New Google Earth has exciting features for teachers — from thejournal.com by Richard Chang

Excerpt:

Google has recently released a brand new version of Google Earth for both Chrome and Android. This new version has come with a slew of nifty features teachers can use for educational purposes with students in class. Following is a quick overview of the most fascinating features…

 

 

 

 

 

 

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 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:
Given the exponential pace of technological change that many societies throughout the globe are now on, we need some tools to help us pulse-check what’s going on in the relevant landscapes that we are trying to scan.


 

 

 

 

 

 

 

 

 

 


Below, I would like to suggest 2 methods/tools to do this.  I have used both methods for years, and I have found them to be immensely helpful in pulse-checking the landscapes. Perhaps these tools will be helpful to you — or to your students or employees — as well.  I vote for these 2 tools to be a part of all of our learning ecosystems. (And besides, they also encourage micro-learning while helping us spot emerging trends.)


 

Google Alerts

 

 

Feedly.com

 

 

 

 

IBM to Train 25 Million Africans for Free to Build Workforce — from by Loni Prinsloo
* Tech giant seeking to bring, keep digital jobs in Africa
* Africa to have world’s largest workforce by 2040, IBM projects

Excerpt:

International Business Machines Corp. is ramping up its digital-skills training program to accommodate as many as 25 million Africans in the next five years, looking toward building a future workforce on the continent. The U.S. tech giant plans to make an initial investment of 945 million rand ($70 million) to roll out the training initiative in South Africa…

 

Also see:

IBM Unveils IT Learning Platform for African Youth — from investopedia.com by Tim Brugger

Excerpt (emphasis DSC):

Responding to concerns that artificial intelligence (A.I.) in the workplace will lead to companies laying off employees and shrinking their work forces, IBM (NYSE: IBM) CEO Ginni Rometty said in an interview with CNBC last month that A.I. wouldn’t replace humans, but rather open the door to “new collar” employment opportunities.

IBM describes new collar jobs as “careers that do not always require a four-year college degree but rather sought-after skills in cybersecurity, data science, artificial intelligence, cloud, and much more.”

In keeping with IBM’s promise to devote time and resources to preparing tomorrow’s new collar workers for those careers, it has announced a new “Digital-Nation Africa” initiative. IBM has committed $70 million to its cloud-based learning platform that will provide free skills development to as many as 25 million young people in Africa over the next five years.

The platform will include online learning opportunities for everything from basic IT skills to advanced training in social engagement, digital privacy, and cyber protection. IBM added that its A.I. computing wonder Watson will be used to analyze data from the online platform, adapt it, and help direct students to appropriate courses, as well as refine the curriculum to better suit specific needs.

 

 

From DSC:
That last part, about Watson being used to personalize learning and direct students to appropropriate courses, is one of the elements that I see in the Learning from the Living [Class]Room vision that I’ve been pulse-checking for the last several years. AI/cognitive computing will most assuredly be a part of our learning ecosystems in the future.  Amazon is currently building their own platform that adds 100 skills each day — and has 1000 people working on creating skills for Alexa.  This type of thing isn’t going away any time soon. Rather, I’d say that we haven’t seen anything yet!

 

 

The Living [Class] Room -- by Daniel Christian -- July 2012 -- a second device used in conjunction with a Smart/Connected TV

 

 

And Amazon has doubled down to develop Alexa’s “skills,” which are discrete voice-based applications that allow the system to carry out specific tasks (like ordering pizza for example). At launch, Alexa had just 20 skills, which has reportedly jumped to 5,200 today with the company adding about 100 skills per day.

In fact, Bezos has said, “We’ve been working behind the scenes for the last four years, we have more than 1,000 people working on Alexa and the Echo ecosystem … It’s just the tip of the iceberg. Just last week, it launched a new website to help brands and developers create more skills for Alexa.

Source

 

 

Also see:

 

“We are trying to make education more personalised and cognitive through this partnership by creating a technology-driven personalised learning and tutoring,” Lula Mohanty, Vice President, Services at IBM, told ET. IBM will also use its cognitive technology platform, IBM Watson, as part of the partnership.

“We will use the IBM Watson data cloud as part of the deal, and access Watson education insight services, Watson library, student information insights — these are big data sets that have been created through collaboration and inputs with many universities. On top of this, we apply big data analytics,” Mohanty added.

Source

 

 


 

Also see:

  • Most People in Education are Just Looking for Faster Horses, But the Automobile is Coming — from etale.org by Bernard Bull
    Excerpt:
    Most people in education are looking for faster horses. It is too challenging, troubling, or beyond people’s sense of what is possible to really imagine a completely different way in which education happens in the world. That doesn’t mean, however, that the educational equivalent of the automobile is not on its way. I am confident that it is very much on its way. It might even arrive earlier than even the futurists expect. Consider the following prediction.

 


 

 

 
 

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 Periodic Table of AI — from ai.xprize.org by Kris Hammond

Excerpts:

This is an invitation to collaborate.  In particular, it is an invitation to collaborate in framing how we look at and develop machine intelligence. Even more specifically, it is an invitation to collaborate in the construction of a Periodic Table of AI.

