Social Physics - Reinventing Analytics to Better Predict Human Behaviors

Irving Wladawsky-Berger

But, at the same time, mutations and innovations will vary among different groups, with natural selection favoring those human groups better able to adapt to changing conditions. Its prediction algorithms integrate social physics technologies into its data analytic engine, enabling it to efficiently extract the underlying social attributes embedded in the raw data being analyzed.

The Promise and Challenges of Health Analytics

Irving Wladawsky-Berger

On February 24 I attended a workshop in MIT on the Future of Health Analytics. In a recent paper , Ausiello notes that health analytics has the potential to become the next frontier in medicine, driven by the confluence of three key revolutions: . Complex Systems Data Science and Big Data Education and Talent Healthcare Systems Innovation Management and Leadership Political Issues Services Innovation Smart Systems Society and Culture Technology and Strategy

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The need for innovation across boundaries and the power of big data analytics

Ross Dawson

The primary themes of the report were around spotting opportunities, innovation, transparency and trust, personalization, and 24/7 availability, and the implications for business. One of the interesting insights from the study was on what executives believe their organizations can best do to foster innovation. Top of the list was increasing co-operation between departments, recognizing the imperative of innovation across boundaries.

The Competitive Value of Data: From Analytics to Machine Learning

Irving Wladawsky-Berger

The article noted that regulators, policy makers and academics in Europe and the US are increasingly concerned that the vast data assets of digital giants could become a competitive barrier to startups and innovation. Business analytics predates the big data era. Beyond business analytics, new data science methods could now be used to extract actionable knowledge from all that data, that is, knowledge to help make better decisions and predictions.

The Transformative Power of Analytics - The Houston Astros: a Case Study

Irving Wladawsky-Berger

The answer, in a nutshell, is analytics. As VP of scouting and player development in St Louis, Luhnow built a strong analytics department and enjoyed great success as one of the top talent producers in baseball, helping the Cardinals win the 2011 World Series as well as reach the playoffs from 2012 to 2015. Luhnow then brought his analytics and management skills to Houston and proceeded to transform the team into a top contender in a short few years.

The Transformative Power of Analytics - The Houston Astros: a Case Study

Irving Wladawsky-Berger

The answer, in a nutshell, is analytics. As VP of scouting and player development in St Louis, Luhnow built a strong analytics department and enjoyed great success as one of the top talent producers in baseball, helping the Cardinals win the 2011 World Series as well as reach the playoffs from 2012 to 2015. Luhnow then brought his analytics and management skills to Houston and proceeded to transform the team into a top contender in a short few years.

Innovation in open online courses

George Siemens

In a few weeks, our edX course Data, Analytics, and Learning (#DALMOOC https://www.edx.org/course/utarlingtonx/utarlingtonx-link5-10x-data-analytics-2186 ) will start. This is a short overview of the innovations that we want to explore during the course. The innovations build heavily on community and network approaches that I and others (Stephen Downes, David Wiley, Alan Levine, Jim Groom, Dave Cormier) have used in previous open courses.

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The Internet, Blockchain, and the Evolution of Foundational Innovations

Irving Wladawsky-Berger

The concept of disruptive technologies, as defined by Clayton Christensen 20 years ago, has become widely accepted as a way of thinking about innovation-driven growth, but it’s been often misunderstood and misapplied. A disruptive innovation is one that successfully challenges a traditional product or business model with a lower cost solution, typically developed by a small company with few resources.

The Management of Disruptive Innovations

Irving Wladawsky-Berger

The management of disruptive innovations is very different when looking at relatively simple or self-contained technologies, products or services versus highly complex platforms and infrastructures. A lot of the hype you often hear when new innovations come about is the result of people not properly understanding the different dynamics that apply to simple versus highly complex innovations. The dot-com era was famous for its many innovations, but also for its hype.

Institutional Innovation - I Have a Dream

Edge Perspectives

Today they are an unnatural bundle of three very different and often conflicting business types: infrastructure management, product innovation and commercialization, and customer relationship businesses. Everyone talks about transformation these days, but the term is used so loosely that it’s begun to lose all meaning. I recently wrote a blog post suggesting that the ultimate test of transformation is whether the caterpillar became a butterfly – is it so different that it’s unrecognizable?

