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.

 

 

 

 

In the 2030 and beyond world, employers will no longer be a separate entity from the education establishment. Pressures from both the supply and demand side are so large that employers and learners will end up, by default, co-designing new learning experiences, where all learning counts.

 

OBJECTIVES FOR CONVENINGS

  • Identify the skills everyone will need to navigate the changing relationship between machine intelligence and people over the next 10-12 years.
  • Develop implications for work, workers, students, working learners, employers, and policymakers.
  • Identify a preliminary set of actions that need to be taken now to best prepare for the changing work + learn ecosystem.

Three key questions guided the discussions:

  1. What are the LEAST and MOST essential skills needed for the future?
  2. Where and how will tomorrow’s workers and learners acquire the skills they really need?
  3. Who is accountable for making sure individuals can thrive in this new economy?

This report summarizes the experts’ views on what skills will likely be needed to navigate the work + learn ecosystem over the next 10–15 years—and their suggested steps for better serving the nation’s future needs.

 

In a new world of work, driven especially by AI, institutionally-sanctioned curricula could give way to AI-personalized learning. This would drastically change the nature of existing social contracts between employers and employees, teachers and students, and governments and citizens. Traditional social contracts would need to be renegotiated or revamped entirely. In the process, institutional assessment and evaluation could well shift from top-down to new bottom-up tools and processes for developing capacities, valuing skills, and managing performance through new kinds of reputation or accomplishment scores.

 

In October 2017, Chris Wanstrath, CEO of Github, the foremost code-sharing and social networking resource for programmers today, made a bold statement: “The future of coding is no coding at all.” He believes that the writing of code will be automated in the near future, leaving humans to focus on “higher-level strategy and design of software.” Many of the experts at the convenings agreed. Even creating the AI systems of tomorrow, they asserted, will likely require less human coding than is needed today, with graphic interfaces turning AI programming into a drag-and-drop operation.

Digital fluency does not mean knowing coding languages. Experts at both convenings contended that effectively “befriending the machine” will be less about teaching people to code and more about being able to empathize with AIs and machines, understanding how they “see the world” and “think” and “make decisions.” Machines will create languages to talk to one another.

Here’s a list of many skills the experts do not expect to see much of—if at all—in the future:

  • Coding. Systems will be self-programming.
  • Building AI systems. Graphic interfaces will turn AI programming into drag-and-drop operations.
  • Calendaring, scheduling, and organizing. There won’t be need for email triage.
  • Planning and even decision-making. AI assistants will pick this up.
  • Creating more personalized curricula. Learners may design more of their own personalized learning adventure.
  • Writing and reviewing resumes. Digital portfolios, personal branding, and performance reputation will replace resumes.
  • Language translation and localization. This will happen in real time using translator apps.
  • Legal research and writing. Many of our legal systems will be automated.
  • Validation skills. Machines will check people’s work to validate their skills.
  • Driving. Driverless vehicles will replace the need to learn how to drive.

Here’s a list of the most essential skills needed for the future:

  • Quantitative and algorithmic thinking.  
  • Managing reputation.  
  • Storytelling and interpretive skills.  
  • First principles thinking.  
  • Communicating with machines as machines.  
  • Augmenting high-skilled physical tasks with AI.
  • Optimization and debugging frame of mind.
  • Creativity and growth mindset.
  • Adaptability.
  • Emotional intelligence.
  • Truth seeking.
  • Cybersecurity.

 

The rise of machine intelligence is just one of the many powerful social, technological, economic, environmental, and political forces that are rapidly and disruptively changing the way everyone will work and learn in the future. Because this largely tech-driven force is so interconnected with other drivers of change, it is nearly impossible to understand the impact of intelligent agents on how we will work and learn without also imagining the ways in which these new tools will reshape how we live.

