Forecast 5.0 – The Future of Learning: Navigating the Future of Learning  — from knowledgeworks.org by Katherine Prince, Jason Swanson, and Katie King
Discover how current trends could impact learning ten years from now and consider ways to shape a future where all students can thrive.

 

 

 

AI Now Report 2018 | December 2018  — from ainowinstitute.org

Meredith Whittaker , AI Now Institute, New York University, Google Open Research
Kate Crawford , AI Now Institute, New York University, Microsoft Research
Roel Dobbe , AI Now Institute, New York University
Genevieve Fried , AI Now Institute, New York University
Elizabeth Kaziunas , AI Now Institute, New York University
Varoon Mathur , AI Now Institute, New York University
Sarah Myers West , AI Now Institute, New York University
Rashida Richardson , AI Now Institute, New York University
Jason Schultz , AI Now Institute, New York University School of Law
Oscar Schwartz , AI Now Institute, New York University

With research assistance from Alex Campolo and Gretchen Krueger (AI Now Institute, New York University)

Excerpt (emphasis DSC):

Building on our 2016 and 2017 reports, the AI Now 2018 Report contends with this central problem, and provides 10 practical recommendations that can help create accountability frameworks capable of governing these powerful technologies.

  1. Governments need to regulate AI by expanding the powers of sector-specific agencies to oversee, audit, and monitor these technologies by domain.
  2. Facial recognition and affect recognition need stringent regulation to protect the public interest.
  3. The AI industry urgently needs new approaches to governance. As this report demonstrates, internal governance structures at most technology companies are failing to ensure accountability for AI systems.
  4. AI companies should waive trade secrecy and other legal claims that stand in the way of accountability in the public sector.
  5. Technology companies should provide protections for conscientious objectors, employee organizing, and ethical whistleblowers.
  6.  Consumer protection agencies should apply “truth-in-advertising” laws to AI products and services.
  7. Technology companies must go beyond the “pipeline model” and commit to addressing the practices of exclusion and discrimination in their workplaces.
  8. Fairness, accountability, and transparency in AI require a detailed account of the “full stack supply chain.”
  9. More funding and support are needed for litigation, labor organizing, and community participation on AI accountability issues.
  10. University AI programs should expand beyond computer science and engineering disciplines. AI began as an interdisciplinary field, but over the decades has narrowed to become a technical discipline. With the increasing application of AI systems to social domains, it needs to expand its disciplinary orientation. That means centering forms of expertise from the social and humanistic disciplines. AI efforts that genuinely wish to address social implications cannot stay solely within computer science and engineering departments, where faculty and students are not trained to research the social world. Expanding the disciplinary orientation of AI research will ensure deeper attention to social contexts, and more focus on potential hazards when these systems are applied to human populations.

 

Also see:

After a Year of Tech Scandals, Our 10 Recommendations for AI — from medium.com by the AI Now Institute
Let’s begin with better regulation, protecting workers, and applying “truth in advertising” rules to AI

 

Also see:

Excerpt:

As we discussed, this technology brings important and even exciting societal benefits but also the potential for abuse. We noted the need for broader study and discussion of these issues. In the ensuing months, we’ve been pursuing these issues further, talking with technologists, companies, civil society groups, academics and public officials around the world. We’ve learned more and tested new ideas. Based on this work, we believe it’s important to move beyond study and discussion. The time for action has arrived.

We believe it’s important for governments in 2019 to start adopting laws to regulate this technology. The facial recognition genie, so to speak, is just emerging from the bottle. Unless we act, we risk waking up five years from now to find that facial recognition services have spread in ways that exacerbate societal issues. By that time, these challenges will be much more difficult to bottle back up.

In particular, we don’t believe that the world will be best served by a commercial race to the bottom, with tech companies forced to choose between social responsibility and market success. We believe that the only way to protect against this race to the bottom is to build a floor of responsibility that supports healthy market competition. And a solid floor requires that we ensure that this technology, and the organizations that develop and use it, are governed by the rule of law.

 

From DSC:
This is a major heads up to the American Bar Association (ABA), law schools, governments, legislatures around the country, the courts, the corporate world, as well as for colleges, universities, and community colleges. The pace of emerging technologies is much faster than society’s ability to deal with them! 

The ABA and law schools need to majorly pick up their pace — for the benefit of all within our society.

 

 

 

The information below is from Heather Campbell at Chegg
(emphasis DSC)


 

Chegg Math Solver is an AI-driven tool to help the student understand math. It is more than just a calculator – it explains the approach to solving the problem. So, students won’t just copy the answer but understand and can solve similar problems at the same time. Most importantly,students can dig deeper into a problem and see why it’s solved that way. Chegg Math Solver.

