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….

What’s Happened to Inflation? And Why Does It Matter?

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

At this stage in an economic cycle, inflation has usually reared its head, and the U.S. Federal Reserve (the Fed) is typically thinking about taking the punchbowl away from the (stock market) party by jacking up interest rates. In turn, this usually kills the economic recovery, tipping us over into recession.

But inflation has been notably, remarkably, absent, so much so that the Fed has consistently missed its inflation targets on the low side. Meanwhile, the presidents of the various regional Federal Reserve Banks (of New York, St. Louis, etc.) have been regulars on Bloomberg and other financial media, straining to explain what’s happened as inflation seems to have disappeared.

This has been bad news for gold bugs, as well as inflation and deficit hawks, some of whom have been forecasting inflation, serious inflation, or hyperinflation (and been notably wrong) since the bottom of the Great Recession in 2009-10. And yet, such commentators haven’t recanted; they still insist inflation is just around the corner.

I have a couple of thoughts about why inflation has gone away. I’m not an economist, but have been a keen student and user of economics for decades, starting with my university studies, then blending into my days working in the stock and bond markets.

In this vein, it occurs to me that there are at least three things going on that have contained inflation, and will continue to do so.

Demographics Is Keeping Demand Soft

There have been a small number of studies relating inflation – and hence interest rates – to demographics. And although they’ve been rare, they have also been remarkably consistent, going back to the 1970s, when inflation began to run rampant.

The basic thesis is that younger people in the family formation stage of life tend to buy more stuff than older people in, or near to, retirement. And the baby boom, while gradually shuffling off-stage from the workplace, is still the second largest age group in the population, is also the wealthiest, and has the greatest economic clout. (Marketers seem to have forgotten Willie Sutton’s dictum by ignoring the boomers: when asked why he robbed banks, he remarked, “Because that’s where the money is.” But that’s another subject for another day.)

The boomers are, as I said, retiring, and while they are buying stuff related to retirement, such as travel and health care, they are not buying as many everyday possessions. They are largely in replacement mode rather than acquisition mode. So, they are buying less furniture, fewer clothes, they don’t commute so they don’t need as many (or as frequent) cars, and so on. And, as stock traders like to remark when asked why the stock market goes down on a given day, “More sellers than buyers.” When members of the richest generation with the greatest economic clout buy less stuff, demand will likely stay limp.

Of course, the generations below, notably the Millennials, are in the acquisition stage of their lives, but many of them are struggling financially, either because they haven’t been able to find suitable jobs (or no jobs at all), because they’re still paying off student loans, because they’ve delayed starting families, or some combination of these things.

So, the first factor I would point to is that demand is weak because not as many people are buying things.

Workers Are in Weak Bargaining Positions

Next is a factor mentioned in passing above: the job market. While many people of working age are well along in building their careers, a significant fraction are either frozen out entirely, and have been unemployed for more than a year, or are making do with entry-level or minimum-wage related jobs. This is especially true, again, of Millennials.

The exodus of manufacturing and related jobs is one major cause. The emergence of a global economy is gradually producing a leveling of wages for many kinds of work around the world. And since developed countries had the highest wages for unskilled and semi-skilled wages, they have been hit the hardest.

But that’s old news. What is newer news is the mushrooming expansion of automation. My colleague, Kit Worzel, and I have talked about this at length here and here. Therefore, let me just sum this up by saying that smart computers, including those using Artificial Intelligence, are leading to more capable automation and robots to augment or replace human workers. And as AI improves at computer-related speeds, automation is going to eat its way up the food-chain very quickly indeed.

Automation does not need to replace the workers of an entire industry, category of jobs, or the economy as a whole in order to have a severe dampening effect on wages. The Economist news magazine explored a parallel example based on the analysis of British economist Tim Harford.

My interpretation of Harford’s work would be that job-seeking becomes like a game of musical chairs as automation steadily removes jobs from the workplace. Even if the rate at which we lose jobs to automation is slight, the knock-on effects can be significant as it disproportionately reduces the bargaining power of all job seekers.

I realize this is an oversimplification of the very complex economics of employment, but it would explain why wage demands remain weak, even as the economy reaches what most would describe as full employment.

And when you add the changing nature of jobs, the argument becomes even more compelling. Even though the number of jobs is rising, the quality of many of the jobs created seems, on average, to be declining. You can see this in the research on the contingent labor force, and the anecdotes of people having to work two- and three part-time jobs to make ends meet after having lost higher-paying, full-time jobs. And, of course, as I mentioned earlier, lower incomes lead to lower consumption, plus delayed household formation for many Millennials.

So, my second factor leading to low inflation is that workers are, on average, losing bargaining power, even in what appears to be a buoyant economy.

Weak Corporate Investment

There are three principal players in the economy: consumers, corporations, and governments. So far, we’ve explored why consumer demand appears to be weak. Now let’s look at corporations.

There are two things happening in the corporate world that weaken corporate demand. First, weak consumer demand means that there’s very little need or impetus to invest in new plant and equipment. When you can meet demand with your existing infrastructure, or by adding a second shift to your workforce, then there’s very little appetite to buy equipment to increase production. We’re clearly seeing that.

And, just as the introduction of automation limits the bargaining power of workers, it also increases the bargaining power of management and owners. This, too, is quite evident in the steadily rising share of economic activity (GDP) accruing to profits rather than to workers, as seen below in the two graphs below from Federal Reserve Economic Data (FRED) produced by the Federal Reserve Bank of St. Louis. For both graphs, I’ve added a red line connecting the years 1990 and 2016 to better illustrate the changes:

Share of GDP due to Employee Compensation, 1950-2016

And here’s the share due to profits. The deep drops in profits are due to recessions:

Share of GDP due to Corporate Profits, 1950-2016

So, corporations have money to invest; the corporate sector is awash in cash balances right now. It’s just that they have little or no motivation to spend that cash. So, except where there is a desire to replace or supplement workers with automation, robots, or cobots, businesses have little reason to invest, and hence, corporate demand shows up as surprisingly weak for this stage of an economic cycle.

What Happens Next? And Why Does It Matter?

I don’t see significant changes occurring in any of these three factors soon. I do expect that as labor markets continue to tighten, wages will start to firm up slightly. But, it also seems to me that as quickly as wage demands start to rise, corporations will introduce more automation, knocking wages back down again.

Of course, that will lead to some increase in corporate investment. But overall, I suspect these two changes will have a relatively small effect on the overall rate of inflation, less than most economists are likely to project.

I expect, therefore, that although inflation may rise slightly, and interest rates with it, I don’t expect either to rise dramatically – unless there’s some unexpected crisis. And low inflation is generally a good thing.

However, continuing low inflation also means continuing low interest rates, and hence the continued, and potentially dangerous, growth of asset bubbles, as is going on in the stock market right now. As well, the weak wage situation of workers means that consumer debt is high, which may well carry the seeds of the next recession.

Meanwhile, the thing that corporations appear to have overlooked in the rosy outlook for corporate profits is that if workers don’t have money to spend, then neither do consumers, for they are essentially the same group.

Henry Ford understood this, which is why he raised the wages of his assembly-line workers so that they could buy Ford’s cars. That lesson seems arcane and lost in the mists of antiquity today, but will come back to bite us.

© Copyright, IF Research, January 2018.

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18 Things to Watch for in 2018

by futurists Richard Worzel & Kit Worzel

2018 is going to be remarkable in many different ways. Indeed, when we put together our lists for the most important changes ahead in 2018, we wound up with so many we decided we couldn’t discuss them all, and settled on 18 things for 2018 (catchy, right?) that we feel are highest profile, or of greatest interest and importance.

But because there’s so much material, please feel free to pick and choose, reading the items that catch your interest and skipping the rest.

