From DSC:
Given the increasing use of robotics, automation, and artificial intelligence…how should the question of “What sort of education will you need to be employable in the future?” impact what’s being taught within K-12 & within higher education? Should certain areas within higher education, for example, start owning this research, as well as the strategic planning and whether changes are needed to the core curricula for this increasingly important trend?

The future’s coming at us fast — perhaps faster than we think. It seems prudent to work through some potential scenarios and develop plans for those various scenarios now, rather than react to this trend at some point in the future. If we wait, we’ll be trying to “swim up the backside of the wave” as my wise and wonderful father-in-law would say.

 



The above reflections occurred after I reviewed the posting out at cmrubinworld.com (with thanks to @STEMbyThomas for this resource):

  • The Global Search for Education: What Does My Robot Think?
    Excerpt:
    The Global Search for Education is pleased to welcome Ling Lee, Co-Curator of Robots and the Contemporary Science Manager for Exhibitions at the Science Museum in London, to discuss the impact of robots on our past and future.

 

 

 



 

 

Australian start-up taps IBM Watson to launch language translation earpiece — from prnewswire.com
World’s first available independent translation earpiece, powered by AI to be in the hands of consumers by July

Excerpts:

SYDNEY, June 12, 2017 /PRNewswire/ — Lingmo International, an Australian technology start-up, has today launched Translate One2One, an earpiece powered by IBM Watson that can efficiently translate spoken conversations within seconds, being the first of its kind to hit global markets next month.

Unveiled at last week’s United Nations Artificial Intelligence (AI) for Good Summit in Geneva, Switzerland, the Translate One2One earpiece supports translations across English, Japanese, French, Italian, Spanish, Brazilian Portuguese, German and Chinese. Available to purchase today for delivery in July, the earpiece carries a price tag of $179 USD, and is the first independent translation device that doesn’t rely on Bluetooth or Wi-Fi connectivity.

 

Lingmo International, an Australian technology start-up, has today launched Translate One2One, an earpiece powered by IBM Watson that can efficiently translate spoken conversations within seconds.

 

 

From DSC:
How much longer before this sort of technology gets integrated into videoconferencing and transcription tools that are used in online-based courses — enabling global learning at a scale never seen before? (Or perhaps NLP-based tools are already being integrated into global MOOCs and the like…not sure.) It would surely allow for us to learn from each other in a variety of societies throughout the globe.

 

 

 

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…

 

 

 

 

 

 

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:

 

 

 

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:
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!

 

 

 

 

 

 

 

 

 

 
 

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:

 

 

 
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