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

 

 

 

From DSC:
Chatbots were another one of the topics I mentioned at the Next Generation Learning Spaces Conference last week. For those of us working within higher education, chatbots need to be on our radars!

 

 

An article from today on this:

  • Using AI Chatbots to Freeze ‘Summer Melt’ in Higher Ed — from campustechnology.com by Sri Ravipati
    Excerpt:
    Students who accept offers of admission into a college or university don’t always show up for fall enrollment — a phenomenon known as “summer melt.” It’s a problem that Georgia State University (GSU) is all too familiar with: The institution’s summer melt rates have increased from 12 percent to nearly 19 percent in recent years. With traditional methods of reaching students (i.e. snail mail, e-mail and phone calls) producing feeble results, GSU decided to try another approach: smart text messaging.

    According to a recent case study, GSU was well aware of the advantages of communicating with students via text messages, but was concerned about the additional workload that text messaging students would place on existing staff. So, the university partnered with AdmitHub, a Boston-based ed tech startup, to test out text-based intervention. AdmitHub works with higher ed institutions to create a virtual “campus coach” that embodies the collective knowledge and unique spirit of a school’s community. It integrates conversational artificial intelligence (AI) with human expertise to guide students to and through college.

 

One of the slides from my presentation on this:

 

 

 

Excerpt from Amazon fumbles earnings amidst high expectations (emphasis DSC):

Aside from AWS, Amazon Alexa-enabled devices were the top-selling products across all categories on Amazon.com throughout the holiday season and the company is reporting that Echo family sales are up over 9x compared to last season. Amazon aims to brand Alexa as a platform, something that has helped the product to gain capabilities faster than its competition. Developers and corporates released 4,000 new skills for the voice assistant in just the last quarter.

 

 

 

 

 

Alexa got 4,000 new skills in just the last quarter!

From DSC:
What are the teaching & learning ramifications of this?

By the way, I’m not saying for professors, teachers, & trainers to run for the hills (i.e., that they’ll be replaced by AI-based tools). But rather, I would like to suggest that we not only put this type of thing on our radars, but we should begin to actively experiment with such technologies to see if they might be able to help us do some heavy lifting for students learning about new topics.

 
 

Per X Media Lab:

The authoritative CB Insights lists imminent Future Tech Trends: customized babies; personalized foods; robotic companions; 3D printed housing; solar roads; ephemeral retail; enhanced workers; lab-engineered luxury; botroots movements; microbe-made chemicals; neuro-prosthetics; instant expertise; AI ghosts. You can download the whole outstanding report here (125 pgs).

 

From DSC:
Though I’m generally pro-technology, there are several items in here which support the need for all members of society to be informed and have some input into if and how these technologies should be used. Prime example: Customized babies.  The report discusses the genetic modification of babies: “In the future, we will choose the traits for our babies.” Veeeeery slippery ground here.

 

Below are some example screenshots:

 

 

 

 

 

 

 

 

 

Also see:

CBInsights — Innovation Summit

  • The New User Interface: The Challenge and Opportunities that Chatbots, Voice Interfaces and Smart Devices Present
  • Fusing the physical, digital and biological: AI’s transformation of healthcare
  • How predictive algorithms and AI will rule financial services
  • Autonomous Everything: How Connected Vehicles Will Change Mobility and Which Companies Will Own this Future
  • The Next Industrial Age: The New Revenue Sources that the Industrial Internet of Things Unlocks
  • The AI-100: 100 Artificial Intelligence Startups That You Better Know
  • Autonomous Everything: How Connected Vehicles Will Change Mobility and Which Companies Will Own this Future

 

 

 

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:

 

 

 

From DSC:
The following article reminded me of a vision that I’ve had for the last few years…

  • How to Build a Production Studio for Online Courses — from campustechnology.com by Dian Schaffhauser
    At the College of Business at the University of Illinois, video operations don’t come in one size. Here’s how the institution is handling studio setup for MOOCs, online courses, guest speakers and more.

