Online Personality Tests

4 Ways to Make Your Management Skills More Effective

Online Personality Tests

A good leader provides constant motivation to his team to help them maintain excellence and quality in results. A good leader always looks for ways to improve efficiency and quality, especially if this can be done by simplifying processes or without increasing an employee’s workload.

2 Strategies to Develop Your Leadership

Online Personality Tests

Leadership, Leadership, Leadership! There is an abundant and constant flow of information on leadership in the form of books and articles including material on leadership development and best practices.

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3 Common Performance Management Mistakes

Online Personality Tests

Good Management is Key. According to Gallup , a US firm specializing in management research, in a survey of more than one million Americans, “people quit their bosses, not the organization.” ” The effect of mismanagement is widely felt.

Leadership Tools: 4 Steps to Handle Difficult Relationships

Online Personality Tests

We’ve all experienced them. We can’t get along with everyone, right? Difficult relationships can be stressful, frustrating and exhausting. But it doesn’t have to be that way.

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6 Ways to Develop Your Leadership Skills

Online Personality Tests

Good Leaders Are Good Learners. Developing your leadership skills and abilities is a lifelong pursuit. It’s one that requires you to never stop learning and growing as a person. Leadership means continuing to tackle new challenges, face new changes and overcome new obstacles.

Top 3 Myths About Change Management

Online Personality Tests

Did you know that… “Studies show that 70% – 80% of change initiatives in organizations fail” -John Kotter. As an organizational psychologist, I can tell you that this alarming failure rate is often attributed to the human factor! Here are 3 classical myths that I frequently deal with in my practice. Myth #1 – It is important to communicate the information in groups.

Leadership Tools: Building Your Ideal Team

Online Personality Tests

How to Build Your Ideal Team. One of the best and sometimes trickiest parts of being a leader is having the opportunity to build your ideal team. Choosing the right employees can make a huge difference in the success of your company.

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Leadership Tools: Clarity in Communication

Online Personality Tests

Clarity Begins with Our Mighty Minds. Did you know our brains communicate information at the rate of some four billion neuron impulses per second? All that information feeds through our senses, and yet we’re only consciously aware of about 2,000. That’s only 0.00005%.

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Leadership Tools: Conquering Fear

Online Personality Tests

What do you fear? Stop for a moment and think about this: what keeps you from living your dreams? What problem tends to dominate the average person’s life without us even noticing? The answer: FEAR! People live every day in fear.

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Leadership Tools: Effective Listening

Online Personality Tests

Effective Listening Isn’t Waiting Your Turn to Speak All too often we are far more enthusiastic about talking than we are listening. Yet effective listening is vital for effective communication. Would you believe that most conflicts are simple misunderstandings?

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You are Your Own President

Online Personality Tests

Welcome President You Congratulations! You are the president of your own nation, and it is called “Imagination”…get it? “Imagi-NATION” This particular nation is the driving force behind your life and is the underlying factor for your future.

The 2019 Artificial Intelligence Index

Irving Wladawsky-Berger

AI has emerged as the defining technology of our era, as transformative over time as the steam engine, electricity, and the Internet.

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Discover2Learn

Jane Hart

Activities and Resources for self-isolation and beyond Be curious – Explore – Discover new things – Have fun – Learn Use your self-isolation time to invest in your future. Reduce boredom and anxiety. Discover2Learn. Modern Workplace Learning

Cynefin St David’s Day (5 of 5)

Dave Snowden

I don’t know if I displaying a keen anticipatory capability in focusing on Disorder, the all too often ignored fifth domain of Cynefin, in this annual update series; if so it was unconscious.

Shallow or Deep

Clark Quinn

I wrote about how I was frustrated with the lack of any decent learning expertise in too many vendors. And, lately I’ve been seeing more orgs making learning claims. Unrelated, of course, because it’s too soon. Still, are things improving?

How To Lead Remote Employees In The Wake Of COVID-19

Dan Pontefract

As of this writing, the World Health Organization (WHO) hasn’t declared COVID-19 (aka: coronavirus) a pandemic, but the likelihood grows by the hour. Whether or not it receives such a … Continue reading "How To Lead Remote Employees In The Wake Of COVID-19".

