About Richard Worzel

I am a futurist, and a professional member of the World Future Society. I make my living by helping corporations and industry associations plan intelligently for the future. I focus on North America, but deal with global issues. My client list includes organizations like Ford, IBM, Bell Canada, Coca-Cola, the U.S. Navy, the National Research Council, and many others.

The services I offer are tailored to the needs of the client, but fall into the following major areas: Keynote Speeches, Workshops and Seminars, and Innovation Sessions. Companies can also ask me to work with their planning groups on an on-going basis, or for a specific part of their planning cycle. I have also been asked to assess companies in which a client is considering making an investment, especially in technology companies where I may have some familiarity with the field.

Please use the menu above to access various information on this Blog or for more information please take a moment to visit my main website at: www.futuresearch.com.

Groups or companies wishing to know more about any of my services should contact me by e-mail at futurist@futuresearch.com, or call 1-416-489-4511.

I look forward to any feedback or questions you may have.

Below you will find the most recent additions to my blog….

Three Things You Need to Know About Artificial Intelligence

by Senior futurist Richard Worzel, C.F.A.

Pay attention. Your life is about to be significantly changed by Artificial Intelligence (AI), whether you want it to be or not.

Every once in while, something happens that tosses a huge rock into the pond of human affairs. Such rocks include things like the discovery of fire, the invention of the wheel, written language, movable type, the telegraph, computers, and the Internet. These kinds of massive disturbances produce pronounced, remarkable, unexpected changes, and radically alter human life.

Artificial Intelligence is just such a rock, and will produce exactly those kinds of disturbances. We’re not prepared for the tsunami that AI is going to throw at us.

AI has been the technology of the future since the 1960s, but one that always seemed just over the horizon, and never arrived. Certainly, AI was widely discussed when I got my degree in computer science, more than 30 years ago.

But now AI is becoming a reality, and it is going to hit us far faster than we now expect. This will lead to an avalanche of effects that will reach into all aspects of our lives, society, the economy, business, and the job market. It will lead to perhaps the most dramatic technological revolution we have yet experienced – even greater than the advent of computers, smartphones, or the Internet.

There are three keys to AI that will help to understand what’s happening:

  • AI is the Swiss Army knife of technology
  • AI is not a shrink-wrapped product, and
  • Once AI is properly established, the domino effects occur with astonishing speed.

But before I dive into these three keys, let me tackle what AI is because there is no real agreement what the term “artificial intelligence” means. I read one article, for instance, that claimed that there are 33 kinds of AI. And, indeed, the term covers a broad range of techniques and technologies.

But in my view, they all have a central, defining characteristic. I define AI as a computer system that is adaptive, and can solve problems that it has not encountered before. Some of those problems are ones humans have solved – but increasingly, many of such problems are ones humans haven’t solved, and might not be able to solve unassisted.

With that in mind, let’s turn to the three keys to AI.

AI Is the Swiss Army Knife of Technology

AI is not restricted to any narrow range of fields or areas of human endeavor, but will be applied anywhere and everywhere where some smarts would be helpful. The highest profile results will be things like robots and self-driving cars, but there are thousands of other places where AI will be used.

Security systems, whether at airports or a local clothing store, will use AI to identify faces, either those that might commit crimes, or those that are more likely to buy something.

It will be used to assess satellite images to locate submarines, pods of whales, which agricultural areas are producing vibrant crops, and which are suffering and hence what will happen to crop prices, or to judge how well traffic is flowing in a city and how it could be improved.

It will be used in cars and home heating systems to determine, on a second-by-second basis, how to most efficiently use fuel while keeping human users happy.

It will manage investment portfolios better than all but the most gifted humans – and be more consistent in their management results than their human betters.

It will work in all aspects of health care, energy management, manufacturing, industrial process control, accounting, law, weather and climate prediction, drug discovery, toy design, and entertainment, ranging from responsive, real-time virtual reality to traditional game playing.

It will help chefs design more interesting, nutritious, and economical food, and hotels provide more satisfying, more profitable stays. It will watch over babies and the elderly to make sure they’re safe, and potential criminals to make sure everyone else is safe.

It will be used to design a unique, virtual newspaper for each subscriber that appeal to that reader’s particular interests.

It will design bridges and desserts and cars and fashions and farms and teeth, and just about anything else that humans use, build, or think about.

Bad guys will use it to identify the highest value targets, and design cheap, effective explosive devices. They’ll use it to commit identity theft at a rapidly accelerating pace – even as white hat hackers use it to thwart evil AI.

Politicians will use AI to identify silent voters who would be inclined to vote for them if asked – and opponents will use it to develop custom-tailored messages to make sure such voters stay home.

It will be used to identify weaknesses in opponents’ armies, or their economies, or their political appeal.

And just about anything else you can think of.

AI will be used everywhere, all the time, and by everyone – whether they know it or not.

AI Is Not a Shrink-Wrapped Product

Using AI is not easy, simple, or straightforward. You can’t just take it out of the box, plug it in, and start getting fabulous results. It takes three major, difficult-to-achieve things: good data, smart analytics, and clear objectives.

AI’s are fundamentally data driven, because they use data to interpret patterns, and to create patterns for which they can search or use to select behaviors or actions. If the data is dirty, meaning it contains errors or too much irrelevant data points, or isn’t timely, which means it’s not indicating what’s happening now, then the results won’t be very useful. This is the classic computer observation, GIGO: “Garbage In, Garbage Out”.

Hence, if you’re trying to get an Artificial Intelligence to help you trade stocks, but are using data from three months or even three hours ago, you’re not going to get good results as markets don’t stand still.

Smart analytics means having someone identify what patterns the AI system is looking for. If you can’t show the AI how to use the data you’ve provided to clearly analyze what’s happening, then you’re not going to be able guide it to figure out what it needs to do to produce the results you want.

For instance, you can’t get an AI to assess a satellite photo of a farming region and determine whether the region is suffering from drought unless you can provide the analytical tools to tell it what that looks like.

If you want AI to look at tissue samples to determine whether a particular kind of cancer is present, you need to be provide a means to tell when that cancer is present. Sometimes this can be done in a rough way, by presenting thousands of cases where the outcome is already known, then telling the AI, “These samples have the cancer we’re looking for, but these ones don’t.” But at other times, you need to have very precise parameters to inform the AI what to search for, and how to do it, depending on the application and means of discovery.

And finally, you need to define what you consider a successful result to be:

“[OpenAI] researcher Dario Amodei showed off an autonomous system that taught itself to play Coast Runners, an old boat-racing video game. The winner is the boat with the most points that also crosses the finish line.

“The result was surprising: The boat was far too interested in the little green widgets that popped up on the screen. Catching these widgets meant scoring points. Rather than trying to finish the race, the boat went point-crazy. It drove in endless circles, colliding with other vessels, skidding into stone walls and repeatedly catching fire.”[1]

The AI obviously thought its objective was simply to get the highest number of points, rather than the highest number of points while finishing the race, and not crashing and burning.

So, unless you can provide the data necessary for an AI to learn what’s successful and what isn’t, have the means of analyzing that data, and have clearly identified what you want the AI to do, you’re not going to get very far in using AI.

Once AI Is Established, the Domino Effects Occur with Astonishing Speed

AlphaGo is a software AI developed by DeepMind, a machine-learning company owned by Alphabet/Google. AlphaGo was developed to play the traditional Asian game of Go, which is much more difficult to master than chess. Computer scientists speculated that it would take AlphaGo 15-20 years to become competitive with the best human players. Yet AlphaGo beat Lee Sedol, the world champion, 4 games out of 5 in March of 2016 – two years after it was created.

In November of 2015, a company called Kensho unveiled an AI to evaluate and summarize the monthly Bureau of Labor Statistics’ (BLS) monthly employment report[2]. The Kensho AI compared the BLS report with statistics from dozens of other databases, and produced a summary, and 13 key exhibits, along with a forecast of how it would affect dozens of investments, based on how they had responded to earlier reports. It used to take 2-5 days for an experienced and intelligent research analyst, working full-time, to do this. Kensho produced and distributed this report on its own, within minutes of the release of the BLS report – and does the same with many other kinds of economic and financial data.

Even the so-called “Masters of the Universe” ­– the highly paid, high profile institutional stock traders on Wall Street – aren’t immune:

“At its height back in 2000, the U.S. cash equities trading desk at Goldman Sachs’s New York headquarters employed 600 traders, buying and selling stock on the orders of the investment bank’s large clients. Today there are just two equity traders left.” [3]

Note that these 600 traders probably made $500,000 or more each back in 2000. Now they’ve been put out of work by AI.

The legal profession seems to be particularly susceptible to early occupation by AIs:

“At JPMorgan Chase & Co., a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours. The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of work each year by lawyers and loan officers.”[4]

So, before June of 2017, lawyers and loan officers spent 360,000 hours a year interpreting commercial loan agreements for JPMorgan Chase. Since June, that specific kind of work has vanished.

ROSS is a computer system based on IBM’s Watson AI platform. ROSS performs legal research and prepares legal briefs. In so doing, it has the potential to replace the work done by hundreds or thousands of paralegals and junior lawyers. Is the law profession concerned?

“[A recent] survey of large U.S. law firms … asked whether Watson would replace various timekeepers in these firms in the next five to 10 years. Half the respondents said it would replace paralegals, 35% said first-year associates. … The other interesting aspect of that survey was the response to the option ‘Computers will never replace human practitioners.’ That got a 46% affirmative response four years ago; this time around, just 20%. That’s a huge drop.”[5]

Finally let me offer a personal anecdote. Not long ago I had a conversation with a computer scientist who has clients in the financial industry. He confided in me that most people didn’t realize how quickly the domino effects cascade once an AI is properly established. “Once a front-line job can be done by AI,” he said, “then usually all of the back-office jobs that support it can also be replaced. Companies have no idea how fast this is happening.”

He didn’t want to go public with his thoughts because he was afraid it would scare his company’s clients.

So, AI is coming, and it’s coming far faster than people realize, and the consequences will be far-reaching.

What Happens Next?

There has been a lot of news stories and popular pieces about how the robots (meaning AI and automation generally) are coming to get us. A much-cited 2013 study done by Oxford University academics Carl Frey and Michael Osborne said that “According to our estimates, about 47 percent of total US employment is at risk.”[6]

Almost half of all jobs in the U.S. are susceptible to automation, according to Frey & Osborne. And whether they are precisely right, or even close, that’s an enormous disturbance in the force of our society and economy. But will it happen that way?

Based on my almost 30-years of study and work as a futurist, and based on the best analyses I can find, I can confidently say: yes and no.

Yes, the consequences will be dramatic. No, they don’t have to decimate the labor force, and therefore the economy and society. But they will produce some enormous challenges for which we are not ready.

There’s a long-running debate between the groups that I’ve called the neo-Luddites, who say that automation will destroy our jobs and our society, and the technologists, who insist that as old jobs disappear, new ones will be created to replace them. I’ve explored this at some length in an earlier blog, found here. This is a legitimate debate, and one that’s been going on for at least two centuries.

My feelings are that, yes, new jobs are being created even as the old ones are being destroyed but:

  • Jobs are appearing and disappearing with increasing rapidity, which makes it hard to keep up on the credentials you need in order to stay employed;
  • The best new jobs have very high standards, requiring specific kinds of hard-to-get credentials, and thus are not available to the vast major of people displaced from existing jobs; and
  • Many of the jobs created are relatively low-level service jobs that just don’t pay very well.

I’ve been writing about this for more than 20 years, as you can see from a book I published in 1994 titled Facing the Future:

“This is not a problem that will burst on the scene in the next five or ten years. Humans are still capable of offering a flexibility, initiative, and creativity that machines cannot duplicate. But at some point, whether it’s twenty years away or one hundred, I’m afraid that the time will come when there are very few jobs that computers can’t do better, faster, cheaper, and more reliably than humans. As that day approaches, we will be confronted with several problems.”

“In the first place, we will need a new economic system. Much as it grieves me to say so, free market capitalism may be dying, for it pays only those who are part of the production process. If virtually no one is part of this process, all the fruits of production will belong to those who own the machines—a recipe for the peon-and-aristocracy patterns of Third World economies. But where will the machine owners find their customers? People can’t be consumers unless they have money to spend.” [7]

I didn’t use the term “the 1%” in that book, but that’s clearly who I was referring to when I described them as an emerging aristocracy.

So, yes, many jobs will be affected, many jobs will be eliminated, and many people will have to find a new way to work, all of which sounds disastrous.

And yet, that’s not all of the story.

The Borg vs. The Hybrid

In an earlier blog, entitled “I, Cobot”, I explored the interaction between robots and humans, and concluded that they are more productive together than either can be on their own. This is broadly true of AI and automation and humans as well.

As I said at the outset of that blog, reality is messy, and people are good at messy situations whereas AI isn’t. On the other hand, AI is good at numbers, and in-depth scrutiny using multivariate analysis. Combining the two kinds of strengths – real world flexibility plus analytic rigor – would produce a better result for everyone.

What’s more, every analysis that I’ve read about this issue basically says that increased productivity means that we can produce the same amount of stuff (goods and services) with fewer people. However, increased productivity could also mean that the same number of people can produce much more stuff.

I call these two different models The Borg (yes, from Star Trek) vs. The Hybrid.

In the Borg model, automation moves in and shoves people aside, throwing them out of work and “resistance is futile.”

In the Hybrid, AI works in cooperation with humans to maximize the strengths of each, and to use increased productivity to increase their output. “Increase output and revenues” is the motto for this model.

When I first introduced this idea to a group of professionals in a keynote address at a conference in the Summer of 2017, they asked how this would work in the real world. For instance, many of the conferees were lawyers. They asked, why should we continue to employ paralegals and junior lawyers when ROSS or other legal AIs can do the job better, faster, and cheaper?

Rather than answering the question specifically and directly, I answered indirectly and generally. I suggested that when someone’s job was going to be eliminated by automation, that rather than just escorting the person involved off the premises, that the organization sit them down and say something like this: “Bob, you know that our new AI has taken over the work you were doing. Ordinarily, we’d give you 8-weeks notice and say good-bye. Instead, we want you to take that 8 weeks, and think about what else you could do that would help us to serve our clients better. And in particular, we’d like you to think about how you could work with us to make this new AI even more valuable – and you with it.

“In short, we want you to invent a new job for yourself. Your experience with us has been valuable, you know our business and our clients, and we’d like you to help us become even more successful. Come back to us in 5-7 weeks with a some thoughts or a proposal that we can explore together. Can you do that?”

