These news anchors are professional and efficient. They’re also not human. — from washingtonpost.com by Taylor Telford

Excerpt:

The new anchors at China’s state-run news agency have perfect hair and no pulse.

Xinhua News just unveiled what it is calling the world’s first news anchors powered by artificial intelligence, at the World Internet Conference on Wednesday in China’s Zhejiang province. From the outside, they are almost indistinguishable from their human counterparts, crisp-suited and even-keeled. Although Xinhua says the anchors have the “voice, facial expressions and actions of a real person,” the robotic anchors relay whatever text is fed to them in stilted speech that sounds less human than Siri or Alexa.

 

From DSC:
The question is…is this what we want our future to look like? Personally, I don’t care to watch a robotic newscaster giving me the latest “death and dying report.” It comes off bad enough — callous enough — from human beings backed up by TV networks/stations that have agendas of their own; let alone from a robot run by AI.

 

 

Introducing several new ideas to provide personalized, customized learning experiences for all kinds of learners! [Christian]

From DSC:
I have often reflected on differentiation or what some call personalized learning and/or customized learning. How does a busy teacher, instructor, professor, or trainer achieve this, realistically?

It’s very difficult and time-consuming to do for sure. But it also requires a team of specialists to achieve such a holy grail of learning — as one person can’t know it all. That is, one educator doesn’t have the necessary time, skills, or knowledge to address so many different learning needs and levels!

  • Think of different cognitive capabilities — from students that have special learning needs and challenges to gifted students
  • Or learners that have different physical capabilities or restrictions
  • Or learners that have different backgrounds and/or levels of prior knowledge
  • Etc., etc., etc.

Educators  and trainers have so many things on their plates that it’s very difficult to come up with _X_ lesson plans/agendas/personalized approaches, etc.  On the other side of the table, how do students from a vast array of backgrounds and cognitive skill levels get the main points of a chapter or piece of text? How can they self-select the level of difficulty and/or start at a “basics” level and work one’s way up to harder/more detailed levels if they can cognitively handle that level of detail/complexity? Conversely, how do I as a learner get the boiled down version of a piece of text?

Well… just as with the flipped classroom approach, I’d like to suggest that we flip things a bit and enlist teams of specialists at the publishers to fulfill this need. Move things to the content creation end — not so much at the delivery end of things. Publishers’ teams could play a significant, hugely helpful role in providing customized learning to learners.

Some of the ways that this could happen:

Use an HTML like language when writing a textbook, such as:

<MainPoint> The text for the main point here. </MainPoint>

<SubPoint1>The text for the subpoint 1 here.</SubPoint1>

<DetailsSubPoint1>More detailed information for subpoint 1 here.</DetailsSubPoint1>

<SubPoint2>The text for the subpoint 2 here.</SubPoint2>

<DetailsSubPoint2>More detailed information for subpoint 2 here.</DetailsSubPoint2>

<SubPoint3>The text for the subpoint 3 here.</SubPoint3>

<DetailsSubPoint3>More detailed information for subpoint 3 here.</DetailsSubPoint1>

<SummaryOfMainPoints>A list of the main points that a learner should walk away with.</SummaryOfMainPoints>

<BasicsOfMainPoints>Here is a listing of the main points, but put in alternative words and more basic ways of expressing those main points. </BasicsOfMainPoints>

<Conclusion> The text for the concluding comments here.</Conclusion>

 

<BasicsOfMainPoints> could be called <AlternativeExplanations>
Bottom line: This tag would be to put things forth using very straightforward terms.

Another tag would be to address how this topic/chapter is relevant:
<RealWorldApplication>This short paragraph should illustrate real world examples

of this particular topic. Why does this topic matter? How is it relevant?</RealWorldApplication>

 

On the students’ end, they could use an app that works with such tags to allow a learner to quickly see/review the different layers. That is:

  • Show me just the main points
  • Then add on the sub points
  • Then fill in the details
    OR
  • Just give me the basics via an alternative ways of expressing these things. I won’t remember all the details. Put things using easy-to-understand wording/ideas.

 

It’s like the layers of a Microsoft HoloLens app of the human anatomy:

 

Or it’s like different layers of a chapter of a “textbook” — so a learner could quickly collapse/expand the text as needed:

 

This approach could be helpful at all kinds of learning levels. For example, it could be very helpful for law school students to obtain outlines for cases or for chapters of information. Similarly, it could be helpful for dental or medical school students to get the main points as well as detailed information.

