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.

 

 

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.

 

 

Affordable and at-scale — from insidehighered.com by Ray Schroeder
Affordable degrees at scale have arrived. The momentum behind this movement is undeniable, and its impact will be significant, Ray Schroeder writes.

Excerpt (emphasis DSC):

How many times have we been told that major change in our field is on the near horizon? Too many times, indeed.

The promises of technologies and practices have fallen short more often than not. Just seven years ago, I was part of the early MOOC movement and felt the pulsating potential of teaching thousands of students around the world in a single class. The “year of the MOOC” was declared in 2012. Three years later, skeptics declared that the MOOC had died an ignominious death with high “failure” rates and relatively little recognition by employers.

However, the skeptics were too impatient, misunderstood the nature of MOOCs and lacked the vision of those at Georgia Tech, the University of Illinois, Arizona State University, Coursera, edX and scores of other institutions that have persevered in building upon MOOCs’ premises to develop high-quality, affordable courses, certificates and now, degrees at scale.

No, these degrees are not free, but they are less than half the cost of on-campus versions. No, they are not massive in the hundreds of thousands, but they are certainly at large scale with many thousands enrolled. In computer science, the success is felt across the country.

 

Georgia Tech alone has enrolled 10,000 students over all in its online master’s program and is adding thousands of new students each semester in a top 10-ranked degree program costing less than $7,000. Georgia Tech broke the new ground through building collaborations among several partners. Yet, that was just the beginning, and many leading universities have followed.

 

 

Also see:

Trends for the future of education with Jeff Selingo — from steelcase.com
How the future of work and new technology will make place more important than ever.

Excerpt:

Selingo sees artificial intelligence and big data as game changers for higher education. He says AI can free up professors and advisors to spend more time with students by answering some more frequently-asked questions and handling some of the grading. He also says data can help us track and predict student performance to help them create better outcomes. “When they come in as a first-year student, we can say ‘People who came in like you that had similar high school grades and took similar classes ended up here. So, if you want to get out of here in four years and have a successful career, here are the different pathways you should follow.’”

 

 

 

Reflections on “Inside Amazon’s artificial intelligence flywheel” [Levy]

Inside Amazon’s artificial intelligence flywheel — from wired.com by Steven Levy
How deep learning came to power Alexa, Amazon Web Services, and nearly every other division of the company.

Excerpt (emphasis DSC):

Amazon loves to use the word flywheel to describe how various parts of its massive business work as a single perpetual motion machine. It now has a powerful AI flywheel, where machine-learning innovations in one part of the company fuel the efforts of other teams, who in turn can build products or offer services to affect other groups, or even the company at large. Offering its machine-learning platforms to outsiders as a paid service makes the effort itself profitable—and in certain cases scoops up yet more data to level up the technology even more.

It took a lot of six-pagers to transform Amazon from a deep-learning wannabe into a formidable power. The results of this transformation can be seen throughout the company—including in a recommendations system that now runs on a totally new machine-learning infrastructure. Amazon is smarter in suggesting what you should read next, what items you should add to your shopping list, and what movie you might want to watch tonight. And this year Thirumalai started a new job, heading Amazon search, where he intends to use deep learning in every aspect of the service.

“If you asked me seven or eight years ago how big a force Amazon was in AI, I would have said, ‘They aren’t,’” says Pedro Domingos, a top computer science professor at the University of Washington. “But they have really come on aggressively. Now they are becoming a force.”

Maybe the force.

 

 

From DSC:
When will we begin to see more mainstream recommendation engines for learning-based materials? With the demand for people to reinvent themselves, such a next generation learning platform can’t come soon enough!