It’s possible that Bob (or whomever) can’t come up with a good enough answer to stay employed – but I suspect that, faced with unemployment as an alternative, Bob would get really creative, and might well come up with a completely unexpected, and imaginative new way that he could become even more productive, especially if challenged to learn how to leverage the new-found strengths of the AI.

And, what’s more, if organizations became much more productive, then prices would come down, clients and consumers would be able to buy more, and the general standard of living would go up – just as it did in the Industrial Revolution.

A Possible Hybrid Example

Suppose, for instance, that Bob is a junior lawyer in a firm that has just started using an AI to do legal research and legal briefs. The obvious thing to do would be to get rid of Bob. However, if the firm does that, and maintains that approach, who will emerge to become the senior lawyers later on?

Meanwhile, what else could Bob do? Well, if what people do best is handle the messy stuff, suppose that Bob steps back and considers if there’s a more creative way to solve the legal issues for a particular client. And, sifting through the brief created by the firm’s AI, he looks at what kinds of cases have been cited as precedents.

Next, he makes use of the AI to look for off-beat or unusual settlements or outcomes, and assesses whether they would be preferable to the straightforward resolution being prepared. In other words, he uses the AI to leverage human creativity.

If Bob can come up with a superior result for the client by enlarging the possible outcomes, and offering a better, more unconventional approach, he will complement the work done by the AI to get a better, more valuable result.

Will This Solve All Problems?

Our world is about to be turned upside down. If we aren’t proactive about how we manage this, then lots of people could become unemployed, and possibly unemployable. In turn, this would mean that lots of people couldn’t afford to buy as much from companies, which would mean those companies wouldn’t make as much in profits. And that, in turn, would mean that the value of such companies would go down, making the owners poorer.

There’s a cliché in the stock market that a rising tide lifts all boats, meaning that everyone makes money in a bull market. The converse is true as well: in a falling economy, everyone gets hurt. Therefore, even those people who fall into the 1% should be thinking about how we can embrace the Hybrid model of AI + Humans, not because it’s the moral and kind thing to do (which it is), but because it’s the smart and selfish thing to do.

If, instead of just saying that this is happening and doing nothing about it, we instead all treat it as an opportunity to create a more prosperous society, then everyone benefits. The alternative is potential economic chaos, social turmoil, and, possibly, riots and revolution.

So, is resistance futile? That’s up to us to decide.

[1] Metz, Cade, “Teaching A.I. Systems to Behave Themselves”, New York Times website, 13 August 2017, https://www.nytimes.com/2017/08/13/technology/artificial-intelligence-safety-training.html

[2] Popper, Nathaniel, “The Robots Are Coming for Wall Street”, New York Times, 25 Feb 2016.

[3] “As Goldman Embraces Automation, Even the Masters of the Universe Are Threatened”, MIT Technology Review, 7 Feb. 2017

[4] “JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours”, Hugh Son, Bloomberg website, 27 Feb. 2017

[5] “How will artificial intelligence affect the legal profession in the next decade?”, Queen’s University Law website, http://law.queensu.ca/how-will-artificial-intelligence-affect-legal-profession-next-decade

[6] Frey & Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerization?”, Oxford Martin School, Oxford University, 17 Sept. 2013, website: http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

[7] Worzel, Richard, Facing the Future: The Seven Forces Revolutionizing Our Lives, Stoddart Publishing, 1994, p.83.

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Radical Thinking: Re-Imagining the Future of Education to the Year 2067

by Senior futurist Richard Worzel, C.F.A.

What follows is an edited version of a presentation I gave to an audience at Fanshawe College in London, Ontario on the occasion of their 50th anniversary. They asked me to speak about the next 50 years in post-secondary education. I was delighted and honored to be asked to do so.


Whenever it’s appropriate, or whenever I’m asked, I tell my audiences that if you talk long enough about any aspect of the future, you will eventually wind up talking about education. Education is the fulcrum on which the future of humanity, and the future of individual humans, moves.

If we get education right, we have a chance to deal with the other problems we are facing. If we get education wrong, then we have no chance at all. Education is literally the key to the future.

How to look out to the year 2067

I doubt if I will surprise anyone by saying that the changes coming over the next 50 years will be radical. Many of these changes won’t be smooth, continuous changes, either, which is what even the most sophisticated planners generally prepare for.

Instead, I expect we’ll see discontinuities, sharp breaks with past experience in many areas of post-secondary education. My purpose is to try to identify the issues we should be considering, discussing, and deciding in order to plan and prepare for the uncertainties in education ahead of us.

But in doing so, I hope to leave you with more questions than answers because I think questions are more important than answers. A good question can lead you many places, whereas a good answer tends to limit you to one.

So please consider what I say as a starting point, rather than the definitive word on what’s going to happen over the next 50 years. And let me caution you that much of what I’m going to tell you is going to seem far-fetched, or more like science fiction than a hard-eyed look at the future. One of the major reasons for this is that we have a fundamental lack of imagination about the future. We have a natural tendency to believe that tomorrow will be like today, and, by extension, that 2067 will be similar to 2017.

I disagree. I believe 2067 will be dramatically different, and if you can accept that premise, then you start to look at things differently. So let’s start by looking at the most predictable wild card: technology

Computers & Technology

I can’t really imagine how much more powerful computers will be in 50 years, except to say by an incredibly large factor, perhaps trillions of times. We may be reaching the limits of Moore’s Law with today’s technology, but that’s happened four times before, and each time we’ve leapt to new technologies.

It may be that the next move will be to quantum computing. It’s hard to say how much faster quantum computing will be because it’s just different! It would be a bit like asking how much longer peanut butter is than blue paint – it may not be a useful comparison.

Regardless, I am quite sure computers will be immensely more powerful in 2067 than they are now. And they will be much smarter & more sophisticated, too. Based on what we are already seeing with Fog computing, evolutionary algorithms like Genetic Programming, and Deep Learning (which involves neural networks), computers are already getting much smarter, and very quickly.

Meanwhile, communications will become even more ubiquitous. Augmented reality and virtual reality will become routine parts of our lives, and will enable us to make extensive use of telepresence, which will be similar to (but not quite the same as) being in the same room with someone.

We will attend events virtually, including conferences, sporting events, and concerts, with augmented views of the events but with the feel of being there in person.

And we will be able to experience data in context and in real time, and see dimensions of reality with relevant data superimposed. For education, this will mean that physical presence will become less and less important – but not meaningless. After all, would you rather see your favorite actor, singer, or celebrity in person, or through a really good screen?

Next, we will have computer genies, which have also been called butlers, avatars, or virtual assistants, that will act as our guardians, agents, and alter egos in cyberspace. They will learn our likes and dislikes, and, through Deep Learning and related techniques, begin to anticipate our wants and needs.

The pedagogical significance of this is that computer-assisted learning (“CAL”) systems will be able to judge whether a learner is engaged, and even how well she is comprehending what is being presented. And if a learner is not engaged, or doesn’t get it, then the system will be able to use another approach until it finds the best way to present a given body of material.

The net result is that CAL will become dramatically more effective, and, in conjunction with a human mentor/tutor, massively more effective than today’s lecture-based instruction.

Tomorrow’s Society

Now let’s turn to what society will be like in 2067.

Global population will be somewhere around 10 billion people. Canadian population is projected to be something around 52 million, and the majority will be non-white, including a large number of immigrants who have arrived within the previous 2-3 generations.

How long will we live? It’s been said that within the next 20 years, life expectancy will go up by 50% – at least if you can afford the technologies involved. And what we are finding is that as life expectancy goes up, quality of life persists longer than in the past.

It turns out that decrepitude is more closely related to how near you are to your death than how far you are from your birth. In the 19th Century, someone in their 40s might well have been decrepit. Today, decrepitude typically comes in the 80s or even later, while people in their 70s climb mountains and run marathons.

So, just suppose you’re going to live to 120. When should you retire? Is 65 still the finish line? Will you continue to work, and in the same occupation for another 50-60 years? Or will you get bored and want to do something else? And if you do something else, do you think you might need some a different education than you have now?

My point is that I strongly believe that people will choose to work longer, and may choose to change occupations. And, in my mind, this means you will have more people in their 60s, 70s, 80s, and beyond seeking to learn new things.

The Changing Workplace

How will the workplace change? I believe the world will divide into two principal kinds of economies:

  • National economies that are integrated into a somewhat lumpy, but fundamentally homogeneous global economy, accompanied by a global labor force, and
  • Protected national or perhaps regional economies that have pulled back from globalization and created a “walled garden”, protected by trade barriers in order to protect jobs and domestic industries.

Protected economies will be much smaller, relatively backward, and distinctly poorer, but may have a more even distribution of wealth than those nations integrated into the global economy. I’m sure some people will disagree with this contention, but that represents a discussion for another day.

Within the global economy, wages will be more or less equal around the world for equal work. Inequalities due to gender, sexual orientation, race, age, or other irrelevancies will largely have vanished under the force of one, massively overwhelming question: What can you do for us today?

Who you are will be much less important than what you can do. It will be closer to a merciless meritocracy even than today’s world, where you can be a highly-paid superstar one day, and unemployable the next. You can already see this starting today.

However, competition between humans will be much less of a factor than competition from automation. Computers will be (potentially) billions of times faster, and will be substantially smarter than they are now, as I said a few minutes ago. Over the next 10 years, computers will likely get 1,000 times more cost-effective, and this will lead to dramatic changes. And everyday robots will become part of our daily lives.

Already, Watson, which is IBM’s AI (Artificial Intelligence), is available through the Cloud, and on a retail, transactional basis for anyone who wants to experiment with AI, but without having to make a major investment in creating one.

As a result, automation is already spreading rapidly. All routine work is gradually (and sometimes not so gradually) being eliminated. Physical work will increasingly be done by capable robots.

For example, right now, hamburger flippers are being replaced by things like Momentum Machines’ Burger Bot, which can make a burger every 10 seconds (if there’s a human to load ingredients and fix problems). And Moley Robotic Kitchen copies the movements and preparation skills of a human chef.

Mental/white collar work will increasingly be done by AIs. Legal research is now being done by ROSS, a system that uses IBM’s Watson, and law isn’t the only white collar field that will be decimated by automation.

So, if routine work of every kind is being eliminated, what will be left? Clearly, the only thing left will be non-routine work, which is creative, innovative work. This is one of the reasons for the rise of the Maker Movement, and for online merchants like Etsy.

As automation rises, humans will need to fall back on those things that are uniquely human like creativity, innovation, leadership, teamwork, inspiration, communication, persuasion, craftsmanship, pride, and the ego necessary to believe you can create unique and valuable works. In other words, the soft, human skills will become ever-more important as automation becomes more widespread.

What Should Learners Learn?

Understanding will be much more important than facts.

When was the Magna Carta signed? Who cares? (1215). What was its significance? (Much more important – and more difficult.) One is a fact, the other is an interpretation of the implications.

Facts will be cheap and relatively unimportant. Anything you can look up on the Internet – or have your computer genie look up for you – will be almost valueless. Understanding the context and implications of facts will be crucial.

This will mean that the individual making the evaluation will need to have a body of knowledge, but will also need to place it into context.

Next, we will need to focus on our own unique humanity: our interests, talents, and passions. And we will need to practice creativity. At the moment, our society labors under the delusion that you have to be some kind of artist (which includes things like author, playwright, performer, painter, etc.) to be creative. In fact, everyone is creative to greater or lesser extents, but few of us practice creativity.

To be sure, some people have more creative talent than others, but creativity is also a skill anyone can improve. And people will need a broad background to enable them to discern when they need to dive in and learn things in an apparently unrelated field.

For example, I was a math & science nerd in high school and university, and took art courses in high school only because they were required. Little did I know that I would need to learn many of the basic principles of art, like color, balance, proportion, form and so on, to create presentation slides to make my living.

I suggest that we will need not just math, physics, and technology, but history, art, music, performance, psychology, sociology, economics, and more. I believe the successful student of the future will need to be well rounded as well as deeply educated, with the ability to learn quickly and evaluate new fields critically

Who Will Tomorrow’s Students Be?

As our needs to be creative grow, we will increasingly need to turn inward to find what we will do to make a living. And as that happens, the education we need will be more individualized. Fortunately, the technology will make this not only possible, but immensely preferable.

And if public education moves towards individually tailored education, then a young student’s age won’t be as relevant. Customized, computer-assisted education implies the end of the class-grade system. We will have “Tommy Smith grade” or “Cai Eng grade” instead of “11th grade”.

Some of your future college students will be young people who today would be considered to be high school students. Those who can do the work, and need the background and challenge you can provide, will study with you despite their age or location.

And “lifelong education” will become a reality instead of a cliché, especially if mature workers look to retool rather than retire. You will have people much older as well, including people who would otherwise be retirees. And you will have students in between, looking to upgrade specific skills, acquire new ones, and gain additional credentials. These have been described as “portable and stackable” credentials, and may come in bite-sized portions requiring hours, days, or weeks of study rather than 3- or 4-year degree or diploma programs. And they will need to be available when and where the students are available, rather than having the students schedule their lives around classroom timetables.

Meanwhile, young adults are already starting to question whether investing the time and money for a degree or diploma is worth it. Many students are leaving post-secondary education deeply in debt, and are still unable to find worthwhile employment. And as automation continues to eat its way up the food chain, more potential students are going to question whether what colleges and universities have to offer is worth the cost in time and money. So longer lives, a radically changing workplace, and differing needs are going to make your student population substantially more diverse, even as their needs become individualized.

What’s the Best Way to Teach Something?

The best way to learn something depends on:

·      Who is doing the learning.
·      What they are trying to learn.
·      And who (if anyone) is doing the teaching.

So, the best way to help someone learn depends on the student, her style of learning (e.g., visual, auditory, or kinesthetic), and how her brain processes information. Hence, if two students are engaged in learning the same material, the approach or presentation of that material might be dramatically different.

Pause for question: Does that sound like today’s classroom lecture to you?

But now let’s bring things down from a height of 30,000 feet to something closer to ground level.

What Will Be the Role of Post-Secondary Education in All of This?

Fanshawe’s motto, “Always changing, always relevant” is entirely relevant to the future of education. But how should you change to remain relevant? Should you teach anyone anything, or should you specialize in specific topics, specific fields, or even specific kinds of learners?

Don’t be trapped into thinking that you have to do the things you are doing now, like classroom lectures or even seminars. And don’t be trapped into thinking you have to approach learning and education in the traditional ways, say by subject matter, or the age or the educational attainment of the learner.

