Adonis Diaries

Archive for the ‘education methods/programs’ Category

Three ways to add value

Tasks (doing), decisions (choosing), and initiation (starting something out of nothing?)

Each of the three adds value, but one is more prized than the others.

Tasks are set up for you. Incoming. You use skill and effort to knock them down one at a time and move to the next one.

Decisions often overlap with tasks. There are alternatives, and you use knowledge and judgment to pick the best one.

And initiation is what happens when you start something out of nothing, break the pattern, launch the new thing and take a leap.

When we think about humans who have made change happen, institutions who have made a difference, cultural shifts (paradigm shift) that have mattered, we must begin with initiation

Posted by Seth Godin on May 19, 2017

How can we make love statistics? Interactive graphs?

Think you’re good at guessing stats? Guess again. Whether we consider ourselves math people or not, our ability to understand and work with numbers is terribly limited, says data visualization expert Alan Smith. Smith explores the mismatch between what we know and what we think we know.

Alan Smith. Data visualisation editor
Alan Smith uses interactive graphics and statistics to breathe new life into how data is presented. Full bio
Filmed April 2016

Back in 2003, the UK government carried out a survey. And it was a survey that measured levels of numeracy in the population.

And they were shocked to find out that for every 100 working age adults in the country, 47 of them lacked Level 1 numeracy skills. Now, Level 1 numeracy skills — that’s low-end GCSE score. It’s the ability to deal with fractions, percentages and decimals.

this figure prompted a lot of hand-wringing in Whitehall. Policies were changed, investments were made, and then they ran the survey again in 2011. So can you guess what happened to this number? It went up to 49.

0:57 And in fact, when I reported this figure in the FT, one of our readers joked and said, This figure is only shocking to 51 percent of the population.”

But I preferred the reaction of a schoolchild when I presented at a school this information, who raised their hand and said, “How do we know that the person who made that number isn’t one of the 49 percent either?”

1:20 (Laughter)

So clearly, there’s a numeracy issue, because these are important skills for life, and a lot of the changes that we want to introduce in this century involve us becoming more comfortable with numbers. (Can’t learn numeracy without using a pen and pencil?)

it’s not just an English problem. OECD this year released some figures looking at numeracy in young people, and leading the way, the USA — nearly 40 percent of young people in the US have low numeracy. Now, England is there too, but there are seven OECD countries with figures above 20 percent. That is a problem, because it doesn’t have to be that way. If you look at the far end of this graph, you can see the Netherlands and Korea are in single figures. So there’s definitely a numeracy problem that we want to address. (It is the method used to learning numeracy)

 as useful as studies like these are, I think we risk herding people inadvertently into one of two categories; that there are two kinds of people: those people that are comfortable with numbers, that can do numbers, and the people who can’t.

And what I’m trying to talk about here today is to say that I believe that is a false dichotomy. It’s not an immutable pairing. I think you don’t have to have tremendously high levels of numeracy to be inspired by numbers, and that should be the starting point to the journey ahead.

one of the ways in which we can begin that journey, for me, is looking at statistics. Now, I am the first to acknowledge that statistics has got somewhat of an image problem.

2:52 (Laughter)

It’s the part of mathematics that even mathematicians don’t particularly like, because whereas the rest of maths is all about precision and certainty, statistics is almost the reverse of that.

But actually, I was a late convert to the world of statistics myself. If you’d asked my undergraduate professors what two subjects would I be least likely to excel in after university, they’d have told you statistics and computer programming, and yet here I am, about to show you some statistical graphics that I programmed. (You think you comprehended probability and statistics, but you forget them if Not practiced)

 what inspired that change in me? What made me think that statistics was actually an interesting thing? It’s really because statistics are about us.

If you look at the etymology of the word statistics, it’s the science of dealing with data about the state or the community that we live in. So statistics are about us as a group, not us as individuals. And I think as social animals, we share this fascination about how we, as individuals, relate to our groups, to our peers. And statistics in this way are at their most powerful when they surprise us.

there’s been some really wonderful surveys carried out recently by Ipsos MORI in the last few years. They did a survey of over 1,000 adults in the UK, and said, for every 100 people in England and Wales, how many of them are Muslim? Now the average answer from this survey, which was supposed to be representative of the total population, was 24. That’s what people thought. British people think 24 out of every 100 people in the country are Muslim. Now, official figures reveal that figure to be about five. So there’s this big variation between what we think, our perception, and the reality as given by statistics. And I think that’s interesting. What could possibly be causing that misperception?

