More than 9 in 10 elementary school teachers feel highly stressed, MU study finds — from munews.missouri.edu by Keith Herman, with thanks to Cailin Riley for the resource
Research shows high stress classroom environments yield poor student performance and behavior

Excerpt:

“It’s troubling that only 7 percent of teachers experience low stress and feel they are getting the support they need to adequately cope with the stressors of their job,” Herman said. “Even more concerning is that these patterns of teacher stress are related to students’ success in school, both academically and behaviorally. For example, classrooms with highly stressed teachers have more instances of disruptive behaviors and lower levels of prosocial behaviors.”

 

“We as a society need to consider methods that create nurturing school environments not just for students, but for the adults who work there,” Herman said. “This could mean finding ways for administrators, peers and parents to have positive interactions with teachers, giving teachers the time and training to perform their jobs, and creating social networks of support so that teachers do not feel isolated.”

 

 

Also see:

Empirically Derived Profiles of Teacher Stress, Burnout, Self-Efficacy, and Coping and Associated Student Outcomes
Keith C. Herman, PhD, Jal’et Hickmon-Rosa, BA, Wendy M. Reinke, PhD
First Published October 6, 2017 Research Article
Download PDFPDF download for Empirically Derived Profiles of Teacher Stress, Burnout, Self-Efficacy, and Coping and Associated Student Outcomes Article information
Article has an altmetric score of 28 Full Access

Abstract
Understanding how teacher stress, burnout, coping, and self-efficacy are interrelated can inform preventive and intervention efforts to support teachers. In this study, we explored these constructs to determine their relation to student outcomes, including disruptive behaviors and academic achievement. Participants in this study were 121 teachers and 1,817 students in grades kindergarten to fourth from nine elementary schools in an urban Midwestern school district. Latent profile analysis was used to determine patterns of teacher adjustment in relation to stress, coping, efficacy, and burnout. These profiles were then linked to student behavioral and academic outcomes. Four profiles of teacher adjustment were identified. Three classes were characterized by high levels of stress and were distinguished by variations in coping and burnout ranging from (a) high coping/low burnout (60%) to (b) moderate coping and burnout (30%), to (c) low coping/high burnout (3%). The fourth class was distinguished by low stress, high coping, and low burnout. Only 7% of the sample fell into this Well-Adjusted class. Teachers in the high stress, high burnout, and low coping class were associated with the poorest student outcomes. Implications for supporting teachers to maximize student outcomes are discussed.

 

 

Incumbents Strike Back: Insights from the Global C-suite Study — by the IBM Institute for Business Value

Excerpts:

Dancing with disruption
Incumbents hit their stride
We explore the forces at play in shaping the current competitive environment, the opportunities emerging, and how a balance between stability and dynamism favors the Reinventors.

Trust in the journey
The path to personalization
Here we show how the Reinventors as design thinkers are testing their assumptions and re-orienting their organizations to engage their customers and create bonds based on trust.

Orchestrating the future
The pull of platform business models
This section reveals the step change in capability that occurs as organizations scale their partner networks in new ways. We chart how organizations will need to reconsider their value propositions and allocation of resources to own or participate in platforms.

Innovation in motion
Agility for the enterprise
We delineate how leaders are liberating their employees to experiment and innovate, get up close to customers and thrive in an ever-evolving ecosystem of dynamic teams and partnerships.

 

 

 

Better Brainstorming — from hbr.org by Hal Gregersen

Excerpt:

Brainstorming for questions, not answers, wasn’t something I’d tried before.

Underlying the approach is a broader recognition that fresh questions often beget novel—even transformative—insights. Consider this example from the field of psychology: Before 1998 virtually all well-trained psychologists focused on attacking the roots of mental disorders and deficits, on the assumption that well-being came down to the absence of those negative conditions. But then Martin Seligman became president of the American Psychological Association, and he reframed things for his colleagues. What if, he asked in a speech at the APA’s annual meeting, well-being is just as driven by the presence of certain positive conditions—keys to flourishing that could be recognized, measured, and cultivated? With that question, the positive psychology movement was born.

Brainstorming for questions rather than answers makes it easier to push past cognitive biases and venture into uncharted territory.