  • Turning over control to learners to create/enhance their own web-based learner profiles; and allowing people to say who can access their learning profiles.
  • AI-based recommendation engines to help people identify curated, effective digital playlists for what they want to learn about.
  • Voice-driven interfaces.
  • Matching employees to employers.
  • Matching one’s learning preferences (not styles) with the content being presented as one piece of a personalized learning experience.
  • From cradle to grave. Lifelong learning.
  • Multimedia-based, interactive content.
  • Asynchronously and synchronously connecting with others learning about the same content.
  • Online-based tutoring/assistance; remote assistance.
  • Reinvent. Staying relevant. Surviving.
  • Competency-based learning.

 

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

 

 

 

 

 

 

 

We’re about to embark on a period in American history where career reinvention will be critical, perhaps more so than it’s ever been before. In the next decade, as many as 50 million American workers—a third of the total—will need to change careers, according to McKinsey Global Institute. Automation, in the form of AI (artificial intelligence) and RPA (robotic process automation), is the primary driver. McKinsey observes: “There are few precedents in which societies have successfully retrained such large numbers of people.”

Bill Triant and Ryan Craig

 

 

 

Also relevant/see:

Online education’s expansion continues in higher ed with a focus on tech skills — from educationdive.com by James Paterson

Dive Brief:

  • Online learning continues to expand in higher ed with the addition of several online master’s degrees and a new for-profit college that offers a hybrid of vocational training and liberal arts curriculum online.
  • Inside Higher Ed reported the nonprofit learning provider edX is offering nine master’s degrees through five U.S. universities — the Georgia Institute of Technology, the University of Texas at Austin, Indiana University, Arizona State University and the University of California, San Diego. The programs include cybersecurity, data science, analytics, computer science and marketing, and they cost from around $10,000 to $22,000. Most offer stackable certificates, helping students who change their educational trajectory.
  • Former Harvard University Dean of Social Science Stephen Kosslyn, meanwhile, will open Foundry College in January. The for-profit, two-year program targets adult learners who want to upskill, and it includes training in soft skills such as critical thinking and problem solving. Students will pay about $1,000 per course, though the college is waiving tuition for its first cohort.

 

 

 

The 11 coolest announcements Google made at its biggest product event of the year — from businessinsider.com by Dave Smith

Excerpt:

On Tuesday (10/9), Google invited journalists to New York City to debut its newest smartphone, the Pixel 3, among several other hardware goodies.

Google made dozens of announcements in its 75-minute event, but a handful of “wow” moments stole the show.

Here are the 11 coolest announcements Google made at its big hardware event…

 

 

 

Google’s Pixel 3 event in 6 minutes — from businessinsider.com

 

 

Google unveiled its new Home Hub but it might be ‘late to the market’ — from barrons.com by Emily Bary

Excerpt:

Alphabet ’s Google has sold millions of voice-enabled speakers, but it inched toward the future at a Tuesday launch event when it introduced the Home Hub smart screen.

Google isn’t the first company to roll out a screen-enabled home device with voice-assistant technology— Amazon.com released its Echo Show in July 2017. Meanwhile, Lenovo has gotten good reviews for its Smart Display, and Facebook introduced the Portal on Monday.

For the most part, though, consumers have stuck to voice-only devices, and it will be up to Google and its rivals to convince them that an added screen is worth paying for. They’ll also have to reassure consumers that they can trust big tech to maintain their privacy, an admittedly harder task these days after recent security issues at Google and Facebook.

 

Amazon was right to realize early on that consumers aren’t always comfortable buying items they can’t even see pictures of, and that it’s hard to remember directions you’ve heard but not seen.

 

 

Google has announced its first smart speaker with a screen — from businessinsider.com by Brandt Ranj

  • Google has announced the Google Hub, its first smart home speaker with a screen. It’s available for pre-order at Best Buy, Target, and Walmart for $149. The Hub will be released on October 22.
  • The Hub has a 7″ touchscreen and sits on a base with a built-in speaker and microphones, which you can use to play music, watch videos, get directions, and control smart home accessories with your voice.
  • Its biggest advantage is its ability to hook into Google’s first-party services, like YouTube and Google Maps, which none of its competitors can use.
  • If you’re an Android or Chromecast user, trust Google more than Amazon, or want a smaller smart home speaker with a screen, the Google Hub is now your best bet.