I was so thrilled with this study, I started to take questions out in presentations. I was referring to it. Now, I did a presentation at St. Paul’s School for Girls in Hammersmith, and I had an audience rather like this, except it was comprised entirely of sixth-form girls.

And I said, “Girls, how many teenage girls do you think the British public think get pregnant every year?” And the girls were apoplectic when I said the British public think that 15 out of every 100 teenage girls get pregnant in the year. And they had every right to be angry, because in fact, I’d have to have closer to 200 dots before I could color one in, in terms of what the official figures tell us.

And rather like numeracy, this is not just an English problem. Ipsos MORI expanded the survey in recent years to go across the world. And so, they asked Saudi Arabians, for every 100 adults in your country, how many of them are overweight or obese? And the average answer from the Saudis was just over a quarter. That’s what they thought. Just over a quarter of adults are overweight or obese. The official figures show, actually, it’s nearer to three-quarters.

5:56 (Laughter)

5:57 So again, a big variation.

I love this one: they asked the Japanese, for every 100 Japanese people, how many of them live in rural areas? The average was about a 50-50 split, just over halfway. They thought 56 out of every 100 Japanese people lived in rural areas. The official figure is seven.

So extraordinary variations, and surprising to some, but not surprising to people who have read the work of Daniel Kahneman, for example, the Nobel-winning economist. He and his colleague, Amos Tversky, spent years researching this disjoint between what people perceive and the reality, the fact that people are actually pretty poor intuitive statisticians. (I read many of their research papers in the late 80’s)

And there are many reasons for this. Individual experiences, certainly, can influence our perceptions, but so, too, can things like the media reporting things by exception, rather than what’s normal. Kahneman had a nice way of referring to that. He said, “We can be blind to the obvious” — so we’ve got the numbers wrong — “but we can be blind to our blindness about it.” And that has enormous repercussions for decision making.

at the statistics office while this was all going on, I thought this was really interesting. I said, this is clearly a global problem, but maybe geography is the issue here.

These were questions that were all about, how well do you know your country? So in this case, it’s how well do you know 64 million people? Not very well, it turns out. I can’t do that. So I had an idea, which was to think about this same sort of approach but to think about it in a very local sense. Is this a local? If we reframe the questions and say, how well do you know your local area, would your answers be any more accurate?

I devised a quiz: How well do you know your area? It’s a simple Web app. You put in a post code and then it will ask you questions based on census data for your local area. And I was very conscious in designing this. I wanted to make it open to the widest possible range of people, not just the 49 percent who can get the numbers.

I wanted everyone to engage with it. So for the design of the quiz, I was inspired by the isotypes of Otto Neurath from the 1920s and ’30s. Now, these are methods for representing numbers using repeating icons. And the numbers are there, but they sit in the background. So it’s a great way of representing quantity without resorting to using terms like “percentage,” “fractions” and “ratios.”

So here’s the quiz. The layout of the quiz is, you have your repeating icons on the left-hand side there, and a map showing you the area we’re asking you questions about on the right-hand side. There are 7 questions. Each question, there’s a possible answer between zero and a hundred, and at the end of the quiz, you get an overall score between zero and a hundred.

And so because this is TEDxExeter, I thought we would have a quick look at the quiz for the first few questions of Exeter. And so the first question is: For every 100 people, how many are aged under 16? Now, I don’t know Exeter very well at all, so I had a guess at this, but it gives you an idea of how this quiz works. You drag the slider to highlight your icons, and then just click “Submit” to answer, and we animate away the difference between your answer and reality. And it turns out, I was a pretty terrible guess: five.

How about the next question? This is asking about what the average age is, so the age at which half the population are younger and half the population are older. (This is the definition of the median) And I thought 35 — that sounds middle-aged to me.

9:35 (Laughter)

9:39 Actually, in Exeter, it’s incredibly young, and I had underestimated the impact of the university in this area. The questions get harder as you go through. So this one’s now asking about homeownership: For every 100 households, how many are owned with a mortgage or loan? And I hedged my bets here, because I didn’t want to be more than 50 out on the answer.

 these get harder, these questions, because when you’re in an area, when you’re in a community, things like age — there are clues to whether a population is old or young. Just by looking around the area, you can see it. Something like homeownership is much more difficult to see, so we revert to our own heuristics, our own biases about how many people we think own their own homes.

the truth is, when we published this quiz, the census data that it’s based on was already a few years old. We’ve had online applications that allow you to put in a post code and get statistics back for years. So in some senses, this was all a little bit old and not necessarily new. But I was interested to see what reaction we might get by gamifying the data in the way that we have, by using animation and playing on the fact that people have their own preconceptions.