The methodology I’ve developed is essentially a process for recasting problems in valuable new ways. It helps people adopt a more creative habit of thinking and, when they’re looking for breakthroughs, gives them a sense of control. There’s actually something they can do other than sit and wait for a bolt from the blue. Here, I’ll describe how and why this approach works. You can use it anytime you (in a group or individually) are feeling stuck or trying to imagine new possibilities. And if you make it a regular practice in your organization, it can foster a stronger culture of collective problem solving and truth seeking.

 

 

 

From MIT Technology Review on 4-2-2018

*Only* 14 percent of the world has to worry about robots taking their jobs. Yay?
The Organization for Economic Cooperation and Development (OECD) has released a major report analyzing the impact of automation on jobs in 32 countries.

Clashing views: In 2016, the OECD said only 9 percent of US and worldwide jobs face a “high degree of automobility.” That was a contradiction of one of the most widely cited reports on jobs and automation, by Oxford researchers Carl Frey and Michael Osborne, who in 2013 said that 47 percent of US jobs were at high risk of being consumed by automation.

What’s new: The OECD’s latest report says that across the countries analyzed, 14 percent of jobs are highly automatable, meaning they have over a 70 percent likelihood of automation. In the US, the study concludes that 10 percent of jobs will likely be lost to automation. An additional 32 percent of global jobs will be transformed and require significant worker retraining.

The big “but”: As the gap between the OECD report and Frey and Osborne’s estimates illustrate, predictions like these aren’t known for their accuracy. In fact, when we compiled all of the studies we could on the subject, we found there are about as many predictions as there are experts.

 


Also see:



Automation, skills use and training
— from oecd-ilibrary.org by Ljubica Nedelkoska and Glenda Quintini

Excerpts:

Here are the study’s key findings.
Across the 32 countries, close to one in two jobs are likely to be significantly affected by automation, based on the tasks they involve. But the degree of risk varies.

The variance in automatability across countries is large: 33% of all jobs in Slovakia are highly automatable, while this is only the case with 6% of the jobs in Norway.

The cross-country variation in automatability, contrary to expectations, is better explained by the differences in the organisation of job tasks within economic sectors, than by the differences in the sectoral structure of economies.

There are upside and downside risks to the figures obtained in this paper. On the upside, it is important to keep in mind that these estimates refer to technological possibilities, abstracting from the speed of diffusion and likelihood of adoption of such technologies….But there are risks on the downside too. First, the estimates are based on the fact that, given the current state of knowledge, tasks related to social intelligence, cognitive intelligence and perception and manipulation cannot be automated. However, progress is being made very rapidly, particularly in the latter two categories.

Most importantly, the risk of automation is not distributed equally among workers. Automation is found to mainly affect jobs in the manufacturing industry and agriculture, although a number of service sectors, such as postal and courier services, land transport and food services are also found to be highly automatable.

Overall, despite recurrent arguments that automation may start to adversely affect selected highly skilled occupations, this prediction is not supported by the Frey and Osborne (2013) framework of engineering bottlenecks used in this study. If anything, Artificial Intelligence puts more low-skilled jobs at risk than previous waves of technological progress…

A striking novel finding is that the risk of automation is the highest among teenage jobs. The relationship between automation and age is U-shaped, but the peak in automatability among youth jobs is far more pronounced than the peak among senior workers.


This unequal distribution of the risk of automation raises the stakes involved in policies to prepare workers for the new job requirements. In this context, adult learning is a crucial policy instrument for the re-training and up-skilling of workers whose jobs are being affected by technology. Unfortunately, evidence from this study suggests that a lot needs to be done to facilitate participation by the groups most affected by automation.

An analysis of German data suggests that training is used to move to jobs at lower risk of automation.

 

 

 

The Changing Landscape of Online Education (CHLOE)

QM and Eduventures have teamed up to conduct a multi-year study to examine the changing landscape of online education, provide results to those who can use them and help those involved with online education place their institution within a broader context and possibly influence strategic decisions and organizational changes. Please complete the form on this page to gain access to the 2018 CHLOE 2 Report.

The third iteration of CHLOE is scheduled for April 2018 and focuses on in-depth coverage of issues such as governance of online programs, blended learning and the influence of subject matter on the design and delivery of online programs. If you are a Chief Online Officer and wish to participate in the next CHLOE Survey, or if you wish to nominate the COO at your institution, please contact QM’s Manager of Research & Development Barbra Burch.