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The Coming Era of Decision Machines

Irving Wladawsky-Berger

“Artificial intelligence (AI) is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals,” wrote Harvard professor David Parkes in A Responsibility to Judge Carefully in the Era of P rediction Decision Machines , an essay recently published as part of Harvard’s Digital Initiative. “Machines need to be able to predict to decide, but decision making requires much more. Decision making requires bringing together and reconciling multiple points of view. Decision making requires leadership in advocating and explaining a path forward. Decision making requires dialogue.”. In April, 2017 I attended a seminar by University of Toronto professor Avi Goldfarb on the economic value of AI. Goldfarb explained that the best way to assess the impact of a new radical technology is to look at how the technology reduces the cost of a widely used function. For example, computers are essentially powerful calculators whose cost of digital operations have dramatically decreased over the past several decades. Over the years, we’ve learned to define all kinds of tasks in terms of digital operations, e.g., financial transactions, word processing, photography. Similarly, the Internet has drastically reduced the cost of communications and of access to all kinds of information, - including text, pictures, music and videos. Viewed through this lens, the AI revolution can be viewed as reducing the cost of predictions. Prediction means anticipating what is likely to happen in the future. Over the past decade, increasingly powerful and inexpensive computers, advanced machine learning algorithms, and the explosive growth of big data have enabled us to extract insights from all that data and turn them into valuable predictions. Given the widespread role of predictions in business, government and everyday life, AI is already having a major impact on many human activities. As was previously the case with arithmetic, communications and access to information, - we will be able to use predictions in all kinds of new applications. Over time, we’ll discover that lots of tasks can be reframed as prediction problems. But, “[it’s] decisions, not predictions, that have consequences,” notes Parkes. “ If the narrative of the present is one of managers who are valued for showing judgment in decision making… then the narrative of the future will be one in which we are valued for our ability to judge and shape the decision-making capabilities of machines. What will the decision machines of the future be optimizing for, on the basis of what data, and on whose behalf? How should we develop and deploy complex AI systems whose purpose is to make decisions continuously and automatically? What values should be enshrined in our systems? The academic community is starting to pay attention to these very important and difficult questions underlying the shift, from predictions to decisions. L ast year Parkes was co-organizer of a workshop on Algorithmic and Economic Perspectives on Fairness. The workshop brought together researchers with backgrounds in algorithmic decision making, machine learning, and data science with policy makers, legal experts, economists, and business leaders. As explained in the workshop report , algorithmic systems have long been used to help us make consequential decisions. Recidivism predictions date back to the 1920s, and automated credit scoring began in the middle of the 20th century. Not surprisingly, prediction algorithms are now used in an increasing variety of domains, including job applications, criminal justice, lending and insurance, medicine and public services. This prominence of algorithmic methods has led to concerns regarding their overall fairness in the treatment of those whose behavior they’re predicting, such as whether the algorithms systematically discriminate against individuals with a common ethnicity or religion; do they properly treat each person as an individual; and who decides how algorithms are designed and deployed. These concerns have been present whenever we make important decisions. What’s new is the much, much larger scale at which we now rely on algorithms to help us make decisions. Human errors that may have once been idiosyncratic may now become systematic. Another consideration is their widespread use across domains. Prediction algorithms, such as credit scores, may now be used in contexts beyond their original purpose. Accountability is another serious issue. “Who is responsible for an algorithm’s predictions? How might one appeal against an algorithm? How does one ask an algorithm to consider additional information beyond what its designers already fixed upon?”. While fairness is viewed as subjective and difficult to measure, accuracy measurements are generally regarded as objective and unambiguous. “Nothing could be farther from the truth,” says the workshop report. “Decisions based on predictive models suffer from two kinds of errors that frequently move in opposite directions: false positives and false negatives. Further, the probability distribution over the two kinds of errors is not fixed but depends on the modeling choices of the designer. As a consequence, two different algorithms with identical false positive rates and false negative rates can make mistakes on very different sets of individuals with profound welfare consequences.”. Workshop participants were asked to identify and frame what they felt were the most pressing issues to ensure fairness in an increasingly data- and algorithmic-driven world. Let me summarize some of the key issues they came up with as well as questions to be further investigated. Decision Making and Algorithms. It’s not enough to focus on the fairness of algorithms because their output is just one of the inputs to a human decision maker. This raises a number of important questions: h ow do human decision makers interpret and integrate the output of algorithms?; when they deviate from the algorithmic recommendation, is it in a systematic way?; and which aspects of a decision process should be handled by an algorithm and which by a human to achieve fair outcomes? Assessing Outcomes. It’s very difficult to measure the impact of an algorithm on a decision because of indirect effects and feedback loops. Therefore, it’s very important to monitor and evaluate actual outcomes. Can we properly understand the reasons behind an algorithmic recommendation?; how can we design automated systems that will do appropriate exploration in order to provide robust performance in changing environments? Regulation and Monitoring. Poorly designed regulations may be harmful to the individuals they’re intended to protect as well as being costly to implement for firms. It’s thus important to specify the precise way in which compliance will be monitored. How should recommendation systems be designed to provide users with more control?; could the regulation of algorithms lead to firms abandoning algorithms in favor of less inspectable forms of decision-making? Educational and Workforce Implications. The study of fairness considerations as they relate to algorithmic systems is a fairly new area. It’s thus important to understand the effect of different kinds of training on how well people will interact with AI based decisions, as well as the management and governance structure for AI-based decisions. Are managers (or judges) who have some technical training more likely to use machine learning- based recommendations?; w hat should software engineers learn about ethical implications of their technologies?; what’s the relationship between domain and technical expertise in thinking about these issues? Algorithm Research. Algorithm design is a well-established area of research within computer science. At the same time, fairness questions are inherently complex and multifaceted and incredibly important to get right. How can we promote cross-field collaborations between researchers with domain expertise (moral philosophy, economics, sociology, legal scholarship) and those with technical expertise? Artificial Intelligence Complex Systems Data Science and Big Data Economic Issues Education and Talent Innovation Management and Leadership Political Issues Services Innovation Smart Systems Society and Culture Technology and Strategy

