I was one of the taller kids in my high school class. I thought – and hoped – that I might use this size advantage to become a star basketball player.
Alas, it was not to be. I had a bad case of what’s often called “white guy’s disease”. Simply put, I couldn’t jump. Though I was over six feet tall, I could barely touch the rim even with my mightiest leap.
Van Jones would call this my fate. In a memorable commencement speech at Loyola New Orleans, Jones distinguished between fate and destiny. He defines fate as “those things that we have no control over” and suggests that the “people who are most miserable in life are the ones who spend their time cursing their fate.” (Click here for the video).
As it happens, the field of design thinking has a similar concept. Dave Evans, a design engineer, calls it the gravity problem. No matter how hard we try, we can’t change gravity. Indeed, we can’t even suspend it temporarily. Wouldn’t it be great to suspend gravity while we’re building a new house and then reinstate it when we move in? Unfortunately, we can’t. Time to move on. (For a podcast featuring Evans, click here).
Gravity is a fact of life. My inability to jump is a fact of my life. Instead of asking, “How can I change my fate?” it’s better to accept it and ask more useful questions. A useful question is one that we can actually do something about. A designer would say that we need to design around the constraints.
As Evans describes it, we’re looking for room to maneuver around the facts that define our products or our lives. I couldn’t jump very high. That’s a design constraint. So I might ask a different question: “How can I make basketball an important part of my life, even though I can’t play very well?” Once I ask the how can I question, I can dream up alternatives. I might become a coach. Or a sportscaster. Or I might decide to take up a sport that doesn’t require jumping.
Van Jones calls this destiny as opposed to fate. We have no control over fate. But we can respond to destiny. As Jones points out, “The world is not going to tell you every day about …” your destiny. We have to live our lives, and respond to our challenges, to discover our destiny.
Whether we call it destiny or design thinking, when we bump up against gravity, we need to change the question. By doing so, we can find an array of alternatives. Once armed with a list of alternatives, we can design a life or a product. Which alternatives fit the constraints? Which ones don’t?
We don’t design a product and then launch it. Rather we design it, then re-design it, then re-design it as we discover new constraints. Similarly, it’s difficult to design a life before we launch it. To overcome fate and discover our destiny, we need to design our lives as we live them.
The 2017 World Happiness Report was released yesterday. The headlines today are all about Norway, which supplanted Denmark as the happiest country in the world. That’s nice and I’m sure that Norwegians are celebrating today. But what intrigues me is the relationship between happiness and creativity. (See also here, here and here).
In 2015, the Martin Prosperity Institute published the Global Creativity Index. Reviewing the two lists together suggests that the relationship between happiness and creativity is very tight indeed. Here are the top ten countries on each list.
|Rank||Happiness (2017)||Most Creative (2015)|
Of the ten happiest countries in the world, eight also make the top ten list for most creative countries in the world. The two that miss — Norway and Switzerland — don’t miss by much. Norway is 11th on the most creative list; Switzerland is 16th.
Conversely, of the ten most creative countries in the world, eight also make the list of the happiest countries in the world. Again, the two that don’t make the list — the United States and Singapore — don’t miss by much. The United States is 14th; Singapore is 26th.
What’s it all mean? I can think of at least four ways to interpret the data:
It’s also interesting to delve into which countries have the best combination of happiness and creativity. We can make some crude judgments by adding up the national position in each survey. Like golf, the low score wins. For instance, Denmark is second in happiness and fifth in creativity, for a combined score of seven. As it happens, that’ s the lowest score — so Denmark takes first place in the combined league table. Here are the top five combined scores. I don’t know about you but I think I’ll soon pay a visit to Denmark.
|3 (tie)||New Zealand||11|
How do you tell someone where you are? Most of us would use some form of a postal address to identify our location. But what if you’re in a place that doesn’t have a postal address? In other words … most of the world.
If there’s no postal address, I might use latitude and longitude. For instance, our home is located at 39.714549 latitude and -104.971346 longitude. If you understand the system, you’ll realize that my house is 39 degrees north of the equator and 104 degrees west of the prime meridian that passes through Greenwich, England.
(I have an 18th century French map that gives longitude as the number of degrees east or west of Paris. It was part of a long-running dispute about where, precisely, the center of the world is.)
Longitude and latitude give us precise locations, but they’re not human friendly. It’s like noting that the current temperature is 287.039 degrees Kelvin. That’s accurate but not terribly meaningful to most humans.
So, is there a way to map the world that would be easier for humans to manage? Well, how about we divide up the entire surface of the earth into squares that are approximately three meters per side? Each square is nine square meters or roughly 90 square feet. As you’ve no doubt calculated by now, we would need about 54 trillion such squares.
That may sound complicated but, really, how hard is it to manage 54 trillion squares? The researchers at What3Words – a start-up company in London – figured out that you only need 40,000 words in three-word combinations. That yields about 64 trillion combinations – enough to address the world and have a few trillion combinations left over.
In the world of What3Words, our home address is quit.snacks.humid. It’s easy to remember and precise enough to guide you to our front door. If I wanted to guide you to our driveway, I would instead use the words refuse.fake.limbs. If I wanted to send you to the highest summit in Colorado – a place that doesn’t have a postal address – I would send you to penned.metro.inspections.