Also, as Artificial Intelligence (AI) grows, the system could check a learner’s cloud-based learner profile to see their reading level or prior knowledge, any IEP’s on file, their learning preferences (audio, video, animations, etc.), etc. to further provide a personalized/customized learning experience. 

To recap:

  • “Textbooks” continue to be created by teams of specialists, but add specialists with knowledge of students with special needs as well as for gifted students. For example, a team could have experts within the field of Special Education to help create one of the overlays/or filters/lenses — i.e., to reword things. If the text was talking about how to hit a backhand or a forehand, the alternative text layer could be summed up to say that tennis is a sport…and that a sport is something people play. On the other end of the spectrum, the text could dive deeply into the various grips a person could use to hit a forehand or backhand.
  • This puts the power of offering differentiation at the point of content creation/development (differentiation could also be provided for at the delivery end, but again, time and expertise are likely not going to be there)
  • Publishers create “overlays” or various layers that can be turned on or off by the learners
  • Can see whole chapters or can see main ideas, topic sentences, and/or details. Like HTML tags for web pages.
  • Can instantly collapse chapters to main ideas/outlines.

 

 

Robots won’t replace instructors, 2 Penn State educators argue. Instead, they’ll help them be ‘more human.’ — from edsurge.com by Tina Nazerian

Excerpt:

Specifically, it will help them prepare for and teach their courses through several phases—ideation, design, assessment, facilitation, reflection and research. The two described a few prototypes they’ve built to show what that might look like.

 

Also see:

The future of education: Online, free, and with AI teachers? — from fool.com by Simon Erickson
Duolingo is using artificial intelligence to teach 300 million people a foreign language for free. Will this be the future of education?

Excerpts:

While it might not get a lot of investor attention, education is actually one of America’s largest markets.

The U.S. has 20 million undergraduates enrolled in colleges and universities right now and another 3 million enrolled in graduate programs. Those undergrads paid an average of $17,237 for tuition, room, and board at public institutions in the 2016-17 school year and $44,551 for private institutions. Graduate education varies widely by area of focus, but the average amount paid for tuition alone was $24,812 last year.

Add all of those up, and America’s students are paying more than half a trillion dollars each year for their education! And that doesn’t even include the interest amassed for student loans, the college-branded merchandise, or all the money spent on beer and coffee.

Keeping the costs down
Several companies are trying to find ways to make college more affordable and accessible.

 

But after we launched, we have so many users that nowadays if the system wants to figure out whether it should teach plurals before adjectives or adjectives before plurals, it just runs a test with about 50,000 people. So for the next 50,000 people that sign up, which takes about six hours for 50,000 new users to come to Duolingo, to half of them it teaches plurals before adjectives. To the other half it teaches adjectives before plurals. And then it measures which ones learn better. And so once and for all it can figure out, ah it turns out for this particular language to teach plurals before adjectives for example.

So every week the system is improving. It’s making itself better at teaching by learning from our learners. So it’s doing that just based on huge amounts of data. And this is why it’s become so successful I think at teaching and why we have so many users.

 

 

From DSC:
I see AI helping learners, instructors, teachers, and trainers. I see AI being a tool to help do some of the heavy lifting, but people still like to learn with other people…with actual human beings. That said, a next generation learning platform could be far more responsive than what today’s traditional institutions of higher education are delivering.

 

 

Reflections on “Are ‘smart’ classrooms the future?” [Johnston]

Are ‘smart’ classrooms the future? — from campustechnology.com by Julie Johnston
Indiana University explores that question by bringing together tech partners and university leaders to share ideas on how to design classrooms that make better use of faculty and student time.

Excerpt:

To achieve these goals, we are investigating smart solutions that will:

  • Untether instructors from the room’s podium, allowing them control from anywhere in the room;
  • Streamline the start of class, including biometric login to the room’s technology, behind-the-scenes routing of course content to room displays, control of lights and automatic attendance taking;
  • Offer whiteboards that can be captured, routed to different displays in the room and saved for future viewing and editing;
  • Provide small-group collaboration displays and the ability to easily route content to and from these displays; and
  • Deliver these features through a simple, user-friendly and reliable room/technology interface.

Activities included collaborative brainstorming focusing on these questions:

  • What else can we do to create the classroom of the future?
  • What current technology exists to solve these problems?
  • What could be developed that doesn’t yet exist?
  • What’s next?