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Cynefin St David’s Day 2020 (1 of n)

Dave Snowden

On St David’s Day last year, I started a five-part series of posts to update the Cynefin Framework, all illustrated by pictures of the mountains of Eryri, or Snowdonia if you want to use the Saxon which derives from Snow Dun, or snow hill.

The State of AI Adoption - High Performers Show the Way

Irving Wladawsky-Berger

For the past few years, the McKinsey Global Institute has been conducting a yearly survey to assess the state of AI adoption. Its 2017 survey of over 3,000 AI-aware executive found that outside the technology sector, AI adoption was at an early, often experimental stage.

An Open Letter To The CEOs Of High Tech And Telecom Regarding COVID-19

Dan Pontefract

To the CEOs of companies such as Apple, Amazon, Google, Microsoft, Intel, IBM, Oracle, AT&T, Verizon, TELUS, Rogers, Bell, BT and Telstra, I have a request. Gather your c-suite. Ask … Continue reading "An Open Letter To The CEOs Of High Tech And Telecom Regarding COVID-19".

Death to Zombies!

Clark Quinn

Last week, I ranted about a myth that seems inextinguishable. And I ran across another one in a place I shouldn’t have. And I keep seeing others, spotting them roaming around loose. Like zombies, they seem to rise from the dead. We need death to zombies. Particularly learning myth zombies!

Consulting in the Time of Corona (virus)

Dave Snowden

In the past 2 weeks, I have been called into a few urgent conference calls with partners and clients. As a response to the Corona virus, and it’s spread, many of them (and ourselves) included have had their projects impacted. .

Announcing My Free (or chip in!) Basics On Working From Home Toolkit

Dan Pontefract

You may recall that I launched a REMOTE LEADERSHIP TOOLKIT on March 16. There have been over 3,000 downloads thus far. Glad I could help somehow in this the age … Continue reading "Announcing My Free (or chip in!) Basics On Working From Home Toolkit". The post Announcing My Free (or chip in!)

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Zoom needs to clean up its privacy act

Doc Searls

As quarantined millions gather virtually on conferencing platforms, the best of those, Zoom , is doing very well. Hats off. But Zoom is also—correctly— taking a lot of heat for its privacy policy , which is creepily chummy with the tracking-based advertising biz (also called adtech ).

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Can Democracy and Free Markets Survive in the Coming Age of AI?

Irving Wladawsky-Berger

Can technology plan economies and destroy democracy?