 

 

Google is shutting down Google+ for consumers following security lapse — from theverge.com by Ashley Carman

Excerpt:

Google is going to shut down the consumer version of Google+ over the next 10 months, the company writes in a blog post today. The decision follows the revelation of a previously undisclosed security flaw that exposed users’ profile data that was remedied in March 2018.

 

 

Google shutters Google+ after users’ personal data exposed — from cbsnews.com

 

 

Google+ is shutting down, and the site’s few loyal users are mourning – from cnbc.com by Jillian D’Onfro

  • Even though Google had diverted resources away from Google+, there’s a small loyal base of users devastated to see it go away.
  • One fan said the company is using its recently revealed security breach as an excuse to shutter the site.
  • Google said it will be closing Google+ in the coming months.

 

 

 

10 jobs that are safe in an AI world — from linkedin.com by Kai-Fu Lee

Excerpts:

Teaching
AI will be a great tool for teachers and educational institutions, as it will help educators figure out how to personalize curriculum based on each student’s competence, progress, aptitude, and temperament. However, teaching will still need to be oriented around helping students figure out their interests, teaching students to learn independently, and providing one-on-one mentorship. These are tasks that can only be done by a human teacher. As such, there will still be a great need for human educators in the future.

Criminal defense law
Top lawyers will have nothing to worry about when it comes to job displacement. reasoning across domains, winning the trust of clients, applying years of experience in the courtroom, and having the ability to persuade a jury are all examples of the cognitive complexities, strategies, and modes of human interaction that are beyond the capabilities of AI. However, a lot of paralegal and preparatory work like document review, analysis, creating contracts, handling small cases, packing cases, and coming up with recommendations can be done much better and more efficiently with AI. The costs of law make it worthwhile for AI companies to go after AI paralegals and AI junior lawyers, but not top lawyers.

 

From DSC:
In terms of teaching, I agree that while #AI will help personalize learning, there will still be a great need for human teachers, professors, and trainers. I also agree w/ my boss (and with some of the author’s viewpoints here, but not all) that many kinds of legal work will still need the human touch & thought processes. I diverge from his thinking in terms of scope — the need for human lawyers will go far beyond just lawyers involved in crim law.

 

Also see:

15 business applications for artificial intelligence and machine learning — from forbes.com

Excerpt:

Fifteen members of Forbes Technology Council discuss some of the latest applications they’ve found for AI/ML at their companies. Here’s what they had to say…

 

 

 

Microsoft's conference room of the future

 

From DSC:
Microsoft’s conference room of the future “listens” to the conversations of the team and provides a transcript of the meeting. It also is using “artificial intelligence tools to then act on what meeting participants say. If someone says ‘I’ll follow up with you next week,’ then they’ll get a notification in Microsoft Teams, Microsoft’s Slack competitor, to actually act on that promise.”

This made me wonder about our learning spaces in the future. Will an #AI-based device/cloud-based software app — in real-time — be able to “listen” to the discussion in a classroom and present helpful resources in the smart classroom of the future (i.e., websites, online-based databases, journal articles, and more)?

Will this be a feature of a next generation learning platform as well (i.e., addressing the online-based learning realm)? Will this be a piece of an intelligent tutor or an intelligent system?

Hmmm…time will tell.

 

 


 

Also see this article out at Forbes.com entitled, “There’s Nothing Artificial About How AI Is Changing The Workplace.” 

Here is an excerpt:

The New Meeting Scribe: Artificial Intelligence

As I write this, AI has already begun to make video meetings even better. You no longer have to spend time entering codes or clicking buttons to launch a meeting. Instead, with voice-based AI, video conference users can start, join or end a meeting by simply speaking a command (think about how you interact with Alexa).

