The Dark Secret at the Heart of AI — from technologyreview.com by Will Knight
No one really knows how the most advanced algorithms do what they do. That could be a problem.

Excerpt:

The mysterious mind of this vehicle points to a looming issue with artificial intelligence. The car’s underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.

But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur—and it’s inevitable they will. That’s one reason Nvidia’s car is still experimental.

 

“Whether it’s an investment decision, a medical decision, or maybe a military decision, you don’t want to just rely on a ‘black box’ method.”

 


This raises mind-boggling questions. As the technology advances, we might soon cross some threshold beyond which using AI requires a leap of faith. Sure, we humans can’t always truly explain our thought processes either—but we find ways to intuitively trust and gauge people. Will that also be possible with machines that think and make decisions differently from the way a human would? We’ve never before built machines that operate in ways their creators don’t understand. How well can we expect to communicate—and get along with—intelligent machines that could be unpredictable and inscrutable? These questions took me on a journey to the bleeding edge of research on AI algorithms, from Google to Apple and many places in between, including a meeting with one of the great philosophers of our time.

 

 

 

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.

 

 

 

 

(Below emphasis via DSC)

IBM and Ricoh have partnered for a cognitive-enabled interactive whiteboard which uses IBM’s Watson intelligence and voice technologies to support voice commands, taking notes and actions and even translating into other languages.

 

 

The Intelligent Workplace Solution leverages IBM Watson and Ricoh’s interactive whiteboards to allow to access features via using voice. It makes sure that Watson doesn’t just listen, but is an active meeting participant, using real-time analytics to help guide discussions.

Features of the new cognitive-enabled whiteboard solution include:

  • Global voice control of meetings: Once a meeting begins, any employee, whether in-person or located remotely in another country, can easily control what’s on the screen, including advancing slides, all through simple voice commands using Watson’s Natural Language API.
  • Translation of the meeting into another language: The Intelligent Workplace Solution can translate speakers’ words into several other languages and display them on screen or in transcript.
  • Easy-to-join meetings: With the swipe of a badge the Intelligent Workplace Solution can log attendance and track key agenda items to ensure all key topics are discussed.
  • Ability to capture side discussions: During a meeting, team members can also hold side conversations that are displayed on the same whiteboard.

From DSC:

Holy smokes!

If you combine the technologies that Ricoh and IBM are using with their new cognitive-enabled interactive whiteboard with what Bluescape is doing — by providing 160 acres of digital workspace that’s used to foster collaboration (and to do so whether you are working remotely or working with others in the same physical space) — and you have one incredibly powerful platform! 

#NLP  |  #AI  |  #VoiceRecognition |  #CognitiveComputing
#SmartClassrooms  |  #LearningSpaces  |#Collaboration |  #Meetings 


 

 

 

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

 

 

 

Summer 2017 Human++ — fromcambridge.nuvustudio.com
Human-Machine Intelligence, Hacking Drones, Bio Fashion, Augmented Video Games, Aerial Filmmaking, Smart Tools, Soft Robotics and more!

Excerpt:

NuVu is a place where young students grow their spirit of innovation. They use their curiosity and creativity to explore new ideas, and make their concepts come to life through our design process. Our model is based on the architecture studio model, and every Summer we use imaginative themes to frame two-week long Studios in which students dive into hands-on design, engineering, science, technology, art and more!

 

 

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.

 

 

 

 

No hype, just fact: What artificial intelligence is – in simple business terms — from zdnet.com by Michael Krigsman
AI has become one of the great, meaningless buzzwords of our time. In this video, the Chief Data Scientist of Dun and Bradstreet explains AI in clear business terms.

Excerpt:

How do terms like machine learning, AI, and cognitive computing relate to one another?
They’re not synonymous. So, cognitive computing is very different than machine learning, and I will call both of them a type of AI. Just to try and describe those three. So, I would say artificial intelligence is all of that stuff I just described. It’s a collection of things designed to either mimic behavior, mimic thinking, behave intelligently, behave rationally, behave empathetically. Those are the systems and processes that are in the collection of soup that we call artificial intelligence.