It turns out, the reaction was more than I could have hoped for. It was a long-held ambition of mine to bring down a statistics website due to public demand.

11:06 (Laughter)

This URL contains the words “statistics,” “gov” and “UK,” which are three of people’s least favorite words in a URL. And the amazing thing about this was that the website came down at quarter to 10 at night, because people were actually engaging with this data of their own free will, using their own personal time.

I was very interested to see that we got something like a quarter of a million people playing the quiz within the space of 48 hours of launching it. And it sparked an enormous discussion online, on social media, which was largely dominated by people having fun with their misconceptions, which is something that I couldn’t have hoped for any better, in some respects. I also liked the fact that people started sending it to politicians. How well do you know the area you claim to represent? (All candidates to public office must go through such quizzes in their locality and the nation)

 then just to finish, going back to the two kinds of people, I thought it would be really interesting to see how people who are good with numbers would do on this quiz. The national statistician of England and Wales, John Pullinger, you would expect he would be pretty good. He got 44 for his own area.

12:16 (Laughter)

Jeremy Paxman — admittedly, after a glass of wine — 36. Even worse. It just shows you that the numbers can inspire us all. They can surprise us all.

12:31 So very often, we talk about statistics as being the science of uncertainty. My parting thought for today is: actually, statistics is the science of us. And that’s why we should be fascinated by numbers. 

Patsy Z shared this link · 7 hrs

“Whether we consider ourselves math people or not, our ability to understand and work with numbers is terribly limited.”

A talk from TEDxExeter.
#TED #TEDx #TEDxTalks #SKE #TEDxSKE #Salon #TEDxSKESalon #TEDxExeter #Statistics #Numbers #Facts

Alan Smith explores the mismatch between what we know and what we th…


Tension vs. fear

Fear’s a dream killer. It puts people into suspended animation, holding their breath, paralyzed and unable to move forward.

Fear is present in many education settings, because fear’s a cheap way to ensure compliance. “Do this,” the teacher threatens, “or something bad is going to happen to you.”

The thing is, learning is difficult. If it was easy, you’d already know everything you need to know. And if you could do it on your own, you wouldn’t need the time or expense to do it with others. (Like visiting with a psy or alcohol anonymous?)

But when we try to learn something on our own, we often get stuck.

It’s not because of fear, it’s because of tension.

The tension we face any time we’re about to cross a threshold. The tension of this might work vs. this might not work. The tension of if I learn this, will I like who I become? (Most probably it is: Is what I learned on my own is correct and valid knowledge?)

Tension is the hallmark of a great educational experience.

The tension of not quite knowing where we are in the process, not being sure of the curriculum, not having a guarantee that it’s about to happen.

As adults, we willingly expose ourselves to the tension of a great jazz concert, or a baseball game or a thrilling movie.

But, mostly because we’ve been indoctrinated by fear, we hesitate when we have the opportunity to learn something new on our way to becoming the person we seek to be.

Effective teachers have the courage to create tension. And adult learners on their way to levelling up actively seek out this tension, because it works. It pushes us over the chasm to the other side.

I’ve been running the altMBA for nearly two years, and in that time we’ve seen tens of thousands of people consider the workshop. Some of them see the tension coming and eagerly dive in. Others mistake that tension for fear and back away, promising themselves that they’ll sign up later.

The ones who leapt are transformed. The tension pays off.

We’re proud of the tension. We built it into the workshop from the start, because education is never about access to information, it’s about the forward motion of learning.

You already know this workshop works. That’s easy to check out. The hesitation comes from this very fact… that it works. That a change occurs. That the unknown is right over there, and to get yourself there, you have to walk through a month’s worth of tension.

That’s the best way I know to learn. And so that’s the way we teach.



Levantine vocabulary, 80% of words used in conversation (Zipf). Very few words have Arabic origin (conflations).