Date Published:  Tue, 03/27/2018

 

Also see:

  • Online Learning’s Complex, Fractured Landscape — from insidehighered.com by Doug Lederman — references new report from Quality Matters & Eduventures Research entitled “The Changing Landscape of Online Education: A Deeper Dive”
    Survey of chief online officers shows enormous variation in how colleges define and structure digital education, in terms of pricing, program structure and use of instructional design.

Excerpt:

A new survey of those who oversee online learning programs at their institutions reveals significant diversity in the online education landscape, from differences in colleges’ strategic goals in going online to how they structure and price their programs and how much they require/encourage faculty members to work with professional designers to craft their courses.

The report, “The Changing Landscape of Online Education: A Deeper Dive,” is the second such report from Quality Matters and Eduventures Research, leading them to dub it CHLOE2. (Inside Higher Ed and “Inside Digital Learning” covered last year’s report here and here.) One hundred eighty-two senior officials responsible for online education at their institutions responded to the survey (up from 104 last year), drawn roughly equivalently from four-year private, four-year public and two-year public colleges.

The survey explores a wide range of topics and issues, related to the administrative structure of online offerings, the economics of their programs and the role of instructional designers. Among the most interesting findings:

 

 

 

 

 

 

2018 TECH TRENDS REPORT — from the Future Today Institute
Emerging technology trends that will influence business, government, education, media and society in the coming year.

Description:

The Future Today Institute’s 11th annual Tech Trends Report identifies 235 tantalizing advancements in emerging technologies—artificial intelligence, biotech, autonomous robots, green energy and space travel—that will begin to enter the mainstream and fundamentally disrupt business, geopolitics and everyday life around the world. Our annual report has garnered more than six million cumulative views, and this edition is our largest to date.

Helping organizations see change early and calculate the impact of new trends is why we publish our annual Emerging Tech Trends Report, which focuses on mid- to late-stage emerging technologies that are on a growth trajectory.

In this edition of the FTI Tech Trends Report, we’ve included several new features and sections:

  • a list and map of the world’s smartest cities
  • a calendar of events that will shape technology this year
  • detailed near-future scenarios for several of the technologies
  • a new framework to help organizations decide when to take action on trends
  • an interactive table of contents, which will allow you to more easily navigate the report from the bookmarks bar in your PDF reader

 


 

01 How does this trend impact our industry and all of its parts?
02 How might global events — politics, climate change, economic shifts – impact this trend, and as a result, our organization?
03 What are the second, third, fourth, and fifth-order implications of this trend as it evolves, both in our organization and our industry?
04 What are the consequences if our organization fails to take action on this trend?
05 Does this trend signal emerging disruption to our traditional business practices and cherished beliefs?
06 Does this trend indicate a future disruption to the established roles and responsibilities within our organization? If so, how do we reverse-engineer that disruption and deal with it in the present day?
07 How are the organizations in adjacent spaces addressing this trend? What can we learn from their failures and best practices?
08 How will the wants, needs and expectations of our consumers/ constituents change as a result of this trend?
09 Where does this trend create potential new partners or collaborators for us?
10 How does this trend inspire us to think about the future of our organization?

 


 

 

AT&T’s $1 billion gambit: Retraining nearly half its workforce for jobs of the future — from cnbc.com by Susan Caminiti

Excerpts (emphasis DSC):

  • AT&T initiated a massive retraining effort after discovering that nearly half of its 250,000 employees lacked the necessary skills needed to keep the company competitive.
  • Ninety percent of maturing companies expect digital disruption, but only 44 percent are adequately preparing for it.
  • Despite the federal government’s investment in job-retraining efforts, most are deemed ineffective.

 

The discovery presented AT&T with two daunting options, explains Bill Blase, senior executive vice president of human resources. “We could go out and try to hire all these software and engineering people and probably pay through the nose to get them, but even that wouldn’t have been adequate,” he explains. “Or we could try to reskill our existing workforce so they could be competent in the technology and the skills required to run the business going forward.”

 

In a world where technology advances are measured in months, not years, companies selling everything from computers and cellphones to cereal and sneakers are trying desperately to adapt. A recent research report by the Society for Human Resource Management states that nearly 40 percent of hiring managers cite lack of technical skills among the reasons why they can’t fill job openings.