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Wherefore, part the first

Dave Snowden

We tend to read ‘ wherefore ’ as a question asking where something is, but the meaning is actually for what , or why as in “ Wherefore was I born” (Shakespeare, Richard III Act 2 scene 3) and Juliet’s more famous rendering which is attempting to locate her love but to ask why does he have to be a Montague; remember it is followed by “What’s in a name? That which we call a rose By any other name would smell as sweet.” In this two-part post, my first since the Christmas series, I want to take a look at the three-part question What? So What? Now What? which is deceptively simple and can easily tend to the simplistic. It appears in Liberating Structures without (as is all too common in that otherwise useful tool) without attribution. It is more commonly attributed to Glenda Eoyang’s Adaptive Action but its first formulation goes back to Terry Borton in 1970 and it was then developed by John Driscoll in the context of clinical practice which is where I suspect Glenda got it, but I could be wrong there. I’ve put the three representations in rough date order to the right of the text below. Basically I want to ask the wherefore question as to its use and (tomorrow) map it to all five domains of Cynefin. Just to give you a taster, my argument is that the linear use of the three-part question effectively sits in the confused or unordered domain of Cynefin. It is probably worth starting with the Driscoll sequence in the context of clinical care. He links it to a learning cycle which is represented in the following stages: Having an Experience. WHAT? describe event, then … Purposefully reflect on the selected aspects of the experience. SO WHAT? conduct an analysis of the event … Discover the learning arising from this process of reflection. NOW WHAT? Determine proposed actions following the event. Enact the new learning from that experience in clinical practice. Then loop back to the start. Adaptive action (Eoyang) aims to conduct multiple connected iterations of the three questions in allow coherence over the system to emerge as “the parts use simple rules to guide their work toward shared goals (my emphasis). The process focuses on the identification of patterns and use of the CDE (Containers, Differences, Exchanges) to understand what is generating those patterns and the link to Plan-Do-Check-Act is made although it is not named as the Deming cycle. Amplification and dampening of those patterns, shaping new patterns are all up there as actions. The process is based on workshops, discussions and (I assume) learned individual behaviour. While the process is linear it does have multiple interconnections and the Now What? stage can trigger other stages and so on. Coherence for Glenda is all about “internal fitness” and adaptive means “external fitness’. Defining terms in complexity work is key as, at the moment, everyone is using the language in different ways. Once you are at the Now What? stage, the process becomes a familiar set of project management questions and task assignment: who is doing what, how long will it take with what resource, who has to be involved, what will it mean to complete and (importantly) how will this trigger a new What? the whole idea is that nothing ever ends. Finally, the Liberating structures guys revert to the linear, overlaying a systems dynamics model on to W³. I tend to put this into the mostly harmless category as they are focused on workshop experiences. That said having recently watched some Liberating Structures facilitators tear the heart out of Future Backwards by conforming it to the goal-based idealism of systems dynamics was depressing. Given that they attributed it to me and asked for my endorsement I think I was fairly restrained in my response. Now there may be other uses – if so please post them. I can be positive and negative about the three that I have listed. Tomorrow I ended to map W³ to Cynefin using (for the first time) the five Cs namely Clear, Complicated, Complex, Chaotic and Confused. I will argue that all the above – given that they are universal and in part linear – while useful are not energy efficient. But that is for tomorrow. My apologies for the absence of blog posts but I have been busy on various things including design of next generation of SenseMaker® which has me more excited than I’ve been for a long time. A complete shift away from a survey like platform to a radical new approach to distributed decision support; current SenseMaker® will simply be one instance of something more sophisticated. We expect to open up for participation in a couple of months. Otherwise, I am going to try and get back to posting here at least once a week, ideally more. Acknowledgments. The Letter W in the text is by Leo Reynolds discovered in Flickr as is the banner picture , both and used under the terms a creative commons license. The post Wherefore, part the first appeared first on Cognitive Edge. musing Polemic

the future is here

Harold Jarche

Work is learning, and learning is the work. This has been my tag line for the past decade.

The Current State of Open Innovation

Irving Wladawsky-Berger

In January, UC Berkeley professor Henry Chesbrough published Open Innovation Results : Going Beyond the Hype and Getting Down to Business, his fourth book on innovation in the last two decades.

Announcing Speak Aid 2020

Dan Pontefract

In this time of uncertainty, angst and working from home, patterns have changed and new norms are quickly being crafted. One of the norms to have seemingly vanished overnight was … Continue reading "Announcing Speak Aid 2020". The post Announcing Speak Aid 2020 appeared first on Dan Pontefract.

Aporetic Meditations

Dave Snowden

These have been trying times. Yesterday, I watched our National Development Minister tear up at a national address as he thanked frontline workers who are doing their part against COVID-19. Singapore released its second stimulus package yesterday, drawing a landmark $48bn from its reserves.