NassimNicholasTaleb @nntaleb 11h11 hours ago

Arab “nationalists” believe and spread this misconception that people in the Levant had NO language before the Arabs showed up. In fact it is Arabic that changed and got richer from the input and import from the local languages. Especially, urban terms of far more complex social structures and interactions
Much of the similarities between Levantine and Arabic come from Aramaic loan words into Arabic, particularly religious ones
Words such as “syrup” or “sorbet” that come from Arabic actually have Aramaic roots SRB: parched (to offset)

Like to come Along for the ride? And what’s on commitment and techniques?

Along for the ride

And the pilot says, “sit back, relax, and enjoy the flight.”

When you’re on one of those Disneyland boats, it takes you where Disney wants you to go. That’s why you got on.

And so you are lulled, a spectator, merely a tourist.

So different, isn’t it, from driving yourself.

You got to choose your own route and have to owning what comes of it.

(Taking on your own responsibilities is the price to pay for choosing to drive your life)

How long have you been along for the ride? When is your turn to actually drive?

Computer learn to understand what a cat is?

Just like the brain integrates vision and language, we developed a model that connects parts of visual things like visual snippets with words and phrases in sentences.

(Video) A 3-year-old  Girl: Okay, that’s a cat sitting in a bed. The boy is petting the elephant. Those are people that are going on an airplane. That’s a big airplane.

0:32 Fei-Fei Li: This is a three-year-old child describing what she sees in a series of photos. She might still have a lot to learn about this world, but she’s already an expert at one very important task: to make sense of what she sees.

Our society is more technologically advanced than ever. We send people to the moon, we make phones that talk to us or customize radio stations that can play only music we like.

Yet, our most advanced machines and computers still struggle at this task (making sense of what they see?).

So I’m here today to give you a progress report on the latest advances in our research in computer vision, one of the most frontier and potentially revolutionary technologies in computer science.

we have prototyped cars that can drive by themselves, but without smart vision, they cannot really tell the difference between a crumpled paper bag on the road, which can be run over, and a rock that size, which should be avoided.

We have made fabulous megapixel cameras, but we have not delivered sight to the blind.

Drones can fly over massive land, but don’t have enough vision technology to help us to track the changes of the rainforests.

Security cameras are everywhere, but they do not alert us when a child is drowning in a swimming pool.

Photos and videos are becoming an integral part of global life. They’re being generated at a pace that’s far beyond what any human, or teams of humans, could hope to view, and you and I are contributing to that at this TED.

Yet our most advanced software is still struggling at understanding and managing this enormous content. So in other words, collectively as a society, we’re very much blind, because our smartest machines are still blind. (Why Not invest in auditory detection?)

“Why is this so hard?” you may ask. Cameras can take pictures like this one by converting lights into a two-dimensional array of numbers known as pixels, but these are just lifeless numbers. They do not carry meaning in themselves. Just like to hear is not the same as to listen, to take pictures is not the same as to see, and by seeing, we really mean understanding.

In fact, it took Mother Nature 540 million years of hard work to do this task, and much of that effort went into developing the visual processing apparatus of our brains, not the eyes themselves. So vision begins with the eyes, but it truly takes place in the brain.

 for 15 years now, starting from my Ph.D. at Caltech and then leading Stanford’s Vision Lab, I’ve been working with my mentors, collaborators and students to teach computers to see. Our research field is called computer vision and machine learning.

It’s part of the general field of artificial intelligence. So ultimately, we want to teach the machines to see just like we do: naming objects, identifying people, inferring 3D geometry of things, understanding relations, emotions, actions and intentions. You and I weave together entire stories of people, places and things the moment we lay our gaze on them.

The first step towards this goal is to teach a computer to see objects, the building block of the visual world. In its simplest terms, imagine this teaching process as showing the computers some training images of a particular object, let’s say cats, and designing a model that learns from these training images.

How hard can this be? After all, a cat is just a collection of shapes and colors, and this is what we did in the early days of object modeling. We’d tell the computer algorithm in a mathematical language that a cat has a round face, a chubby body, two pointy ears, and a long tail, and that looked all fine. But what about this cat? (Laughter) It’s all curled up.

Now you have to add another shape and viewpoint to the object model. But what if cats are hidden? What about these silly cats? Now you get my point. Even something as simple as a household pet can present an infinite number of variations to the object model, and that’s just one object.

 about 8 years ago, a very simple and profound observation changed my thinking. No one tells a child how to see, especially in the early years. They learn this through real-world experiences and examples.