Perhaps you would be better off helping people with specific kinds learning styles rather than people who want to learn particular subjects. Maybe you will specialize in helping kinesthetic learners rather than auditory or visual learners. Or perhaps you should specialize in facilitating people whom we currently describe as autistic rather than neurotypicals.

Perhaps you should recruit specific individuals who have a particular or even unique talent or skill set, and build your offerings around them rather than set the curriculum and then recruit people who are merely good at teaching it. In a sense, you might franchise the brains of your instructors.

Or perhaps you would be better off working with learners from anywhere around the world rather than just the ones who make the trek to your campus.

And, as an aside relevant to the next 10-20 years, I think there is an enormous potential for offering higher education and certification to people around the world who want it, especially when it comes with the Canadian brand. And that brings us to some of the more obvious obstacles you face, and they opportunities they create.

Obstacles and Opportunities

Government funding for post-secondary education will be severely threatened in an age when health care costs threaten to gobble up government revenues. The boomers are currently between the ages of 50 and 70, which means the biggest generation in history is now entering the high rent district of health care.

Governments are going to be pressed to find enough funding to serve the aging, and politically active, boomers. But with reductions in government funding come opportunities to find other sources with fewer strings attached. Provincial governments will be looking for ways of off-loading expenses, and might be amenable to new funding sources if proposed by Ontario colleges.

Distance Learning & Competition

As technology emerges, making distance education more effective and cheaper, and technology makes customized education simpler and more effective, you’re going to have to face an old question, but now on steroids: Why should anyone want to be educated here?

You will be competing not only with other Ontario colleges & universities, and other post-secondary institutions around the continent, but with online courses and offerings from places like Udacity and MIT.

So, let me ask some of the questions I would be asking if I were Fanshawe, and considering the next 50 years.

  • What will technology be capable of doing, and how might we be able to use it to help students / learners?
  • What will the workplace be like, and what will individuals need in order to create an income and live fulfilling lives?
  • Who will be interested in learning? What will be their ages, stages, and needs?
  • How will credentials change, and how will people want to gain them?
  • How might you decide what credentials to offer, to whom, and when?
  • What aspect(s) of education should you focus on?
  • What can you provide learners that is better than they can get anywhere else? How can you provide that learning experience in an effective manner?
  • And if you can project where your college might be in 2067, how should you plan to move from where you are to that future?

Finally, I would strongly suggest that you consider more than one possible future by doing some scenario planning.

How Do We Cope with the Future?

How do we cope with a rapidly mutating future? How do you prepare for tomorrow’s world?

I don’t have all the answers, but I can tell you one answer that won’t work: trying to maintain the status quo.

I wish you good luck, and God speed. Thank you.

© Copyright, IF Research, June 2017.

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I, Cobot

by Senior Futurist Richard Worzel, C.F.A.

Reality is messy.

You know how, when the office photocopier gets jammed, you always call that guy in your office who seems to be the only one who knows how to unjam it? That’s the kind of reality that robots and automation can’t cope with.

Reality is unexpected.

Physicists at the beginning of the 20th Century were convinced that they had all the answers, being Newtonian physics, except for one or two minor glitches that remained to clean up. Einstein grabbed onto those minor glitches, and proceeded to start the unraveling of the entirety of known physics, leading to relativity theory and quantum physics, remaking the field into something completely new and unexpected. That’s the kind of problem solving that computers don’t do.

Reality is unpredictable.

While populations can be predictable, individuals aren’t. So dealing with an irate customer, or a patient that needs support, or a grieving husband, are not things that can be learned by rote, then passed off to computer or a robot.

Forty years ago, when robotics was still very much in its infancy, one of the biggest problems was teaching robots how to interpret the information fed to them by a camera, radar, or a sonar signal. Robots didn’t know who to interact with reality. They could only respond to specific situations for which they had a program. As Garry Kasparov, the former human chess champion who was defeated by IBM’s Deep Blue computer in 1997, said that Deep Blue was actually about “as intelligent as an alarm clock”[1]

Computers have traditionally worked by algorithms, which are step-by-step recipes for doing something, or heuristics, which are rules of thumb for doing something that’s more difficult to define. Both can take a long time to create in order to allow computers (or robots and automation, which are physical extensions of computers, just as hands are physical extensions of brains) to come to terms with the real world.

But that’s changing in a couple of important ways. One of the changes involves evolutionary algorithms, neural networks, and other ways of coming up with new ideas. These fall into the broad, and rather messily defined field of Artificial Intelligence (or AI), which we’ll talk about another day. The other is the emergence of co-bots (which, for simplicity, I’ll spell as “cobots”), which are robots that work in collaboration with people, hence the name.

Industrial robots have tended to be massive machines that work in restricted areas to perform heavy tasks, such as automobile assembly. They have to be kept separate from humans because they work according to programming that didn’t take account of the harm they might do to humans. While very useful, computer scientists and experts in robotics have come to see that segregating robots from humans has vastly reduced the flexibility and value of the robots.

Cobots have developed that are lighter, moved more deliberately, and have sensors that cause them to avoid hitting humans, or, if they do, not to hurt them. They are, in other words, designed to work alongside humans, rather than in cages. And this relatively simple change is about to significantly accelerate both automation in general, and robotics in particular.

Where Robots Go

The first change this brings about is it means that cobots don’t need special working spaces, but can be used almost anywhere it’s safe for humans. This means that most workspaces, including offices, restaurants, low-tech assembly lines, stores, hospitals, and many more locations, may now be automated if it makes sense to do so.

Next, because cobots aren’t massive machines, they tend to be cheaper than industrial robots, and hence more attractive to potential purchasers.

Baxter, built by Rethink Robotics, was one of the first cobots on the market, and currently retails for about US$25,000 (plus optional extras). This is typically approaching what a minimum wage worker would earn in straight wages. If you add on any benefits, such as medical insurance, pension benefits, or unemployment insurance, Baxter may actually be less expensive to buy than it would cost to pay a minimum wage worker for one year.

And Baxter has an advertised useful life of three years, which means that the 2nd and 3rd years would be gravy from a business owner’s point of view. Indeed, a recent article in the IEEE[2] Spectrum publication estimated that Baxter would cost the equivalent of about $4/hour – far less than a minimum wage worker, even without including benefits. And cobots don’t get tired, sick, or have personal problems that employers must cope with. All of this makes cobots financially interesting to business owners and managers.

Better with Humans

But beyond simple cost, cobots also increase the productivity of the humans they work alongside, largely because humans and cobots complement each other’s strengths. Robots are good at doing the same things over and over again, and doing them precisely. They can also be used in risky environments we don’t want to expose people to. It is, after all, easier to cope with the aftermath of a dead robot than a dead human.

On the other hand, humans aren’t particularly precise, get bored with doing the same things repeatedly, but are good at stepping into situations that require initiative, an ability to examine and diagnose problems, and then find a way to fix them, even if it’s only unjamming a photocopier.

Going back to photocopiers, they’ve become remarkable pieces of automation, capable of scanning, adjusting color balance according to set parameters, photocopying on both sides of a page, collating, and stapling or binding the results, delivering the finished product in neat, stacked, little piles, all done quickly, and with a minimum of fuss. What photocopiers are not good at is opening new boxes and packages of paper, riffling it so it separates slightly and feeds more easily, replacing toner cartridges, clearing paper jams and clogged ink jets or printer drums, and figuring out why the photocopier isn’t working the way it should.

It’s not that a robot couldn’t be built and programmed to do such things. It’s just that these are the awkward, messy parts of the reality of the photocopying process, and are more efficiently and effectively done by humans, whose evolution over millions of years have led to them being superb problem solvers, artificers, and manipulators of oddly shaped things.

So, why not have humans do the awkward, difficult-to-define parts of the process?

Likewise, why not have a robot (or some other form of computer automation) perform the parts that require precision and repetition, or involve risks that would be unacceptable for a human?

As one cobot client in a promotional video puts it, “[Sawyer the cobot] is really there to do the hard labor jobs that we really don’t want the people to do. We want them to use their brains a lot more and [do] a lot less of the physical labor out there on the floor.”[3]


The result of this collaboration is that both humans and robots perform better, and the hybrid result is better than either one going it alone.

And that isn’t just nice theory. It’s been verified in the real world:

“In a human-machine study conducted by MIT researchers at a BMW factory, it was shown that teams made of humans and robots collaborating efficiently can be more productive than teams made of either humans or robots alone. Also, the cooperative process reduced human idle time by 85 percent.”[4]

Why Does This Matter?

Or, to put it another way: So what?

Well, there are several “so-whats” about the emergence of cobots.

First, their cheapness means that the market for them will expand much more rapidly than earlier kinds of robots. And the fact that cobots don’t need a special, risk-free environment means they can be put to work almost anywhere that’s clean and dry. (And I’m sure special purpose cobots will evolve that are suitable to more challenging environments.)

They do relatively simple tasks, such as moving things from A to B, or checking manufactured items for defects, then removing those that aren’t acceptable. And because cobots perform relatively simple work, they’re relatively easy to program, often by having a person physically move a cobot through the motions you want it to make – showing it what to do, in effect.

Rethink Robotics, the makers of Baxter & Sawyer, quotes one of their clients as saying their robots are “stupid simple” to program, just by moving them through their paces. And because they can be designed to work at almost any scale, they can be used in fine, precision processes, such as manufacturing involving electronics components, where they can employ small, or even microscopic waldoes (manipulators).

All of this means that the first “so what” is that such robots can, in the right circumstances, move very quickly into a work environment and become productive. Since they’re not intended to replace people, but to complement them, they will have humans overseeing their work, and can rapidly get up to speed. And the economics and their precision mean that they can pay for themselves rapidly as well.

The Acceleration of Automation

Because of the first so-what, automation will spread much more quickly than even devotees expect. As more and more organizations, both profit and non-profit, see examples of cobots being used, and run into people who have worked with them or used them, the idea of using them will spread. And as the economics and improved results of using cobots become more widely known, the interest in cobots will spread as well.

So the 2nd so-what is that cobots will advance the entire field of automation much more rapidly than might happen otherwise. And because humans often have to see a success before they will consider experimenting with a new idea, cobots will spread the idea that robots in particular, but automation more generally, can be worthwhile, can be non-threatening, and may be worth investigating.

This will also mean that that the speed with which robotics and automation spread will catch people by surprise. Within 10 years’ time, robots in the workplace won’t even rate an eye-twitch, let alone an exclamation.

The Widening Gap Between the 1% and the 99%

A bigger, broader change will be that productivity will increase, and that’s generally a good thing. Higher productivity growth is perhaps the single biggest driver of prosperity. But, as has always been the case, this prosperity won’t be evenly distributed.

For the human coworkers of cobots, increased productivity may mean they can do more interesting, more intellectually stimulating, and potentially better paying work. It will probably also mean that organizations will either be able to get more results with the same number of people, or the same results with fewer people. In the first instance, everyone benefits, especially the owners. In the second case, the workers that remain may have better working conditions and possibly better pay, the owners will benefit from better results, but those workers who are displaced will lose their livelihoods and be far worse off.

Over a longer period of time, this will exacerbate the divide between owners and workers, or, in the popular cliché, between the 1% and the 99%. This has been going on for some time, but the acceleration of computer intelligences, robots, and automation will likewise accelerate the widening of this gap.

I’ve written about this before, going back more than 20 years, and this will eventually lead to a society of aristocrats and peasants – unless we choose to do something about it. That’s a subject I’ve written about before, and will return to again, but isn’t directly germane to this discussion.

Death by Inattention

There is a largely unnoticed, certainly little discussed downside to having robots (or computers) manage important processes under human supervision, and it is best illustrated by self-driving cars.

There’s a categorization for different levels of self-driving cars (also called autonomous vehicles, or AVs), ranging from Level 0 (no automated features) through Level 5 (fully automatic, and capable of dealing with any highway situation). In my mind, the most dangerous levels are Levels 3&4, where the car is fully capable of driving on its own, but where the driver should be alert, and prepared to take over and deal with ambiguous, unusual, or emergency situations.

The problem is that in such situations, drivers are unlikely to be paying full attention. If you are on a long-distance journey, and allowing your AV to drive itself while you are theoretically monitoring what’s going on but not actually doing anything, then the odds are you’re going to be bored. Worse, you probably won’t be paying attention, certainly not the concentrated attention you would be giving to the task if you were driving the car.

And since emergencies tend to arise quite suddenly, and usually without warning, the odds that you could go from being bored and inattentive, to fully alert, completely aware of what’s going on, and capable of taking decisive action in a complex situation, are, in my opinion, vanishingly small.

Likewise, a human overseeing a robot in a dangerous or critical situation is likely to be lulled into complacency by the vast amount of time spent doing routine things. As a result, when an immediate or instantaneous need to intervene occurs, it’s unlikely that the human will be able to shift gears fast enough, and respond quickly enough, to the change from boredom to full engagement.

This is a risk with automation or robotics that I haven’t seen discussed.

“Death to Robots!”

A more speculative so-what is whether there’s a backlash against automation in general, and robots in particular. Certainly there have been people who have been vocal in their dislike for things like self-check-out counters in supermarkets, but that’s not the same thing as, say, people boycotting supermarkets that have them.

Part of the reason for this is that automation has been slowly creeping into our lives for as long as any of us can remember. The difference in future is going to be the speed with which automation accelerates, and how pervasive it becomes. And, of course, robots are much more noticeable, and just plain flashier than a self-check-out counter.

And there is historical precedence. The Luddites of 19th Century England were textile workers who smashed automatic weaving machinery that they believed were threatening their livelihoods.

I expect, but am not certain, that there will be an anti-robot, anti-automation backlash, a kind of “We don’t buy from machines” movement. Where or when this will emerge is beyond my abilities of seeing, but I would be surprised if something like this doesn’t happen, even though I don’t know how or when it might.

So What Happens Next? And What Should We Do About It?

Everyday robots, led by cobots, will start appearing in businesses, hospitals health care clinics, and other places where you will see them, slowly at first, then with increasing frequency. They won’t have the speedy take-up of, say, smartphones after the introduction of the BlackBerry or iPhone, but they make too much economic sense for people who do things to ignore them.

So the first thing to do is to watch how quickly cobots start to appear in your life, or in the businesses and organizations you encounter. They won’t be front-of-the-house at first, but if you keep your eyes open, you will probably start noticing them in the next 3-5 years.

Next, think about how cobots in particular, but robots, AI (such as IBM’s Watson), and automation in general might be of use to what you do, the organizations you’re involved with, and to your work specifically. As I’ve said before, if you can’t beat ’em, co-opt them.