 

 

 

From DSC:
Though many peoples’ — including faculty members’ — eyes gloss over when we start talking about learning spaces and smart classrooms, it’s still an important topic. Personally, I’d rather be learning in an engaging, exciting learning environment that’s outfitted with a variety of tools (physically as well as digitally and virtually-based) that make sense for that community of learners. Also, faculty members have very limited time to get across campus and into the classroom and get things setup…the more things that can be automated in those setup situations the better!

I’ve long posted items re: machine-to-machine communications, voice recognition/voice-enabled interfaces, artificial intelligence, bots, algorithms, a variety of vendors and their products including Amazon’s Alexa / Apple’s Siri / Microsoft’s Cortana / and Google’s Home or Google Assistant, learning spaces, and smart classrooms, as I do think those things are components of our future learning ecosystems.

 

 

 

AI can’t replace doctors. But it can make them better. — from technologyreview.com by Rahul Parikh; via Maree Conway
A machine can collate environmental data, genetic data, and patient history way better than I can.

Excerpts:

But for physicians like me in primary care, managing 1,500 to 2,000 patients, AI presents an opportunity. I went to medical school to connect with people and make a difference. Today I often feel like an overpaid bookkeeper instead, taking in information and spitting it back to patients, prescribing drugs and adjusting doses, ordering tests. But AI in the exam room opens up the chance to recapture the art of medicine. It could let me get to know my patients better, learn how a disease uniquely affects them, and give me time to coach them toward a better outcome.

AI might also help to manage asthma flares. For many patients, asthma gets worse as air pollution levels rise, as happened this past summer when brush fires swept through Northern California. AI could let us take environmental information and respond proactively.

Not long ago, in the Journal of the American Medical Association, I saw a colorful picture drawn by a child in crayon. It portrayed her pediatrician, eyes glued to the computer, while she sat on the exam table, looking wide-eyed. I hope that AI will soon allow me to turn my attention back to that little girl.

Rahul Parikh is a pediatrician in the San Francisco Bay area.

 

 

logo.

Global installed base of smart speakers to surpass 200 million in 2020, says GlobalData

The global installed base for smart speakers will hit 100 million early next year, before surpassing the 200 million mark at some point in 2020, according to GlobalData, a leading data and analytics company.

The company’s latest report: ‘Smart Speakers – Thematic Research’ states that nearly every leading technology company is either already producing a smart speaker or developing one, with Facebook the latest to enter the fray (launching its Portal device this month). The appetite for smart speakers is also not limited by geography, with China in particular emerging as a major marketplace.

Ed Thomas, Principal Analyst for Technology Thematic Research at GlobalData, comments: “It is only four years since Amazon unveiled the Echo, the first wireless speaker to incorporate a voice-activated virtual assistant. Initial reactions were muted but the device, and the Alexa virtual assistant it contained, quickly became a phenomenon, with the level of demand catching even Amazon by surprise.”

Smart speakers give companies like Amazon, Google, Apple, and Alibaba access to a vast amount of highly valuable user data. They also allow users to get comfortable interacting with artificial intelligence (AI) tools in general, and virtual assistants in particular, increasing the likelihood that they will use them in other situations, and they lock customers into a broader ecosystem, making it more likely that they will buy complementary products or access other services, such as online stores.

Thomas continues: “Smart speakers, particularly lower-priced models, are gateway devices, in that they give consumers the opportunity to interact with a virtual assistant like Amazon’s Alexa or Google’s Assistant, in a “safe” environment. For tech companies serious about competing in the virtual assistant sector, a smart speaker is becoming a necessity, hence the recent entry of Apple and Facebook into the market and the expected arrival of Samsung and Microsoft over the next year or so.”

In terms of the competitive landscape for smart speakers, Amazon was the pioneer and is still a dominant force, although its first-mover advantage has been eroded over the last year or so. Its closest challenger is Google, but neither company is present in the fastest-growing geographic market, China. Alibaba is the leading player there, with Xiaomi also performing well.

Thomas concludes: “With big names like Samsung and Microsoft expected to launch smart speakers in the next year or so, the competitive landscape will continue to fluctuate. It is likely that we will see two distinct markets emerge: the cheap, impulse-buy end of the spectrum, used by vendors to boost their ecosystems; and the more expensive, luxury end, where greater focus is placed on sound quality and aesthetics. This is the area of the market at which Apple has aimed the HomePod and early indications are that this is where Samsung’s Galaxy Home will also look to make an impact.”