 

 

 

Benchmarking Higher Ed AV Staffing Levels — Revisited — from campustechnology.com by Mike Tomei
As AV-equipped classrooms on campus increase in both numbers and complexity, have AV departments staffed up accordingly? A recent survey sheds some light on how AV is managed in higher education.

Excerpt:

I think we can all agree that new AV system installs have a much higher degree of complexity compared to AV systems five or 10 years ago. The obvious culprits are active learning classrooms that employ multiple displays and matrix switching backends, and conferencing systems of varying complexity being installed in big and small rooms all over campus. But even if today’s standard basic classrooms are offering the same presentation functionality as they were five years ago, the backend AV technology running those systems has still increased in complexity. We’re trying to push very high resolution video signals around the room; copyright-protected digital content is coming into play; there are myriad BYOD devices and connectors that need to be supported; and we’re making a strong push to connect our AV devices to the enterprise network for monitoring and troubleshooting. This increase in AV system complexity just adds to the system design, installation and support burdens placed upon an AV department. Without an increase in FTE staff beyond what we’re seeing, there’s just no way that AV support can truly flourish on campuses.

Today we’re reopening the survey to continue to gather data about AV staffing levels, and we’ll periodically tabulate and publish the results for those that participate. Visit www.AV-Survey.com to take the survey. If you would like to request the full 2018 AV staffing survey results, including average AV department budgets, staffing levels by position, breakouts by public/private/community colleges and small/medium/large schools, please send an e-mail to me (mike@tomeiav.com) and to Craig Park from The Sextant Group (cpark@thesextantgroup.com).

 

 

 

What K-12 and higher education can learn from each other — from edweek.org by Ethan Ake-Little

Excerpts:

In 1952, three prep schools—the Lawrenceville School, Phillips Academy, and Phillips Exeter Academy—and three universities—Harvard, Princeton, and Yale—came together to participate in a bold experiment which ultimately introduced the radical concept of allowing high school seniors the chance to study college-level material and take achievement exams for college credit. Thus, the Advanced Placement program was born. More than six decades later, the program continues to offer students an opportunity to pursue college-level content with greater individualized instruction than many first-year college courses.

While the AP program has helped to bridge the gap between K-12 and higher education, both institutions remain largely unaware of how the other operates.

 

Despite stark differences in structure and philosophy, K-12 and higher education cannot avoid the fact that they need one other to serve all students’ educational needs. But to bridge their divide, both K-12 educators and higher education instructors must move outside their comfort zones and immerse themselves in each other’s world. Only then can both sides truly innovate in much the same way they did in 1952 to develop a program that is the hallmark of this partnership today.

 

 

 

 

U.S. students spend more time working paid jobs than going to class — from bloomberg.com by Riley Griffin
Facing mounting debt, U.S. college students spend double the time working paid jobs than in the library.

Excerpts:

Haunted by costly degrees and insurmountable student debt, American college students now spend more time working paid jobs than in lectures, the library or studying at home.

The vast majority of current students—85 percent—work while enrolled, according to an HSBC survey published Thursday. Students spend an average of 4.2 hours a day working paid jobs, which is more than double the time they spend in the library, nearly two hours more than they spend in class and 1.4 hours more time than they spend studying at home.

Haunted by costly degrees and insurmountable student debt, American college students now spend more time working paid jobs than in lectures, the library or studying at home.

The vast majority of current students—85 percent—work while enrolled, according to an HSBC survey published Thursday. Students spend an average of 4.2 hours a day working paid jobs, which is more than double the time they spend in the library, nearly two hours more than they spend in class and 1.4 hours more time than they spend studying at home.

 

“The economics of the debt crisis have become a major distraction to students’ education,” said John Hupalo, founder and chief executive officer of Invite Education, an education financial planner. “Students’ first priority should be to get value out of their education, not squeezing out hours at a job in order to make money to sustain that education.”