And since every year is a mix of good and bad news, we’re going to start with the scary stuff, then end with the fun stuff:

Scary Stuff

The Potential for War – There are two parts of the world where war, even a nuclear war could break out: North Korea and Iran. In both cases, war might well be started by the United States – or more specifically, President Trump and/or the advisors around him.

In the case of North Korea, no matter what bombastic statements Kim Jong-Un makes, it’s unlikely he would be the first to pull the trigger. Not only has China said they would not back him up if he was the aggressor, but he knows that no matter how many other people wound up dead, he would die. Since he only cares about his own life, this is likely to deter him. On the other hand, President Trump doesn’t believe he would die, and he seems just as uninterested about the welfare of others, so he could well lash out against North Korea. If this happens (and we think it’s possible, but unlikely), it will be incredibly bad, and if the US is the aggressor, it could even lead to war with China.

Meanwhile, Iran has been a sore point for the US government since the Shah was overthrown in 1979, and has been getting progressively worse as they have worked steadily towards developing nuclear weapons. The US doesn’t want that, but has few good ways of stopping it. The agreement that the Obama administration reached with Iran is sort of the least-worst solution, delaying Iran’s nuclear program, but not stopping it entirely. President Trump doesn’t have the patience or forbearance for such a nuanced approach. Moreover, there is a significant faction within the military leadership that reportedly wants to invade and neutralize Iran. This is much easier said than done, as Iraq and Afghanistan have shown, but if this faction gets its way, the US is in for another very expensive, and very frustrating, Middle Eastern war.

Cybercrime Gets Even Scarier – 2017 would have been known as the year of cyber-terror except for all of the other things that happened. Last year showed just how damaging cyberattacks can get, with over $2 billion dollars lost from ransomware, data stolen from hundreds of millions of personal accounts including Equifax, voter data from the GOP’s data files, tax information stolen by NotPetya, and even the massive Yahoo hack from 2013 that was exposed in 2017, which resulted in more than one billion accounts being compromised.

There are several very important points that we can learn from these attacks. First, they are profitable, having made billions of dollars for the hackers. As a result, it’s estimated there will be a quadrupling of such attacks in 2018.

Second, companies that are hacked try to cover it up rather than notifying customers. And cover-ups prevent customers who’ve had data stolen from taking steps to protect themselves.

Third, these attacks reveal a massive downside for the Internet of Things (IoT). We wrote about the IoT a few years ago, and warned about the vulnerability of objects connected to the internet. After all, who puts a firewall on a Fitbit? The WannaCry hack exploited this lack of security, and used the processing power of internet-capable objects for its own purposes, as well as ransoming them. This means that one person’s lack of security on a router or smart thermostat is no longer just their problem, it becomes everyone’s problem.

Lastly, everyone needs to be better about updating security. Almost all of the security breaches of the past year happened because people didn’t install the latest security patch, or because they didn’t configure their security properly. We need a society-wide trend towards better security practices to prevent attacks like this from happening more frequently in the future.

Widespread Civil Strife in America – Strife in America seems likely, but the real question is: how violent will it get?

Strife could break out for several different reasons. The Mueller investigation could conceivable and credibly charge President Trump, members of his family, and members of his inner circle with collusion with Russia and obstruction of justice, or something approaching that. Trump would go ballistic over that, firing Mueller and attempting to castrate FBI leadership in order to bend it to his will.

Or, the midterm elections in November of 2018 could produce divisive results no matter how they end up. If the Democrats take over one of the House and Senate, then the US government will be even more deadlocked than it is now.

If gerrymandering, combined with voter suppression, deny the Democrats control of either house, even though they might gain a majority of the votes in all the relevant elections (which happened in 2016, where the Republicans won 49.9 percent of the votes in the House, but got 55.2 percent of the seats), then the outcry will be enormous. Or President Trump could do something truly egregious, such as starting one or more wars (see above).

Several movements also hold the potential to precipitate more strife through protest and counter-protests, from Black Lives Matter, to Time’s Up (the successor to the #MeToo movement), to LGBT+ protests against conservatives’ attempts to roll back history, to white supremacists, and more.

America’s political landscape has become incredibly polarized over the past several decades, so any of these things, or a range of other possibilities, could produce demonstrations that evolve into pitched battles.

Or, to put it more simply: It’s hard to think up a scenario where civil society in America has a quiet year in 2018.

Climate Change News Gets Worse – 2017 was the second hottest year on record, following only 2016. Indeed, 17 of the 18 hottest years on record have occurred since 2000. Anyone who actually is qualified to have an opinion on climate change says that climate is changing, it’s changing faster than we thought, and extreme weather events are going to become both more extreme, and more frequent. Therefore, while it’s not possible to say what weather extremes we are likely to have in 2018, the floods, hurricanes, wildfires, cold snaps, heat waves, blizzards, and so on that we experienced in 2017 are probably milder than what’s going to happen in 2018.

Russian battlefield robots – In August of 2017, 117 experts signed a letter to the UN asking for a ban on lethal autonomous weapon systems (LAWS), also known as battlefield robots, that lack human controls. Despite the widespread support for this measure, Russia and China have stated that they will continue to develop – and potentially deploy – LAWS.

It is likely that 2018 will see the first fully autonomous killer robots deployed against humans in battle, and there is pressure that other militaries will not want to fall behind in this field. Experts worry that LAWS will lack judgement with respect to targets, and use of lethal force.

But regardless of the consequences, the push to create and deploy battlefield robots is on, and a new arms race begins.

Not Scary, But Startling or Unsettling Stuff

US Midterm Elections – There will be massive interest in the 2018 US midterm elections, which started even before the votes were counted in 2016.

We would love to tell you what the outcome of the highly-anticipated mid-term elections will be, but the truth is, that trying to predict the outcome is like throwing a dart in a hurricane. At present, it looks as if a Democratic wave will decimate the ranks of Republicans up for re-election, but as has been frequently observed, a week is a lifetime in politics, and we are many weeks away from November. However, there are three primary scenarios in prospect:

1) There is no significant shift in seats. In this outcome, the Republicans maintain control over the House and the Senate, and things continue as they have been for the past year, leading to massive unrest (see above). This seems unlikely at the moment.

2) The Democrats win enough seats to take control of the House, but not the Senate. This seems like the most probable scenario at the moment, and would likely mean that Congress would be stuck in complete gridlock, with neither party being able to push through legislation, especially as both parties maneuvered for political position in the 2020 presidential election.

3) There’s a Democratic wave, leading to an enormous shift, with the Democrats gaining control of both the House and the Senate. This could conceivably lead to the impeachment of President Trump by the House – although it would be unlikely that the Senate would convict him as that requires a 2/3 majority.

The Decline of Men – It’s our belief that men, as a sex, are in decline, both physically and socially. We’ve discussed this before, but for 2018, we have three specific concerns.

Young men in particular (but not exclusively) seem to be spending their time in online gaming and online pornography to the detriment of their ambition, their social interactions with real people. Add to that the emergence of sex robots, and you have a dangerous mix that threatens to stunt the social skills of a certain fraction of men, and undermine their ability to function successfully in society.

The second area of concern is that the cultural perception of men has declined to the level of Homer Simpson and the characters portrayed in such movies as The Hangover. In other words, men are being perceived as immature man-children, with nothing on their stupid minds but sex, food, and short-term gratification. This is not, in our view, a healthy environment in which to raise boys and have them become responsible people.

The third element will be the sometimes-violent reaction of men to the decline in their perceived status, especially as the rise of women (see below) means a relative lowering of their status. Add to this the emergence of the #MeToo and Time’s Up movements, with the vast number of accused sexual predators being male, and the environment is ripe for a backlash by some less-confident men. This could feed into the white supremacist movement, as well as increased violence towards women by a small minority of men.

In short, we see 2018 as an unsettling time to be a man, especially a young man.