In particular, if you are someone who does repetitive work, even if it requires specialized skills, start thinking about what you could do and would do if, suddenly, there was a robot working with you that could do the routine things. This would free you to, as the video above of a Sawyer cobot user said, “use your brains a lot more.”

What could you accomplish for your organization if you had more time to think about what was going on? How could you improve the way your organization ran? What tasks would you like to have automated so you don’t have to do them?

Or, to focus this further, imagine that your boss came to you and said, “We’re getting a cobot in here to do the routine stuff you’ve been doing. Take a couple of weeks and write yourself a new job description, then come back to us and let us know what role you’d like to have in this organization.”

What would you say to them? What would you propose?

The One Thing You Can’t Do

The one thing you can’t do is turn back the clock, and pretend that this isn’t happening, or that it won’t happen to you.

If you own or run a business, or a hospital or health-care clinic, or a non-profit organization, start asking yourself how you could make your people more valuable by relieving them of routine tasks, and how you could improve your operations by using cobots.

If you’re an employee, start thinking about what a cobot could do for you and with you that would make you more valuable to your organization.

Because, like it or not, there are cobots in your future. And they’re coming faster than you expect.

© Copyright, IF Research, May 2017.

[1] Doherty, Mike, “Garry Kasparov’s next move: teaming up with machines”, Toronto Star, 8 May 2017, https://www.thestar.com/entertainment/books/2017/05/08/garry-kasparovs-next-move-teaming-up-with-machines.html

[2] The IEEE started in 1884 as a group of electrical engineers, and now is widely considered to be the world’s leading society of technologists.

[3] “Sawyer at Work: Customer Success Story – Tuthill”, Rethink Robots website, viewed 10 May 2017, http://www.rethinkrobotics.com/sawyer/

[4] Tobe, Frank, “Why Co-Bots Will Be a Huge Innovation and Growth Driver for Robotics Industry”, IEEE Spectrum website, 30 December 2015, http://spectrum.ieee.org/automaton/robotics/industrial-robots/collaborative-robots-innovation-growth-driver

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“How Can I Help My Kid Get a Job?”

by senior futurist Richard Worzel, C.F.A.

Virtually everyone knows people who are unemployed or underemployed. This is especially true of young adults, although there are also lots of older people, even in their 50s and 60s, who are in similar situations.

This wasn’t true in earlier eras, specifically the periods from about 1950 until the 1980s. If someone finished high school, or even dropped out, and wanted to work, their older brother, a neighbor, or friend would say, “Come on down to the factory with me. I’ll speak to the foreman, and we’ll get you a job.” People coming out of college or university in the 1960s and 1970s would typically have several companies lined up, waiting to hire them. And anyone who truly wanted a job could find one.

This isn’t the case today, and it’s going to get worse. And contrary to the current political fashion to blame unfair foreign competition and international trade agreements, most of the problems today come from the rise of automation, and how machines are taking jobs that used to be done by people. Moreover, this is happening at a steadily accelerating pace. (See our earlier blog, The Beginning Death Cycle of the Consumer, for more details.)

As a result, for many years one of the most consistent questions I had from individuals who have heard me speak at a conference is the one I quoted as the title of this blog, some variation of “How can I help my kid get a job?”

I’ve also heard this directly from young people. I was recently the featured speaker at a celebration of Fanshawe College’s 50th anniversary, at which they asked me to speak about what was coming to post-secondary education over the next 50 years. Afterwards, a young lady, who was currently enrolled at Fanshawe, came up to me and said she had a number of friends who had graduated, and were unable to find useful employment. She wanted to know what I could tell her that might help her find a job.

I got an analogous question the following week at a conference where I was speaking about the future of electric power utilities (See Deadly Shock: The Coming Devastation of Power Utilities), only this time from the father of a young man who, although highly qualified and holding a useful degree, couldn’t find steady, full-time employment.

So let me first trace the reasons why this is happening, and then look at what individuals of any age, but especially people in their 20s and 30s, can do about it.

What’s the Problem? Where Are the Jobs?

It was true that jobs were lost during the rapid rise of the Chinese, Indian, and other rapidly developing economies, but that was largely in the latter decades of the 20th Century. Today, while some shifts in employment happen because of emerging new players, more recently in Latin America and Africa, there isn’t the wholesale loss of jobs to foreign competitors that there was earlier.

Now most of the job loss is implicitly because of the march of Moore’s Law, i.e., the rapid acceleration of cost-effective computing. Add to this the emergence of more sophisticated computing techniques, things like neural networks and Deep Learning, evolutionary algorithms and Genetic Programming (a software technique), and more, and it’s easy to see that computers are getting much smarter as well as much faster. All of this has been rather sloppily labeled “Artificial Intelligence”, or AI.

The result is that automation, both as smart computers, and as robots in the physical world, is rapidly eating its way up the workplace food chain, displacing more and more people from routine work. And not all of this work is blue collar, either. Indeed, dealing with the real world, which is typically what happens in blue-collar work, is actually pretty messy and unpredictable, which makes it harder to automate. Today it is white-collar work, such as accounting and legal research, that’s more likely to be automated.

Meanwhile, in the aftermath of the financial panic of 2008 and the Great Recession of 2009-10, companies have been exceptionally cautious, and thus have been slow to hire workers back, and slow to invest in new plant and equipment. In response, consumer demand has been slow to recover – the workers who have not been hired back are less likely to splurge on new purchases. This has led to a vicious cycle: companies didn’t hire because consumer demand was weak, and consumers didn’t buy because employment was hard to find.

It’s only now, in the 10th year after the panic of 2008 that something like full employment has been achieved in America, and even today much of this is due to the number of people who have given up looking for work, which technically takes them out of the labor force. (Someone who has given up looking for work is not counted as being unemployed.)

So today companies are, in aggregate, sitting on large amounts of cash at a time when robots and smart computers are offering the opportunity to make a one-time purchase to get a job done rather than paying an employee every week or month. And as computers and robots become steadily more flexible and more capable, the range of jobs available to humans will continue to shrink.

And having even a modest percentage of people who are desperate for work gives employers a big advantage, allowing them to offer less in pay, security, and benefits to almost everyone by playing workers off against each other. This increases profits, and also creates even more people who are eager to accept less than they want in order to be employed, leading to a ever-more precarious employment.

All of which brings us to back the opening question: What can a person do to find a job?

How Can I Find a Job?

Over the years, due to being asked this and similar questions repeatedly, and after having given it a lot of thought, I tend to offer a range of suggestions:

1) Stop looking for a paycheck – The world is changing, and many people who might have been employed in earlier decades may find that they have to create their own paycheck by running their own business. Indeed, regardless of whether you sign your own paycheck, or someone else does, you are going to be responsible for managing your career. And this means making sure you have skills that the world wants, and can find a way of making sure you get paid for it. This is not easy, but may be the best solution for you.

But beyond this, thinking that you have to find a job, that you have to have someone else pay you, is limiting. It pushes your thinking towards the perspective that you need to have a job, or else you have nothing. This kind of thinking may lead you to a dead-end, retail greeter-type of job, if you can even find one.

Instead, the question you should be asking yourself is: How can I create an income stream?

2) Why should you pay someone to sign your paycheck? – If someone employs you, it’s because you can produce enough to justify your employment. That means you need to be able to compensate your employer not only for your paycheck, but also for all of the overhead, office space, accounting, legal form filing, and the rest of the stuff that goes into having employees. As well, an employer is essentially guaranteeing to pay you on a regular basis, so they need to be compensated for that assurance as well. And if you’re working with a for-profit company, you have to produce a profit for your employer, or else there’s no reason for them to have you on staff.

All of this is perfectly normal and ethical, but it does beg the question: If you are worth more than your paycheck, is there a way you could capture that “more” for yourself instead of paying it to an employer?

The answer may be no. If you are a natural born salesperson of oil tankers, for instance, it may be hard for you to set up shop for yourself. If you are a gifted accountant, able to manage the books and the forward financial planning for a multinational corporation, you need a multinational corporation to do that for. You may need someone else to complement your talents and abilities, or to provide a large organizational context that allows you to do what you are best at doing. But before you come to that as a final conclusion, ask yourself if there isn’t a way you could do that by working at arms-length for an organization that needs your talents rather than becoming an employee.

Another perfectly legitimate reason for being an employee is that you are unwilling to take the risk of running your own business, of going it alone. As someone who has run their own consultancy for almost 40 years, and gone through some pretty lean times, I can tell you that it can be very scary when there’s no money coming in the door, no clients at the other end of the phone. But, on the other hand, it’s pretty scary being unemployed for a long period of time, too.

It may be that being employed is what you really want and need, but at least consider other possibilities.

3) Don’t just look at the world, look at what’s inside of you – A question that I get related to the primary question is: What kind of jobs have the best prospects? That’s a useful question, but you need to complement it by asking: Am I likely to be good at that, and would I want to invest at least part of my life doing that?

Looking outside yourself is useful, in part because it gives you a sense of what the world is willing to pay to have done. But don’t stop there. You should also look inside and ask yourself: What talents and abilities do I have that are worthwhile, and that the world might be willing to pay for. And also: What do I want to build my life around doing? You want to create a life, as well as afford to live.

So, by all means, research what’s hot, what occupations are growing, and where is there a crying need for people, but don’t stop there. You’re looking for something that fits both you and the world’s needs. Which brings me to my next principle.

4) What are you passionate about? – In a global economy, you’re going to be competing with the best people in the world in any field, so in many ways your best bet is to do what you are best at. Most of the time, that’s also what you are most passionate about, so ask yourself: What do I really, really want to do for a living? This can be a surprisingly difficult question to answer. We are so used to settling for what seems possible after a lifetime of being told that we can’t have what we really want that we lose the ability to even recognize what we really, really want.

Yet, being passionate about something, and being really good at it, isn’t enough. You also have to invent an answer to the question: How can I create a reason why the world should pay me to do what I love doing?

I have a friend from my university days who is an artist. Whenever we’re together, he has a tendency to rail against society, and how it doesn’t do enough to support the arts, by which he means that the world doesn’t do enough to support him. Whether he’s right that society should do more to support the arts (and him) or not doesn’t matter. It only matters whether society will do more – and it’s pretty clear that the answer is no.

You have to invent a reason, a method, a structure, an excuse, or a mechanism to entice the world to pay you for doing what you are good at doing. No one is going to do that for you.

This can be difficult, but it’s a step that you must take.

5) Become multi-talented – Even if you focus on your passion and greatest strength, whatever it is, there will be plenty of competition from others with similar strengths. An important way to improve your competitive position is to have more than one strength, to offer a range of complementary skills.

Hence, a passionate and talented graphic artist will find herself up against bunches of other talented and passionate graphic artists, but one who can write good marketing copy as well will have an advantage. And the more complementary skills you have, and the more versatile you are, the better the potential is to improve your competitive position.

6) What else do you need? – Whether you decide to sign your own paycheck, or have someone else do it for you, you will, as I said above, still need to accept responsibility for your own career – or else you may be caught off-guard when your job is eliminated, or you are “outsourced”, or whatever the current euphemism is for being fired, let go, or laid off.

And if you accept responsibility for managing your own career, you should ask yourself: What else do I need?

Beyond this, if you have identified your passion, and know your greatest strengths, you are half way to also identifying what you’re not especially good at. And if you accept, as I said above, that you need to invent a reason why the world should pay you to do what you are passionate about, then you probably have a pretty good idea, in rough terms, what would be necessary for that to happen.

I’m not particularly good at marketing myself, for instance, which is often true with people who sell their own services. I find it hard to tell people why I’m the greatest thing since sliced pineapple, and why they should shell out big bucks just to have me around. The ways that I’ve learned to deal with this are two-fold.

First, I’ve worked hard at developing groups of people who are willing to market my services. They work on commission, but they are good at marketing, and they spend their days thinking about who might find my (and, I admit, other peoples’) services useful. And they have no qualms telling people just how wonderful I am, and why people should pay me a lot of money – and them a justifiable commission out of that.

And second, I’ve worked at learning how I can make it easier for such people to market my services. I’ve also worked hard at getting better at what I do, and finding out how I can add more value to those organizations that engage me to work with them. And as part of that, I’ve also studied both marketing and sales techniques, and have learned ways of helping myself help potential clients see what value I can bring to the table.

Likewise, you need to figure out what you aren’t good at, but need, then find ways of filling those needs. It may be that you need people to market your services. It may be that you need to learn things, like accounting and tax management, that you wouldn’t necessarily choose to learn, but which are going to be important if you are going to succeed. If you’re an introvert, you may need to learn how to be outgoing when warranted. If you’re an extrovert, you may need to learn how to be quiet and listen when your clients speak.

Very, very few people form a complete package, so figure out what else you need, then find a way to get it.

7) Be in front of the right door at the right time with the right stuff –  The Pixar movie Monsters, Inc. describes a community of monsters who generate power by scaring children. They leap from their community to a specific child’s bedroom by means of movable doors (dimensional portals, I guess – we’re never really told). But to get to scare a specific child at a particular time, a monster has to be in front of the right door, at the right time, and with the right stuff.

Finding work, whether you are job hunting or beating the bushes for work for your own business, involves the same thing. One of the most common problems is you can be in front of the right door with the right stuff, but it’s not the right time. Finding the right time is difficult, and about the only way I’ve ever found for solving this one is to check in with that door (i.e., client or employer) not once, but several times.

Of course, you don’t want to become a pest, someone people avoid, so make sure you have a reasonable excuse for calling back, and permission to do so. If I check with a client who says they like what I do, but aren’t in the market right now, I’ll say something like, “Fair enough. Would it be OK if I checked back with you in 2-3 months?” Mostly they’ll say yes, which gives me permission to do so. If they say no, then I’ll say something like, “When would be a good time for me to check back with you?”

Another way of doing this is to send them something you believe they might find of interest from time-to-time, or that might be valuable to them, and then follow up with more information. And while you’re chatting with them, you can ask if anything has changed.

Make sure you are sensitive to how they respond to speaking with you more than once. You want to be regarded as a resource, not a pest.

8) Find people who can help you – This is a natural outgrowth of point 6, and it particularly applies to your network, as well as the network of people that people in your network know.

Working your network can help you find the right door to be in front of, and when that happens, make sure you’re there at the right time, with the right stuff, even if you have to disrupt the rest of your life to be there. And if they say they haven’t got anything for you, keep working the network by asking them if they happen to know anyone who is looking for someone like you.