 

 


From DSC:
Obviously, this could be a major problem for many students — depending upon whether their work experiences are paying off in terms of other kinds of learning/experiences/skills development/obtaining jobs later on. But this need to work to get through school is also why I think online education needs to be more prevalent in higher ed. If students need to work to obtain a degree, then they need the flexibility to make their class schedules jibe with their work schedules. As with healthcare, I’d also like to see us find ways to bring the costs down.

 

Also see:

One HBCU Hopes Its ‘$10,000 Degree Pathway’ Will Win Over Students Considering For-Profit Alternatives — from edsurge.com by Jeff Young

Excerpt:

A public university in North Carolina has teamed up with six community colleges to offer a program that promises students they will pay no more than $10,000 out of pocket for their four-year degree.

Participating students will attend a two-year college in the state to get their Associate’s degree, then transfer to an online program at Fayetteville State University to finish their bachelor’s. The students will continue to have access to mentors and resources at the local community college to help them stay on track.

 

Making College Affordable Remains a High Priority in Washington — from campustechnology.com by Sara Friedman
More states are providing free college tuition, but equity concerns remain when it comes to the costs of textbooks, transportation and housing.

 

 

 

Why giving kids a roadmap to their brain can make learning easier — from edsurge.com by Megan Nellis

Excerpts:

Learning, Down to a Science
Metacognition. Neuroplasticity. Retrieval Practice. Amygdala. These aren’t the normal words you’d expect to hear in a 15-year-old rural South African’s vocabulary. Here, though, it’s common talk. And why shouldn’t it be? Over the years, we’ve found youth are innately hungry to learn about the inner workings of their mind—where, why and how learning, thinking and decision-making happens. So, we teach them cognitive science.

Over the next three years, we teach students about the software and hardware of the brain. From Carol Dweck’s online Brainology curriculum, they learn about growth mindset, memory and mnemonics, the neural infrastructure of the brain. They learn how stress impacts learning and about neuroplasticity—or how the brain learns. From David Eagleman and Dan Siegel, they learn about the changing landscape of the adolescent brain and how novelty, emotionality and peer relationships aid in learning.

Pulling from books such as Make It Stick and How We Learn, we pointedly teach students about the science behind retrieval practice, metacognition and other strategies. We expressly use them in our classes so students see and experience the direct impact, and we also dedicate a whole class in our program for students to practice applying these strategies toward their own academic learning from school.

 

 

 

NEW: The Top Tools for Learning 2018 [Jane Hart]

The Top Tools for Learning 2018 from the 12th Annual Digital Learning Tools Survey -- by Jane Hart

 

The above was from Jane’s posting 10 Trends for Digital Learning in 2018 — from modernworkplacelearning.com by Jane Hart

Excerpt:

[On 9/24/18],  I released the Top Tools for Learning 2018 , which I compiled from the results of the 12th Annual Digital Learning Tools Survey.

I have also categorised the tools into 30 different areas, and produced 3 sub-lists that provide some context to how the tools are being used:

  • Top 100 Tools for Personal & Professional Learning 2018 (PPL100): the digital tools used by individuals for their own self-improvement, learning and development – both inside and outside the workplace.
  • Top 100 Tools for Workplace Learning (WPL100): the digital tools used to design, deliver, enable and/or support learning in the workplace.
  • Top 100 Tools for Education (EDU100): the digital tools used by educators and students in schools, colleges, universities, adult education etc.

 

3 – Web courses are increasing in popularity.
Although Coursera is still the most popular web course platform, there are, in fact, now 12 web course platforms on the list. New additions this year include Udacity and Highbrow (the latter provides daily micro-lessons). It is clear that people like these platforms because they can chose what they want to study as well as how they want to study, ie. they can dip in and out if they want to and no-one is going to tell them off – which is unlike most corporate online courses which have a prescribed path through them and their use is heavily monitored.

