Amazon’s new bricks-&-mortar bookstore nails what the web couldn’t — from hackernoon.com by Pat Ryan

or

A title from DSC:
How Amazon uses its vast data resources to reinvent the bookstore

 

Excerpt (emphasis DSC):

Amazon’s First Foray into Physical Retail — While Utilitarian — Takes Discovery to New Levels
As a long time city dweller living in a neighborhood full of history, I had mixed feelings about the arrival of Amazon’s first bricks-and-mortar bookstore in a city neighborhood (the first four are located in malls). Like most of my neighbors around Chicago’s Southport Corridor, I prefer the charm of owner operated boutiques. Yet as a tech entrepreneur who holds Amazon founder Jeff Bezos in the highest esteem, I was excited to see how Amazon would reimagine the traditional bookstore given their customer obsession and their treasure trove of user data. Here’s what I discovered…

The Bottom Line:
I will still go to Amazon.com for the job of ordering a book that I already know that I want (and to the local Barnes and Noble if I need it today). But when I need to discover a book for gifts (Father’s Day is coming up soon enough) or for my own interest, nothing that I have seen compares to Amazon Books. We had an amazing experience and discovered more books in 20 minutes than we had in the past month or two.

 

 

The physical manifestation of the “if you like…then you’ll love…”

 

 

 

The ultra metric combining insights from disparate sources seems more compelling than standard best seller lists

 

 

 

Making sure the machines don’t take over — from raconteur.net by Mark Frary
Preparing economic players for the impact of artificial intelligence is a work in progress which requires careful handling

 

From DSC:
This short article presents a balanced approach, as it relays both the advantages and disadvantages of AI in our world.

Perhaps it will be one of higher education’s new tasks — to determine the best jobs to go into that will survive the next 5-10+ years and help you get up-to-speed in those areas. The liberal arts are very important here, as they lay a solid foundation that one can use to adapt to changing conditions and move into multiple areas. If the C-suite only sees the savings to the bottom line — and to *&^# with humanity (that’s their problem, not mine!) — then our society could be in trouble.

 

Also see:

 

 

 

From DSC:
First of all, let me say again that I’m not suggesting that we replace professors with artificial intelligence, algorithms, and such.

However, given a variety of trends, we need to greatly lower the price of obtaining a degree and these types of technologies will help us do just that — while at the same time significantly increasing the productivity of each professor and/or team of specialists offering an online-based course (something institutions of higher education are currently attempting to do…big time). Not only will these types of technologies find their place in the higher education landscape, I predict that they will usher in a “New Amazon.com of Higher Education” — a new organization that will cause major disruption for traditional institutions of higher education. AI-powered MOOCs will find their place on the higher ed landscape; just how big they become remains to be seen, but this area of the landscape should be on our radars from here on out.

This type of development again points the need for team-based
approaches; s
uch approaches will likely dominate the future.

 

 


 

California State University East Bay partners with Cognii to offer artificial intelligence powered online learning — from prnewswire.com
Cognii’s Virtual Learning Assistant technology will provide intelligent tutoring and assessments to students in a chatbot-style conversation

Excerpt:

HAYWARD, Calif., April 14, 2017 /PRNewswire/ — Cal State East Bay, a top-tier public university, and Cognii Inc., a leading provider of artificial intelligence-based educational technologies, today announced a partnership. Cognii will work with Cal State East Bay to develop a new learning and assessment experience, powered by Cognii’s Virtual Learning Assistant technology.

Winner of the 2016 EdTech Innovation of the Year Award from Mass Technology Leadership Council for its unique use of conversational AI and Natural Language Processing technologies in education, Cognii VLA provides automatic grading to students’ open-response answers along with qualitative feedback that guides them towards conceptual mastery. Compared to the multiple choice tests, open-response questions are considered pedagogically superior for measuring students’ critical thinking and problem solving skills, essential for 21st century jobs.

Students at Cal State East Bay will use the Cognii-powered interactive tutorials starting in summer as part of the online transfer orientation course. The interactive questions and tutorials will be developed collaboratively by Cognii team and the eLearning specialists from the university’s office of the Online Campus. Students will interact with the questions in a chatbot-style natural language conversation during the formative assessment stage. As students practice the tutorials, Cognii will generate rich learning analytics and proficiency measurements for the course leaders.

 

 

 

 

The 82 Hottest EdTech Tools of 2017 According to Education Experts — from tutora.co.uk by Giorgio Cassella

Excerpt:

If you work in education, you’ll know there’s a HUGE array of applications, services, products and tools created to serve a multitude of functions in education.

Tools for teaching and learning, parent-teacher communication apps, lesson planning software, home-tutoring websites, revision blogs, SEN education information, professional development qualifications and more.