Innovation in an Age of Austerity

Irving Wladawsky-Berger

On December 7 I participated in an Innovation Summit sponsored by the UK Innovation Research Centre (UK-IRC) at the University of Cambridge. The UK-IRC is “a collaborative initiative for cutting-edge research and knowledge exchange activities on how innovation can make businesses more competitive, improve public services delivery and help the UK meet the social, environmental and economic challenges it faces.”

Reflections on Innovation, Productivity and Job Creation

Irving Wladawsky-Berger

For example, over the past year I have given a number of seminars on Technology and Innovation in the Service Economy. I have also been thinking a lot about the impact of these innovations on the productivity of the service sector of the economy in general, and in particular, on the kinds of jobs that we can expect to be created over the next decades to replace those jobs that might decline or disappear as a result of such productivity improvements.

The Future of Learning Management Systems: Development, Innovation and Change

Stephen Downes: Half an Hour

A big long-term trend to watch: initially there was a lot of emphasis on analytics, where they say ‘we will analyze the data’. The big thing is data access; analytics is more secondary. Phil Hill Summary notes from the presentation at from at the World Conference on Online Learning , Toronto. Slides: [link] We forget about the perspective of time. Let’s look at 2011-2017. Thrun was saying (2012) 50 years from now there will only be ten institutions delivering higher education.

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An innovative workshop on radical management

Steve Denning

In these three days ( March 19-21 in Washington DC ), you will discover how to use radical management to thrive in the 21 st Century creative economy and the world of continuous innovation. Have you ever wondered how your firm is going to survive and thrive as the world economy goes through a fundamental phase change—from industrial bureaucracy to a creative economy of continuous innovation? Why innovation happens “despite” the system, not because of it.

Libary Lab funds library innovation projects

David Weinberger

The Library Innovation Lab [ blog ] that Kim Dulin and I co-direct had a few of its proposals accepted: Library Analytics Toolkit : Tools to enable libraries to understand, analyze, and visualize the patterns of activities, including checkouts, returns, and recent acquisitions, and to do so across multiple libraries. LibraryCloud Server : Build and maintain a web server that makes available to all Harvard library innovators data and metadata gathered from the Harvard libraries.

Learning Analytics: Big Data Applied to Training, Teaching, and Learning

Beth Kanter

Learning Analytics comes from a report about the impact of emerging technologies for practitioners in a field. Two or three years to adoption: Learning Analytics (K-12/Higher Ed), Open Content (K-12), Games and Gamification (Higher Ed). The technology trend that caught my eye was “Learning Analytics,” which is big data applied to the field of education. The State of Learning Analytics in 2012: A Review and Future Challenges.

Ethical Codes and Learning Analytics

Stephen Downes: Half an Hour

Abstract The growth and development of learning analytics has placed a range of new capacities into the hands of educational institutions. The ethics of learning analytics will therefore need to be developed on criteria specific to education. It may be argued that the relation between ethics and law is such that in a treatment of the ethics of learning analytics we ought also to be concerned with the law in relation to learning analytics.

Why Innovation is Critical to Help Governments Adjust to the Realities of the 21st Century?

Irving Wladawsky-Berger

Aligning government with 21st century economic realities will require innovations at least as disruptive and profound as those embraced by the private sector. By harnessing major technological shifts and adopting best business practices, we can not only make our government far more productive, but also foster greater innovation in areas ranging from healthcare to education and energy – innovation that will generate economic growth and job creation.”. “We

Irving Wladawsky-Berger: Creativity, Innovation and Design

Irving Wladawsky-Berger

Irving Wladawsky-Berger A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects. In early June, Imperial College and the Royal College of Art announced the creation of a new center - Design-London - to bring together the disciplines of design, engineering, technology and business to address jointly the challenges of innovation in an increasingly global, competitive economy.

The role of informal social networks in building organizational creativity and innovation

Trends in the Living Networks

This is evident in the California Management Review paper I co-authored on Managing Collaboration: Improving Team Effectiveness through a Network Perspective , in which we examined how to improve performance in sales, innovation, and execution. Innovation is of course a particularly pointed issue today, with the increasing pace of external and industry change driving the necessity of effective, applied creativity. Write the history of your organization’s most compelling innovations.

How to Emerge Stronger in the Post-Pandemic New Normal

Irving Wladawsky-Berger

Such a digital recovery will include “technology-enabled next-generation operations, analytics-enabled engineering productivity, and automation of service-related processes.”. In the coming years, we can expect superior user experiences and other innovations across many online applications.