According to What3Worlds, the system is already in use to deliver mail in unaddressed areas like Mongolia or the favelas of Brazil. Similarly, Steven Spielberg is using What3Words addresses to get his actors and crew to the right place at the right time as he films his latest movie. I can imagine Colorado’s Alpine Rescue Team guiding rescuers to acutely.jumbo.popcorn rather than saying, ”The injured party is about 3.3 miles northeast of the Mt. Elbert summit on the east flank of a small ravine.”
What3Words already has some interesting use cases and, if it develops fully, it should help us with logistics, emergency services, scheduling, and materials management. But its real potential comes from the fact that it’s released not as a solution but as a platform. As we know, (click here, here, and here) platforms are innovations that generate innovations. As other application developers adopt and adapt the platform, we could see a rich ecosystem of solutions that even the What3Words folks can’t imagine today.
By the way, I’m taking a few days off. If you need me, I’ll be at tent.quarrel.charm.
If you met somebody from your third grade class, would you recognize her? How about someone you last saw a decade ago at a company where you used to work? How about a person you saw in a mug shot at the Post Office?
If you answered “yes” to any of these, you may be a super-recognizer. Super-recognizers literally never forget a face. They may also give us the next great leap forward in law enforcement.
We haven’t known about super recognizers for very long. Over the past 20 years or so, researchers have learned a great deal about the opposite condition known as prosopagnosia or face blindness. Some people – perhaps two percent of the population — just can’t remember faces. They’re “blind” to the faces around them. They can interact with you perfectly well while they’re with you but they won’t recognize you the next time they see you.
Researchers initially thought that this was a binary condition – either you were normal or you were face blind. Then someone had the bright idea that the ability to recognize faces might be distributed along a normal curve. If face blind people are clustered under one tail of the curve then the other tail should include people who are exceptionally good at recognizing faces – the super-recognizers.
It turns out that they were right. In 2009, Richard Russell and his colleagues published the first academic paper on the subject: “Super-recognizers: People with extraordinary face recognition ability”.
It seems like a typical academic topic but the story took an unusual twist when the Metropolitan Police Service in London took up the idea. As detailed in a recent story in The New Yorker, the Met experimented with super-recognizers as detectives. London has more security cameras than any other city in the world but couldn’t turn the images into a crime-fighting advantage. The city had millions of low-resolution images of potential criminals and nobody to interpret them.
The Met tried to change that with an organized team of super-recognizers. The super-recognizers browse through mug shots and then review footage from security cameras that have recorded a crime. In a surprising number of cases, the super-recognizer has an “aha” moment and links a miscreant to a mug shot.
How good are they? The Met calls super-recognizers “the third revolution in forensics” after fingerprints and DNA evidence. The Met solves about 2,000 cases a year with fingerprints and another 2,000 with DNA. By comparison, the super-recognizers solve about 2,500 cases.
At this point, you may be wondering just how good you are at recognizing faces. Here’s how to find out – the Cambridge Face Memory Test. Click here and you can take the same test that the Met uses to screen applicants for the super-recognizer team. If you get a high score, you might just apply for a position with your local police force.
Four years ago, I wrote a somewhat pessimistic article about Jevons paradox. A 19th-century British economist, William Jevons, noted that as energy-efficient innovations are developed and deployed, energy consumption goes up rather than down. The reason: as energy grows cheaper, we use more of it. We find more and more places to apply energy-consuming devices.
Three years ago, I wrote a somewhat pessimistic article about the future of employment. I argued that smart machines would either: 1) augment knowledge workers, making them much more productive, or; 2) replace knowledge workers altogether. Either way, we would need far fewer knowledge workers.
What if you combine these two rather pessimistic ideas? Oddly enough, the result is a rather optimistic idea.
Here’s an example drawn from a recent issue of The Economist. The process of discovery is often invoked in legal disputes between companies or between companies and government agencies. Each side has the right to inspect the other side’s documents, including e-mails, correspondence, web content, and so on. In complex cases, each side may need to inspect massive numbers of documents to decide which documents are germane and which are not. The actual inspecting and sorting has traditionally been done by highly trained paralegals – lots of them.
As you can imagine, the process is time-consuming and error-prone. It’s also fairly easy to automate through deep learning. Artificial neural networks (ANNs) can study the examples of which documents are germane and which are not and learn how to distinguish between the two. Just turn suitably trained ANNs loose on boxes and boxes of documents and you’ll have them sorted in no time, with fewer errors than humans would make.
In other words, artificial neural networks can do a better job than humans at lower cost and in less time. So this should be bad news for paralegal employment, right? The number of paralegals must be plummeting, correct? Actually no. The Economist tells us that paralegal employment has actually risen since ANNs were first deployed for discovery processes.
Why would that be? Jevons paradox. The use of ANNs has dramatically lowered the obstacles to using the discovery process. Hence, the discovery process is used in many more situations. Each discovery process uses fewer paralegals but there are many more discovery processes. The net effect is greater – not lesser – demand for paralegals.
I think of this as good news. As the cost of a useful process drops, the process itself – spam filtering, document editing, image identification, quality control, etc. – can be deployed to many more activities. That’s useful in and of itself. It also drives employment. As costs drops, demand rises. We deploy the process more widely. Each human is more productive but more humans are ultimately required because the process is far more widespread.
As a teacher, this concept makes me rather optimistic. Artificial intelligence can augment my skills, make me more productive, and help me reach more students. But that doesn’t mean that we’ll need fewer teachers. Rather, it means that we can educate many, many more students. That’s a good thing – for both students and teachers.