There are so many companies creating new products for education, though, that it can be difficult to keep up – especially with the massive volumes of planning and marking teachers have to do, never mind finding the time to actually teach!

So how do you know which ones are the best?

Well, as a team of people passionate about education and learning, we decided to do a bit of research to help you out.

We’ve asked some of the best and brightest in education for their opinions on the hottest EdTech of 2017. These guys are the real deal – experts in education, teaching and new tech from all over the world from England to India, to New York and San Francisco.

They’ve given us a list of 82 amazing, tried and tested tools…


From DSC:
The ones that I mentioned that Giorgio included in his excellent article were:

  • AdmitHub – Free, Expert College Admissions Advice
  • Labster – Empowering the Next Generation of Scientists to Change the World
  • Unimersiv – Virtual Reality Educational Experiences
  • Lifeliqe – Interactive 3D Models to Augment Classroom Learning

 


 

 

 

 

The Hidden Costs of Active Learning — from by Thomas Mennella
Flipped and active learning truly are a better way for students to learn, but they also may be a fast track to instructor burnout.

Excerpt:

The time has come for us to have a discussion about the hidden cost of active learning in higher education. Soon, gone will be the days of instructors arriving to a lecture hall, delivering a 75-minute speech and leaving. Gone will be the days of midterms and finals being the sole forms of assessing student learning. For me, these days have already passed, and good riddance. These are largely ineffective teaching and learning strategies. Today’s college classroom is becoming dynamic, active and student-centered. Additionally, the learning never stops because the dialogue between student and instructor persists endlessly over the internet. Trust me when I say that this can be exhausting. With constant ‘touch-points,’ ‘personalized learning opportunities’ and the like, the notion of a college instructor having 12 contact hours per week that even remotely total 12 hours is beyond unreasonable.

We need to reevaluate how we measure, assign and compensate faculty teaching loads within an active learning framework. We need to recognize that instructors teaching in these innovative ways are doing more, and spending more hours, than their more traditional colleagues. And we must accept that a failure to recognize and remedy these ‘new normals’ risks burning out a generation of dedicated and passionate instructors. Flipped learning works and active learning works, but they’re very challenging ways to teach. I still say I will never teach another way again … I’m just not sure for how much longer that can be.

 

From DSC:
The above article prompted me to revisit the question of how we might move towards using more team-based approaches…? Thomas Mennella seems to be doing an incredible job — but grading 344 assignments each week or 3,784 assignments this semester is most definitely a recipe for burnout.

Then, pondering this situation, an article came to my mind that discusses Thomas Frey’s prediction that the largest internet-based company of 2030 will be focused on education.

I wondered…who will be the Amazon.com of the future of education? 

Such an organization will likely utilize a team-based approach to create and deliver excellent learning experiences — and will also likely leverage the power of artificial intelligence/machine learning/deep learning as a piece of their strategy.

 

 

 

Tech giants grapple with the ethical concerns raised by the AI boom — from technologyreview.com by Tom Simonite
As machines take over more decisions from humans, new questions about fairness, ethics, and morality arise.

Excerpt:

With great power comes great responsibility—and artificial-intelligence technology is getting much more powerful. Companies in the vanguard of developing and deploying machine learning and AI are now starting to talk openly about ethical challenges raised by their increasingly smart creations.

“We’re here at an inflection point for AI,” said Eric Horvitz, managing director of Microsoft Research, at MIT Technology Review’s EmTech conference this week. “We have an ethical imperative to harness AI to protect and preserve over time.”

Horvitz spoke alongside researchers from IBM and Google pondering similar issues. One shared concern was that recent advances are leading companies to put software in positions with very direct control over humans—for example in health care.

 

 

21 bot experts make their predictions for 2017 — from venturebeat.com by Adelyn Zhou

Excerpt:

2016 was a huge year for bots, with major platforms like Facebook launching bots for Messenger, and Amazon and Google heavily pushing their digital assistants. Looking forward to 2017, we asked 21 bot experts, entrepreneurs, and executives to share their predictions for how bots will continue to evolve in the coming year.

From Jordi Torras, founder and CEO, Inbenta:
“Chatbots will get increasingly smarter, thanks to the adoption of sophisticated AI algorithms and machine learning. But also they will specialize more in specific tasks, like online purchases, customer support, or online advice. First attempts of chatbot interoperability will start to appear, with generalist chatbots, like Siri or Alexa, connecting to specialized enterprise chatbots to accomplish specific tasks. Functions traditionally performed by search engines will be increasingly performed by chatbots.”

 

 

 

 

 


From DSC:
For those of us working within higher education, chatbots need to be on our radars. Here are 2 slides from my NGLS 2017 presentation.

 

 

 

 

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)

 

 

 
 

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

 

 

 
© 2016 Learning Ecosystems