Though I’m a huge fan of online learning, why only build a production studio that’s meant to support online courses only? Let’s take it a step further and design a space that can address the content development for online learning as well as for blended learning — which can include the flipped classroom type of approach.

To do so, colleges and universities need to build something akin to what the National University of Singapore has done. I would like to see institutions create large enough facilities in order to house multiple types of recording studios in each one of them. Each facility would feature:

  • One room that has a lightboard and a mobile whiteboard in it — let the faculty member choose which surface that they want to use

 

 

 

 

 

 

 

  • A recording booth with a nice, powerful, large iMac that has ScreenFlow on it. The booth would also include a nice, professional microphone, a pop filter, sound absorbing acoustical panels, and more. Blackboard Collaborate could be used here as well…especially with the Application Sharing feature turned on and/or just showing one’s PowerPoint slides — with or without the video of the faculty member…whatever they prefer.

 

 

 

 

  • Another recording booth with a PC and Adobe Captivate, Camtasia Studio, Screencast-O-Matic, or similar tools. The booth would also include a nice, professional microphone, a pop filter, sound absorbing acoustical panels, and more. Blackboard Collaborate could be used here as well…especially with the Application Sharing feature turned on and/or just showing one’s PowerPoint slides — with or without the video of the faculty member…whatever they prefer.

 

 

 

 

  • Another recording booth with an iPad tablet and apps loaded on it such as Explain Everything:

 

 

  • A large recording studio that is similar to what’s described in the article — a room that incorporates a full-width green screen, with video monitors, a tablet, a podium, several cameras, high-end mics and more.  Or, if the budget allows for it, a really high end broadcasting/recording studio like what Harvard Business school is using:

 

 

 

 

 


 

A piece of this facility could look and act like the Sound Lab at the Museum of Pop Culture (MoPOP)

 

 

 


 

 

 

Don’t discount the game-changing power of the morphing “TV” when coupled with AI, NLP, and blockchain-based technologies! [Christian]

From DSC:

Don’t discount the game-changing power of the morphing “TV” when coupled with artificial intelligence (AI), natural language processing (NLP), and blockchain-based technologies!

When I saw the article below, I couldn’t help but wonder what (we currently know of as) “TVs” will morph into and what functionalities they will be able to provide to us in the not-too-distant future…?

For example, the article mentions that Seiki, Westinghouse, and Element will be offering TVs that can not only access Alexa — a personal assistant from Amazon which uses artificial intelligence — but will also be able to provide access to over 7,000 apps and games via the Amazon Fire TV Store.

Some of the questions that come to my mind:

  • Why can’t there be more educationally-related games and apps available on this type of platform?
  • Why can’t the results of the assessments taken on these apps get fed into cloud-based learner profiles that capture one’s lifelong learning? (#blockchain)
  • When will potential employers start asking for access to such web-based learner profiles?
  • Will tvOS and similar operating systems expand to provide blockchain-based technologies as well as the types of functionality we get from our current set of CMSs/LMSs?
  • Will this type of setup become a major outlet for competency-based education as well as for corporate training-related programs?
  • Will augmented reality (AR), virtual reality (VR), and mixed reality (MR) capabilities come with our near future “TVs”?
  • Will virtual tutoring be one of the available apps/channels?
  • Will the microphone and the wide angle, HD camera on the “TV” be able to be disconnected from the Internet for security reasons? (i.e., to be sure no hacker is eavesdropping in on their private lives)

 

Forget a streaming stick: These 4K TVs come with Amazon Fire TV inside — from techradar.com by Nick Pino

Excerpt:

The TVs will not only have access to Alexa via a microphone-equipped remote but, more importantly, will have access to the over 7,000 apps and games available on the Amazon Fire TV Store – a huge boon considering that most of these Smart TVs usually include, at max, a few dozen apps.

 

 

 

 

 

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

 


Addendums


 

“I’ve been predicting that by 2030 the largest company on the internet is going to be an education-based company that we haven’t heard of yet,” Frey, the senior futurist at the DaVinci Institute think tank, tells Business Insider.

.