Information based on GlobalData’s report: Smart Speakers – Thematic Research

 

 

 

 

Gartner: Immersive experiences among top tech trends for 2019 — from campustechnology.com by Dian Schaffhauser

Excerpt:

IT analyst firm Gartner has named its top 10 trends for 2019, and the “immersive user experience” is on the list, alongside blockchain, quantum computing and seven other drivers influencing how we interact with the world. The annual trend list covers breakout tech with broad impact and tech that could reach a tipping point in the near future.

 

 

 

New game lets players train AI to spot legal issues — from abajournal.com by Jason Tashea

Excerpt:

Got a free minute? There’s a new game that will help train an artificial intelligence model to spot legal issues and help close the access-to-justice gap.

Called Learned Hands—yes, it’s a pun—the game takes 75,000 legal questions posted on Reddit dealing with family, consumer, criminal and other legal issues and asks the user to determine what the issue is.

While conjuring up nightmares of the first-year in law school for many lawyers, David Colarusso says it’s for a good cause.

“It’s an opportunity for attorneys to take their downtime to train machine learning algorithms to help access-to-justice issues,” says Colarusso, director of Suffolk University Law School’s Legal Innovation and Technology (LIT) Lab and partner on this project with the Stanford Legal Design Lab.

 

From learnedhands.law.stanford.edu/legalIssues

When you play the game, you’ll be spotting if different legal issues are present in people’s stories. Some of these issues will be high level categories, and others will be more specific issues.

Here are the high level categories:

 

 

MIT plans $1B computing college, AI research effort — from educationdive.com by James Paterson

Dive Brief (emphasis DSC):

  • The Massachusetts Institute of Technology is creating a College of Computing with the help of a $350 million gift from billionaire investor Stephen A. Schwarzman, who is the CEO and co-founder of the private equity firm Blackstone, in a move the university said is its “most significant reshaping” since 1950.
  • Featuring 50 new faculty positions and a new headquarters building, the $1 billion interdisciplinary initiative will bring together computer science, artificial intelligence (AI), data science and related programs across the institution. MIT will establish a new deanship for the college.
  • The new college…will explore and promote AI’s use in non-technology disciplines with a focus on ethical considerations, which are a growing concern as the technology becomes embedded in many fields.

 

Also see:

Alexa Sessions You Won’t Want to Miss at AWS re:Invent 2018 — from developer.amazon.com

Excerpts — with an eye towards where this might be leading in terms of learning spaces:

Alexa and AWS IoT — Voice is a natural interface to interact not just with the world around us, but also with physical assets and things, such as connected home devices, including lights, thermostats, or TVs. Learn how you can connect and control devices in your home using the AWS IoT platform and Alexa Skills Kit.

Connect Any Device to Alexa and Control Any Feature with the Updated Smart Home Skill API — Learn about the latest update to the Smart Home Skill API, featuring new capability interfaces you can use as building blocks to connect any device to Alexa, including those that fall outside of the traditional smart home categories of lighting, locks, thermostats, sensors, cameras, and audio/video gear. Start learning about how you can create a smarter home with Alexa.

Workshop: Build an Alexa Skill with Multiple Models — Learn how to build an Alexa skill that utilizes multiple interaction models and combines functionality into a single skill. Build an Alexa smart home skill from scratch that implements both custom interactions and smart home functionality within a single skill. Check out these resources to start learning:

 

The coming revolution in software development — from forbes.com by Matt Bornstein

Excerpt:

Amid the deep learning hype, though, many observers miss the biggest reason to be optimistic about its future: deep learning requires coders to write very little actual code. Rather than relying on preset rules or if-then statements, a deep learning system writes rules automatically based on past examples. A software developer only has to create a “rough skeleton,” to paraphrase Andrej Karpathy from Tesla, then let the computers do the rest.

In this new world, developers no longer need to design a unique algorithm for each problem. Most work focuses, instead, on generating datasets that reflect desired behavior and managing the training process. Pete Warden from Google’s TensorFlow team pointed this outas far back as 2014: “I used to be a coder,” he wrote. “Now I teach computers to write their own programs.”

Again: the programming model driving the most important advances in software today does not require a significant amount of actual programming.

What does this mean for the future of software development?

 

 

 

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