And the message isn’t lost on workers, either. A 2016 Pew Research Center survey shows that more than half of the adults in the workforce today realize that it will be essential for them to get training and develop new skills throughout their career in order to keep up with changes in the workplace.

In fact, according to Willis Towers Watson, 90 percent of maturing companies expect digital disruption, but only 44 percent are adequately preparing for it — and getting the right people to get the work done remains a challenge for most.


AT&T’s massive global retraining program — the company prefers to call it “reskilling” — is perhaps corporate America’s boldest response to this war for talent. Known inside the company as Future Ready, the initiative is a $1 billion web-based, multiyear effort that includes online courses; collaborations with Coursera, Udacity and leading universities; and a career center that allows employees to identify and train for the kinds of jobs the company needs today and down the road. An online portal called Career Intelligence lets workers see what jobs are available, the skills required for each, the potential salary range and whether that particular area is projected to grow or shrink in the years ahead. In short, it gives them a roadmap to get from where they are today to where the company needs them to be in the future.

 

 

From DSC:
This article is encouraging in at least a couple of ways to me:

  • A large company is choosing to retrain its employees, helping them to learn and grow — to reinvent themselves and to stay relevant
  • A large company is recognizing the exponential pace of change that we’re now on. The question is, are we ready for it?

 

 

 

On Change and Relevance for Higher Education — from campustechnology.com by Mary Grush and Phil Long
A Q&A with Phil Long

Excerpts:

Mary Grush: You’ve been connected to scores of technology leaders and have watched trends in higher education for more than 30 years. What is the central, or most important concern you are hearing from institutional leadership now?

Phil Long: Higher ed institutions are facing some serious challenges to stay relevant in a world that is diversifying and changing rapidly. They want to make sure that the experiences they have designed for students will carry the next generation forward to be productive citizens and workers. But institutions’ abilities to keep up in our changing environment have begun to lag to a sufficient degree, such that alternatives to the traditional university are being considered, both by the institutions themselves and by their constituents and colleagues throughout the education sector.

Grush: What are a few of the more specific areas in which institutions may find it difficult to navigate?

Long: Just from a very high level view, I’d include on that list: big data and the increasing sophistication of algorithms, with the associated benefits and risks; artificial intelligence with all its implications for good… and for peril; and perhaps most importantly, new applications and practices that support how we recognize learning.

 

 

“The pace of change never seems to slow down. And the issues and implications of the technologies we use are actually getting broader and more profound every day.” — Phil Long

 

 

 

 

Michelle Weise: ‘We Need to Design the Learning Ecosystem of the Future’ — from edsurge.com  by Michelle Weise

Excerpts:

These days, education reformers, evangelists and foundations pay a lot of lip service to the notion of lifelong learning, but we do little to invest in the systems, architecture and infrastructure needed to facilitate seamless movements in and out of learning and work.

Talk of lifelong learning doesn’t translate into action. In fact, resources and funding are often geared toward the traditional 17- to 22-year-old college-going population and less often to working adults, our growing new-traditional student population.

We’ll need a different investment thesis: For most adults, taking time off work to attend classes at a local, brick-and-mortar community college or a four-year institution will not be the answer. The opportunity costs will be too high. Our current system of traditional higher education is ill-suited to facilitate flexible, seamless cost-effective learning pathways for these students to keep up with the emergent demands of the workforce.

Many adults may have no interest in coming back to college. Out of the 37 million Americans with some college and no degree, many have already failed one or twice before and will be wholly uninterested in experiencing more educational trauma.We can’t just say, “Here’s a MOOC, or here’s an online degree, or a 6- to 12-week immersive bootcamp.”

 

We have to do better. Let’s begin seeding the foundational elements of a learning ecosystem of the future—flexible enough for adults to move consistently in and out of learning and work. Enough talk about lifelong learning: Let’s build the foundations of that learning ecosystem of the future.

 

 

From DSC:
I couldn’t agree more with Michelle that we need a new learning ecosystem of the future. In fact, I have been calling such an effort “Learning from the Living [Class] Room — and it outlines a next generation learning platform that aims to deliver everything Michelle talks about in her solid article out at edsurge.com.