Separated by a common language?

Dave Snowden

From time to time in social media I resist the temptation to respond when people talk about the Stacy Matrix; if they conflate the two I generally put up something mild along the lines of They are very different you know and Stacy himself deprecates its use.

Is intrinsic motivation a myth?

Clark Quinn

I was asked to comment on intrinsic motivation, and was pointed to an article claiming that it’s a myth(!). Given that I’m a staunch advocate of intrinsic motivation, I felt this was something that I should comprehend. Is intrinsic motivation a myth?

Since 2008 I’ve Worked From Home. Here Are 5 Helpful Tips.

Dan Pontefract

I spent ten years at TELUS as a team member, leader and executive. I was a mobile worker the entire time. I worked from the road, a TELUS office, hotels, the … Continue reading "Since 2008 I’ve Worked From Home. Here Are 5 Helpful Tips.". The post Since 2008 I’ve Worked From Home.

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Cynefin St David’s Day (4 of 5)

Dave Snowden

In this penultimate post in the current series, I want to look at what you can manage in the complex domain of Cynefin and return to the which domain is X in debate.

The Increasing Demand for Hybrid, “T-Shaped” Workers

Irving Wladawsky-Berger

A recent article in The Atlantic used the USS Gabrielle Giffords to illustrate the important changes taking place in the US Navy, - and in the world of work in general. After discussing various features of its advanced design, the article noted that the ship’s most futuristic aspect is its crew. “It

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The Long-Term Future of Work and Education: Three Potential Scenarios

Irving Wladawsky-Berger

“Experts differ widely in their predictions about how technological innovation will change the labor market, but they all see a need for changes in education,” write British professors Ewart Keep and Phillip Brown in a recently published article, Rethinking the Race Between Education and Technology. While experts don’t generally agree on much, they’re pretty much of one mind when it comes to the growing importance of skills and education in our 21st century digital economy. Every past technological transformation ultimately led to more jobs, higher living standards and economic growth. But, as a number of recent studies concluded, to ensure that this will indeed be the case, our emerging knowledge economy should be accompanied by the expansion of educational opportunities for everyone. While noting that this is the most likely scenario for the next 10 - 15 years, Keep and Brown also consider two potential longer-term scenarios. Perhaps AI will lead to even more pervasive and fundamental transformations in the nature of work, making it difficult for even those with a college or higher education to find a good job. Beyond that , some have suggested that in the more distant future we might see an even more radical, science-fiction-like transformation: the end of work as we’ve long known it. The authors argue that considering such a spectrum of possibilities will help us better prepare for what’s essentially an unpredictable future. In that spirit, their paper discusses three different labor market scenarios: labor scarcity, job scarcity, and the end of work. Labor scarcity. “Supporters of this scenario expect that as in the past, new positions and professions will emerge and create new jobs to replace any eliminated by new technology. Although there may be a challenging period of transition, especially for those displaced by automation, technological innovation will require new skills and create employment opportunities.”. Investments in the skills required to meet these technology and workforce challenges are the key source of individual opportunity, social mobility, and economic welfare. This is especially important for workers without a four-year college degree who’ve disproportionately borne the brunt of automation. Post-secondary education and training venues, - e.g., community colleges, apprenticeships, online education, industry-specific training programs, - are likely to be most relevant and accessible to these workers. However, existing education and training programs won’t be enough given the demands for life-long adult learning. “What makes these arguments consistent is the idea of a race between technology and education to develop more advanced skills if people are to remain employable in tomorrow’s labor market. The fundamental challenge remains the reform of education systems to prepare the future workforce to take advantage of new opportunities emerging within a technologically advanced economy… People will need to adapt continuously and learn new skills and approaches within a variety of contexts.”. In addition, new skills will be required to keep up with the increased digitalization of the economy. The article references the Essential Digital Skills Framework , a tool developed by the UK Government that defines the skills needed to benefit from, participate in, and contribute to the digital world. The framework includes five categories of skills: communicating, collaborating and sharing online; handling information and content securely; buying, selling and managing transactions; finding solutions to problems using digital tools; and being safe and legal online. Job Scarcity. Automation fears have understandably accelerated in recent years, as our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans. P revious technological innovation always delivered more long-run employment, but things can change. As a 2014 Economist article noted, while the majority of economists wave such worries away, some now fear that a new era of automation enabled by ever more powerful and capable computers could work out differently. “The job scarcity view recognizes that new technologies may enhance the skills of a relatively small proportion of the workforce, but the general direction of technological innovation is toward the redesign of existing jobs, where much of the knowledge content is captured in software that permits a high level of standardization and potential to deskill or automate a wide range of occupations, including technical, professional, and managerial roles.”. This scenario reminds me of Software is Eating the World , a 2011 essay by Marc Andreessen which predicted that software was poised to take over large swathes of the economy. Entrepreneurial companies all over the world are disrupting established industries with innovative AI-driven software solutions. An increasing number of businesses and industries are being run on software and delivered as online services. “Job scarcity points to a significant mismatch between an expanding supply of educated and skilled workers and a scarcity of high-quality job opportunities, primarily resulting from the routinization and segmentation of job roles rather than technological unemployment.” A relatively small number of highly skilled, educated professionals and managers will develop the necessary algorithms, digital systems and business models, while a much larger number of less skilled workers will be needed to implement the procedures and managerial tasks which have been captured in algorithms and software. The End of Work. In a 1930 essay , English economist John Maynard Keynes wrote about the onset of “a new disease” which he named technological unemployment , that is, “unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.” Keynes predicted that the standard of living in advanced economies would be so much higher by 2030 that “for the first time since his creation man will be faced with his real, his permanent problem - how to use his freedom from pressing economic cares, how to occupy the leisure,” and most people would be working a 15-hour week or so, which would satisfy their need to work in order to feel useful and contended. Such an end-of-work scenario assumes that decades from now, most economic activity will be handled by super-smart machines developed and supervised by small groups of highly skilled professional and technical workers. “It would represent a profound dislocation for the education and training system… where for the past three decades or more the focus has been on the role of education in equipping individuals to perform effectively in a changing labor market.” Instead, the aim of education “would be to help people gain the skills to live fulfilling lives, with the judgment and knowledge to be capable of addressing the complex problems that humanity will face.”. “All three theories acknowledge rapid technological change, even if there is disagreement about its impact on labor demand and job quality,” write the authors in conclusion. “They all acknowledge the need for digital skills and an even greater focus on social skills. These skills are seen to be more important because people will need to be flexible and adaptable within rapidly changing labor markets and work contexts. Moreover, although the technical and knowledge requirements of what people do for a living may change, the social context in which people interact, network, and produce will remain, and social skills are more difficult for smart machines to develop.” Finally, “ all three theories see a need for educational reform and a greater focus on lifelong learning.”. Artificial Intelligence Complex Systems Economic Issues Education and Talent Future of Work Management and Leadership Political Issues Services Innovation Smart Systems Society and Culture Technology and Strategy