Voice-to-text transcription, another artificial intelligence feature offered by Otter Voice Meeting Notes (from AISense, a Zoom partner), Voicefox and others, can take notes during video meetings, leaving you and your team free to concentrate on what’s being said or shown. AI-based voice-to-text transcription can identify each speaker in the meeting and save you time by letting you skim the transcript, search and analyze it for certain meeting segments or words, then jump to those mentions in the script. Over 65% of respondents from the Zoom survey said they think AI will save them at least one hour a week of busy work, with many claiming it will save them one to five hours a week.

 

 

Reimagining the Higher Education Ecosystem — from edu2030.agorize.com
How might we empower people to design their own learning journeys so they can lead purposeful and economically stable lives?

Excerpts:

The problem
Technology is rapidly transforming the way we live, learn, and work. Entirely new jobs are emerging as others are lost to automation. People are living longer, yet switching jobs more often. These dramatic shifts call for a reimagining of the way we prepare for work and life—specifically, how we learn new skills and adapt to a changing economic landscape.

The changes ahead are likely to hurt most those who can least afford to manage them: low-income and first generation learners already ill-served by our existing postsecondary education system. Our current system stifles economic mobility and widens income and achievement gaps; we must act now to ensure that we have an educational ecosystem flexible and fair enough to help all people live purposeful and economically stable lives. And if we are to design solutions proportionate to this problem, new technologies must be called on to scale approaches that reach the millions of vulnerable people across the country.

 

The challenge
How might we empower people to design their own learning journeys so they can lead purposeful and economically stable lives?

The Challenge—Reimagining the Higher Education Ecosystem—seeks bold ideas for how our postsecondary education system could be reimagined to foster equity and encourage learner agency and resilience. We seek specific pilots to move us toward a future in which all learners can achieve economic stability and lead purposeful lives. This Challenge invites participants to articulate a vision and then design pilot projects for a future ecosystem that has the following characteristics:

Expands access: The educational system must ensure that all people—including low-income learners who are disproportionately underserved by the current higher education system—can leverage education to live meaningful and economically stable lives.

Draws on a broad postsecondary ecosystem: While college and universities play a vital role in educating students, there is a much larger ecosystem in which students learn. This ecosystem includes non-traditional “classes” or alternative learning providers, such as MOOCs, bootcamps, and online courses as well as on-the-job training and informal learning. Our future learning system must value the learning that happens in many different environments and enable seamless transitions between learning, work, and life.

 

From DSC:
This is where I could see a vision similar to Learning from the Living [Class] Room come into play. It would provide a highly affordable, accessible platform, that would offer more choice, and more control to learners of all ages. It would be available 24×7 and would be a platform that supports lifelong learning. It would combine a variety of AI-enabled functionalities with human expertise, teaching, training, motivation, and creativity.

It could be that what comes out of this challenge will lay the groundwork for a future, massive new learning platform.

 

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

 

Also see:

 

 

 

Google’s robot assistant now makes eerily lifelike phone calls for you — from theguardian.com by Olivia Solon
Google Duplex contacts hair salon and restaurant in demo, adding ‘er’ and ‘mmm-hmm’ so listeners think it’s human

Excerpt:

Google’s virtual assistant can now make phone calls on your behalf to schedule appointments, make reservations in restaurants and get holiday hours.

The robotic assistant uses a very natural speech pattern that includes hesitations and affirmations such as “er” and “mmm-hmm” so that it is extremely difficult to distinguish from an actual human phone call.

The unsettling feature, which will be available to the public later this year, is enabled by a technology called Google Duplex, which can carry out “real world” tasks on the phone, without the other person realising they are talking to a machine. The assistant refers to the person’s calendar to find a suitable time slot and then notifies the user when an appointment is scheduled.

 

 

Google employees quit over the company’s military AI project — from thenextweb.com by Tristan Greene

Excerpt:

About a dozen Google employees reportedly left the company over its insistence on developing AI for the US military through a program called Project Maven. Meanwhile 4,000 others signed a petition demanding the company stop.

