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.

 

 

 

A cyber-skills shortage means students are being recruited to fight off hackers — from technologyreview.com by Erin Winick
Students with little or no cybersecurity knowledge are being paired with easy-to-use AI software that lets them protect their campus from attack.

Excerpt:

There aren’t enough cybersecurity workers out there—and things are getting worse. According to one estimate, by 2021 an estimated 3.5 million cybersecurity jobs will be unfilled. And of the candidates who apply, fewer than one in four are even qualified.

That’s why many large corporations are investing in longer-term solutions like mobile training trucks and apprenticeship programs. But Texas A&M University has found a way to solve its labor shortage in the short term. It’s pairing student security beginners with AI software.

The college’s Security Operations Center deals with about a million attempts to hack the university system each month. While the center does have some full-time employees, the majority of its security force is made up of students. Ten students currently work alongside AI softwareto detect, monitor, and remediate the threats.

 

 

 

 

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).

 

 

 

This is how the Future Today Institute researches, models & maps the future & develops strategies

 

This is how the Future Today Institute researches, models & maps the future & develops strategies

 

Also see what the Institute for the Future does in this regard

Foresight Tools
IFTF has pioneered tools and methods for building foresight ever since its founding days. Co-founder Olaf Helmer was the inventor of the Delphi Method, and early projects developed cross-impact analysis and scenario tools. Today, IFTF is methodologically agnostic, with a brimming toolkit that includes the following favorites…

 

 

From DSC:
How might higher education use this foresight workflow? How might we better develop a future-oriented mindset?

From my perspective, I think that we need to be pulse-checking a variety of landscapes, looking for those early signals. We need to be thinking about what should be on our radars. Then we need to develop some potential scenarios and strategies to deal with those potential scenarios if they occur. Graphically speaking, here’s an excerpted slide from my introductory piece for a NGLS 2017 panel that we did.

 

 

 

This resource regarding their foresight workflow was mentioned in  a recent e-newsletter from the FTI where they mentioned this important item as well:

  • Climate change: a megatrend that impacts us all
    Excerpt:
    Earlier this week, the United Nations’ scientific panel on climate change issued a dire report [PDF]. To say the report is concerning would be a dramatic understatement. Models built by the scientists show that at our current rate, the atmosphere will warm as much as 1.5 degrees Celsius, leading to a dystopian future of food shortages, wildfires, extreme winters, a mass die-off of coral reefs and more –– as soon as 2040. That’s just 20 years away from now.

 

But China also decided to ban the import of foreign plastic waste –– which includes trash from around the U.S. and Europe. The U.S. alone could wind up with an extra 37 million metric tons of plastic waste, and we don’t have a plan for what to do with it all.

 

Immediate Futures Scenarios: Year 2019

  • Optimistic: Climate change is depoliticized. Leaders in the U.S., Brazil and elsewhere decide to be the heroes, and invest resources into developing solutions to our climate problem. We understand that fixing our futures isn’t only about foregoing plastic straws, but about systemic change. Not all solutions require regulation. Businesses and everyday people are incentivized to shift behavior. Smart people spend the next two decades collaborating on plausible solutions.
  • Pragmatic: Climate change continues to be debated, while extreme weather events cause damage to our power grid, wreak havoc on travel, cause school cancellations, and devastate our farms. The U.S. fails to work on realistic scenarios and strategies to combat the growing problem of climate change. More countries elect far-right leaders, who shun global environmental accords and agreements. By 2029, it’s clear that we’ve waited too long, and that we’re running out of time to adapt.
  • Catastrophic: A chorus of voices calling climate change a hoax grows ever louder in some of the world’s largest economies, whose leaders choose immediate political gain over longer-term consequences. China builds an environmental coalition of 100 countries within the decade, developing both green infrastructure while accumulating debt service. Beijing sets global standards emissions––and it locks the U.S out of trading with coalition members. Trash piles up in the U.S., which didn’t plan ahead for waste management. By 2040, our population centers have moved inland and further north, our farms are decimated, our lives are miserable.

