify it for credentialing. At the core of the effort is blockchain, a trust technology developed for bitcoin and used in solving other forms of validation between individuals and organizations. Still in its nascent stage, the technology could, within just a year or two, provide the core services that would enable schools to stop acting as if they own proof of learning and help students verify their credentials as needed — without waiting on a records office to do it for them.

 

From DSC:
This article reminded me of two of the slides from my NGLS 2017 presentation back from February:

 

 

 

Also see:

 

 
 

From DSC:
Can you imagine this as a virtual reality or a mixed reality-based app!?! Very cool.

This resource is incredible on multiple levels:

  • For their interface/interaction design
  • For their insights and ideas
  • For their creativity
  • For their graphics
  • …and more!

 

 

 

 

 

 

 

 

 

 

A smorgasboard of ideas to put on your organization’s radar! [Christian]

From DSC:
At the Next Generation Learning Spaces Conference, held recently in San Diego, CA, I moderated a panel discussion re: AR, VR, and MR.  I started off our panel discussion with some introductory ideas and remarks — meant to make sure that numerous ideas were on the radars at attendees’ organizations. Then Vinay and Carrie did a super job of addressing several topics and questions (Mary was unable to make it that day, as she got stuck in the UK due to transportation-related issues).

That said, I didn’t get a chance to finish the second part of the presentation which I’ve listed below in both 4:3 and 16:9 formats.  So I made a recording of these ideas, and I’m relaying it to you in the hopes that it can help you and your organization.

 


Presentations/recordings:


 

Audio/video recording (187 MB MP4 file)

 

 


Again, I hope you find this information helpful.

Thanks,
Daniel

 

 

 

From DSC:
In the future, will Microsoft — via data supplied by LinkedIn and Lynda.com — use artificial intelligence, big data, and blockchain-related technologies to match employers with employees/freelancers?  If so, how would this impact higher education? Badging? Credentialing?

It’s something to put on our radars.

 

 

 

 

 

Excerpt:

A sneak peak on Recruitment in AI era
With global talent war at its peak, organisations are now looking at harnessing Artificial Intelligence (AI) capabilities, to use search optimisation tools, data analytics, and talent mapping to reach out to the right talent for crucial job roles. Technology has been revolutionising the way recruitment works with the entire process being now automated with ATS and other talent management softwares. This saves time and costs involved with recruiting for HR managers, whilst allowing them to do away with third-party service providers for talent sourcing such as employment bureaus and traditional recruitment agencies. With modern talent acquisition technology empowered by AI, the time taken for recruitment is halved and search narrowed to reach out to only the best talent that matches job requirements. There is no need for human intervention and manual personality matching to choose the best candidates for suitable job roles.

Talent mapping, with the help of big data, is definitely the next step in recruitment technology. With talent mapping, recruiters can determine their candidate needs well in advance and develop a strategic plan for hiring long-term. This includes filling any skill gaps, bolstering the team for sudden changes in the workplace, or just simply having suitable talent in mind for the future. All of these, when prepared ahead of time, can save companies the trouble and time in future. Recruiters who are able to understand how AI works, harness the technology to save on time and costs will be rewarded with improved quality of hires, enhanced efficiency, more productive workforce and less turnover.

 

 

Key issues in teaching and learning 2017 — from Educause Learning Initiative (ELI)

Excerpt:

Since 2011, ELI has surveyed the higher education teaching and learning community to identify its key issues. The community is wide in scope: we solicit input from all those participating in the support of the teaching and learning mission, including professionals from the IT organization, the center for teaching and learning, the library, and the dean’s and provost’s offices.

 

 

 

HarvardX rolls out new adaptive learning feature in online course — from edscoop.com by Corinne Lestch
Students in MOOC adaptive learning experiment scored nearly 20 percent better than students using more traditional learning approaches.

Excerpt:

Online courses at Harvard University are adapting on the fly to students’ needs.