  • Once thought to be a fad, MOOCs showed staying power in 2016 — from educationdive.com
    Dive Brief:

    • EdSurge profiles the growth of massive online open courses in 2016, which attracted more than 58 million students in over 700 colleges and universities last year.
    • The top three MOOC providers — Coursera, Udacity and EdX — collectively grossed more than $100 million last year, as much of the content provided on these platforms shifted from free to paywall guarded materials.
    • Many MOOCs have moved to offering credentialing programs or nanodegree offerings to increase their value in industrial marketplaces.
 

Alexa, Tell Me Where You’re Going Next — from backchannel.com by Steven Levy
Amazon’s VP of Alexa talks about machine learning, chatbots, and whether industry is strip-mining AI talent from academia.

Excerpt:

Today Prasad is giving an Alexa “State of the Union” address at the Amazon Web Services conference in Las Vegas, announcing an improved version of the Alexa Skills Kit, which helps developers create the equivalent of apps for the platform; a beefed-up Alexa Voice Service, which will make it easier to transform third-party devices like refrigerators and cars into Alexa bots; a partnership with Intel; and the Alexa Accelerator that, with the startup incubator Techstars, will run a 13-week program to help newcomers build Alexa skills. Prasad and Amazon haven’t revealed sales numbers, but industry experts have estimated that Amazon has sold over five million Echo devices so far.

Prasad, who joined Amazon in 2013, spent some time with Backchannel before his talk today to illuminate the direction of Alexa and discuss how he’s recruiting for Jeff Bezos’s arsenal without drying up the AI pipeline.

 

 

What DeepMind brings to Alphabet — from economist.com
The AI firm’s main value to Alphabet is as a new kind of algorithm factory

Excerpt:

DeepMind’s horizons stretch far beyond talent capture and public attention, however. Demis Hassabis, its CEO and one of its co-founders, describes the company as a new kind of research organisation, combining the long-term outlook of academia with “the energy and focus of a technology startup”—to say nothing of Alphabet’s cash.

Were he to succeed in creating a general-purpose AI, that would obviously be enormously valuable to Alphabet. It would in effect give the firm a digital employee that could be copied over and over again in service of multiple problems. Yet DeepMind’s research agenda is not—or not yet—the same thing as a business model. And its time frames are extremely long.

 

 

Artificial Intelligence: Silicon Valley’s Next Frontier — from toptechnews.com by Ethan Baron

Excerpt:

Silicon Valley needs its next big thing, a focus for the concentrated brain power and innovation infrastructure that have made this region the world leader in transformative technology. Just as the valley’s mobile era is peaking, the next frontier of growth and innovation has arrived: It’s Siri in an Apple iPhone, Alexa in an Amazon Echo, the software brain in Google’s self-driving cars, Amazon’s product recommendations and, someday, maybe the robot surgeon that saves your life.

It’s artificial intelligence, software that can “learn” and “think,” the latest revolution in tech.

“It’s going to be embedded in everything,” said startup guru Steve Blank, an adjunct professor at Stanford. “We’ve been talking about artificial intelligence for 30 years, maybe longer, in Silicon Valley. It’s only in the last five years, or maybe even the last two years, that this stuff has become useful.”

 

 

 

What Is The Difference Between Artificial Intelligence And Machine Learning? — from forbes.com by Bernard Marr

Excerpt:

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference.

In short, the best answer is that:
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.
And,
Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.

 

 

Why we are still light years away from full artificial intelligence — from techcrunch.com by Clara Lu

Excerpt:

Yet, the truth is, we are far from achieving true AI — something that is as reactive, dynamic, self-improving and powerful as human intelligence.

Full AI, or superintelligence, should possess the full range of human cognitive abilities. This includes self-awareness, sentience and consciousness, as these are all features of human cognition.

 

 

Udacity adds 14 hiring partners as AI, VR and self-driving talent wars heat up — from techcrunch.com by Darrell Etherington

Excerpt:

Udacity is positioned perfectly to benefit from the rush on talent in a number of growing areas of interest among tech companies and startups. The online education platform has added 14 new hiring partners across its Artificial Intelligence Engineer, Self-Driving Car Engineer and Virtual Reality Developer Nanodegree programs, as well as in its Predictive Analytics Nanodegree, including standouts like Bosch, Harma, Slack, Intel, Amazon Alexa and Samsung.