If they say no, there’s no harm done. If they say yes, then call that person right away and say, “Hi, I was just talking to Joe Smith, and he thought you might be interested in …” and then go into your elevator speech. (The elevator speech is your finest honed and practiced presentation of what you offer that can be delivered in a single breath, and can be finished before you can be interrupted, or they get out at their floor of the elevator.)

But the right people can be mentors, too.

Early in my career, I developed a relationship with a head-hunter (personnel recruiter). He made his living by finding a range of capable people with different abilities to fit specific careers for his clients. I worked with him in a couple of ways. First, I made sure I stayed in touch with him, and kept him apprised of what I was doing, and any new skills or accomplishments I had. As a result, I found he would often include me in a group of potential candidates for a wider range of jobs than I might otherwise have seen. Sometimes I was just padding to provide a range of options for the client. That never bothered me because it gave me a broader range of exposure than I would have had on my own. And you never knew when you might be a surprise fit for a position.

And second, whenever we got together and talked, I tried to learn from him as his perspective on what organizations would pay to have people do was different from my own, and his experience much deeper in many areas than mine. He was older than I was, and was happy to do this, partly out of a sense of mentorship, but also as a potential investment in someone who might one day might earn him a commission on a position he had been hired to fill.

And that became a valuable insight for me: I tried to make sure that any relationship benefitted both parties, that I gave things of value to people with whom I worked and people I met, so that they would come to value their relationship with me.

But sometimes what the other person receives may not be something they can put in the bank. I’ve had a number of mentors through my career, and have mentored some others as I got older. What my mentors received (I hope), is a sense of satisfaction that someone they felt was worthwhile has benefited from something they knew or help they were able to give. Likewise, I’ve been on the receiving end of this kind of feeling by being a mentor. Benefits don’t have to be financial to be valuable.

9) Be persistent – About three years before I started working as a professional speaker, I was approached by a very successful, national speakers’ agency. They had heard of me, and wanted to meet with me to see if I had potential as a speaker. I met one of their agents over coffee, and outlined to her what I thought I could bring to an event as a speaker. She thanked me warmly when we finished, and left.

I heard back later that they didn’t think I had much potential as a speaker – or, as they rather more diplomatically put it, I wasn’t a “good fit” for their agency.

What they didn’t realize was that they had made me aware of something that I wanted to do, and thought I would be good at doing. Something I could be passionate about. So when something else I did opened the door to professional speaking again, I charged through it. It was hard going for the first couple of years, but I had learned by that stage in my life that persistence is a cardinal virtue in careers as well as business, so I kept at it.

This has been expressed in many, many business clichés, such as the popular saying “Keep on keepin’ on”, or “Persistence is will power and desire combined. Persistence is steel determination.[1]”, attributed to Warren Buffet, or, perhaps most famously, Ray Kroc’s comment:

Nothing in this world can take the place of persistence. Talent will not; nothing is more common than unsuccessful people with talent. Genius will not; unrewarded genius is almost a proverb. Education will not; the world is full of educated derelicts. Persistence and determination alone are omnipotent. The slogan press on has solved and always will solve the problems of the human race.[2]

You may have to detour for a while, or do something else to pay the bills for a while, but if you persist, your odds of succeeding at what you want go way up, whether it’s securing a specific kind of job, or creating your own business.

And, by the way, over the years I have done tens of thousands of dollars worth of business with the speakers’ agency that told me I had no potential as a speaker. We all laugh when I remind them of that.

10) Play Against the Machines’ Weaknesses, Not Their Strengths – If automation is eating its way up the food chain, and you don’t want to be part of its lunch, then you need to figure out what you’re good at that the machines are not.

As I said earlier, machines aren’t good at messy things. For example, there’s a burger-making robot, which is now being marketed by Momentum Machines. It’s very fast, producing 360 hamburgers an hour. It’s consistent. And it takes less floor space than a human hamburger flipper. But it needs to have a human load the ingredients, and fix any problems that crop up. It also wouldn’t be very good at dealing with an irate customer. So, yes, robots are good at repetition – but not very good at the messy bits around the edges, especially if they require judgment, or involve novel situations not contemplated in the machines’ programming.

Fundamentally, machines, whether robots or smart computers, are good at learning and doing routine things, and then repeating them. So the jobs that are going to be at greatest risk are going to be those where you are doing the same things over and over again. And it doesn’t matter whether this repetition happens every day, every week, or every year. If your job involves repeating a process over and over, you are in danger of seeing your work automated.

All routine work is under siege, and may eventually be automated. So what does that leave? Well, obviously, non-routine work, and there are two primary kinds of that.

The first kind is messy, like the real world is messy, as I described with the Burger ’Bot. The real world is messier and more complicated than robots are capable of dealing with outside of their neatly defined areas of competence. As a result, humans may be able to keep automation at bay in messy, real world situations, especially those that require coming up with new answers to old questions, or dealing with situations that haven’t arisen before.

Which brings me to my second kind of non-routine work: creative, innovative work, work that is different every day, and in which you are constantly learning and doing new things.

So, in your consideration of what you want to do, look for ways of introducing constant change to your work by continually learning how to do new things, and how to do old things better.

Which means that the old business cliché, “If it ain’t broken, don’t fix it” is now broken. Fix it by constantly reinventing yourself and what you do.

11) Co-opt the competition – The working world is going to be a more difficult, and less forgiving place. At the same time, because things are changing so quickly, it also offers more opportunities for those able to see and grasp them. And if automation is part of the competition, then one way of dealing with that is to co-opt it.

Look at what computers and robots can do, and think about what you could do with computers and robots, how you could employ them, that would make you more valuable.

This may be easier if you are working for yourself – or it may be that you can become the office tech guru by introducing new technologies that can give your organization a more productive edge.

If you can’t beat ’em, co-opt them.

12) Ignore your parents (or your past) – Perhaps the thorniest lesson may be that the lessons of the past may actually be harmful to your future. And for parents, this may be particularly difficult: the things that worked for you probably won’t work for your kids. So my final principle is: Don’t listen to your parents. It was different in their day, and what worked for them probably won’t work for you.

Or, if you’ve been in the workplace for quite some time, be willing to forget the past. What brought you success then may not bring you success now. Continuing to do what you always did may lead to dead ends. Instead, learn about the marketplace as it is now rather than remembering how it was when you first broke into the workplace. A sense of amnesia may be very valuable.

And good luck.

[1] http://www.evancarmichael.com/library/harriette-blye/What-Is-The-Value-Of-Persistence.html

[2] http://www.searchquotes.com/search/Ray_Kroc_Nothing_Takes_The_Place_Of_Persistence/

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Preparing for Trumpian Uncertainty

by senior futurist Richard Worzel, C.F.A.

The victory of Donald Trump as president of the United States is without precedent, and creates enormous uncertainty. Will his qualifications as a businessman transfer to management of the world’s largest economy? Will his policies work as advertised? Will his advisors be up to the task?

We don’t know, and uncertainty is something that businesspeople despise, and investors flee from. So, how should you cope with the uncharted economic waters in which we find ourselves?

As it happens, this is precisely the kind of situation where scenario planning offers the greatest benefits. The future will inevitably catch us all by surprise. Those who will do best out of it are those who recover fastest when the unexpected happens, and who respond most constructively with the shortest delay.

With that in mind, let’s consider three of the major scenarios that we should consider, as well as the contingency plans we might make for each.

Scenario 1: Trump’s Triumph

Trump’s supporters believe that his understanding of business will lead to lower regulation, productive and much-needed investments in public infrastructure, and a devotion to leveling the playing field in terms of trade. Businesses will respond by loosening their purse strings and investing in new plant, equipment, and staff. As a result, Trump’s supporters believe that the economy will boom, corporate profits will grow, and Americans will be better off.

In that scenario, companies should be planning how best to profit from the boom times ahead. They should be making investments in productivity enhancements and key personnel, and they should be preparing their finances to expand. Investors should identify sectors, industries, and companies that will benefit, and shift their portfolio balances to reflect these projections.

Scenario 2: Trump’s Tragedy

Trump’s detractors believe he will produce a disaster. His lack of understanding of macroeconomics and the nuances of fiscal and monetary policy will lead to a short-term boom with rapidly rising inflation & interest rates, bloated government deficits, and rising government indebtedness. His beggar-thy-neighbor trade policies will precipitate a trade war, leading to a global recession. His amateurish foreign policy will lead to a series of global crises, scaring investors and citizens alike. And poorly regulated industries, especially in finance, will lead to another 2008-style financial panic.

In that scenario, companies should prepare for the worst, husband their cash, take a flinty-eyed look at their cost structures, and eliminate products lines, operations, and staff that are not productive. Investors should retreat to defensive positions, emphasizing cash with some investments in gold and precious metals as an insurance policy against serious disaster.

Scenario 3: Trump’s Irrelevance

The Trump Administration’s actions turn out to have more PR value than economic impact, with the result that the economy continues to muddle along as it has been doing for some time. Growth continues, but at a modest pace. Corporate profits grow, but slowly. The recovery continues in what seems like a modest Goldilocks pattern of slow, but steady, growth.

If that happens, then companies should continue as they are now, being cautious but open to making new investments in plant, equipment, and personnel as demand requires. Investors should be sensitive to the already full valuations of stocks, and the potential downside risks of bonds, and be selective in their choices.

The Critical Issues

There are other, more extreme scenarios on both the upside and the downside that I haven’t delineated, and that are of lower probabilities. The point is not to try to cover all possibilities, but to assess probable futures. Right now, nobody knows what to expect so it is more than worthwhile to game out the major possibilities. Indeed, the first, and most critical, decision is to consider more than a single possible future. None of the scenarios you sketch out will be 100% accurate, but the exercise of considering possibilities will stretch your mind, broaden your field of vision, and cause you to consider both what might happen, how it might happen, and what you could or should do about the range of events that could occur.

And whatever scenarios you consider, don’t expect to be completely correct. The future may be different in minor details, or you might not have correctly assessed the right probabilities and the future may be wildly different. But just thinking about ranges of probabilities is a worthwhile exercise.

The second critical decision is what indicators, or Distant Early Warning signs you should be watching. How could you tell if one scenario was emerging rather than the others? What should you watch so you can be warned as early as possible? Just keeping an eye on developments with the intention of trying to identify which scenario will be closest to being right is valuable because it helps you identify things that don’t fit with one scenario or another – and may not fit with any of them. Watching the future with intent can be extremely valuable in assessing the future.

Or, to put it another way, if you don’t watch what’s going on, you will inevitably be caught by surprise, more so than if you are assessing and gauging what’s going on, and trying to decide where it’s taking us.

So take advantage of the tools provided by scenario planning. We are entering a period without precedent, and those who are prepared for the things that catch everyone else by surprise will benefit most from the changes to come.

There’s a rule in the field of future studies: Someone always benefits from change. Let it be you.

© Copyright, IF Research, January 2017.

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The Beginning Death Cycle of the Consumer

by senior futurist Richard Worzel, C.F.A.

“There’s nothing you can do that can’t be done…
Nothing you can make that can’t be made”

– “All You Need Is Love”, Lennon & McCartney

Our own brilliance and ingenuity is putting us out of work.

In some ways, one of the most insidious problems we are facing is the disappearance of meaningful work. Not having meaningful work is soul-destroying for a number of reasons, but two in particular. Not being able to provide for yourself and your family erodes your sense of self-worth and self-confidence. And not having anything meaningful to occupy your time and attention can lead to a loss of purpose and meaning. It’s why newly retired people often die shortly they quit work.

The gradual dwindling of meaningful work has been happening for decades, but is now accelerating. Mostly, it’s happened for two reasons: foreign competition, and domestic automation.

Foreign competition – the migration of jobs to developing countries like China, India, and others, where the workers do effectively the same work for a fraction of the cost – has been much discussed, and continues to receive a lot of attention (although much of this is disguised as discussions about trade rather than work). I’ve discussed this before, and will again. Now, though, I’d like to focus on domestic automation.

The Rise of the Robots

It’s clear to me that eventually robots and computer intelligences will be able to do almost anything that humans can do, and do it better, faster, and cheaper. The science fiction writers of the first half of the 20th Century saw this as leading to a utopia, where everyone lived as they wished, free of the drudgery and toil of back-breaking labor. Indeed, I still get asked about this future quite regularly. I call it the “George Jetson future.”

George Jetson, of the animated TV show “The Jetsons”, was employed by Spacely Sprockets to fly into his office sometime around 11 a.m., and sit with his feet up on his desk until noon. Then, when the noon lunch whistle went, he would push a button in the middle of his desk, and go home for the day. That’s the old science fiction utopia.

No one thought to ask: If all George Jetson does is push a button when the noon whistle sounds, why do we need to employ George Jetson? Once you ask that question, the answer becomes obvious: you don’t. And that answer leads to the world we are living in today, rather than the George Jetson future.

Machine learning, coupled with the continuing acceleration of computing power and the increasing sophistication and subtlety of computer techniques, is rapidly creating machines that can learn new tasks, and perfect them, slowly at first, but at a rapidly accelerating pace. Moreover, this is happening in many fields, with each one perhaps using a different set of techniques. Anyone who cares to do even casual research can verify that this is happening, and in a growing number of areas of what used to be human work.

That doesn’t mean that we are creating a race of humanoid robots that look, talk, think, and act like humans, only better. Robots will come in many shapes, and with differing abilities and specialties. And computer intelligences will be focused on specific tasks, rather than being able to do a little of everything, as humans do. But collectively, the result is a range of robots and computers that are smarter, faster, and can do a growing number of things more cheaply than humans.

The Neo-Luddites vs. the Technologists

This leads us to a very old debate, dating back at least to 1811[1], when textile workers rebelled against the mechanical automation of weaving and spinning machines in often violent protests. These people were called Luddites because they were believed to be followers of one Ned Ludd, who was reputed to have smashed such equipment in protest. The term Luddite is still widely used, and generally applied to people who are stuck in the past, and rail against beneficial automation. Their argument, then and now, is that machines are eliminating human jobs, and hence the advance of such machinery should be stopped.

On the opposite side of the debate are people who have no such catchy label, but which, for convenience, I will call the Technologists. Their argument, which has been right for the last two hundred years, is that as automation eliminates bad, old jobs that rely on repetitive actions and backbreaking labor. As a result, automation increases productivity, which decreases the cost of goods and services produced. In turn, this increases the standard of living by leaving people more money to buy more goods and services, and, along the way, creates new jobs by increasing demand. And as a result of this rising demand, new jobs are created that are better paying, offer better working conditions, and greater hope for advancement.

In essence, new, better jobs are built on the rubble of bad, old jobs. And for the past two hundred years this has been proven to be correct.