If you consider a child’s eyes as a pair of biological cameras, they take one picture about every 200 milliseconds, the average time an eye movement is made. So by age three, a child would have seen hundreds of millions of pictures of the real world. That’s a lot of training examples. So instead of focusing solely on better and better algorithms, my insight was to give the algorithms the kind of training data that a child was given through experiences in both quantity and quality.

Once we know this, we knew we needed to collect a data set that has far more images than we have ever had before, perhaps thousands of times more, and together with Professor Kai Li at Princeton University, we launched the ImageNet project in 2007.

Luckily, we didn’t have to mount a camera on our head and wait for many years. We went to the Internet, the biggest treasure trove of pictures that humans have ever created. We downloaded nearly a billion images and used crowdsourcing technology like the Amazon Mechanical Turk platform to help us to label these images.

At its peak, ImageNet was one of the biggest employers of the Amazon Mechanical Turk workers: together, almost 50,000 workers from 167 countries around the world helped us to clean, sort and label nearly a billion candidate images. That was how much effort it took to capture even a fraction of the imagery a child’s mind takes in in the early developmental years.

 In hindsight, this idea of using big data to train computer algorithms may seem obvious now, but back in 2007, it was not so obvious. We were fairly alone on this journey for quite a while. Some very friendly colleagues advised me to do something more useful for my tenure, and we were constantly struggling for research funding. Once, I even joked to my graduate students that I would just reopen my dry cleaner’s shop to fund ImageNet. After all, that’s how I funded my college years.

we carried on.

In 2009, the ImageNet project delivered a database of 15 million images across 22,000 classes of objects and things organized by everyday English words. In both quantity and quality, this was an unprecedented scale. As an example, in the case of cats, we have more than 62,000 cats of all kinds of looks and poses and across all species of domestic and wild cats. We were thrilled to have put together ImageNet, and we wanted the whole research world to benefit from it, so in the TED fashion, we opened up the entire data set to the worldwide research community for free. (Applause)

Now that we have the data to nourish our computer brain, we’re ready to come back to the algorithms themselves.

As it turned out, the wealth of information provided by ImageNet was a perfect match to a particular class of machine learning algorithms called convolutional neural network, pioneered by Kunihiko Fukushima, Geoff Hinton, and Yann LeCun back in the 1970s and ’80s.

Just like the brain consists of billions of highly connected neurons, a basic operating unit in a neural network is a neuron-like node. It takes input from other nodes and sends output to others. Moreover, these hundreds of thousands or even millions of nodes are organized in hierarchical layers, also similar to the brain. (maybe Not that hierarchical?)

In a typical neural network we use to train our object recognition model, it has 24 million nodes, 140 million parameters, and 15 billion connections.

That’s an enormous model. Powered by the massive data from ImageNet and the modern CPUs and GPUs to train such a humongous model, the convolutional neural network blossomed in a way that no one expected. It became the winning architecture to generate exciting new results in object recognition.

This is a computer telling us this picture contains a cat and where the cat is. Of course there are more things than cats, so here’s a computer algorithm telling us the picture contains a boy and a teddy bear; a dog, a person, and a small kite in the background; or a picture of very busy things like a man, a skateboard, railings, a lampost, and so on.

Sometimes, when the computer is not so confident about what it sees, we have taught it to be smart enough to give us a safe answer instead of committing too much, just like we would do, but other times our computer algorithm is remarkable at telling us what exactly the objects are, like the make, model, year of the cars.

We applied this algorithm to millions of Google Street View images across hundreds of American cities, and we have learned something really interesting: first, it confirmed our common wisdom that car prices correlate very well with household incomes. But surprisingly, car prices also correlate well with crime rates in cities, or voting patterns by zip codes.

So wait a minute. Is that it? Has the computer already matched or even surpassed human capabilities? Not so fast. So far, we have just taught the computer to see objects. This is like a small child learning to utter a few nouns. It’s an incredible accomplishment, but it’s only the first step.

Soon, another developmental milestone will be hit, and children begin to communicate in sentences. So instead of saying this is a cat in the picture, you already heard the little girl telling us this is a cat lying on a bed.

 to teach a computer to see a picture and generate sentences, the marriage between big data and machine learning algorithm has to take another step. Now, the computer has to learn from both pictures as well as natural language sentences generated by humans. Just like the brain integrates vision and language, we developed a model that connects parts of visual things like visual snippets with words and phrases in sentences.