 

 

5 – Learning at work is becoming personal and continuous.
The most significant feature of the list this year is the huge leap up the list that Degreed has made – up 86 places to 47th place – the biggest increase by any tool this year. Degreed is a lifelong learning platform and provides the opportunity for individuals to own their expertise and development through a continuous learning approach. And, interestingly, Degreed appears both on the PPL100 (at  30) and WPL100 (at 52). This suggests that some organisations are beginning to see the importance of personal, continuous learning at work. Indeed, another platform that underpins this, has also moved up the list significantly this year, too. Anders Pink is a smart curation platform available for both individuals and teams which delivers daily curated resources on specified topics. Non-traditional learning platforms are therefore coming to the forefront, as the next point further shows.

 

 

From DSC:
Perhaps some foreshadowing of the presence of a powerful, online-based, next generation learning platform…?

 

 

 
 

From DSC to teachers and professors:
Should these posters be in your classroom? The posters each have a different practice such as:

  • Spaced practice
  • Retrieval practice
  • Elaboration
  • Interleaving
  • Concrete examples
  • Dual coding

That said, I could see how all of that information could/would be overwhelming to some students and/or the more technical terms could bore them or fly over their heads. So perhaps you could boil down the information to feature excerpts from the top sections only that put the concepts into easier to digest words such as:

  • Practice bringing information to mind
  • Switch between ideas while you study
  • Combine words and visuals
  • Etc. 

 

Learn how to study using these practices

 

 

Aligning the business model of college with student needs: How WGU is disrupting higher education — from christenseninstitute.org by Alana Dunagan

Excerpt:

Since its inception, Western Governors University (WGU) has aimed to serve learners otherwise shut out of the traditional system. Now, the groundbreaking institution has both graduated 100,000 students and has over 100,000 students currently enrolled. These milestones demonstrate WGU’s ability to scale its high-quality, low-cost model, signaling a momentous shift in the higher education landscape.

In the mid-1990s, governors of 19 states across the western United States were concerned about bringing accessible college education to rural populations, especially working adults.These governors, led by Utah Governor Mike Leavitt, decided to explore building a new university to address the challenge. As the memorandum of understanding between those governors that officially marked the founding of WGU stated, “The strength and well-being of our states and the nation depend increasingly on a strong higher education system that helps individuals adapt to our rapidly changing economy and society. States must look to telecommunications and information technologies to provide greater access and choice to a population that increasingly must have affordable education and training opportunities and the certification of competency throughout their lives.”

 

Now in its third decade, WGU has students in every U.S. state and has over 100,000 enrolled students—a 230% increase since 2011. 

 



Excerpts from their paper:

The potential of competency-based education
Competency-based education is an approach to learning that allows students to determine the pace of their learning and move ahead once they demonstrate mastery in a concept. As described by Clayton Christensen and Michelle Weise:

Competency-based programs have no time-based unit. Learning is fixed, and time is variable; pacing is flexible. Students cannot move on until they have demonstrated proficiency and mastery of each competency but are encouraged to try as many times as necessary to demonstrate their proficiency. Although skeptics may question the “rigor” behind an experience that allows students to keep trying until they have mastered a competency, this model is actually far more rigorous than the traditional model, as students are not able to flunk or get away with a merely average understanding of the material; they must demonstrate mastery—and therefore dedicated work toward gaining mastery—in any competency.

Competency-based education first took hold in the K-12 education system, but it is also growing in higher education. As of fall 2015, roughly 600 institutions were using or exploring competency-based programs in higher education.13 However, only a handful of institutions are using competency-based education exclusively and have designed their business models around it.

WGU offers programs across four industry areas: education, business, information technology, and healthcare. All of these programs are offered online; unlike most higher education institutions, WGU has no physical campus. Instead, it has invested heavily in a technology platform that allows it to deliver curriculum asynchronously, to wherever students are. In addition to its online platform, another unique aspect of WGU’s resources is its approach to faculty. In traditional institutions, faculty are responsible for academic research, course development, teaching, assessment, and advising students. Alternatively, WGU’s model unbundles the faculty role into component parts, with specialists in each role.

 

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