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Getting on the AI Learning Curve: A Pragmatic, Incremental Approach

Irving Wladawsky-Berger

Two-thirds of the opportunities to use AI are in improving the performance of existing analytical use cases,” is the paper’s overriding finding. For the vast majority of the use cases, 69%, the key value of machine learning is to improve performance beyond that provided by traditional analytic techniques. And, in the remaining 15% of cases, machine learning provided limited additional performance over existing analytical methods. .

Presentation to UNCTAD's Advisory Group on "Developing skills, knowledge and capacities through innovation: E-Learning, M-Learning, cloud-Learning"

Stephen Downes: Half an Hour

Finally, in the LPSS, or Learning in Performance Support System, is analytics, competence and assessment , and this is essentially the application of artificial intelligence, and the pattern recognition to identify the ways in which a person can become competent at some skill or task, and the gap between where they are, and becoming competent. based analytics, is that a personal learning record, can extend beyond the limits of the platform.

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Irving Wladawsky-Berger: Reflections on Leadership and Innovation

Irving Wladawsky-Berger

Irving Wladawsky-Berger A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects. In this course, I want to examine how to leverage disruptive innovations to significantly transform a business or even a whole industry. I want to focus, in particular, on what a company has to do when faced with a disruptive innovation so that it becomes the disruptor rather than the disruptee.

After Years of Promise and Hype, Is AI Once More Failing to Deliver?

Irving Wladawsky-Berger

One of deep learning’s key features is that, unlike classic analytic methods, there’s no asymptotic data size limit beyond which it stops improving.

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Getting on the AI Learning Curve: A Pragmatic, Incremental Approach

Irving Wladawsky-Berger

Two-thirds of the opportunities to use AI are in improving the performance of existing analytical use cases,” is the paper’s overriding finding. For the vast majority of the use cases, 69%, the key value of machine learning is to improve performance beyond that provided by traditional analytic techniques. And, in the remaining 15% of cases, machine learning provided limited additional performance over existing analytical methods. .

The AI Factory: A New Kind of Digital Operating Model

Irving Wladawsky-Berger

Artificial Intelligence Complex Systems Data Science and Big Data Economic Issues Education and Talent Innovation Management and Leadership Political Issues Technology and Strategy

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Social Physics and Cybercrime Detection

Irving Wladawsky-Berger

These patterns can be used to detect emerging behavioral trends before they can be observed by other data analytics techniques. And if a new behavior, - whether the result of an innovative idea like the discovery of tools, or a mutation like a larger brain size, - helps a human group better adapt to a changing environment, natural selection will favor the survival of that group over others.

Reflections on the Later Stages of Our Careers

Irving Wladawsky-Berger

While there are outliers, “the likelihood of producing a major innovation at age 70 is approximately what it was at age 20 - almost nonexistent.”. Economic Issues Education and Talent Innovation Society and Culture

Everyday Technology: Innovative Ways To Do More With Less

Beth Kanter

Note from Beth: Innovation was the buzzword at the Nonprofit Technology Conference! I ran into the good folks at Idealware who offered to do a guest post on the topic – looking at small ways to be innovative. Everyday Technology: Innovative Ways To Do More With Less, Guest Post by Laura Quinn and Chris Bernard. But at its core, innovation is really about finding creative solutions to existing problems and needs—challenges faced by nonprofits every day.

Can AI Help Develop and Execute a Competitive Business Strategy?

Irving Wladawsky-Berger

In today’s data-rich markets, top business leaders rely heavily on analytics and quantitive measures to define, communicate and drive strategy. As ‘accountable optimization’ becomes an AI-enabled business norm, there is no escaping analytically enhanced oversight. Artificial Intelligence Complex Systems Data Science and Big Data Economic Issues Innovation Management and Leadership Services Innovation Smart Systems Technology and Strategy

How to Support the Widespread Adoption of AI

Irving Wladawsky-Berger

In their 2009 book Wired for Innovation: How Information Technology is Rewiring the Economy , Erik Brynjolfsson and Adam Saunders introduced the concept of organizational capital as the necessary critical ingredient that enabled a company to take full advantage of major IT advances. Having business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues.