About four months ago, we finally tied all this together and produced one of the first computer vision models that is capable of generating a human-like sentence when it sees a picture for the first time. Now, I’m ready to show you what the computer says when it sees the picture that the little girl saw at the beginning of this talk.

14:30 (Video) Computer: A man is standing next to an elephant. A large airplane sitting on top of an airport runway.

14:40 FFL: Of course, we’re still working hard to improve our algorithms, and it still has a lot to learn. (Applause)

14:50 And the computer still makes mistakes.

14:53 (Video) Computer: A cat lying on a bed in a blanket.

14:57 FFL: So of course, when it sees too many cats, it thinks everything might look like a cat.

15:04 (Video) Computer: A young boy is holding a baseball bat. (Laughter)

15:08 FFL: Or, if it hasn’t seen a toothbrush, it confuses it with a baseball bat.

15:14 (Video) Computer: A man riding a horse down a street next to a building. (Laughter)

15:19 FFL: We haven’t taught Art 101 to the computers.

15:24 (Video) Computer: A zebra standing in a field of grass.

15:27 FFL: And it hasn’t learned to appreciate the stunning beauty of nature like you and I do.

15:33 So it has been a long journey. To get from age zero to three was hard.

The real challenge is to go from three to 13 and far beyond. Let me remind you with this picture of the boy and the cake again. So far, we have taught the computer to see objects or even tell us a simple story when seeing a picture.

15:58 (Video) Computer: A person sitting at a table with a cake.

16:02 FFL: But there’s so much more to this picture than just a person and a cake. What the computer doesn’t see is that this is a special Italian cake that’s only served during Easter time. The boy is wearing his favorite t-shirt given to him as a gift by his father after a trip to Sydney, and you and I can all tell how happy he is and what’s exactly on his mind at that moment.

This is my son Leo. On my quest for visual intelligence, I think of Leo constantly and the future world he will live in. When machines can see, doctors and nurses will have extra pairs of tireless eyes to help them to diagnose and take care of patients.

Cars will run smarter and safer on the road. Robots, not just humans, will help us to brave the disaster zones to save the trapped and wounded.

We will discover new species, better materials, and explore unseen frontiers with the help of the machines.

17:14 Little by little, we’re giving sight to the machines. First, we teach them to see. Then, they help us to see better. For the first time, human eyes won’t be the only ones pondering and exploring our world. We will not only use the machines for their intelligence, we will also collaborate with them in ways that we cannot even imagine.

17:40 This is my quest: to give computers visual intelligence and to create a better future for Leo and for the world.

Patsy Z shared this link TED

Why the body-bending poses in cat pictures are a huge challenge to AI technology:

This is how we’re teaching computers to understand pictures.|By Fei-Fei Li

Any good use of ownership, property, exclusivity, cartel…?

Ownership is dangerous when others are ruled out. “It’s mine! Don’t touch my things!”

Individual owners do things themselves. That’s good unless it become exclusive, protective, and short-sighted.

Individual contributors:

The trouble with individual contributors is they create patterns and processes others don’t embrace or duplicate. They hoard expertise and knowledge. Some can’t share the spotlight; others don’t know how. Some refuse to invest in others.

Individual ownership is powerful. But, ownership is a dead-end unless teams and partnerships are included and developed.

Individual contributors are essential;
team builders exponential.

Alone is ok; with someone is better. Leaders create “withs”.

Create ownership continuums:

Continuity, sustainability, knowledge transfer, and longevity are leadership’s responsibility. Take the long view rather than the easy out.

  1. Train everyone to replace themselves. If they can’t teach others to do what they do, they need to go. Move training from theory to practice with new opportunities.
  2. Provide job shadowing opportunities at least once a month. “Follow me around for an hour or two.”
  3. Engage in job rotation. At given intervals, every three years for example, people’s job should change in measurable ways. Mastery becomes lethargy without new challenges.
  4. Leverage leaving. When someone leaves your organization, don’t simply replace them. Change the position. Reassign responsibilities.

Caveat: It may not be feasible to rotate highly specialized, highly technical people. Do it everywhere possible.


Employee security includes sameness. “Don’t mess with my job.” On the other hand, disruption challenges, freshens, and invigorates.

What are the pros and cons of working toward ownership continuum?

How might you implement ownership continuum in your organization?

Where are these ideas unrealistic?




May 2017
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