Officials at the Cambridge, Massachusetts, institution announced a new adaptive learning technology that was recently rolled out in a HarvardX online course. The feature offers tailored course material that directly correlates with student performance while the student is taking the class, as well as tailored assessment algorithms.

HarvardX is an independent university initiative that was launched in parallel with edX, the online learning platform that was created by Harvard and Massachusetts Institute of Technology. Both HarvardX and edX run massive open online courses. The new feature has never before been used in a HarvardX course, and has only been deployed in a small number of edX courses, according to officials.

 

 

From DSC:
Given the growth of AI, this is certainly radar worthy — something that’s definitely worth pulse-checking to see where opportunities exist to leverage these types of technologies.  What we now know of as adaptive learning will likely take an enormous step forward in the next decade.

IBM’s assertion rings in my mind:

 

 

I’m cautiously hopeful that these types of technologies can extend beyond K-12 and help us deal with the current need to be lifelong learners, and the need to constantly reinvent ourselves — while providing us with more choice, more control over our learning. I’m hopeful that learners will be able to pursue their passions, and enlist the help of other learners and/or the (human) subject matter experts as needed.

I don’t see these types of technologies replacing any teachers, professors, or trainers. That said, these types of technologies should be able to help do some of the heavy teaching and learning lifting in order to help someone learn about a new topic.

Again, this is one piece of the Learning from the Living [Class] Room that we see developing.

 

 

 

 

 

CES 2017: The year of voice — from J. Walter Thompson Intelligence by Sheperd Laughlin
Improvements in natural language processing have set the stage for a revolution in how we interact with tech.

Excerpt:

Over the past several decades, the proliferation of screens and “screen time” has been practically synonymous technology’s ever-expanding role in our lives. But this year’s CES highlights a shift in how we interact with computers: more and more, we’re bypassing screens altogether through the medium of voice.

Shawn DuBravac, chief economist of the Consumer Technology Association, said that 2017 represented an inflection point in computers’ ability to translate speech into text. When such experiments first began in 1994, he said, their error rate was about 100%. As recently as 2013, computers failed to accurately transcribe 23% of human speech.

But in 2017, they will reach parity with humans, understanding what we say at least 94% of the time. “We’re ushering in an entirely new era of faceless computing,” DuBravac said.

 

 

 


Addendum on 2/23/17:


 

 

From DSC:
Hmmm…how true: “…the digital age rewards change and punishes stasis.” (
source)

Which reminds me of a photo I took just yesterday morning at one of the malls in our area, where a local Sears store is closing.

It made me wonder…if Sears could do it all over again, what would they do differently? If they had a time machine, would they go back in time and work to become the new Amazon.com?

 

 

 

By the way, this picture is for those people who continue to dismiss the need to change and to adapt.  Surveying the relevant landscapes is an increasingly important thing for all of us to do, especially given that we are now on an exponential pace of technological change.

 

 

Companies must be open to radical reinvention to find new, significant, and sustainable sources of revenue. Incremental adjustments or building something new outside of the core business can provide real benefits and, in many cases, are a crucial first step for a digital transformation. But if these initiatives don’t lead to more profound changes to the core business and avoid the real work of rearchitecting how the business makes money, the benefits can be fleeting and too insignificant to avert a steady march to oblivion.

 

 

 



Addendum on 2/10/17

  • Macy’s earnings: Shifts in retail are hurting major players — from marketwatch.com by Tonya Garcia
    Macy’s has assets like real estate and brand identity, but shifts in the sector are putting pressure on earningsExcerpt:
    Even a major player like Macy’s M, +1.51%   isn’t immune to retail’s struggles. The sector is experiencing a dramatic shift to e-commerce and changes in consumer tastes and shopping behavior that have put pressure on department store earnings, and on the industry as a whole. Macy’s has already announced 100 store closures and thousands of job cuts, in addition to a reassessment of its real-estate assets. Now there’s buzz from reports about buyout talks with Hudson’s Bay Co. HBC, parent to Lord & Taylor and Saks Fifth Avenue.

 

 

 
© 2016 Learning Ecosystems