In every subject, there are many key concepts and terms that are crucial for students to know and understand. Often it can be hard to determine what the most important concepts and terms are for a given subject, and even once you’ve identified them you still need to understand what they mean. To help you learn and understand these terms and concepts, we’ve provided thousands of definitions, written and compiled by Chegg experts. Chegg Definition.

 

 

 

 

 


From DSC:
I see this type of functionality as a piece of a next generation learning platform — a piece of the Living from the Living [Class] Room type of vision. Great work here by Chegg!

Likely, students will also be able to take pictures of their homework, submit it online, and have that image/problem analyzed for correctness and/or where things went wrong with it.

 

 


 

 

Intelligent Machines: One of the fathers of AI is worried about its future — from technologyreview.com by Will Knight
Yoshua Bengio wants to stop talk of an AI arms race and make the technology more accessible to the developing world.

Excerpts:

Yoshua Bengio is a grand master of modern artificial intelligence.

Alongside Geoff Hinton and Yann LeCun, Bengio is famous for championing a technique known as deep learning that in recent years has gone from an academic curiosity to one of the most powerful technologies on the planet.

Deep learning involves feeding data to large neural networks that crudely simulate the human brain, and it has proved incredibly powerful and effective for all sorts of practical tasks, from voice recognition and image classification to controlling self-driving cars and automating business decisions.

Bengio has resisted the lure of any big tech company. While Hinton and LeCun joined Google and Facebook, respectively, he remains a full-time professor at the University of Montreal. (He did, however, cofound Element AI in 2016, and it has built a very successful business helping big companies explore the commercial applications of AI research.)

Bengio met with MIT Technology Review’s senior editor for AI, Will Knight, at an MIT event recently.

What do you make of the idea that there’s an AI race between different countries?

I don’t like it. I don’t think it’s the right way to do it.

We could collectively participate in a race, but as a scientist and somebody who wants to think about the common good, I think we’re better off thinking about how to both build smarter machines and make sure AI is used for the well-being of as many people as possible.

 

 

Alexa, get me the articles (voice interfaces in academia) — from blog.libux.co by Kelly Dagan

Excerpt:

Credit to Jill O’Neill, who has written an engaging consideration of applications, discussions, and potentials for voice-user interfaces in the scholarly realm. She details a few use case scenarios: finding recent, authoritative biographies of Jane Austen; finding if your closest library has an item on the shelf now (and whether it’s worth the drive based on traffic).

Coming from an undergraduate-focused (and library) perspective, I can think of a few more:

  • asking if there are any group study rooms available at 7 pm and making a booking
  • finding out if [X] is open now (Archives, the Cafe, the Library, etc.)
  • finding three books on the Red Brigades, seeing if they are available, and saving the locations
  • grabbing five research articles on stereotype threat, to read later

 

Also see:

 

 

 

Mama Mia It’s Sophia: A Show Robot Or Dangerous Platform To Mislead? — from forbes.com by Noel Sharkey

Excerpts:

A collective eyebrow was raised by the AI and robotics community when the robot Sophia was given Saudia citizenship in 2017 The AI sharks were already circling as Sophia’s fame spread with worldwide media attention. Were they just jealous buzz-kills or is something deeper going on? Sophia has gripped the public imagination with its interesting and fun appearances on TV and on high-profile conference platforms.

Sophia is not the first show robot to attain celebrity status. Yet accusations of hype and deception have proliferated about the misrepresentation of AI to public and policymakers alike. In an AI-hungry world where decisions about the application of the technologies will impact significantly on our lives, Sophia’s creators may have crossed a line. What might the negative consequences be? To get answers, we need to place Sophia in the context of earlier show robots.

 

 

A dangerous path for our rights and security
For me, the biggest problem with the hype surrounding Sophia is that we have entered a critical moment in the history of AI where informed decisions need to be made. AI is sweeping through the business world and being delegated decisions that impact significantly on peoples lives from mortgage and loan applications to job interviews, to prison sentences and bail guidance, to transport and delivery services to medicine and care.

It is vitally important that our governments and policymakers are strongly grounded in the reality of AI at this time and are not misled by hype, speculation, and fantasy. It is not clear how much the Hanson Robotics team are aware of the dangers that they are creating by appearing on international platforms with government ministers and policymakers in the audience.

 

 

Combining retrieval, spacing, and feedback boosts STEM learning — from retrievalpractice.org

Punchline:
Scientists demonstrated that when college students used a quizzing program that combined retrieval practice, spacing, and feedback, exam performance increased by nearly a letter grade.