The Rise of Women – The long push for female equality in all aspects of life, but especially in equal pay for equal work, has yielded some results, although nowhere near parity. What it has done is to lay the groundwork for a serious push for equality, which is, in our opinion, just starting now, triggered by the #MeToo movement, and its successor, Time’s Up. When we say “groundwork”, we would point to three things that portend the rise of women, and, we would argue, the eventual supremacy of women in society and our economy.

The first is that there are now female role-models for every role in society, up to, but not yet including, president of the United States. As a result, a young girl can consider a career, and find someone who has done what she wants to do – and therefore believe that she can do it. This is an often overlooked, but critical development.

The second sign is that there are now more businesses being created by women than men, and the businesses started by women are twice as likely to survive. This means that there will be more business owners and owner-operators who are women, and who have economic clout. In turn, this means that even businesses owned, operated, or managed by men will have to be respectful of women-run businesses if they want them as clients.

But the third sign is the clincher. Almost 60% of undergraduates in colleges and universities are women in North America, and that percentage is even higher in grad schools. This means that the leaders of the future are women.

And now that women are speaking out and confronting sexual predators (who are virtually all men), they are going to find that, collectively, they have power they didn’t suspect or hadn’t chosen to exercise. When that realization hits, they won’t be satisfied until they are given due respect, compensation, and security of person, and political parties that try to oppose them will be trampled in the dust. Not all of this will happen in 2018 – it will take time for this to unfold. But 2018 will see the beginning of this major social upheaval. Richard argued this at more length here.

The Rise of Robots, AI, and Automation Will Be Messy – We recently posted a blog discussing how automation was set to take over whole industries, and the impact will grow throughout 2018. In particular, as the push for a higher minimum wage gains force, we will see machines, AI, and automation replace humans in jobs that, to quote Forbes magazine, cover the 4 Ds: dull, dirty, dangerous and dear (as in expensive). This is both a good thing, as machines will be doing the boring, repetitive, dangerous work that humans dislike or could be injured by, but it also means that the job market will get more difficult for humans.

There’s a long-standing argument between what might be called the neo-Luddites and the techno-weenies over whether humans are going to be largely replaced by automation, which we discussed here. And there are strong arguments both ways.

We believe that no matter who is ultimately right, there will be widespread displacement of workers as automation at least changes who does what work as automation and robots offer more cost-effective solutions to some aspects of blue- and white-collar work. There will also be many new jobs created – but most of these will either be relatively low-paid service jobs, or will require such highly specialized education that they won’t be available to most job-seekers. And there will be widespread unemployment, often for long periods of time.

But the so-called rise of the robots will not be a simple process, and will often happen piecemeal, or in unexpected ways, and will often happen by having robots and automation work with humans rather than replacing them. Indeed, we believe that a hybrid robot/human model will turn out to be more effective than either on their own.

Self-Driving Cars Will Emerge, But Not Easily – The rise self-driving vehicles has been widely forecast, and gets lots of media coverage. There have been many scenarios of how wondrous and liberating they will be, freeing up parking lots in urban centers for redevelopment, reducing traffic flow, and unjamming traffic gridlock. And there are power economic motivations for the emergence of autonomous vehicles (AVs).

But there are at least two problems with this future. The first is that for these scenarios to materialize, people would have to very quickly get rid of their cars, and switch to a world where they relied solely on Uber-like AVs for everything.

I doubt if people will switch their behavior patterns that quickly. What about summer camping trips to national parks? What about snowbirds who drive to Florida or Arizona for the winter? Will they use Uber for that, or wind up renting a vehicle they don’t really like for the duration? Or will they stick with their trusty ol’ car, at least until it wears out or they want a new one, or just buy a new vehicle with self-driving capabilities?

And if we do make the switch away from personally-owned vehicles, how will there be enough AVs to deliver everyone to work during rush hour? And wouldn’t that mean there would be as much traffic on the highways as before, just with different vehicle owners?

Meanwhile, until most human drivers are off the highways, AVs won’t be able to operate efficiently or drive as quickly as they might because humans aren’t as predictable as AVs.

This brings us to the second problem: driving in urban environments is the biggest obstacle to AVs because of its chaotic nature, and the unpredictability of human drivers, cyclists, and pedestrians. Indeed, long-distance trucking firms are considering a two-part operation for self-driving trucks: the trucks drive themselves over the superhighways and arterial roads to urban centers, and then are driven into the urban centers by human drivers.

Facing the Future – There are plenty of people who forget their phones, wallets, or keys when they go out, but no-one forgets their face. Bypassing fingerprints entirely, automated commerce, or a-commerce, involves having your bank account linked to your face, with your phone number as a password. This will be extremely convenient, as it involves using something you literally cannot leave home without, and the security involved is impressive.

It uses a 3D camera, so substituting a photograph won’t work, and even very sophisticated makeup will not move the same way your own face does, so the test that requires a person to smile or say a random word will be difficult to fool. After setting up an account with Alipay, one set of Alibaba-developed software that runs the verification, you’ll never be without a means of payment.

Naturally, there are downsides. For starters, your face is now codified in facial recognition software, meaning tracking you is now trivial. And since there is a massive facial recognition database for this to work off of, if it gets hacked, the hackers don’t just have your banking information – they have your face as well. Considering that researchers at the University of Washington have created digital dopplegangers of people, such as former President Obama, by use of AI and video, this could be a concern.

The Neat Stuff

Artificial Intelligence Gets REALLY Big – AI was a big story in 2017. It will be an even bigger story in 2018 and beyond. AI is already making big inroads in legal research, accounting & tax preparation, medical diagnostics, load management for electric power utilities, helping autistic children learn socialization skills, managing investments, and selecting music for your listening pleasure. But the interesting part is that AI is going to start appearing in places you won’t expect. We’re just guessing, but such areas might include:

  • Assessing the migratory patterns of birds, fish, and the global spread of infections.
  • Looking for ways to streamline Medicare billing.
  • Reducing your daily commute time specifically, and managing rush hour congestion generally.
  • Much more effective email spam filters & telemarketing blockers for your phone.
  • Plagiarism checkers for secondary, post-secondary, and graduate instructors.
  • Identifying autistic people, and assessing where they are on the autism spectrum.
  • Recommending a regular, automatic shopping system for your routine needs, then ordering them without your involvement, as well as recommending better choices, however you define “better”.
  • Acting as a lie-detector, but one you can download as an app for your smartphone, and then use with your friends, family, and casual acquaintances.

One specific application, which we’ll deal with in more detail later on, is the widespread adoption of AI in all aspects of health care.

Rent a Celebrity’s Personality to Be Your BFF – Another application of AI is the development of virtual assistants (also called genies, computer butlers, or avatars), like Apple’s Siri, Microsoft’s Cortana, or Amazon’s Alexa, but now with individually customized personalities that respond to your personality. In effect, they will evolve into your best buddy, your closest companion (literally), and your online servant. This will become a big deal, as such virtual assistants will effectively become your representative in cyberspace, putting themselves between you and everything you do, or could do, online.

All of the major tech companies will want to be in this position: Apple, Amazon, Facebook, Google, Microsoft – and anyone else who thinks they can swing it, including companies we’ve probably never heard of.

As part of this, the virtual assistants will grow personalities that evolve to suit your preferences. But we also foresee the potential for a further, somewhat unexpected development: franchising the personality of a celebrity or a movie character to be your virtual BFF. Suppose you really like the Jarvis AI character from the Iron Man movies. Paul Bettany, who does the voice of Jarvis, would record enough text to enable an AI to simulate his voice for any given words, and the script writers would answer questions to allow your genie to imitate Jarvis’ verbal mannerisms.