That brings the total number of hiring partners for Udacity to over 30, which means a lot of potential soft landings for graduates of its nanodegree programs. The nanodegree offered by Udacity is its own original form of accreditation, which is based on a truncated field of study that spans months, rather than years, and allows students to direct the pace of their own learning. It also all takes place online, so students can potentially learn from anywhere.

 

 

 

 

The Ethics of Artificial Intelligence – from livestream.com

 

 

 

 

The Great A.I. Awakening — from nytimes.com by Gideo Lewis-Kraus
How Google used artificial intelligence to transform Google Translate, one of its more popular services — and how machine learning is poised to reinvent computing itself.

Excerpt:

Google’s decision to reorganize itself around A.I. was the first major manifestation of what has become an industrywide machine-learning delirium. Over the past four years, six companies in particular — Google, Facebook, Apple, Amazon, Microsoft and the Chinese firm Baidu — have touched off an arms race for A.I. talent, particularly within universities. Corporate promises of resources and freedom have thinned out top academic departments. It has become widely known in Silicon Valley that Mark Zuckerberg, chief executive of Facebook, personally oversees, with phone calls and video-chat blandishments, his company’s overtures to the most desirable graduate students. Starting salaries of seven figures are not unheard-of. Attendance at the field’s most important academic conference has nearly quadrupled. What is at stake is not just one more piecemeal innovation but control over what very well could represent an entirely new computational platform: pervasive, ambient artificial intelligence.

 

 

 

Microsoft bets on AI — from businessinsider.com

Excerpt:

On [December 12th, 2016], Microsoft announced a new Microsoft Ventures fund dedicated to artificial intelligence (AI) investments, according to TechCrunch. The fund, part of the company’s investment arm that launched in May, will back startups developing AI technology and includes Element AI, a Montreal-based incubator that helps other companies embrace AI. The fund further supports Microsoft’s focus on AI. The company has been steadily announcing major initiatives in support of the technology. For example, in September, it announced a major restructuring and formed a new group dedicated to AI products. And in mid-November, it partnered with OpenAI, an AI research nonprofit backed by Elon Musk, to further its AI research and development efforts.

 

 

The Growth of Artificial Intelligence in E-commerce — from redstagfulfillment.com by Jake Rheude

Excerpt:

Whether Artificial Intelligence (AI) is something you’ve just come across or it’s something you’ve been monitoring for a while, there’s no denying that it’s starting to influence many industries. And one place that it’s really starting to change things is e-commerce. Below you’ll find some interesting stats and facts about how AI is growing in e-commerce and how it’s changing the way we do things. From personalizing the shopping experience for customers to creating personal buying assistants, AI is something retailers can’t ignore. We’ll also take a look at some examples of how leading online stores have used AI to enrich the customer buying experience.

 

 

Will AI built by a ‘sea of dudes’ understand women? AI’s inclusivity problem — from digitaltrends.com by Dyllan Furness

Excerpt:

Only 26 percent of computer professionals were women in 2013, according to a recent review by the American Association of University Women. That figure has dropped 9 percent since 1990.

Explanations abound. Some say the industry is masculine by design. Others claim computer culture is unwelcoming — even hostile — to women. So, while STEM fields like biology, chemistry, and engineering see an increase in diversity, computing does not. Regardless, it’s a serious problem.

Artificial intelligence is still in its infancy, but it’s poised to become the most disruptive technology since the Internet. AI will be everywhere — in your phone, in your fridge, in your Ford. Intelligent algorithms already track your online activity, find your face in Facebook photos, and help you with your finances. Within the next few decades they’ll completely control your car and monitor your heart health. An AI may one day even be your favorite artist.

The programs written today will inform the systems built tomorrow. And if designers all have one worldview, we can expect equally narrow-minded machines.

 

 

 

 
© 2016 Learning Ecosystems