But it may not be as true today, and it certainly won’t be true in the future. Automation still increases productivity, which still lowers the prices of goods and services. That still increases the standard of living – but only for those who are gainfully employed, and have the money to buy things, and that’s where the Technologists’ argument is finally breaking down.

Automation Escapes the Factory Floor

As computers have become more powerful, and more sophisticated, they are displacing workers in jobs that not only involve muscle power, but now, increasingly, white collar workers and professionals that are involved in repetitive tasks involving thinking and judgment.

IBM’s Watson computer system is perhaps the best known, and possibly the most advanced, of such systems. Watson became famous in 2011 for defeating the two human champions of Jeopardy!, the television game show that makes use of the eccentricities of human language, in a three-day match. IBM’s purpose in creating Watson was to produce a machine-learning system that could understand human language, absorb enormous amounts of information, and come up with answers, based on weighted probabilities, to certain kinds of questions. The first area, for instance where Watson was applied was in medical diagnostics, starting with certain kinds of cancers that are difficult to diagnose.

Doctors are not being replaced by Watson, but their work is being supported, and, if you want to be cynical about it, double checked by Watson’s diagnostic abilities, especially in particularly complex diagnoses.

BakerHostetler, one of America’s largest legal firms with over 900 lawyers, recently engaged the services of ROSS[2], a computer application which was developed by a company called ROSS Intelligence. ROSS makes use of Watson to perform legal research for law firms like BakerHostetler. ROSS provides extensively researched legal briefs that have been described as impressive, and gets progressively better as the lawyers that use its output accept or criticize with the material it presents them. As a result, BakerHostetler won’t need to hire anywhere near as many new lawyers or articling students. And ROSS will continue to get better and better as time goes on.

Accountants and tax preparers are finding that the routine aspects of their work is increasingly being done by computer systems, which means we need fewer accountants and tax preparers.

Customer service representatives (“CSRs”) that answer phone systems are increasingly being replaced by computer systems of growing sophistication that can answer most routine questions or problems, and refer the rest to human CSRs, thus reducing the number of humans required.

Nor are office workers and professionals the only ones being affected. Restaurants are looking at computers and robots to replace minimum wage workers, both for taking orders (as is starting to happen at McDonalds[3]), as well as for cooking food (as with the robot burger-maker made by Momentum Machines[4]).

But What About the New, Better Jobs?

But what about the new, better jobs that Technologists say are created, that offer higher pay and more opportunity? Well, some of that is happening, but it’s no longer as certain as it was in the past.

New jobs are being created, but they tend to come in two varieties. High-level jobs are being created that pay well, but they tend to require exceptional levels of specialization, plus high levels of education, usually including a college degree, and often requiring post-graduate qualifications as well. That immediately excludes the large majority of the population without qualifications beyond high school, as well as those with the wrong kinds of college degrees, or without the particular specialization required.

Worse, the shelf-life of such jobs is growing shorter as the pace of change accelerates. Hence, today’s high-level, well-paying job may disappear within less than five years, and possibly less than two.

As a result, while new jobs are being created, they offer less job security than in the past. And once one of these jobs disappears, the person thrown out of work often does not have skills that match up with the even newer jobs created. This can make them as unemployable as a displaced auto worker. And if you look at the U.S. Bureau of Labor Statistics chart below, you can see the net effect: fewer and fewer people of working age are employed, reversing a trend established after World War II. (Note that the recent upward blip is a cyclical response to the improving U.S. economy, and doesn’t represent a reversal of this long-term secular trend.)

U.S. Participation Rate
(Percentage of Working Age Population in the Labor Force)Part rateSource: U.S. Bureau of Labor Statistics

The second kind of new jobs that are being created are low-paying service jobs, typically called McJobs. Even people with high-level skills can wind up taking these kinds of jobs simply because they no longer have skills that match up with today’s jobs. And, as mentioned earlier, these McJobs are also being whittled away.

What Happens Next?

What happens next is a question that has a number of different answers.

First and most directly, automation is going to continue to eat its way up the employment food chain, replacing more and more workers, and in increasingly complex and sophisticated kinds of work. Indeed, I would suggest that any kind of routine work will be automated sooner or later. Hence, if your job involves doing the same thing, or similar things, over and over again, you are at risk of being replaced by a computer intelligence or robot, regardless of whether you are on the factory floor, do white collar work in an office, flip burgers, write music or novels, or are a professional doing work that requires extensive post-graduate training. If your work is repetitive, you risk being replaced.

But if almost all routine work is replaced, what does that leave?

What’s left is mostly non-routine work, or creative, innovative work. This is harder to replace because each day is unique, and tomorrow’s work often doesn’t exist until you define it. This is difficult because you are constantly inventing the future, and figuring out new reasons why your clients (or your boss) will want to keep paying you. Most, but not all, of my work falls into this category, for instance.

And I would argue that all work done by humans in the future will either be creative in some way, or you will be working with, most likely serving, people who want to be served by humans, not machines. Hence, high-end restaurants are likely to retain human servers and human chefs because the patrons don’t want to pay fancy prices for a romantic dining experience, for example, only to be served by a robot.

Likewise, computers are likely to do a lot of the heavy lifting when it comes to medical diagnostics, which involves sifting through large amounts of often contradictory data, but those aspects of medical care that require a human touch – a bedside manner, or even compassion, if you will – will continue to be done by humans.

Hence, it may be that nurses are more likely to have job security than many doctors. But doctors will still be critical to health care because of their awareness of the human elements of patient care, and people want to know that a competent human is in charge, much as airplane passengers want to know that there’s a human pilot in the cockpit. The maitre d’, or concierge of an upscale restaurant, hotel, or nightclub might also fall into this category.

Artists and crafts people who make unique objects (paintings, sculpture, furniture, woodwork, and so on) involving great skill will largely be immune, especially in the case of objects that have a certain snob appeal. For instance, you are unlikely to be willing to pay as much for a copy of a great painting, made by a very skillful robot, as you are for the original, painted by a great painter.

Would you be willing to buy a novel that’s a great read for an airplane if it was written by a computer? I suspect the answer is yes, but I also doubt if computers will be able to write great literature, or come up with truly original ideas.

So, job security will come in three primary forms: those that involve doing new things most of the time (which has its own insecurities); those involving products that are themselves the result of great creativity, especially where there is a cachet in owning something that is made by a specific human, typically an artist or craftsperson; and those where a human touch or human service is particularly prized or important. In other words, our salvation in the face of automation will be the things that make us uniquely human.

But stop a moment: do our current education and employment systems recognize the need for such humanity and creativity? Are we helping the workers of today and tomorrow improve their chances of employment? Not from what I can see. It seems to me that we are still running schools based on 20th Century, mass-production models of education and employment, and pushing people to seek jobs that may have limited shelf lives.

So everyone’s job is at risk if it’s based on repetition. Which brings us to the other major aspect of what happens next.

The Backlash

In some ways, I’m surprised there hasn’t already been more of a backlash against the machine. Yet, there are explanations why it hasn’t happened so far.

First, this is a gradual change that has been happening slowly up until now. But now the pace of change is accelerating as technology accelerates.

Next, the job losses to automation are buried in other effects, such as the high unemployment that occurred during the Great Recession of 2009-10, and the gradual recovery as the economy has improved since then. These effects, which are well-known going into, and coming out of, recessions tend to get talked about because they are familiar. By comparison, displacement of humans by machines at the speeds I’m beginning to see is a new thing, and people have to notice it before it gets much attention. That, too, is changing as the effects become more evident, especially as robots in particular are sexy, and make good media copy.

Then again, the job losses to foreign competition have garnered major headlines for so long that there’s a natural tendency to ascribe similar changes to the same cause. It’s just easier to do that than to dig up a new reason and verify it.

As well, employers don’t make a big deal about replacing humans with automation. They know it makes them look bad in the media, even though it may make their financial results look good. Plus the political effect of automation is to improve the position of the so-called 1%, who own the machines, at the expense of the 99%. The rich and powerful tend to have great economic, marketing, and political clout.

And one final reason why this hasn’t garnered more attention yet: people who are out of work tend to have other things to worry about than commenting on socio-economic changes. They’re worried about being able to pay the bills, and about finding another job. And they and their views tend to be discounted as they are relatively powerless, and may even be deemed unimportant in our society.

But eventually, people will take notice of the encroachment of the machines, and how it is affecting them, their families, or their friends and neighbors in a significant way. It’s at that point that a backlash will start.

Indeed, we may already be seeing a bit of a backlash. In supermarkets, shoppers often line up to be checked out by a human cashier than go to the (admittedly balky and difficult) self-check-out machines. Some telecom companies are going back to humans to field service calls over the phone, and making marketing hay out of it.

Whether this is just because the technology isn’t good enough yet or not is unclear. But there are times when people don’t want to deal with a machine. And I believe that we will start to see slogans along the lines of “We don’t buy from machines!” I expect that there will be a movement against machines comparable to the movement against GMO foods.

The effects of replacing humans with machines are great, and growing. Robots and smart computers can often replace a human for a one-time cost that is equal to, or less than, one year’s wages for a human. As that cost continues to come down, and as the capabilities of robots and computer intelligences continues to grow at accelerating rates, the case for robots will become increasingly compelling.

The Cost of Being Cheap

In the short run, and for an individual employer, the case for the machine may be compelling because it makes so much economic sense, and does so much for the bottom line. Yet, what’s true on the micro-scale isn’t true on the macro-scale, because collectively employees are consumers, and the more employees our economy eliminates, the fewer consumers will be able to buy the goods and services produced. So what works for an individual company does not work for the economy as a whole.

Left unchanged, this could lead to revolution, humans against the machines and their owners. Certainly concentrating wealth in the hands of a few, and economically disenfranchising the many, has historically lead to social unrest, and leaves us ripe for loud-mouthed leaders who rail against the establishment, and offer simple-sounding solutions to very complex problems, yet have no real insight about what to do. That way lies despotism and bloodshed.

Is there another alternative? Perhaps.

Productivity comes either from using fewer employees to produce the same number of goods and services, or from producing more goods and services from the same number of employees. So far, most producers have chosen the first alternative. But what would happen if we chose the second?

Well, more people would be employed, but the price of goods would fall even more rapidly as supply expanded, increasing the standard of living while maintaining higher levels of employment. A larger number of people would be better off, as would the economy as a whole. It could (and probably would) be argued that business owners might not make as much profit as they would with fewer employees as other costs (real estate, cost of materials, and so on) wouldn’t decline, but they would be more likely to have a greater number of affluent customers. Remember that Henry Ford gave his workers an unprecedented raise in wages so they could buy his cars, even though on paper, it raised his costs, and didn’t seem to make sense.

Is this a feasible solution? Again, it might be, but only if companies and their employees look for ways of re-deploying people who are made redundant, rather than just letting them go. Hence, instead of getting a pink slip, you might get a blue slip which says, “We’re replacing your job. For the next three months, we’d like you to research ways that you can continue to be productive with the company. Come up with a way to invent a new job for yourself, and then we’ll see if we can make it work.”

This isn’t easy, and it wouldn’t work all the time, or for all workers. But it is, to my mind, a more sustainable solution to a problem most people aren’t yet aware we have.

So: pink pill, or blue pill. Which future should we choose?

© Copyright, IF Research, May 2016.

N.B. For another viewpoint and more in-depth review of this subject, read Martin Ford’s new book, Rise of the Robots: Technology and the Threat of a Jobless Future

[1] https://en.wikipedia.org/wiki/Luddite

[2] https://www.washingtonpost.com/news/innovations/wp/2016/05/16/meet-ross-the-newly-hired-legal-robot/

[3] http://www.buzzfeed.com/venessawong/robots-are-coming-for-some-fast-food-worker-jobs#.myJedaoQo

[4] http://singularityhub.com/2014/08/10/burger-robot-poised-to-disrupt-fast-food-industry/

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The Machinery of the Mind: What’s Ahead in Brain Research?

by futurist Kit Worzel

One of humanity’s final frontiers of research is the human brain. Dimitry Itskov, a Russian billionaire, is keeping abreast of research as part of his Immortality 2045 project, which has the end goal of uploading his human consciousness to a computerized brain, and living forever in an artificial body. I wish him luck, for he’ll need every bit of time available to attain his goal.

The human brain is tremendously complex, the most complex object in the known universe, and we are only beginning to understand some of its underlying mechanisms. Without a complete understanding of the brain, we will never be able to replicate it, or make it possible to upload its contents. Let me explore some of the areas of research currently underway.

Let’s start by discussing Alzheimer’s disease. Recent research is finding triggers for Alzheimer’s, as well as autism, and even senility, and beginning to develop treatments for them. But there is still a tremendous number of things we don’t know, and the fact that the brain does not rely on one system makes it harder still. I’ll give three examples of Alzheimer’s research, each promising, each examining a different pathway.

  • H. C. Baron of the University of Oxford neurology department studies the electrical activity of the brain. He and his team have found an electrical basis for forgetting, involving cortical excitation and inhibition. A memory can be suppressed by the expression of electrical activity that is the opposite of the forming of the original memory. The memory is suppressed, not erased, and they have shown that removing the inhibition can recover the memory, which can explain why sometimes long forgotten memories float up out of no-where. It is believed that memory suppression has a role in ordering thought processes, otherwise our minds could be firing wildly, unable to cope with unordered memories. It is hoped that further research into electrical memory suppression can help prevent and even reverse the damage done by Alzheimer’s disease.
  • North of Oxford, Dr. Oliver Hardt and his team at the University of Edinburgh have been studying chemical receptors tied to memory and memory loss. These receptors, called AMPA receptors, are shown to be in abundance between cells where there are strong memories, and greatly reduced, or even absent where memories are lost. Dr. Hardt’s team has also found that actively forgetting is important in behavioral adaptations, much like Dr. Baron’s team did. Their research suggests that drugs that target AMPA receptor removal could be useful in treating Alzheimer’s disease or dementia, but also that there could be consequences to blocking AMPA removal, including possibly the inability to form new memories.
  • Across the Pond, at Boston Children’s Hospital, Dr. Beth Stevens and her team study synapse loss, and the relationship to Alzheimer’s disease. Synapses are brain connections, normally associated with the formation of memories. In a normal, healthy brain, synaptic pruning is an ongoing process, removing weak or damaged synapses. This process occurs most vigorously during puberty, and is associated with learning. However, in some neurodegenerative diseases, this pruning runs out of control, removing healthy connections as well as degenerate ones. Dr. Stevens’ team discovered that in a rodent model of Alzheimer’s, there was a high level of synaptic loss in the hippocampus, the region of the brain responsible for learning and memory. This occurred before the formation of amyloid plaques, a key feature of Alzheimer’s disease. By exploring the pathways related to neural pruning, they hope to find methods of repressing pruning, and hopefully, means of treating neurodegenerative diseases.