Or, in the case of a real human celebrity, let’s say a singer named Fred Smith, Fred would enter into an agreement with Big Tech to franchise his personality to inhabit their virtual assistant in exchange for royalties. His voice would be recorded so it can be matched, and he would answer a range of personal questions (without getting too personal). The virtual assistant then adopts his voice, his attitudes, and his mannerisms in order to convince you that you have Fred Smith as your virtual companion.

And when you lose interest in Fred or Jarvis, you rent another celebrity personality to inhabit your genie!

Gene Therapy – Gene therapy has long been the promised silver bullet of medicine, with hopes to cure cancer, and diabetes, and even hemophilia, but has failed to deliver. Twenty years ago, a prototype gene therapy treatment resulted in the death of a young patient, and it put the entire field on hold. But 2017 marks the first year that any gene therapy techniques were approved for use in the US, starting with two specific forms of leukemia and lymphoma, and following with a type of congenital blindness. As success breeds success, we are likely to see more progress in this field, hopefully covering a wider range of diseases. 2018 is posed to be a great year for the field, and for those receiving treatment.

Reversing Paralysis – A traumatic spinal injury usually means the patient is paralyzed, likely for the rest of his or her life. This has long been the case, and while quality of life for such people has improved, there hasn’t really been any hope for getting them to walk again – until recently.

It has been found that putting special implants into a patient’s brain, and running wires to non-responsive limbs can bypass spinal injuries, restoring some degree of motion and mobility to those who are otherwise unable to move. The clinical trials have shown it is possible, and in 2018, it is expected that this treatment will begin proliferating, and hopefully even get to the point that the spinal cord injury can be bypassed directly, instead of restoring mobility to limbs

Renewable Energy & Battery Technology – Renewable energy is now cheaper than fossil fuels in most cases. Saudi Arabia held an auction for companies wishing to build a 300 MW solar farm under contract, and the winning bid came in at 3.6¢ (US) per kilowatt-hour – without government subsidy. Solar power has dropped by 80% over the past 10 years, and by 99% since the 1970s, and continues to fall as economies of scale kick in and technology continues to advance.

Meanwhile, India commissioned a 100 MW wind farm in October, 2017 at an effective price of 4¢/kWh, which represents a 24% drop in cost since February of 2017. Renewable energy is now usually the cheapest source of utility-scale power.

As a result, coal consumption has dropped in every country except India. According to the International Energy Agency, “…5.3bn tonnes of coal equivalent were burnt in 2016, down 1.9% on the year before and 4.2% on 2014, the fastest decline since 1990-1992, when the global economy was in recession.” So, despite the best efforts of the Trump administration, renewable energy is the future, and fossil fuels are – mostly – the past.

Meanwhile, battery technology continues to advance, with the most interesting developments happening in the aviation industry. There are now small planes that are battery powered, and capable of flights longer than 200 miles. EasyJet is working with Wright Electric to develop an electric commuter plane.

The energy density needed to keep a plane in the air has long been an issue when looking at electric planes, but with newer battery technology, it’s becoming possible. This same technology will enable renewable energy to store enough energy to keep a regular supply of power to an entire grid, and to revolutionize electric cars and trucks.

All of this doesn’t mean that oil and gas, or even coal, production will disappear, but it does significantly reduce its upside potential, and means fossil fuel and electric power producers must innovate in response to the rapidly changing energy landscape if they want to survive.

Quantum Computing Approaches Commercial Feasibility – Quantum computers are unlike today’s digital computers, and do not suffer from the limitations of digital computers. Hence, while today’s computers are reaching the physical limits of speed, compactness, and storage capability, falling behind the growth rate predicted by Moore’s Law, quantum computers operate on a very different principle, derived from quantum mechanics, and are barely starting. Yet, the race of quantum computing is very definitely on, especially between Google, IBM, and a Canadian minnow, D-Wave.

But, so what? What’s special about quantum computers? Well, they have the potential to be remarkably faster at certain kinds of problems, such as encryption. A quantum computer could, theoretically, try all possible combinations of a password simultaneously, which would render our current means of securing privacy and personal and corporate accounts useless.

In real world applications, quantum computers could become essential to corporate and personal privacy, tackle problems beyond even the theoretical capacity of today’s computers, such as analyzing the interactions of different genes in your DNA, which would help us assess how our bodies work; selecting compounds to develop as pharmaceuticals without wasting massive amounts of time and money on dead end compounds; identify patterns in stock trading that defy current techniques; and solve problems involving the massive amounts of new data emerging from the Internet of Things.

But the most important applications of quantum computing are unknown because they have yet to be discovered or invented. 2018 will be a watershed year for the field.

3D Printing Gets Real – 3D printing is getting to be old hat as far as news goes, but every truly novel technology goes through a hype stage, followed by real results. We are now in the “real results” stage for 3D printing.

The developments we’re going to see include things like:

  • Super-strong parts, from replacement bones, to surgical instruments, to tools. 3D printing is creating things that are both stronger and lighter than traditional fabrication techniques, limited primarily now by our imagination.
  • 4D printing, also called active origami or shape-morphing systems. These are objects that are produced using 3D techniques, but which can be programmed, or even autonomously adapt to circumstance and need. Hence, they can change form, shape, or, potentially, size, according to temperature, humidity, light exposure, pressure, or other outside triggers.
    Think, for example, of a sweater that bulks up to retain more heat as the temperature drops. Or a sheet of material that can be slipped into a narrow opening, then caused to change shape – sort of like rolling up a model ship as a sheet, and then inserting it into a bottle before having it snap up into its ship-shaped form. Or performing plumbing repair by slipping a slim, flexible rod into a corroded pipe, then having it expand into a full-sized pipe upon exposure to water, repairing the broken pipe without having to replace it.
  • Speed and commercial accessibility. It used to be that 3D printing was a novelty, and only produced cute, cheap, plastic toys. Then it started to be used for more serious applications, in a wide variety of materials, from titanium to chocolate, but was painstakingly slow and still pretty expensive. Serious 3D printers, for industrial applications, are now dipping below $1,000, and can produce items much more quickly and inexpensively. Hewlett-Packard is furiously trying to lead the consumer/industrial thrust because it wants to dominate the market for ink cartridges – effectively repeating the successful model for inkjet (paper) printers. And eventually, 3D printing may rival more conventional fabrication techniques for speed, leading to a vast array of inexpensive, customized products.

Back to the Future

There’s no question that 2018 will be an interesting year. Whether we’ll enjoy it or not depends on what kind of “interesting” things happen. And while nasty shocks and unsettling events seem to capture our attention, never lose sight of the fact that overall, things have been getting better for centuries, and that’s likely to continue.

There are fewer really poor people in the world as a percentage than ever before in history. We are developing the ability to cure or treat diseases and conditions that have bedeviled humanity throughout history, and our standard of living continues to rise beyond what our ancestors would have believed possible.

We continue to be guardedly optimistic about tomorrow’s world, and remain firm in our beliefs that each of us has more influence over our own futures than any other factor.

And if we can help your organization plan and prepare for the future you want, so that you can turn the changes coming to your advantage, please don’t hesitate to contact us.

© Copyright, IF Research, January 2018.

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Artificial Intelligence Is Going to Take Your Job (Probably)

by futurist Kit Worzel

Artificial intelligence and robots are making incredible progress. Recently, Boston Dynamics released footage of one of their latest robots that can do backflips, and land them handily. It’s been over a decade since a human beat a top-class chess AI. We have self-driving car prototypes being tested, and getting ready for the market. And then AI will be coming for your job (probably).

AI is already used extensively in many fields to supplement work done by humans, most notably Google search. Any program that can search and compile data is an AI, even if it’s a basic one. Many of us now consult a different kind of more personal AI on a daily basis, such as Siri or Cortina. We’re seeing automation creep in to various tasks, including self check-outs, and fast food ordering via touch screen. But AI is going to do so much more than that.