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

 

Along these lines…I just saw that Amazon is building out more cashierless stores (and Walmart is also at work on introducing more cashierless stores.) Now, let’s say that you are currently a cashier. 2-5 years from now (depending upon where you’re currently working and which stores are in your community), what are you going to do? The opportunities for such a position will be fewer and fewer. Who can help you do what Michelle mentioned here:

Working learners will also need help articulating their learning goals and envisioning a future for themselves. People don’t know how to translate their skills from one industry to another. How does a student begin to understand that 30% of what they already know could be channeled into a totally different and potentially promising pathway they never even knew was within reach?

And that cashier may have had a tough time with K-12 education and/or with higher education. As Michelle writes:

Many adults may have no interest in coming back to college. Out of the 37 million Americans with some college and no degree, many have already failed one or twice before and will be wholly uninterested in experiencing more educational trauma. We can’t just say, “Here’s a MOOC, or here’s an online degree, or a 6- to 12-week immersive bootcamp.”

And like the cashier in this example…we are quickly approaching an era where, I believe, many of us will need to reinvent ourselves in order to:

  • stay marketable
  • keep bread and butter on the table
  • continue to have a sense of purpose and meaning in our lives

Higher ed, if it wants to remain relevant, must pick up the pace of experimentation and increase the willingness to innovate, and to develop new business models — to develop new “learning channels” so to speak. Such channels need to be:

  • Up-to-date
  • Serving relevant data and information– especially regarding the job market and which jobs appear to be safe for the next 5-10 years
  • Inexpensive/affordable
  • Highly convenient

 

 

 

Tech companies should stop pretending AI won’t destroy jobs — from technologyreview.com / MIT Technology Review by Kai-Fu Lee
No matter what anyone tells you, we’re not ready for the massive societal upheavals on the way.

Excerpt (emphasis DSC):

The rise of China as an AI superpower isn’t a big deal just for China. The competition between the US and China has sparked intense advances in AI that will be impossible to stop anywhere. The change will be massive, and not all of it good. Inequality will widen. As my Uber driver in Cambridge has already intuited, AI will displace a large number of jobs, which will cause social discontent. Consider the progress of Google DeepMind’s AlphaGo software, which beat the best human players of the board game Go in early 2016. It was subsequently bested by AlphaGo Zero, introduced in 2017, which learned by playing games against itself and within 40 days was superior to all the earlier versions. Now imagine those improvements transferring to areas like customer service, telemarketing, assembly lines, reception desks, truck driving, and other routine blue-collar and white-­collar work. It will soon be obvious that half of our job tasks can be done better at almost no cost by AI and robots. This will be the fastest transition humankind has experienced, and we’re not ready for it.

And finally, there are those who deny that AI has any downside at all—which is the position taken by many of the largest AI companies. It’s unfortunate that AI experts aren’t trying to solve the problem. What’s worse, and unbelievably selfish, is that they actually refuse to acknowledge the problem exists in the first place.

These changes are coming, and we need to tell the truth and the whole truth. We need to find the jobs that AI can’t do and train people to do them. We need to reinvent education. These will be the best of times and the worst of times. If we act rationally and quickly, we can bask in what’s best rather than wallow in what’s worst.

 

From DSC:
If a business has a choice between hiring a human being or having the job done by a piece of software and/or by a robot, which do you think they’ll go with? My guess? It’s all about the money — whichever/whomever will be less expensive will get the job.

However, that way of thinking may cause enormous social unrest if the software and robots leave human beings in the (job search) dust. Do we, as a society, win with this way of thinking? To me, it’s capitalism gone astray. We aren’t caring enough for our fellow members of the human race, people who have to put bread and butter on their tables. People who have to support their families. People who want to make solid contributions to society and/or to pursue their vocation/callings — to have/find purpose in their lives.

 

Others think we’ll be saved by a universal basic income. “Take the extra money made by AI and distribute it to the people who lost their jobs,” they say. “This additional income will help people find their new path, and replace other types of social welfare.” But UBI doesn’t address people’s loss of dignity or meet their need to feel useful. It’s just a convenient way for a beneficiary of the AI revolution to sit back and do nothing.

 

 

To Fight Fatal Infections, Hospitals May Turn to Algorithms — from scientificamerican.com by John McQuaid
Machine learning could speed up diagnoses and improve accuracy

Excerpt:

The CDI algorithm—based on a form of artificial intelligence called machine learning—is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experimentation, machine learning’s predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University’s Clinical Inference and Algorithms Program.