These represent three different approaches to Alzheimer’s treatment – electrical, chemical and physical, and all are promising. But they also show that the brain is incredibly complex; after all if there are three different methods of treatment, and they all involve attacking memory loss, then memory formation has to account for all three factors as well. As well, all of the pathways discovered by these research teams are ones that relate to the regulation of memory, and therefore involved in making sure the brain is working properly. As a result, we can’t just remove one of these regulators without causing things to go haywire.

Will we be able to upload our minds?

Now let’s take another look directly at the Immortality 2045 project, the ultimate goal of which is to upload a brain into a computer. This would involve highly sophisticated computers, which, according to Moore’s law, we should have by then. It will also likely involve nanotechnology, and probably technologies we have yet to conceive. But most importantly, it will involve learning enough about the science of the brain to completely understand how the brain functions, and build systems that can replicate each functional pathway, electrical, chemical and physical, either with software or hardware.

Do I think that we’ll be able to walk around in thirty years with electronic brains? It seems unlikely to me, but then, it didn’t seem likely in 1985 that we’d be walking around with supercomputers in our pockets. Or that we’d use them to look at cat pictures.

With this in mind, I’m going to suggest a timeline of what the future milestones of brain research might be.

2016 (present day) – We continue to gather information on how brains work and various brain diseases, using models and clinical trials. There are some drugs and treatments that show promise, but they all require more study before being used in human trials.

2020 – The first truly effective treatments for Parkinson’s and Alzheimer’s disease are approved. These won’t allow us to reverse damage already done, but will put a permanent hold on the progression of these diseases. With early screening, and using treatments prophylactically on those with beginning stages or at high risk, the nasty consequences and progression of these diseases may be halted.

2025 – Researchers have developed a computerized model of the brain, with every known pathway and regulatory system included. It fails to behave like a human within a week, prompting a return to the drawing board, and an acknowledgement that the brain is still more complex than we imagined. In other news, a treatment to cure and reverse the damage from neurodegenerative diseases is approved, though the cost will keep it from widespread use for several years. Luckily, due to earlier treatments arresting progression, people inflicted with such diseases will have those years to wait.

2030 – The development and successful implementation of a cybernetic occipital lobe, which is primarily responsible for vision, takes the world by storm. There are issues for the first few years, but vision is restored in those who were born sighted but lost their vision, even if the treatment necessitates a port in the skull for updates and modifications. An updated computerized model of the brain is released, and runs successfully for three months before exhibiting psychotic behavior, achieving a major milestone, but still falling short of the brain’s complexity.

2035 – A patient suffering from head trauma and a partial encephalectomy receives the first cybernetic parietal lobe, which integrates sensory inputs for the brain. This is less of a resounding success, but still restores a degree of recognition and voluntary movement to the recipients. Unfortunately, it is not enough to allow the recipient to live unassisted, but further updates are expected to fix the major issues inside of two years. The computerized model of the brain has successfully been running for a year now, with no signs of abnormal or aberrant psychology, raising hopes that humanity has reached at least a good, working understanding of the brain.

Beyond 2035 is very hard to see. Progress by then will depend upon as-yet undreamt of technologies closing the gap between mind, and the brain’s machinery. Can we identify, codify, and store a thought? Can we say definitively that the brain and the mind are the same, or that they are different? Could nanotechnology create a bridge from our minds to computers, allowing a full interface even while using our original, organic brains? Or perhaps we’ll be able to copy our mental engrams and personalities, much as we do MP3 files now, in order to transfer them to a non-organic home. These are questions for which we don’t even have hazy notions today. Whether we will be able to find answers within a mere 20 years is an incredibly ambitious goal.

It’s possible that Dimitry Itskov is right, and we’ll be able to upload our brains into computers and live forever, but it seems more likely to me that our brains are far more complex than even the best neuroscientists imagine, and our minds will have to stay where they are, inside our organic brains, at least for the next 20 years and for some time beyond. But until then, we can at least look forward to having healthier brains for longer.

© Copyright, IF Research, April 2016.





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“Where did I leave my keys?”

by senior futurist Richard Worzel, C.F.A.

The emergence of computer genies, such as Siri on the Apple iPhone, Cortana from Microsoft, or IBM’s Watson, will lead to many changes in the way we live our lives and interact with our surroundings, both in the real world and in cyberspace. But one seemingly minor advantage of having and using computer genies will be that they will be able to answer questions like the one in the blog title: Where did I leave my keys?

Let me back up and explain what I mean by a computer genie. The concept is one of having a seemingly intelligent computer, such as Siri, Cortana, or Watson, act as what other commentators have called your personal avatar, butler, servant, or companion, mostly acting in cyberspace. I use the term genie because its goal is to try to grant our wishes.

These smart companions will act as our agents, again, largely in cyberspace, reminding us of up-coming events and things we need to do, monitoring the safety of our computer systems from hackers, viruses, and other attacks, and acting on our behalf in commercial transactions, as well as interacting with other people’s genies to set up appointments, make arrangements, transfer information, and so on.

Much has been written about these things, including extensive passages of my 1997 book The Next 20 Years of Your Life, which is about to reach its best-by date. However, I’d like to focus on one hitherto unacknowledged application for genies: keeping track of us and our belongings.

“Where did I leave my keys?”

Suppose you have a penchant for just dropping your keys wherever you happen to be once you get home, and thus have a problem finding them when you’re getting ready to leave again. Today, you probably spend a great deal of time and frustration looking for them, and getting progressively more and more irritated with yourself and everyone else. In the not-too-distant future, though, all you will have to do is ask your genie (let’s call him “Robin”, just to be cute) where your keys are. If you’ve prepared him to do this kind of thing, he’ll say something like, “You dropped them on the chair by the front door when you came in last night. If they’re not there, they have probably slipped onto the floor nearby.”

You can then stop looking in the bathroom, the living room, and everywhere else you’ve left your keys in the past, and make a bee-line for the chair in the front hallway to find your keys with minimal frustration.

“Where’s that book I was reading?”

A similar transaction might be if you asked Robin, “Where’s that book on complex systems I was reading about a month ago?” This time, though, it requires Robin to have archived what you did with specific belongings over an extended period of time, and to be able to figure out precisely which book you’re referring to. However, if you’ve asked a similar question in the past, then Robin will have learned that you want him to track this kind of thing. If he is thus programmed to do this, he might respond, “Do you mean ‘The Theory of Complexity’ by Merti Williams?”

“Yeah, that’s the book.”

“It’s in your office, on the floor by the left-hand side of the desk, under a set of blue file folders.”

Problem solved – unless someone has recently moved the book, or the files on top of it without you being aware of it.

“Where was that shop?”

Or suppose you’re strolling through a shopping mall, on the way to meet a friend for lunch. After lunch, you remember that you want to find a new windbreaker to replace the aging, ratty old one that your wife keeps nagging you about. And you recall that you noticed that a store that had a sale on outdoor gear, but you can’t remember which one, or where it was as you weren’t paying attention at the time. Ask Robin, and he might reply, “Grant’s Outdoor Stores has a 25% off sale on outdoor equipment sale. You probably saw it as you walked by. Would you like me to show you how to get there?”

If agree, your augmented reality goggles (or better yet, contact lenses) will display arrows in your field of vision, with appropriate text, such as “Walk 50 feet, then turn left at the corner ahead. It will take you about 3 minutes to get to the store.”

Of course, Robin could also tap into the mall’s website to find which other merchants are offering sales, or even make your interest known (anonymously or not) to the mall’s website in order to elicit immediate discount offers from the mall’s merchants, or even a much wider range of merchants, including some online. In fact, you will have a choice of ways you can gather information of interest to you, at the moment you want it, all managed by Robin in order to make your life as simple as possible.

“What’s that woman’s name?”

Suppose you’re at a cocktail party, and you’re introduced to someone new. If you’re like most people, you smile and nod, and her name just slips past you. (In fact, the major reason most people are “bad at names” is that they never really listen to them in the first place.) Suppose later that evening, she’s looking towards you while speaking to a friend of yours, and the two of them start walking towards you. Instead of panicking, you ask Robin (sub-vocally, if necessary, so that no one but Robin can understand you), “What’s the name of the woman walking towards me with Ron Merrick?” Robin reaches into his recorded memory, shows you the woman’s image from your earlier introduction, replaying her name as she pronounced it, then repeats that pronunciation for you a second time to make sure you’ve got it.

And, again, Robin might also do some quick, online research, using facial recognition software to match the woman’s face to her LinkedIn profile, or find a news article about her to give you more information about her. The point is that Robin will be able to help you remember, or discover, the names of people you meet and encounter, and provide some background on who they are if you wish.

All of this raises the issue of personal privacy in public spaces, which has been discussed at length elsewhere, and is worrisome. However, that’s a different topic for a different blog.

“Where am I meeting my wife?”

As the clock winds down, and you’re approaching the end of your working day, Robin reminds you that you and your wife said you wanted to go to dinner and a movie this evening. You discussed a number of films, and narrowed it down to three recently Oscar-nominated offerings you wanted to see.

At your request, Robin starts multi-tasking. First he contacts Cary, your wife’s computer genie, who confirms that your wife, who is at her workplace across town, hasn’t made any conflicting plans, and does, indeed, want to do dinner and a film. Next, Cary reviews the start times of the three films discussed at nearby theatres, while Robin digs up the relevant film reviews for each one. The films and reviews are presented to you both. The start time for one film isn’t convenient, and the reviews from your favorite reviewers for a second are so-so, so Cary and Robin both suggest the third film, to which you both agree.

Now both genies ask their hosts what they feel like eating. Your wife wants sushi, whereas you want Italian. Robin sides with your wife because Robin is also monitoring your calorie and nutritional intake as you’re trying to lose some weight following the holidays. You reluctantly agree, while muttering about disloyal pieces of junk, which Robin ignores. Cary then suggests a sushi restaurant you both like near the theatre you’ll be attending, and makes a reservation for two. Cary and Robin also download your nutritional needs, allergies, and sensitivities to the restaurant chef’s computer, as well as the kinds of menu items each of you have enjoyed in the past. The restaurant’s computer suggests a number of customized menu items, based on your nutritional profiles, previous choices, what the kitchen has in stock, along with the prices for each item. Note that every patron’s menu might be unique, although there is frequently a great deal of overlap in menu choices for most people.

Robin and Cary both make tentative selections, presenting them to their hosts for approval, and then place the orders.

Each genie now calculates how long it will take you to reach the restaurant, based on your current locations and how crowded transit is projected to be when you leave. Each then tells its host when they will need to leave their office to get to the restaurant on time.

When both you and your wife arrive within seconds of each other, you are greeted at the restaurant’s entrance by your (correctly pronounced) names, your coats are taken and handed to your server, who hangs them up, and you are escorted to your table by the owner. Your genies could have directed you to the coat rack and your table, but the restaurant prides itself on human service, so your are offered this unnecessary courtesy, particularly as you are valued customers who have eaten there before.

Your pre-dinner drinks arrive almost as soon as you settle yourself into your table. Relaxing, you talk about your day’s events, comparing notes about what you’ve done before moving onto future plans. When you start to get restless, and your genies interpret from your body language that you’re ready to eat, they ask if this is the case. When you confirm that you do, Robin signals to your server’s genie that you are ready, and he quickly arrives with the first course.

Supper proceeds from there in a smooth, satisfying flow that runs at a tempo you both find comfortable. When you’re ready to leave, Robin contacts the restaurant’s computer, confirms the amount of the bill, checks that it matches what you ordered, presents the total, along with your customary tip, to you for confirmation. You barely glance at it in your contact lenses, then nod to confirm payment while continuing to talk to your wife. Robin confirms the payment to the restaurant, and authorizes the payment from your credit card to the restaurant.

When you’re ready to leave, your server appears with your coats and thanks you, and the owner appears at the door just before you leave, bowing to thank you for visiting again.

At no time did either of you reach for your wallets or put your hands in your pockets. You could have viewed a customized, physical menu if you wished, but as you’ve been to this restaurant many times before, your genies, working with the restaurant’s suggestions, offered up the entrées your were most likely to want. Your dining experience was smooth, soothing, and simple, both for you and for the restaurant staff.

“Has Mandy come home yet?”

After the movie finishes, you walk out into the street while Robin hails a self-driving Uber car to convey you home. Once you’re both settled into the car, you ask Robin if your daughter, Mandy, has arrived home from her date yet. Robin consults Mandy’s genie (to Mandy’s annoyance), and confirms that Mandy arrived home before 10:30, as agreed. You decide not to ask who Mandy was dating as Mandy has just turned 16, thinks both of her parents are being obnoxious and Big Brother-ish, and often flounces upstairs in protest over some restriction or other.

But it’s a comfort to know that you could, if you insisted, get Mandy’s genie, Johnny-5, named after a childhood hero, to tell you where she is, and who she’s with at any time. You could even tap into her recording camera to see what she’s seeing, as well as scan the area around her, if you really wanted to be obnoxious.

Still, you’re trying to let go, and give her more space. But until she’s 18, you can overrule her wishes, and get whatever information you want from her genie. Knowing that, and that Johnny-5 would contact Robin if Mandy was threatened in any way, gives you enough comfort that you can back off and still feel as if you are fulfilling your paternal duties.

“How am I doing on my diet?”

You’re doing the weekly shopping, and are most of the way through your list when you decide you want to get some cold cereal. You head to the cereal aisle, and tell Robin, “How about that new cereal for which I saw the ad yesterday?”

Robin, of course, knows what you watched, and also noticed what interested you because he monitors your vital signs: heartbeat, breathing rate, galvanic skin response, as well as, in this case, the dilation of your pupils. From this data, he identifies the brand of cereal you’re referring to, and asks, for confirmation, “You mean Happy Clappy Granola Crunch?”

You nod, which he notices and understands.

“Happy Clappy Granola Crunch has too much sugar in four different forms, too much salt, and not enough of the nutrients you need. It’s effectively a dessert, not a breakfast cereal.”

“Yeah,” you reply, “but it probably doesn’t taste like mattress-stuffing, or cardboard, like that other crap you want me to eat. My days are hard enough – I don’t want to deliberately start out with a bad taste in my mouth! Find me something that I will enjoy eating!” you command.