Blockchain Can Reshape Financial Services… But it Will Take Significant Time and Investment

Irving Wladawsky-Berger

A recent Gartner report noted that blockchain technologies are hitting the peak of the hype cycle , when the excitement and publicity about a potentially disruptive innovation often leads to a peak of inflated expectations, before falling into the trough of disillusionment when the fledgling technology fails to deliver. wrote the WEF in a very timely message. “Over the last 50 years, technology innovation has been fundamental to financial services industry transformation.

The Mobilisation Journal: A Way To Spread Learning About Social Change Movements Innovative Use of Technology

Beth Kanter

This is an example of a nonprofit organization that has invested in building an internal learning and innovation network that will lead to improved results for Greenpeace’s environmental mission. Storytelling and Knowledge Transfer :: sharing innovations, lessons learned, fail stories, and emerging best practices. Innovation Incubation :: piloting new ways of working, from practices to technologies.

The Emergence of Industry 4.0

Irving Wladawsky-Berger

Many of these innovations are in their infancy, but they are already reaching an inflection point in their development as they build on and amplify each other in a fusion of technologies across the physical, digital and biological worlds.”. The digitization of product and service offerings by adding smart sensors, communication devices, and data analytics tools to create smart digital products and smart services. Robust, enterprise-wide analytics requires significant change.

A Talk On Fast Innovation, All In One Great Picture - Bob Sutton

Bob Sutton

The ergonomics of innovation -- The McKinsey Quarterly. interview Pixar Brad Bird - Innovation lessons from Pixar Director Brad Bird - Strategy - Innovation - The McKinsey Quarterly. Customer-Focused Innovation. David A Owens: Creative People Must Be Stopped: 6 Ways We Kill Innovation (Without Even Trying). McCraw: Prophet of Innovation: Joseph Schumpeter and Creative Destruction. Davenport: Competing on Analytics: The New Science of Winning.

Reconnecting Society and Reopening the Economy

Irving Wladawsky-Berger

Their recent paper and accompanying supplementary material explains their models and analytical methods in great detail. A few months ago, MIT Connection Science held it’s annual Sponsors’ Meeting online. The virtual meeting included a number of very interesting presentations.

Blockchain and Public Health Solutions

Irving Wladawsky-Berger

The roundtable’s findings and recommendations were released in early April in Blockchain Solutions in Pandemics : A Call for Innovation and Transformation in Public Health. Blockchain opens up innovative possibilities for decentralized solutions that give more control to individuals based on the development of self-sovereign digital identities. Using next generation data analytics and AI they could understand the possible trajectories of a virus and take steps to crush it.”.

Globalization in Transition

Irving Wladawsky-Berger

The growing reliance on knowledge favors countries with highly skilled labor forces, strong innovation and R&D capabilities, and robust IP protections. Goods-producing value chains are becoming more regionally concentrated, with companies establishing production in closer proximity to their customers. “Regionalization is most apparent in global innovations value chains, given their need to closely integrate many suppliers for just-in-time sequencing.

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Become a More Productive, Empathetic, Creative Person With the Help of AI-Based Tools

Irving Wladawsky-Berger

In a recent article in the MIT Sloan Management Review , MIT Research Fellow Michael Schrage proposed a provocative and counterintuitive approach for enhancing innovation and productivity through man-machine collaborations. Instead of just asking how can people create more valuable innovation?, why not also ask How can innovation create more valuable people?

Becoming a Data-Driven Business: Challenges and Opportunities

Irving Wladawsky-Berger

A few weeks ago, McKinsey published The age of analytics: Competing in a data-driven world , a comprehensive report on the state of big data, and in particular, on the challenges and opportunities a company faces as it strives to become a data-driven business. Big data ,- including analytics , data science , artificial intelligence and related information-based technologies - are now everywhere. “Is It’s very hard to anticipate the consequences of disruptive innovations.

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Beyond Machine Learning: Capturing Cause-and-Effect Relationships

Irving Wladawsky-Berger

Machine learning is a statistical modelling technique, like data mining and business analytics , which finds and correlates patterns between inputs and outputs without necessarily capturing their cause-and-effect relationships. Artificial Intelligence Complex Systems Data Science and Big Data Innovation Services Innovation Smart Systems Technology and StrategyArtificial intelligence is rapidly becoming one of the most important technologies of our era.

Artificial Intelligence is Ready for Business; Are Businesses Ready for AI?

Irving Wladawsky-Berger

A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture.”. A common theme throughout the report is that the same players who were leaders in the earlier waves of digitization and analytics are now leading in the AI wave. AI is now seemingly everywhere.