I can put jobs into three, broad categories when it comes to AI: Jobs that will be (mostly) replaced by AI, jobs that will be enhanced by AI, and jobs that will be mostly unaffected by AI. I am looking to 2040 for this, so the timeline is about15-25 years.

Jobs that will be replaced by AI and Robots

  • Manufacturing
    Manufacturing is a field already dominated by automation, and that will not change anytime soon. AI and robotics are rapidly getting to the point where they will be cheaper than sweatshop labor in developing countries, and will make the location of a plant largely irrelevant. And since being close to your market is always helpful, this may lead to a mass return of manufacturing to the rich world.
  • Online advertising
    Online advertising is already partially done by AI. Advertisers will probably use AI for crafting and customizing messages soon, which will fail repeatedly before starting to produce quality, targeted ads. The real question is how advertisers intend to get past adblock, and other similar programs, which will also employ AI to thwart obtrusive ads. Finding where ads are best placed, will be well-received, and where the traffic would best suit a product/ad is something AI will become very good at, much better than humans.
  • Transportation
    Planes already have autopilots. Cars and trucks will become self-driving, and also self-loading, meaning shipping things will be very strongly automated. Finding the correct transportation for you will also be much easier, as automated tools will produce a line-up of choices for you, with prices and times. In fact, that much already exists, it will just get more refined and easier to use. As soon as self-driving cars get the go-ahead, taxis, truck drivers, bus drivers, and even rental car agencies will all become endangered jobs. All types of public transport are included here as well, from trains to tickets.
  • Telemarketing
    Will be completely replaced by AI as soon as possible. No-one wants the job, no-one likes telemarketers, very few people listen to telemarketers, and paying people to do this is a waste, especially when automation never gets tired or discouraged, will sound more and more human, and will gradually find new ways to get people answering the phone to respond more positively.
  • Foodservice
    There are already kiosks where you can just use a touch screen to order food, and I foresee automated kitchens in the near future. There may be some delay because the issues around food, and food poisoning are a real concern, as are preparing allergen-free servings. However, since Ray Kroc of McDonald’s managed to carve out a world-wide chain with minimum wage employees doing food prep without significant issue, I don’t anticipate significant delays in fast food that is untouched by human hands.
  • Procurement & supply chain management
    This will be whittled down to top level management with AI assistance. A lot of these jobs are related to access to relevant information, where AI excels. All other jobs in this field will be gone.
  • Construction & carpentry
    This is already happening. Precision work involving repetition is an area where robots excel. They will take over almost entirely, with only human supervisors left. Much like manufacturing, this will go where the products are needed, instead of where the workers are.
  • Accounting, bookkeeping, tax management / planning
    This has been happening for years. Turbo Tax rules this world. Personal taxes and small business taxes are already predominantly done by tax programs, and there is no reason to reverse this trend.
  • Corporate training, career management
    Just think of it as LinkedIn with AI insight added.
  • Real estate (pricing, attractive features, staging)
    Pricing, without a doubt, will be completely automated, as will searching. Staging and preparing houses for sale are holding out, still needing a human touch, but it is getting there, as staging databases are being built that will be accessible to homeowners, and AIs will design the staging, but not in my timeframe.
  • Wholesale and retail
    It will soon be possible to walk into a store, and ask your smartphone where to find a certain product. If you ask in advance, it could be waiting for you by the checkout, and all you’d have to do is scan it and pay.

Jobs that will be enhanced by AI

  • Health care
    So, we already have AIs assisting in surgery. That will become both more prevalent, and more powerful, as instead of an AI assisting a surgeon, a surgeon will supervise an AI. This will be easier with planned surgery, and more difficult with emergency surgery, at least at first. However, having AI’s ride shotgun in busy emergency rooms will give them a large database to pull from, and eventually give them the experience needed. AIs will also take over point-of-care, and patient management. Meanwhile, AIs will take over in the lab, because running tests with standardized results is something they could easily designed into medical testing processes. Even insurance billing could be handled, though the one big area I don’t see AI in the near future is mental health, as we will get a better understanding of it before AIs can become serious partners.
  • Finance (banking, insurance, investments, corporate finance)
    Finance and insurance involves crunching tons of numbers in all sectors. Gathering and processing that information can take days, if not weeks, or even longer. AI can mitigate that, dealing with the information in seconds. AIs will remove all of the low-level, grunt processing jobs, because they will be much more efficient at it. The remaining agents will also have AI assist them in finding new customers, and giving them competitive rates.
  • Legal
    In the legal profession, precedent is one of the most important aspects of the job, along with precise wording of laws. AI programs can search and summarize thousands of pages of legal documents within minutes, meaning a lawyer could ask their AI to research an issue, go get a cup of coffee, and come back to read the comprehensive summary. It’s even possible that public defenders and the like may be replaced with an AI that advises a person of their rights. It seems unlikely that we’ll see AI acting in a courtroom on its own, but may see them acting as an assistant for a paralegal as a low-cost alternative to a lawyer.
  • Sales & marketing analysis
    I’ll group sales and marketing together, as they fall under a similar heading here. Analysis means number crunching and assessing results, which plays to AI’s strengths. I would not yet rely on them to come up with creative solutions, but measuring customer satisfaction and looking into sales trends are both tasks they are well suited for.
  • Customer service
    Although automated systems handle calls already, the tricky calls can’t be handled by AI – yet. As well, people appreciate talking to an actual person, it makes them feel valued, especially in unusual situations. Until we have Turing-capable AI (i.e., generally smart as opposed to smart at one specific thing), customer service will remain as AI-assisted, rather than replacing human CSRs. On the other hand, if I could call customer service and get an AI on the first ring who listens to my issue and gives me an prompt, correct answer, rather than a 30-minute phone queue where I have to punch in seventeen different numbers, I’d happily use AI customer service.
  • Education
    Education is a field where people are untrusting of AI, and many instructors/teachers/professors have tenure, so can’t be replaced quickly. However, education outside of the public systems will change much more quickly. The armed forces and corporations already use AI for training. So, eventually educational games that use AI to teach children, and where AI can get steadily better in helping kids learn at their own pace, in the most effective way for each child will gradually move into the public space. There’s a lot of skepticism over AI and education, and I foresee push-back here. I expect to see AI educational assistants in the classroom, but not at the blackboard.
  • Security (both physical and online)
    This is interesting. Robots, security drones, and AI are better at watching things and noticing exceptions to routine than people, but people are still better at dealing with emerging problems. But there’s more to it than that. A respectable amount of security and police are employed mainly to ensure road safety and parking enforcement. With self-driving cars, that’s not only much easier to catch, but also to detect. It is likely that over 90% of road and parking policing will be done via AI.With online security, I feel confident that AIs will eventually take over entirely because of the speed with which attacks can be launched, and with which hacking approaches can change, with only human supervision to provide common sense and human awareness. There is too much to do, and too much to cover in online security, so AIs will be integrated into firewalls. Of course, AIs will also be used by hackers, so it will be a never-ending race to maintain security.
  • Farming
    AI and automation will assist farmers, but not replace them. Robots will do grunt work and help with crop planning, though. Farm work currently requires too many different jobs to be completely automated, and will still have humans at the helm for a long time.
  • Forestry, fishing, mining, oil exploration & production
    In all primary industries, robotics will take over much of the more dangerous work, and AI will do the complex number crunching, such as finding fish, and oil, and minerals. Humans will still run the robots and supervise.
  • Social services
    AI will enhance social services rather than replacing them. For example, a Florida researcher has used machine learning to help predict if someone will commit suicide up to a year in the future, based on exhibited behaviors. Apparently, most people with suicidal impulses visit a medical professional at least once before attempting suicide. Having an AI to check for these behaviors at a doctor’s office would drastically lower the suicide rate.
  • Scientific research
    AI will be a huge asset in scientific research. Having a computer able to quickly correlate vast amounts of data will massively reduce the amount of time it takes for scientific progress, leaving researchers free to think about the implications of the data.
  • Human Resources
    Human resources already benefits from AI when sorting through resumes, and better AI will only improve this. Meanwhile, the human side of HR will largely remain in human hands, especially when it comes to thorny issues like sexual harassment, although even here, AI’s will be good at identifying early warning signs that a given employee is a source of harassment.
  • Journalism
    Yahoo has already started to use AI for writing news reports, with a certain amount of success. As a writer myself, I’m hoping that it stops there, but there is a Japanese AI that placed highly in a writing contest, so I’ll admit to some apprehension. Assuming that the field does not end up dominated by AI, having a research assistant that can winnow out the facts in minutes is always an asset.