You could swear Robin pauses, which is improbable, and that he’s pursing his lips in disapproval, which is impossible, but you get the message anyway. “You could have low fat, coffee flavored Greek yogurt with flax seeds and slivered almonds,” he proposes.

You’re about to snap your refusal when you stop. That actually sounds interesting. “OK,” you say, “I’ll try it. And how am I doing on my diet, anyway?”

“You’re about a day and a half behind your target profile – mostly because you’ve been cheating at lunch times.”

“Hey! I have not! I’m out with clients, and I have to be sociable! It’s part of my job.”

“You could order sparkling water or club soda instead of wine, you know,” Robin counters.

The conversation goes on, and you grumble – again! – about disloyal junk heaps.

Meanwhile, behind the scenes…

What’s going on behind the scenes at the store is actually far more sophisticated than it appears. Robin estimates the volume of every mouthful of food you take, then calculates all of the nutrients that mouthful contains. This isn’t just calories, sodium, and sugar, but fiber, vitamins, minerals, trace elements, and hundreds of micro-nutrients as well, based on the latest research, cross-referenced for your specific genetic make-up.

When you’re out at a restaurant, he gets all of this information from the restaurant’s computer, including data about where each forkful of food comes from, and what it’s nutritional profile is closely estimated to be. When you’re eating at home, its based on data downloaded either from the food processor’s website, as with Happy Valley Granola Crunch, or, in the case of fresh produce, meat, or fish, from data supplied by the farmer that grew the produce or grew the food animal, or the fisherman that caught the fish. This amazing mass of data far outstrips anything available to consumers, nutritionists, dieticians, or even food scientists today, and is all done by computers handing off data, one to the next. Every step of the way, assessments of nutritional composition are made, checked against comparable records of similar foods from similar (or even the same) producers.

The result is that your genie can finely assess your nutritional needs in a way that has never been possible before – and that can help support optimal health. Food would truly have been transformed into an unbroken string of nutritious compenents – if people didn’t cheat on their diets.

Indeed, the hard part about optimal nutrition is getting people to eat enough of the right things, and to avoid enough of the wrong things. And recently, Robin has been subscribing to an evolutionary algorithm that assesses your human nature, and helps him find ways of getting you to compromise and eat healthier foods. After all, knowing what the best foods to eat are is useless unless you actually eat them, so for Robin to learn how to nudge you onto a better path is better than watching as you continually cheat on the perfect diet.

Humans! What can you do with them? Robin thinks. (Actually, Robin doesn’t think at all – but what he does looks an awful lot like it from the outside.)

The delights and dangers of genie

John Williamson wrote a book called The Humanoids in 1948 in which androids were given the task of “saving man from harm”. Unfortunately, the androids took their instructions literally, with the result that humans were never allowed to do anything that was fun or even remotely dangerous, like walking by the seaside. They became captives of their protectors. And at one extreme, I could imagine that genies could evolve this way.

But there are other risks as well. When we allowed students to use calculators for math, they lost the ability to do math unaided, especially mental math. Some modern fighter planes cannot be flown by human pilots without computer assistance as they are fundamentally unstable, and need constant rebalancing.

The point is that as well as having genies enrich our lives, we will also become dependent on them. If we’re not careful, this will lead to the dumbing-down of humanity, with the result that creativity, initiative, and the spark of innovation could be lost.

Every new technology provides benefits at a cost. Genies will be no exception. And I can hardly wait to have one…

© Copyright, IF Research, March 2016.

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Why Traditional Farming Is Here to Stay

by Kit Worzel, futurist

Last year, I wrote a blog about the future of food, and how we would need to use a number of alternative methods to farming in order to feed everyone. I didn’t manage to fit the rest of the information in that blog, about how we would still need to farm and grow food to feed a population of more than 9 billion. You’ll note I didn’t say “use traditional farming methods” there, because we will actually be using modern and futuristic methods instead.

Farmers in glass houses

Industrial greenhouse usage is fifty years old now, and only set to expand. As prices rise on produce due to water shortages and climate change, more people are turning to greenhouses to grow their plants. They’re not exposed to the wind, and can be climate controlled, to a degree. Water is kept in as part of a closed system, rather than allowed to run off or evaporate away, and they also have the advantage of being able to grow plants in otherwise harsh environments, such as deserts, assuming you can secure a water supply. As a matter of fact, if you combine solar farms and greenhouses en mass, you have quite the set-up, turning otherwise unusable land into profitable enterprises. So long as a water supply can be arranged, even hard, rocky soil can be home to delicate plants – in the raised beds of greenhouses.

Pretty in pink

The level beyond greenhouses are pinkhouses, retrofitted warehouses and buildings that don’t even need windows, and light the plants with red and blue LEDs, the wavelengths that plants absorb best. While there is a sharp cost up front to buy the property and lights, the increase in efficiency is enormous. Plants grow almost twice as well under these coloured lights rather than the broad spectrum light from the sun, and with complete climate control, it is possible to set up pinkhouses anywhere there is power, from Siberia to the Sahara. Rather than be at the mercy of the growing season, and produce at most two crops a year, pinkhouses can manage six harvests, since with complete climate controls; you don’t need to stop for the winter. As an added bonus, these setups will most likely exist in cities, greatly reducing the travel distance for the food, and the pollution associated with shipping.

From waste to waist

As my colleague Richard noted in a previous blog, water is going to be scarce in the future. Unfortunately, plants need three things – light, nutrients, and water. Light has been discussed, and nutrients come from the air and soil, but water is an issue. I see an opportunity here to kill two birds with one stone. Cleaned wastewater is being spoken of as the future of potable water, but it’s a hard sell to get it to consumers, no matter how much it’s been cleaned. It doesn’t matter that it is chemically cleaner than tap water; there is a psychological barrier that is hard to overcome.

But plants don’t care about the taste, so why not use it for them? Reclaim water from municipal waste and send it to pinkhouses and greenhouses for plant use. It doesn’t matter to me if my tomatoes were fed Perrier or poop-water, so it’s an easy work-around for two issues at once.

Vegetables of the sea

While it’s certainly not a new idea, algae farming, or algaculture, is taking off, and increasing in popularity due to a number of factors. For starters, algae is versatile. Various uses for algae include bioplastics, fuel, pharmaceuticals, pollution control, and yes, food. A number of traditional dishes from around the world include algae, including nori and laverbread, and algae with high levels of protein can be used as a nutritional supplement. But it’s the ability of algae to replace ethanol biofuel that I find the most exciting. Of the more than ninety million acres of corn planted in the US every year, 40% of that is for ethanol and biofuel. That’s almost forty million acres just for biofuels. If we converted that to algae for biofuel, we could grow that in two million acres, and we could do it in non-arable land, leaving an area larger than Greece free for cultivation of crops for human consumption.

Getting smart about the great outdoors

As much as pinkhouses and non-traditional methods of cultivating plants appeal, we will still need dirt farms. Grain staples, such as corn, rice and wheat, are grown on a small profit margin, and can only be profitable (and therefore have people willing to grow them) by growing in mass quantities. It’s just not feasible to do that inside, which leaves outdoor farming as the only real option for such things.

These are not the farms that some of you may have grown up with, or seen in TV. Just like telephones, farms have gotten smart. This includes tractors and plows with GPS and straight-drive programs, integrated computers to keep track of harvest and watering schedules, soil and temperature monitors that send SMS alerts, and a whole host of gadgets to make farming easier, faster and cheaper. Watering becomes automated, based on results from soil probes, even the distance between plants is carefully calculated to obtain maximum yield.

This is particularly needful in the less developed areas of the world. Large parts of the world are still using farming techniques hundreds, if not thousands, of years old. They are ignorant of the massive improvements in cultivation techniques, even non-technological techniques that could turn a mediocre crop into a moneymaking one. The changes to irrigation alone in the last hundred years could increase worldwide food production by a significant amount. Educating and assisting the less technological farms will have benefits for everyone, in the long run.

Orchards need more than roots

Orchards and vineyards suffer from the same issues as staple plants – need for space means interior growing is insufficient for most purposes. But they aren’t looking at a technological revolution – they’re looking at a mathematical one.

For at least fifty years, orchards and vineyards have been collecting data on what works best, and sharing it. Teams of scientists have developed models, which were then tested, and finally implemented, and the results are clear. By using these techniques, along with controlled breeding of the plants to emphasize desired characteristics, orchards have increased average tree density (in the US) from 40 trees/acre fifty years ago to over 3,000 trees/acre, in extreme cases.

This concept is interesting to me, because it doesn’t involve high-tech implementation. Once the solution has been found, it can be used anywhere. Yes, the same smart systems that I described above can be used for orchard and vineyard management, but trees are largely stable, once grown, and need less care than more delicate plants do.

Yes, we will have GMOs

Of all the topics I write about, GMOs are probably the most contentious. They are either heralded as a savior, as in the case of golden rice and Hawaiian papayas, or demonized as unethical and dangerous, as with certain companies, and the switch back to organic papayas in Hawaii. Many people hate GMOs, consider them unnatural, and don’t want them anywhere near their plates. But with climate change wreaking havoc on farmlands, and blights and viruses hammering crops, GMOs may be necessary to keep everyone fed.

Do not take this as a whole-hearted endorsement for all GMOs. I believe that while some are good, and even necessary, each one should be judged by its own merits, and care should be taken. Invasive species are a cautionary tale that all should heed, but letting people starve, or go blind because of dislike for a method is going too far in the other direction.

I believe we should promote safe and properly regulated GMO use, but use them all the same.

The United Nations estimates we will have a world population of 9.6 billion by 2050. That is nearly a third again of our current population. We currently have more than 10% of the world population suffering from malnutrition. This means that we need to feed over 3 billion more people in the next 35 years. That will be the single largest increase in food production in human history. But we can do it, we have the technology.

© Copyright, IF Research, February 2016.


Indoor farming


Precision agriculture


Modernizing world staple farming practices


Algae vs other plants



vs Corn



Mathematics of Orchards





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What’s Wrong with Apple?

by senior futurist Richard Worzel, C.F.A.

To read their Wikipedia entry, you would have to say Apple, Inc. is an amazing success:

“Apple is the world’s largest information technology company by revenue, the world’s largest technology company by total assets, and the world’s second-largest mobile phone manufacturer. On November 25, 2014, in addition to being the largest publicly traded corporation in the world by market capitalization, Apple became the first U.S. company to be valued at over US$700 billion…it operates the online Apple Store and iTunes Store, the latter of which is the world’s largest music retailer.”[1]

What’s more, Apple is incredibly profitable, and is sitting on something like $150 billion in cash. They’re doing all right for themselves.

Yet, in my opinion Apple is dying, it just hasn’t realized that yet. And what’s wrong with Apple can be stated in four words: Steve Jobs is dead.

What Made Jobs Unique

What set Apple apart from everyone else was the genius of Steve Jobs, specifically his ability to see the future in a unique way, one that escaped just about everyone else. As a futurist whose job it is to do just that, I appreciated his ability, and was envious of it.

Jobs was a one-man disruptive force, a bulldozer in the use and creation of disruptive technologies. Although Jobs was famous for stealing and adapting great ideas rather than inventing them, he did have a knack for knowing what the consumer would want before consumers themselves had any idea.

Because of this ability, he helped define what a personal computer was in the first place with the Apple II, then set about creating one that was “insanely great” with the Macintosh. He redefined it again when he returned from NeXT to retake the reigns at Apple Computer.

In Jobs’ view, a computer was an extension of our creative selves, not a mundane tool for work and drudgery. He made it fun, trendy, fashionable, and indispensible. Indeed, the way we look at and use all personal computers, especially smartphones, has been shaped by Jobs’ view of what a computer should be.

Jobs wrecked the music industry with the iPod and iTunes. The industry will never be the same after his ministrations – and neither will the way we experience music.

He upended the cellphone industry and dethroned the BlackBerry by defining what a smartphone was, and could and should be. In the process, he created the essential status symbol of our age, the one that continues to reshape our social interactions, the way we live, even the way we (dangerously) drive.

No Longer Insanely Great

My point is this: What has Apple done for us lately, except change the colors and packaging of Jobs’ creations? What have they done that is disruptive since 2011?

Steve Jobs was the creative genius behind Apple. The people he left behind are brilliant designers, engineers, and creative people. But they are not geniuses. They do not seem to be able to, as he put it, “put a dent in reality” the way that he could.

And my experience with Apple mirrors this loss in ways that I hate. Since 2011, Apple’s products, while continuing to be slick, beautifully designed, and pretty, have lost their intuitiveness. The software is harder to use. The interfaces are more complicated, and require more knowledge of technical stuff that I could care less about. The different platforms don’t work seamlessly together.

I bought my first Macintosh in 1985, and have never used any other computer (except to try to help friends wrestle with their horrible Windows-based machines). But the thrill is gone with Apple, and what was great is now merely pretty good – and getting worse. My children, who grew up in an Apple ecosystem, are now turning to other machines. Apple isn’t special anymore, because the spark that made it unique is gone.

Several years ago, when Jobs returned to Apple and brought it back from the dead, Fortune magazine did an article about the company. As best I remember it, they said that Apple was (and is) the only computer company that made everything, from the hardware to the software to the interface to the packaging, and so could make it all work together as an organic whole, as one ecosystem. But most importantly, Apple had Jobs sitting at the top, demanding nothing less than “insane” greatness, and would drop kick engineers, systems analysts, designers, accountants, or anyone else that brought him anything less. That no longer seems to be true. It seems to me that Apple is settling for mediocrity with increasing frequency.

What Has Apple Done to Us Lately?

And lest you think I’m being unduly sentimental, or am some kind of Jobs cult-follower, ask yourself a simple question: When was the last time Apple disrupted a new industry? When did they produce something that forced consumers and competitors to change the way they thought about the world, their place in it, and what they could do if they chose? Netflix has done more to revolutionize TV than Apple TV. Amazon and PayPal have done more to revolutionize retailing than Apple Pay. And Google Glass, failure though it was, did more to define wearable computers than the Apple Watch.

Somehow, being able to buy a “rose gold” iPhone 6S with an improved Siri doesn’t even move the needle on that scale. And what industry did the Apple Watch change? In fact, what does it actually do?

Apple can coast for a long time on the brilliance of its former star, helped by the exceptionally competent crew of people he left behind. But, in my view, Apple is like a projectile that has been launched and is now running out of momentum: it will soar for a while, then the arc will turn down, and it will fall back to Earth.

I will mourn its passing, but Apple actually died in 2011.

© Copyright, IF Research, January 2016.

[1] https://en.wikipedia.org/wiki/Apple_Inc.

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