“The implications of machine learning are profound,” Syed said. “Yet it also promises to be an unpredictable, disruptive force—likely to alter the way medical decisions are made and put some people out of work.

 

 

Lawyer-Bots Are Shaking Up Jobs — from technologyreview.com by Erin Winick

Excerpt:

Meticulous research, deep study of case law, and intricate argument-building—lawyers have used similar methods to ply their trade for hundreds of years. But they’d better watch out, because artificial intelligence is moving in on the field.

As of 2016, there were over 1,300,000 licensed lawyers and 200,000 paralegals in the U.S. Consultancy group McKinsey estimates that 22 percent of a lawyer’s job and 35 percent of a law clerk’s job can be automated, which means that while humanity won’t be completely overtaken, major businesses and career adjustments aren’t far off (see “Is Technology About to Decimate White-Collar Work?”). In some cases, they’re already here.

 

“If I was the parent of a law student, I would be concerned a bit,” says Todd Solomon, a partner at the law firm McDermott Will & Emery, based in Chicago. “There are fewer opportunities for young lawyers to get trained, and that’s the case outside of AI already. But if you add AI onto that, there are ways that is advancement, and there are ways it is hurting us as well.”

 

So far, AI-powered document discovery tools have had the biggest impact on the field. By training on millions of existing documents, case files, and legal briefs, a machine-learning algorithm can learn to flag the appropriate sources a lawyer needs to craft a case, often more successfully than humans. For example, JPMorgan announced earlier this year that it is using software called Contract Intelligence, or COIN, which can in seconds perform document review tasks that took legal aides 360,000 hours.

People fresh out of law school won’t be spared the impact of automation either. Document-based grunt work is typically a key training ground for first-year associate lawyers, and AI-based products are already stepping in. CaseMine, a legal technology company based in India, builds on document discovery software with what it calls its “virtual associate,” CaseIQ. The system takes an uploaded brief and suggests changes to make it more authoritative, while providing additional documents that can strengthen a lawyer’s arguments.

 

 

Lessons From Artificial Intelligence Pioneers — from gartner.com by Christy Pettey

CIOs are struggling to accelerate deployment of artificial intelligence (AI). A recent Gartner survey of global CIOs found that only 4% of respondents had deployed AI. However, the survey also found that one-fifth of the CIOs are already piloting or planning to pilot AI in the short term.

Such ambition puts these leaders in a challenging position. AI efforts are already stressing staff, skills, and the readiness of in-house and third-party AI products and services. Without effective strategic plans for AI, organizations risk wasting money, falling short in performance and falling behind their business rivals.

Pursue small-scale plans likely to deliver small-scale payoffs that will offer lessons for larger implementations

“AI is just starting to become useful to organizations but many will find that AI faces the usual obstacles to progress of any unproven and unfamiliar technology,” says Whit Andrews, vice president and distinguished analyst at Gartner. “However, early AI projects offer valuable lessons and perspectives for enterprise architecture and technology innovation leaders embarking on pilots and more formal AI efforts.”

So what lessons can we learn from these early AI pioneers?

 

 

Why Artificial Intelligence Researchers Should Be More Paranoid — from wired.com by Tom Simonite

Excerpt:

What to do about that? The report’s main recommendation is that people and companies developing AI technology discuss safety and security more actively and openly—including with policymakers. It also asks AI researchers to adopt a more paranoid mindset and consider how enemies or attackers might repurpose their technologies before releasing them.

 

 

How to Prepare College Graduates for an AI World — from wsj.com by
Northeastern University President Joseph Aoun says schools need to change their focus, quickly

Excerpt:

WSJ: What about adults who are already in the workforce?

DR. AOUN: Society has to provide ways, and higher education has to provide ways, for people to re-educate themselves, reskill themselves or upskill themselves.

That is the part that I see that higher education has not embraced. That’s where there is an enormous opportunity. We look at lifelong learning in higher education as an ancillary operation, as a second-class operation in many cases. We dabble with it, we try to make money out of it, but we don’t embrace it as part of our core mission.

 

 

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:

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.

 

 

 

 

Looking for something?

Use the form below to search the site:

Still not finding what you're looking for? Drop a comment on a post or contact us so we can take care of it!

© 2018 | Daniel Christian