Jobs that will be mostly unaffected by AI

  • Management
    We will always want to supervise our robots. So, there will always be a person at the top who has final say.
  • Script writing and Art
    It’s been tried. I’m not overly impressed, but art is subjective, and ultimately deals with the humanity of the audience.
  • Therapy and counseling
    Until we understand our own minds, we can’t possibly program a computer to understand them. For the foreseeable future, these jobs will remain in human hands.

That’s my breakdown, at least for now, but AI is evolving with incredible rapidity, which means that things will change.

Odds are, you will not have a job in your field in twenty years’ time, particularly if your job involves repetitive labor or number crunching of any kind. AIs and robots are just faster & better at such tasks, and getting cheaper with frightening speed. Just as robotics and overseas labor destroyed manufacturing in North America, AI will decimate whole industries. We have yet to figure out what comes next.

What is clear is that you will need to continually reinvent the work you do to avoid having it automated out of existence.









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Women Rise to Power: Aftermath from Outing Sexual Predators

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

The growing number of men being outed for sexual harassment and assault will lead to one of the most profound changes in society in decades, perhaps centuries. Women, who compose the majority of the population in virtually all developed countries, will, I believe, conclude that the only way that they can achieve equal status for pay, advancement, and most especially, for personal security is to take the political power that their majority should afford them.

This will come during a period of great upheaval, as revelation after revelation exposes a past where a surprisingly large number of men in positions of power and influence over women, abused their positions, and will now finally be called to account. There will be many such cases, and the revelations will rock all of our institutions, including government, entertainment, business, religion,  education, and especially the media.

The real changes, though, will come once the novelty of such revelations has worn off, and such cases no longer make the front page of newspapers and websites.

When Did This Start?

It’s hard to identify a specific beginning. It might have begun with Andrea Constand, the woman who started the public accusations against Bill Cosby in 2015, which grew so that Wikipedia now lists 59 women who have accused Cosby of sexual assault over a period of decades[1].

It’s certain that the accusations against Harvey Weinstein, beginning with the Italian actor Asia Argento in September of 2017, inspired a growing movement towards naming and shaming sexual predators as women encouraged one another to come forward, and go public with such accusations. To date, more than a dozen women have reported various degrees of sexual assault against Weinstein, ranging up to full-out rape. And that, in turn, has inspired other women, who had been silent, in some cases for decades, to come forward with allegations against other men.

Weinstein was just a trigger for much more to come. The highest profile case so far after Weinstein is Roy Moore, the Republican candidate for the U.S. Senate in Alabama – not counting Donald Trump. I suspect that this time, the allegations against Trump will catch up with him.

But What Happens Next?

I believe this is the beginning of a cultural revolution beside which the recognition of gay rights will seem calm. I believe women are just realizing that when they act together and in concert with those who support them, they can significantly improve how they are treated, their rights to work, equal pay, advancement, status, and security. And I also believe that from there it’s a short, and inevitable, step to their realization that they are the majority – and should have the political power that goes with that.

The number of men who are outed is growing as women are realizing that they are not to blame for being victims. And as more women come forward to support the accusations of others, this emboldens even more to do so, producing an avalanche of accusations of sexual assault. This is the first effect, but by no means the last, even though it will go on for a long time.

How Widespread Is Sexual Assault?

I used to work in the investment business, and while most men in that industry were good people, there was a significant minority who were pigs, pure and simple, particularly in the more macho, gun-slinging occupations, like bond and stock trading. So-called locker room talk was the norm, not the exception, and off-color, misogynist, racist, sexist, and bigoted jokes were far more common than the ones you could tell at home.

And, in retrospect, I’m quite sure that sexual assault of varying degrees was widespread. This would have ranged from risqué remarks, to “chasing your secretary around the desk,” to outright rape.

Moreover, while the investment industry, with its focus on money, influence, and its gun-slinging mentality, may have been unusually nasty in this regard, it was by no means alone. Indeed, if there were some way to investigate the past, I think we would find that any situation where someone (almost always a man) who exerted power or significant influence over someone they desired (typically, but not necessarily a woman) could have produced sexual predation, and, not infrequently, did.

And since politics and entertainment live and die by power and influence, they are high on the list, and will produce disproportionate numbers of outed predators in the days and weeks to come.

So, I believe we are going to see a rising crescendo of women stepping forward with accusations against predators, some fresh, and some running back decades.

But Then What?

Right now, these kinds of sexual allegations are all the rage among news outlets. They provide all the right ingredients for clickable headlines: sex, power, revenge, righteous indignation, and ax-grinding against people you don’t like. And that will initially produce, as it already has, political brickbats as first members of one group (say Republicans like Donald Trump and Roy Moore) are accused and put on the defensive, and then their opponents (like Al Franken and New York Times White House correspondent Glenn Thrush) are outed and accused.

Eventually, though, everyone will realize that no large group will escape unscathed. For too long powerful men have been able to assault people in subordinate positions with impunity, so that the temptation to do so infected lots of people in positions of power or authority, and in all walks of life.

When people realize this, criticism of one group of men by another will gradually become muted, for the brickbats you hurl today may come back at you tomorrow.

But eventually, the headlines will gradually fade away, except when the name of someone particularly newsworthy pops up. Just one more sexual predator won’t be newsworthy any more, we will eventually reach a point of satiety.

What happens after that will determine what follows. Here’s how I believe events will unfold.

Some accusations will be actionable, and many police departments will become more willing to listen to accusations of assault as it gradually dawns on them that women who have tried to register complaints in the past may have had reasonable grounds to do so, and should not have been brushed off. In those situations, it will depend on technical issues, such as whether sufficient evidence exists for prosecution, and whether a statute of limitations has been passed.

But many police forces will continue to discount such accusations, and will likely use the line of reasoning that a number of outed males have already tried: If this happened so long ago, why did you wait until now to say anything?

The answer, of course, is that until now, any woman who was brave enough to come forward would be guaranteed of verbal abuse on top of the sexual abuse they had already experienced, starting with the fact that they wouldn’t be believed, and would be slut shamed. Abused women know all about blaming the victim.

And once the press gets tired of reporting the same old, same old allegations of sexual assault, predators may believe that if they just tough out the initial revelations, they can get away scot-free – and may even be able to get back to the business of assault.

Awakening the Female Majority

If it happens that women get frustrated with recalcitrant police, and the waning PR effect of reporting sexual assault, then we move on to what I see as the next stage of the process: women will decide they have to take matters into their own hands.

There may be some vigilantism along the way: groups of assaulted women showing up at the homes and workplaces of their predators to confront their employers or wives, reporting the assault to the predators’ mothers (as one woman blogger did with online trolls), and generally confronting their assaulters directly. However, I don’t see this as producing the major shift in results women will demand.

Instead, I believe that women will conclude that they must take political action to make police pay attention, to pass laws that protect victims, to make prosecution of predators more feasible, and restitution to victims more realistic.

Up until now, women have largely relied on apparently supportive men, like Bill Clinton, to act for them. But the outing of even supposedly supportive men (like Bill Clinton) will, I suspect, lead to women deciding they must have women in positions of power to advocate for women’s rights. And so, a massive new political movement will be born: politically energized women standing up for women and every woman’s right to personal security.