—-

Abstract
The most effective educational interventions often face significant barriers to widespread implementation because they are highly specific, resource intense, and/or comprehensive. We argue for an alternative approach to improving education: leveraging technology and cognitive science to develop interventions that generalize, scale, and can be easily implemented within any curriculum. In a classroom experiment, we investigated whether three simple, but powerful principles from cognitive science could be combined to improve learning. Although implementation of these principles only required a few small changes to standard practice in a college engineering course, it significantly increased student performance on exams. Our findings highlight the potential for developing inexpensive, yet effective educational interventions that can be implemented worldwide.

In summary, the combination of spaced retrieval practice and required feedback viewing had a powerful effect on student learning of complex engineering material. Of course, the principles from cognitive science could have been applied without the use of technology. However, our belief is that advances in technology and ideas from machine learning have the potential to exponentially increase the effectiveness and impact of these principles. Automation is an important benefit, but technology also can provide a personalized learning experience for a rapidly growing, diverse body of students who have different knowledge and academic backgrounds. Through the use of data mining, algorithms, and experimentation, technology can help us understand how best to implement these principles for individual learners while also producing new discoveries about how people learn. Finally, technology facilitates access. Even if an intervention has a small effect size, it can still have a substantial impact if broadly implemented. For example, aspirin has a small effect on preventing heart attacks and strokes when taken regularly, but its impact is large because it is cheap and widely available. The synergy of cognitive science, machine learning, and technology has the potential to produce inexpensive, but powerful learning tools that generalize, scale, and can be easily implemented worldwide.

Keywords: Education. Technology. Retrieval practice. Spacing. Feedback. Transfer of learning.

 

 

From DSC:
I agree with futurist Thomas Frey:

“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.”

(source)

 

Along these lines, see what Arizona State University is up to:

We think of this as a transformation away from a mass-production model to a mass-personalization model. For us, that’s the big win in this whole process. When we move away from the large lectures in that mass-production model that we’ve used for the last 170 years and get into something that reflects each of the individual learners’ needs and can personalize their learning path through the instructional resources, we will have successfully moved the education industry to the new frontier in the learning process. We think that mass personalization has already permeated every aspect of our lives, from navigation to entertainment; and education is really the next big frontier.

(source)

 

From DSC:
Each year the vision I outlined here gets closer and closer and closer and closer. With the advancements in Artificial Intelligence (AI), change is on the horizon…big time. Mass personalization. More choice. More control.

 

 

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.

 

 

LinkedIn Learning Opens Its Platform (Slightly) [Young]

LinkedIn Learning Opens Its Platform (Slightly) — from edsurge by Jeff Young

Excerpt (emphasis DSC):

A few years ago, in a move toward professional learning, LinkedIn bought Lynda.com for $1.5 billion, adding the well-known library of video-based courses to its professional social network. Today LinkedIn officials announced that they plan to open up their platform to let in educational videos from other providers as well—but with a catch or two.

The plan, announced Friday, is to let companies or colleges who already subscribe to LinkedIn Learning add content from a select group of other providers. The company or college will still have to subscribe to those other services separately, so it’s essentially an integration—but it does mark a change in approach.

For LinkedIn, the goal is to become the front door for employees as they look for micro-courses for professional development.

 

LinkedIn also announced another service for its LinkedIn Learning platform called Q&A, which will give subscribers the ability to pose a question they have about the video lessons they’re taking. The question will first be sent to bots, but if that doesn’t yield an answer the query will be sent on to other learners, and in some cases the instructor who created the videos.

 

 

Also see:

LinkedIn becomes a serious open learning experience platform — from clomedia.com by Josh Bersin
LinkedIn is becoming a dominant learning solution with some pretty interesting competitive advantages, according to one learning analyst.

Excerpt:

LinkedIn has become quite a juggernaut in the corporate learning market. Last time I checked the company had more than 17 million users, 14,000 corporate customers, more than 3,000 courses and was growing at high double-digit rates. And all this in only about two years.

And the company just threw down the gauntlet; it’s now announcing it has completely opened up its learning platform to external content partners. This is the company’s formal announcement that LinkedIn Learning is not just an amazing array of content, it is a corporate learning platform. The company wants to become a single place for all organizational learning content.

 

LinkedIn now offers skills-based learning recommendations to any user through its machine learning algorithms. 

 

 



Is there demand for staying relevant? For learning new skills? For reinventing oneself?

Well…let’s see.