Interesting times

Clark Quinn

It was when I was living in Australia that I first heard the apocryphal Chinese curse “may you live in interesting times.” ” And, I have to say, the going’s gotten weird. A few reflections on the situation, all of course related to COVID-19.

The roots of LXD

Clark Quinn

Instructional design, as is well documented, has it roots in meeting the needs for training in WWII. User experience (UX) came from the Human Computer Interaction (HCI) revolution towards User Centered Design.

“… save his own fame and gratification.”

Dave Snowden

A reminder that his blog is my personal opinion and is not an official statement from Cognitive Edge, its staff, partners or network. There really is only one subject to talk about in our disunited & troubled kingdom today; and the various historical precedents are scary.

30 articles from 2019 to take us into 2020

Jane Hart

During 2019 I shared hundreds of links to useful articles, posts and resources but here are 30 (listed in chronological order) that I believe highlight 3 key themes for 2020. Modern Workplace Learning

… from little acorns grow

Dave Snowden

Last week I was in Nairobi ase the keynote at a panel discussion on Behavioural Insights for Environment Impact for UNEP sandwiched between two pretty hellish flights. Jules was also on the panel and had done the work to set the event up.

Wherefore, part the second

Dave Snowden

In the first of these articles, I looked at Borton’s W³ question from the 1970s and more recent adaptations (with varying degrees of acknowledgment of their source) by Driscoll, Eoyang and Liberating Structures.

Why Some AI Efforts Succeed While Many Fail

Irving Wladawsky-Berger

Winning with AI , - a 2019 report based on a survey jointly conducted by the MIT Sloan Management Review and the Boston Consulting Group , - found that 90% of respondents agree that AI represents a business opportunity for their company.

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