And although existing political parties could, potentially, decide to join and support this movement, I think that conservatives in any country will find it hard to reconcile this with their past positions on abortion, gay and transgender rights, and, particularly, the whole concept of women as subservient.

If left-leaning political parties, such as the Democrats in the U.S., the Liberals and NDP in Canada, and Labour in the UK, want to ride this wave, they will not only have to support the goals of this new women’s movement, but allow women to lead it. This will transform these parties, and the old guard may not like that. But the risk is to have it pass them by. Notably, there is a very real risk that women will form completely new parties that don’t have the shackles of past policies and the deadweight of hypocritical men.

And After That…

None of this will go down well with the old guard in any party, particularly right-leaning parties, like the Republicans in America, the Conservatives in Canada, or the Tories in the UK. Indeed, I expect there will be a backlash. Some men will say that a women’s movement is unnecessary, that men can be effective advocates for women: “Trust us”.

But other men will rear up and advocate for that Old-Time Religion, where women knew their place, which is barefoot and pregnant in the kitchen. And such people (including many women) will likewise discount and deprecate the women who come forward. Slut shaming will make a big-time comeback: “Enough, already. They asked for it!”

The battle lines will be drawn, and women will realize that although they do, indeed, constitute a majority of voters, they will accomplish more, and more quickly, if they have allies. And allies will be easy to find among the supporters of such groups as Black Lives Matter, LGBT+, and others.

In theory, immigrants could be allies as well, because they, too, are frequently abused by white, male-dominated institutions. However, immigrants frequently tend to be conservative, especially with regards to the status of women, so I’m unclear whether they will be supportive of this new women’s movement.

But at the end of the day, women will prevail. They represent too much political power, and possess too much supressed anger to be stopped. And if they can create mutually supportive alliances with groups that have been similarly abused, this shift will happen very quickly, indeed.

Is This Inevitable?

If existing political parties take the time to evaluate the future, and realize that this time the women’s movement will be a Juggernaut, and represents an existential threat to those who stand against them, such parties can change their policies, get behind the rights and security of women, and wind up as significant beneficiaries of what is about to become a major sea change in political life.

But I wouldn’t bet on it.

© Copyright, IF Research, November 21st, 2017.

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

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The Stock Market Is a Bad Bet

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

“The market can remain irrational longer than you can remain solvent.”
               – John Maynard Keynes *

The U.S. stock market has been on one of the great bull market runs in history, more than tripling since the market bottom on March 9th, 2009. And it continues to defy gravity and its critics, echoing Mark Twain’s comment about reports of its death being exaggerated.

But no tree grows all the way to the sky, as a classic market cliché goes, and this market is at or near the top by virtue of almost any measure of value or price. The problem is, as my opening quote cites, that markets can stay irrational for a long time.

So, given this uncertainty, what’s the proper strategy to adopt now?

This is where a futurist’s approach to risk management can be helpful: You don’t have to know the future to be able to plan intelligently for what’s ahead. Instead, you assess the odds, and the potential for risk and reward, then select the option that puts the odds in your favor. Let’s take a look at what that might look like, and what actions it might lead to.

What Is Risk?

I’m a Chartered Financial Analyst, and used to work in the investment industry. I’ve had a long-standing disagreement with many market-oriented quantitative analysts over the definition of risk, and the current market exemplifies this difference.

The traditional quant’s definition of risk is volatility: the more volatile an investment is, the riskier it is. Yet, the current market has been surprisingly un-volatile. Indeed, the one of the most broadly used measures of volatility, the Chicago Board of Trade’s Volatility Index, called the VIX, has been remarkably quiet:

Does this lack of volatility mean the current market is not risky? That would be a foolish conclusion as even market bulls concede the market can’t keep going up forever, and that this run has been near-historic in proportion.

My own definition of risk is more real-world obvious: Risk is the cost of being wrong. So, let’s look at how investors could be right, and how they might be wrong, and then try to quantify the risks either way.

Let me start by assuming that I’m wrong about the market. What would be the consequences or cost to me? Well, the obvious one is that the market could continue to go up, but my investment holdings won’t as I have sold most of my stock positions.

How much might I lose? From where it is today, it’s hard to see the market advancing by as much as 10% over the next 12 months, so losing out on an additional 10% is my estimated cost. Moreover, I think that this is being generous in my assessment of how much further the market could run.

Now let’s consider the consequences or costs to an investor who is fully invested, and has an aggressive portfolio of stocks. How much might that cost such a person?

A minor market correction is generally considered to be a decline of about 10%. Such a decline will typically happen once or twice a year during a bull market (but hasn’t happened since February of last year).

A major market correction is generally taken to imply a decline of 25% or more. For comparison, the S&P 500 Index lost almost half its value in the 2007-09 market panic.

So, while I might stand to lose as much as 10% by staying away from stocks, our hypothetical aggressive investor might lose 25% in a run-of-the-mill bear market.

There’s a tool in statistics called Expected Return. To calculate it, you multiply the potential gain by the probability of gain, then add that to the potential loss times the probability of loss. To provide a simplistic illustration, let’s assume that there’s a 50-50 chance of a continuation of the bull market versus the advent of a bear market, that if the market rises, it rises by 10%, and if it falls it falls by 25%:

Expected Return = (50% x 10%) + (50% x -25%)
                             = +5% – 12.5% = -7.5%

In other words, in this simplistic illustration you should expect to lose 7½% by investing or staying invested. That would not be a smart investment call; you don’t normally buy an investment when you are expecting to lose money on it.

But have I assessed the potential gains and losses properly, and assigned appropriate odds?

What Is This Market’s Expected Return?

There are always pundits saying that markets are cheap, and others saying it’s expensive. Let’s go through a couple of the reasons why I believe this market is expensive.

One of the best, long-term ways of valuing a market is to look at the total market capitalization relative to Gross Domestic Product, i.e., the value of the market compared to the value of the overall economy. After all, stocks represent how well the economy is working, so shouldn’t get too far ahead of it.

Fortunately, this yardstick is readily available from FRED, Federal Reserve Bank Economic Data, which is a database maintained by the U.S. Federal Reserve Bank of St. Louis. Here’s what that ratio looks like graphically:

A recent value of this ratio had the market capitalization at 138% of GDP, whereas the 50-year average is 65%. This would mean we would have to have a 53% decline to bring us back to average – and markets almost always overshoot in whichever direction they move.

Another value yardstick that is highly thought of is the Case-Shiller PE Ratio[1], which also indicates that the market is almost historically overvalued:

The only times the Case-Shiller PE has been higher were (a) at the height of the tech boom in December of 1999, and (b) at the height of the stock market boom of the Roaring 20s, just before the Black Tuesday crash of October, 1929.

This indicator is currently 31.62, compared to the long-term mean (average) of 16.80. In other words, it would (coincidentally) have to decline by about 53% to get back to the long-term average.

There are several other indicators that might be relevant, but my point is that the cost of being wrong on the market now would likely be very high. The potential loss would be high by historic standards, perhaps on the order of 50% or more.



What’s the Probability of a Market Collapse?

Now that we’ve looked at the cost, let’s look at the probability of being wrong by considering the environment in which the market is operating. In other words, what and where are developments that might derail the market? (The list below is in no particular order.)