 

 

 

 

 

 



From DSC:
So…look out higher ed and traditional forms of accreditation — your window of opportunity may be starting to close. Alternatives to traditional higher ed continue to appear on the scene and gain momentum. LinkedIn — and/or similar organizations in the future — along with blockchain and big data backed efforts may gain traction in the future and start taking away some major market share. If employers get solid performance from their employees who have gone this route…higher ed better look out. 

Microsoft/LinkedIn/Lynda.com are nicely positioned to be a major player who can offer society a next generation learning platform at an incredible price — offering up-to-date, microlearning along with new forms of credentialing. It’s what I’ve been calling the Amazon.com of higher ed (previously the Walmart of Education) for ~10 years. It will take place in a strategy/platform similar to this one.

 



Also, this is what a guerilla on the back looks like:

 

This is what a guerilla on the back looks like!

 



Also see:

  • Meet the 83-Year-Old App Developer Who Says Edtech Should Better Support Seniors — from edsurge.com by Sydney Johnson
    Excerpt (emphasis DSC):
    Now at age 83, Wakamiya beams with excitement when she recounts her journey, which has been featured in news outlets and even at Apple’s developer conference last year. But through learning how to code, she believes that experience offers an even more important lesson to today’s education and technology companies: don’t forget about senior citizens.Today’s education technology products overwhelmingly target young people. And while there’s a growing industry around serving adult learners in higher education, companies largely neglect to consider the needs of the elderly.

 

 

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.

 

 

The global companies that failed to adapt to change. — from trainingmag.com by Professor M.S. Rao, Ph.D.

Excerpt:

Eastman Kodak, a leader for many years, filed for bankruptcy in 2012. Blockbuster Video became defunct in 2013. Similarly, Borders — one of the largest book retailers in the U.S. — went out of business in 2011. Why did these companies, which once had great brands, ultimately fail? It is because they failed to adapt to change. Additionally, they failed to unlearn and relearn.

Former GE CEO Jack Welch once remarked, “If the rate of change on the outside exceeds the rate of change on the inside, the end is near.” Thus, accept change before the change is thrust on you.

Leaders must adopt tools and techniques to adapt to change. Here is a blueprint to embrace change effectively:

  • Keep the vision right and straight, and articulate it effectively.
  • Create organizational culture conducive to bring about change.
  • Communicate clearly about the need to change.
  • Enlighten people about the implications of the status quo.
  • Show them benefits once the change is implemented.
  • Coordinate all stakeholders effectively.
  • Remove the roadblocks by allaying their apprehensions.
  • Show them small gains to ensure that entire change takes place smoothly without any resistance.

 

From DSC:
Though I’m not on board with all of the perspectives in that article, institutions of traditional higher education likely have something to learn from the failures of these companies….while there’s still time to change and to innovate. 

 

 

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.

 

 

Amazon’s new goal: Teach 10 million kids a year to code — from businessinsider.com by Joseph Pisani

Excerpt:

NEW YORK (AP) — Amazon wants to get more kids thinking about becoming computer engineers.

The company launched a program Thursday that aims to teach more than 10 million students a year how to code. Amazon said it will pay for summer camps, teacher training and other initiatives to benefit kids and young adults from low-income families who might not have learned to code otherwise. It hopes the programs spur more black, Hispanic and female students to study computer science.

 

From DSC:
It will be interesting to see how artificial intelligence impacts the demand for programmers as the years progress. 

 

 

Virtual digital assistants in the workplace: Still nascent, but developing — from cisco.com by Pat Brans
As workers get overwhelmed with daily tasks, they want virtual digital assistants in the workplace that can alleviate some of the burden.

Excerpts:

As life gets busier, knowledge workers are struggling with information overload.

They’re looking for a way out, and that way, experts say, will eventually involve virtual digital assistants (VDAs). Increasingly, workers need to complete myriad tasks, often seemingly simultaneously. And as the pace of business continues to drive ever faster, hands-free, intelligent technology that can speed administrative tasks holds obvious appeal.

So far, scenarios in which digital assistants in the workplace enhance productivity fall into three categories: scheduling, project management, and improved interfaces to enterprise applications. “Using digital assistants to perform scheduling has clear benefits,” Beccue said.

“Scheduling meetings and managing calendars takes a long time—many early adopters are able to quantify the savings they get when the scheduling is performed by a VDA. Likewise, when VDAs are used to track project status through daily standup meetings, project managers can easily measure the time saved.”

 

Perhaps the most important change we’ll see in future generations of VDA technology for workforce productivity will be the advent of general-purpose VDAs that help users with all tasks. These VDAs will be multi-channel (providing interfaces through mobile apps, messaging, telephone, and so on) and they will be bi-modal (enlisting text and voice).

 

 

 

 
© 2024 | Daniel Christian