Watchlist: United Nations’ Intergovernmental Panel on Climate Change; European Geosciences Union; National Oceanic and Atmospheric Administration (NOAA); NASA; Department of Energy; Department of Homeland Security; House Armed Services Sub-committee on Emerging Threats and Capabilities; Environmental Justice Foundation; Columbia University’s Earth Institute; University of North Carolina at Wilmington; Potsdam Institute for Climate Impact Research; National Center for Atmospheric Research.

 

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…?

 

 

 
 

To higher ed: When the race track is going 180mph, you can’t walk or jog onto the track. [Christian]

From DSC:
When the race track is going 180mph, you can’t walk or jog onto the track.  What do I mean by that? 

Consider this quote from an article that Jeanne Meister wrote out at Forbes entitled, “The Future of Work: Three New HR Roles in the Age of Artificial Intelligence:”*

This emphasis on learning new skills in the age of AI is reinforced by the most recent report on the future of work from McKinsey which suggests that as many as 375 million workers around the world may need to switch occupational categories and learn new skills because approximately 60% of jobs will have least one-third of their work activities able to be automated.

Go scan the job openings and you will likely see many that have to do with technology, and increasingly, with emerging technologies such as artificial intelligence, deep learning, machine learning, virtual reality, augmented reality, mixed reality, big data, cloud-based services, robotics, automation, bots, algorithm development, blockchain, and more. 

 

From Robert Half’s 2019 Technology Salary Guide 

 

 

How many of us have those kinds of skills? Did we get that training in the community colleges, colleges, and universities that we went to? Highly unlikely — even if you graduated from one of those institutions only 5-10 years ago. And many of those institutions are often moving at the pace of a nice leisurely walk, with some moving at a jog, even fewer are sprinting. But all of them are now being asked to enter a race track that’s moving at 180mph. Higher ed — and society at large — are not used to moving at this pace. 

This is why I think that higher education and its regional accrediting organizations are going to either need to up their game hugely — and go through a paradigm shift in the required thinking/programming/curricula/level of responsiveness — or watch while alternatives to institutions of traditional higher education increasingly attract their learners away from them.

This is also, why I think we’ll see an online-based, next generation learning platform take place. It will be much more nimble — able to offer up-to-the minute, in-demand skills and competencies. 

 

 

The below graphic is from:
Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages

 

 

 


 

* Three New HR Roles To Create Compelling Employee Experiences
These new HR roles include:

  1. IBM: Vice President, Data, AI & Offering Strategy, HR
  2. Kraft Heinz Senior Vice President Global HR, Performance and IT
  3. SunTrust Senior Vice President Employee Wellbeing & Benefits

What do these three roles have in common? All have been created in the last three years and acknowledge the growing importance of a company’s commitment to create a compelling employee experience by using data, research, and predictive analytics to better serve the needs of employees. In each case, the employee assuming the new role also brought a new set of skills and capabilities into HR. And importantly, the new roles created in HR address a common vision: create a compelling employee experience that mirrors a company’s customer experience.

 


 

An excerpt from McKinsey Global Institute | Notes from the Frontier | Modeling the Impact of AI on the World Economy 

Workers.
A widening gap may also unfold at the level of individual workers. Demand for jobs could shift away from repetitive tasks toward those that are socially and cognitively driven and others that involve activities that are hard to automate and require more digital skills.12 Job profiles characterized by repetitive tasks and activities that require low digital skills may experience the largest decline as a share of total employment, from some 40 percent to near 30 percent by 2030. The largest gain in share may be in nonrepetitive activities and those that require high digital skills, rising from some 40 percent to more than 50 percent. These shifts in employment would have an impact on wages. We simulate that around 13 percent of the total wage bill could shift to categories requiring nonrepetitive and high digital skills, where incomes could rise, while workers in the repetitive and low digital skills categories may potentially experience stagnation or even a cut in their wages. The share of the total wage bill of the latter group could decline from 33 to 20 percent.13 Direct consequences of this widening gap in employment and wages would be an intensifying war for people, particularly those skilled in developing and utilizing AI tools, and structural excess supply for a still relatively high portion of people lacking the digital and cognitive skills necessary to work with machines.

 


 

 

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