It looks like there’s some internal confusion over whether the company’s “Don’t Be Evil” motto covers making machine learning systems to aid warfare.

 

 

 

The link between big tech and defense work — from wired.com by Nitasha Tiku

Except:

FOR MONTHS, A growing faction of Google employees has tried to force the company to drop out of a controversial military program called Project Maven. More than 4,000 employees, including dozens of senior engineers, have signed a petition asking Google to cancel the contract. Last week, Gizmodo reported that a dozen employees resigned over the project. “There are a bunch more waiting for job offers (like me) before we do so,” one engineer says. On Friday, employees communicating through an internal mailing list discussed refusing to interview job candidates in order to slow the project’s progress.

Other tech giants have recently secured high-profile contracts to build technology for defense, military, and intelligence agencies. In March, Amazon expanded its newly launched “Secret Region” cloud services supporting top-secret work for the Department of Defense. The same week that news broke of the Google resignations, Bloomberg reported that Microsoft locked down a deal with intelligence agencies. But there’s little sign of the same kind of rebellion among Amazon and Microsoft workers.

 

 

Amazon urged not to sell facial recognition tool to police — from wpxi.com by Gene Johnson

Excerpt:

Facebook SEATTLE (AP) – The American Civil Liberties Union and other privacy advocates are asking Amazon to stop marketing a powerful facial recognition tool to police, saying law enforcement agencies could use the technology to “easily build a system to automate the identification and tracking of anyone.”

The tool, called Rekognition, is already being used by at least one agency – the Washington County Sheriff’s Office in Oregon – to check photographs of unidentified suspects against a database of mug shots from the county jail, which is a common use of such technology around the country.

 

 

From DSC:
Google’s C-Suite — as well as the C-Suites at Microsoft, Amazon, and other companies — needs to be very careful these days, as they could end up losing the support/patronage of a lot of people — including more of their own employees. It’s not an easy task to know how best to build and use technologies in order to make the world a better place…to create a dream vs. a nightmare for our future. But just because we can build something, doesn’t mean we should.

 

 

The Complete Guide to Conversational Commerce | Everything you need to know. — from chatbotsmagazine.com by Matt Schlicht

Excerpt:

What is conversational commerce? Why is it such a big opportunity? How does it work? What does the future look like? How can I get started? These are the questions I’m going to answer for you right now.

The guide covers:

  • An introduction to conversational commerce.
  • Why conversational commerce is such a big opportunity.
  • Complete breakdown of how conversational commerce works.
  • Extensive examples of conversational commerce using chatbots and voicebots.
  • How artificial intelligence impacts conversational commerce.
  • What the future of conversational commerce will look like.

 

Definition: Conversational commerce is an automated technology, powered by rules and sometimes artificial intelligence, that enables online shoppers and brands to interact with one another via chat and voice interfaces.

 

 

 

Notes from the AI frontier: Applications and value of deep learning — from mckinsey.com by Michael Chui, James Manyika, Mehdi Miremadi, Nicolaus Henke, Rita Chung, Pieter Nel, and Sankalp Malhotra

Excerpt:

Artificial intelligence (AI) stands out as a transformational technology of our digital age—and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer “deep learning” techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles—along with future opportunities as the technologies continue their advance. Ultimately, the value of AI is not to be found in the models themselves, but in companies’ abilities to harness them.

It is important to highlight that, even as we see economic potential in the use of AI techniques, the use of data must always take into account concerns including data security, privacy, and potential issues of bias.

  1. Mapping AI techniques to problem types
  2. Insights from use cases
  3. Sizing the potential value of AI
  4. The road to impact and value

 

 

 

AI for Good — from re-work.co by Ali Shah, Head of Emerging Technology and Strategic Direction – BBC

 

 

 

Algorithms are making the same mistakes assessing credit scores that humans did a century ago — from qz.com by Rachel O’Dwyer

 

 

 

 

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