Let’s be honest. Thinking about Artificial Intelligence has proven to be difficult for us.  We argue constantly about what is and is not AI.  We certainly cannot agree on how to test for it.  We have difficultly deciding what technologies should be included within it.  And we struggle with how to evaluate it.

Even so, we are looking at a future in which intelligent technologies are becoming commonplace.

With that in mind, we propose an approach to viewing machine intelligence from the perspective of its functional components. Rather than argue about the technologies behind them, the focus should be on the functional elements that make up intelligence.  By stepping away from how these elements are implemented, we can talk about what they are and their roles within larger systems.

 

 

Also see this article, which contains the graphic below:

 

 

 

From DSC:
These graphics are helpful to me, as they increase my understanding of some of the complexities involved within the realm of artificial intelligence.

 

 

 


Also relevant/see:

 

 

 

Chatbots: The next big thing — from dw.com
Excerpt:

More and more European developers are discovering the potential of chatbots. These mini-programs interact automatically with users and could be particularly useful in areas like online shopping and news delivery. The potential of chatbots is diverse. These tiny programs can do everything from recognizing customers’ tastes to relaying the latest weather forecast. Berlin start-up Spectrm is currently devising bots that deliver customized news. Users can contact the bot via Facebook Messenger, and receive updates on topics that interest them within just a few seconds.

 

 

MyPrivateTutor releases chatbot for finding tutors — from digitaljournal.com
MyPrivateTutor, based in Kolkata, matches tutors to students using proprietary machine learning algorithms

Excerpt:

Using artificial intelligence, the chatbot helps us reach a wider segment of users who are still not comfortable navigating websites and apps but are quite savvy with messaging apps”, said Sandip Kar, co-founder & CEO of MyPrivateTutor (www.myprivatetutor.com), an online marketplace for tutors, has released a chatbot for helping students and parents find tutors, trainers, coaching classes and training institutes near them.

 

 

Story idea: Covering the world of chatbots — from businessjournalism.org by Susan Johnston Taylor

Excerpt:

Chatbots, computer programs designed to converse with humans, can perform all sorts of activities. They can help users book a vacation, order a pizza, negotiate with Comcast or even communicate with POTUS. Instead of calling or emailing a representative at the company, consumers chat with a robot that uses artificial intelligence to simulate natural conversation. A growing number of startups and more established companies now use them to interact with users via Facebook Messenger, SMS, chat-specific apps such as Kik or the company’s own site.

To cover this emerging business story, reporters can seek out companies in their area that use chatbots, or find local tech firms that are building them. Local universities may have professors or other experts available who can provide big-picture context, too. (Expertise Finder can help you identify professors and their specific areas of study.)

 

 

How chatbots are addressing summer melt for colleges — from ecampusnews.com

Excerpt:

AdmitHub, an edtech startup which builds conversational artificial intelligence (AI) chatbots to guide students on the path to and through college, has raised $2.95 million in seed funding.

 

 

Why higher education chatbots will take over university mobile apps — from blog.admithub.com by Kirk Daulerio

Excerpt (emphasis DSC):

Chatbots are the new apps and websites combined
Chatbots are simple, easy to use, and present zero friction. They exist on the channels that people are most familiar with like Messenger, Twitter, SMS text message, Kik, and expanding onto other messaging applications. Unlike apps, bots don’t take up space, users don’t have to take time to get familiar with a new user interface, and bots will give you an instant reply. The biggest difference with chatbots compared to apps and websites is that they use language as the main interface. Websites and apps have to be searched and clicked, while bots and people use language, the most natural interface, to communicate and inform.

 

 


From DSC:
I think messaging-based chatbots will definitely continue to grow in usage — in numerous industries, including higher education. But I also think that the human voice — working in conjunction with technologies that provide natural language processing (NLP) capabilities — will play an increasingly larger role in how we interface with our devices. Whether it’s via a typed/textual message or whether it’s via a command or a query relayed by the human voice, working with bots needs to be on our radars. These conversational messaging agents are likely to be around for a while.

 


 

Addendum:

 

 

 

NYU Steinhardt Edtech Accelerator’s 2016 Cohort Starting Up Chatbots, Augmented Reality Tools and More — from campustechnology.com by Sri Ravipati

Excerpt:

The cohort includes:

  • Admission Table (Bangalore, India), an artificial intelligence (AI) chatbot for university admissions;
  • Alumnify (San Francisco, CA), an enterprise platform for alumni services.
  • AugThat (New York, NY), augmented reality curricula for elementary and middle school students;
  • Bering (Brooklyn, NY), a data analytics platform for research scientists;
  • EduKids Connect Systems (New York, NY), an information system for child care providers;
  • NeuroNet Learning (Gainesville, FL), a research-based early reading program designed to assist students with essential reading, handwriting skills and math;
  • TheTalkList (San Diego, CA), a language learning exchange platform;
  • Trovvit (Brooklyn, NY), a social digital portfolio tool; and
  • Versity U (Jeffersonville, IN), a nursing exam platform.
 
© 2016 Learning Ecosystems