Cognitive computing is primarily an IBM term. It’s a phenomenal approach to curating massive amounts of information that can be ingested into what’s called the cognitive stack. And then to be able to create connections among all of the ingested material, so that the user can discover a particular problem, or a particular question can be explored that hasn’t been anticipated.

Machine learning is almost the opposite of that. Where you have a goal function, you have something very specific that you try and define in the data. And, the machine learning will look at lots of disparate data, and try to create proximity to this goal function ? basically try to find what you told it to look for. Typically, you do that by either training the system, or by watching it behave, and turning knobs and buttons, so there’s unsupervised, supervised learning. And that’s very, very different than cognitive computing.

 

 

 

 

 

 

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.

 


 

 

 

 

A world without work — by Derek Thompson; The Atlantic — from July 2015

Excerpts:

Youngstown, U.S.A.
The end of work is still just a futuristic concept for most of the United States, but it is something like a moment in history for Youngstown, Ohio, one its residents can cite with precision: September 19, 1977.

For much of the 20th century, Youngstown’s steel mills delivered such great prosperity that the city was a model of the American dream, boasting a median income and a homeownership rate that were among the nation’s highest. But as manufacturing shifted abroad after World War  II, Youngstown steel suffered, and on that gray September afternoon in 1977, Youngstown Sheet and Tube announced the shuttering of its Campbell Works mill. Within five years, the city lost 50,000 jobs and $1.3 billion in manufacturing wages. The effect was so severe that a term was coined to describe the fallout: regional depression.

Youngstown was transformed not only by an economic disruption but also by a psychological and cultural breakdown. Depression, spousal abuse, and suicide all became much more prevalent; the caseload of the area’s mental-health center tripled within a decade. The city built four prisons in the mid-1990s—a rare growth industry. One of the few downtown construction projects of that period was a museum dedicated to the defunct steel industry.

“Youngstown’s story is America’s story, because it shows that when jobs go away, the cultural cohesion of a place is destroyed”…

“The cultural breakdown matters even more than the economic breakdown.”

But even leaving aside questions of how to distribute that wealth, the widespread disappearance of work would usher in a social transformation unlike any we’ve seen.

What may be looming is something different: an era of technological unemployment, in which computer scientists and software engineers essentially invent us out of work, and the total number of jobs declines steadily and permanently.

After 300 years of people crying wolf, there are now three broad reasons to take seriously the argument that the beast is at the door: the ongoing triumph of capital over labor, the quiet demise of the working man, and the impressive dexterity of information technology.

The paradox of work is that many people hate their jobs, but they are considerably more miserable doing nothing.

Most people want to work, and are miserable when they cannot. The ills of unemployment go well beyond the loss of income; people who lose their job are more likely to suffer from mental and physical ailments. “There is a loss of status, a general malaise and demoralization, which appears somatically or psychologically or both”…

Research has shown that it is harder to recover from a long bout of joblessness than from losing a loved one or suffering a life-altering injury.

Most people do need to achieve things through, yes, work to feel a lasting sense of purpose.

When an entire area, like Youngstown, suffers from high and prolonged unemployment, problems caused by unemployment move beyond the personal sphere; widespread joblessness shatters neighborhoods and leaches away their civic spirit.

What’s more, although a universal income might replace lost wages, it would do little to preserve the social benefits of work.

“I can’t stress this enough: this isn’t just about economics; it’s psychological”…

 

 

The paradox of work is that many people hate their jobs, but they are considerably more miserable doing nothing.

 

 

From DSC:
Though I’m not saying Thompson is necessarily asserting this in his article, I don’t see a world without work as a dream. In fact, as the quote immediately before this paragraph alludes to, I think that most people would not like a life that is devoid of all work. I think work is where we can serve others, find purpose and meaning for our lives, seek to be instruments of making the world a better place, and attempt to design/create something that’s excellent.  We may miss the mark often (I know I do), but we keep trying.

 

 

 
© 2016 Learning Ecosystems