  • The potential for a nuclear war with North Korea.
  • The potential for war in the Middle East involving Saudi Arabia, which would affect the price of oil, as well increase the instability of Mideastern politics.
  • Economic and political conflict between Britain and the EU over Brexit.
  • Rising interest rates, and the reduction of quantitative easing by central banks, but especially the U.S. Federal Reserve, leading to a sell-off of bonds and interest-sensitive stocks.
  • High levels of corporate and personal indebtedness, which would lead to financial difficulties if the economy slows.
  • The earnings of the S&P 500 peaked in the third quarter of 2014, but the market has continued to rise despite disappointing earnings.
  • The yield curve is flattening, which is a reflection of rising short-term rates. A flat yield curve is typically a forerunner of an economic slowdown.
  • Consumers’ ability to drive the economy is weakening. In a succinct commentary on market conditions by money manager J. Lawrence Manley, Jr. notes that:

    It appears the consumer is struggling. Real wage growth is stagnant, and employment growth is declining. Additionally, the personal savings rate has dropped sharply over the past two years as individuals were forced to reduce their savings rate to fund their consumption. We believe that a significant economic acceleration is unlikely, given the weak fundamentals in the consumer sector of the economy.[2]

  • The market is already assuming that significant tax cuts by the Congress and Administration will pass, but this is far from certain. If they don’t pass, the market will be disappointed.

In short, there are lots of things that threaten this bull market, including the keys: stagnant corporate earnings, eroding consumer spending, rising interest rates, and an aging economic boom.

And yet, market psychology remains buoyant, and markets continue to roll merrily along, acting as if everything is not only great, but getting better. That’s not the case. And from my perspective, this means that the risks are high and rising that the market will roll over.

I would estimate that the odds of the market turning down over the next 12 months are between 70-80%. Let’s pick 75% as a point estimate. This means that I would peg the odds of the market continuing to rise at 25%, coupled with my earlier estimate of the potential for missed gain is about 10%.

Plugging these figures into the Expected Return formula gives:

Expected Return = (25% x 10%) + (75% x -50%)
                             = +2.5% – 37.5%
                             = –35%

In other words, I expect that an investment made (or kept!) in the broad U.S. stock market would lose 35% of its value over the next year.

Clearly, this is a high-risk bet. The cost of being wrong, and the probability of being wrong are both high.

Nobody Knows When a Market Will Turn

Do I know when the market will stop going up and turn down? No; no one does. But I think the risks clearly outweigh the potential returns. I could be wrong, and there are undoubtedly plenty of people who will think so.

But step back and consider the probabilities of the bull market continuing up versus seeing the market crumble and fall into bear territory, and think about what your risk is either way.

How much might you miss by being out of the market?

How much might you lose by staying in?

Which risk is greater?

© Copyright, IF Research, November 2017.

• There is some controversy about who said this. Keynes is usually credited with saying it, but it has also been attributed to economist A. Gary Shilling. See, for instance, https://www.maynardkeynes.org/keynes-the-speculator.html

[1] http://www.multpl.com/shiller-pe/

[2] “The Financial Asset Bubble Is Ending”, https://seekingalpha.com/article/4116560-financial-asset-bubble-ending-time-re-examine-risk-allocation

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Who Pays for Climate Change?

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

Hurricane Harvey was not a surprise. At least, it shouldn’t have been.

Hurricane Ike, which ran just slightly to the east of Houston in 2008, caused an 8 ½ foot flood along the Galveston Strand (i.e., sandbar) to the southeast of Houston. NOAA (the National Oceanic and Atmospheric Administration) estimated that if Ike had run inland just 30 miles west of where it did, Houston would have suffered far worse flooding. And, in particular, a clutch of the world’s largest petrochemical processing facilities would have been submerged by Ike.

In the aftermath of Ike, the city of Houston, the state of Texas, and the U.S. federal government talked about building a system of flood gates to protect the area, sometimes referred to as “Ike’s dike”. However, as the shock wore off, and the emergency returned to the humdrum of everyday life, the urgency wore off, and the proposals bogged down in the usual nonsense of who was going to pay, and where, precisely, it would go, and who, exactly, might get the lucrative contracts.

So when Hurricane Harvey hit this year, Houston was actually in a worse state of preparedness than it had been for Ike, having squandered the nine years in between.

But the bigger issue is that Harvey is going to cost something on the order of $180 billion in disaster relief and rebuilding.

It’s somewhat ironic that many of the members of the Texas delegation to Congress, led by Senator John Cornyn, who voted against providing federal aid to the victims of Superstorm Sandy, which hit New York and New Jersey in 2012, were lining up to ask for federal aid for the victims of Hurricane Harvey. Apparently, it’s only an abdication of personal responsibility to depend on federal money if someone else is getting it.

Hurricane Irma crushed a number of the Leeward Islands of the Caribbean, such as Barbuda, and although it inflicted significant damage on large parts of Florida, it veered just enough from the most damaging course that the worst ravages were avoided. Even so, costs are estimated somewhere between $50 and $100 billion to repair the damage in Florida alone. As Florida is an electoral swing state, virtually every politician in Congress came out in favor of aid.

Hurricane Maria smashed those parts of the U.S. Virgin Islands that Irma missed, such as St. Croix, and absolutely crushed Puerto Rico, destroying much of the island’s vegetation, knocking out almost all of the electric power grid, stranding communities by blocking or destroying roads, and leaving many, perhaps a majority of the residents in dire need of water, food, and shelter. Puerto Ricans are U.S. citizens, but cannot vote unless they move to the mainland, with the result that federal politicians, notably the Republicans, and especially President Trump, were remarkably (or predictably) lackadaisical about rushing federal aid to the island.

But that’s not all of the toll racked up by climate change in 2017. Wildfires scorched much of the western U.S., destroying an estimated 8 million acres of western forests at a cost in the billions.

A Ballplayer on Steroids

Despite all of the evidence to the contrary, I still get people telling me that these are merely routine natural occurrences. There have always been floods, always been wildfires, always been hurricanes, and so on, they say, and that’s true. What is different now is precisely what climatologists warned us was coming: the frequency and ferocity of weather extremes is rising. And one way of illustrating this is by drawing an analogy to a baseball player on steroids.

If a juiced ballplayer hits a home run, you can’t ascribe that specific home run to steroid use. But you can absolutely say it’s due to steroids if a juiced player goes from hitting 10-15 home runs in a season to hitting 30-40.

Likewise, warmer temperatures increase the probability of drier forests, more lightning storms, and therefore more wildfires. And warmer waters in the Atlantic Ocean, Caribbean Sea, and Gulf of Mexico mean more, and more powerful, hurricanes. Higher sea levels mean higher tides, and particularly higher storm surges, as happened with Superstorm Sandy. And more, and heavier rainstorms mean more flooding.

So, although you might not be able to say that Hurricane Harvey was specifically due to climate change (although climatologists are now starting to be able to make such statements, and back them up), you can absolutely say: They warned us this would happen, and now it is happening. How else can you explain that Houston has endured three “once in 500 year” floods in three years?

Who Pays?

And this brings me to my central point: as the toll of more extreme weather events adds up, even the deep pockets of central governments all around the world will start to get tapped out. (Some would argue that the U.S. government is already too indebted, especially when you include future Social Security and Medicare entitlements.) What happens after that?

Let’s take Puerto Rico as a possible case study. Let’s assume, merely for argument’s sake (of course) that Washington doesn’t do much to help Puerto Rico rebuild. What happens to Puerto Ricans then, especially as Puerto Rico is already deeply indebted?

Well, two things happen. First, they will have to rely on themselves. And second, they will be seriously impoverished. They will be poor. And they are pointing towards a future we may all share – unless we do something about it.

Let me come back to the first point later, and deal with the second now.

One inevitable consequence of more extreme weather events is that we will all get poorer. People in the direct path of such extremes will get poorer faster, but the rest of us will get poorer, too, as our tax dollars get diverted from other programs to disaster relief as governments try to help those harmed by disaster. Or, eventually, as governments stop being able to help those harmed by disaster.

At this point in a discussion with my audiences, someone will say, “Boy, I’m glad I don’t live in a disaster-prone area like Florida or Houston!” My reply is, “Yup – but you want to be sure you don’t live anywhere where there are hurricanes, rainstorms, thunderstorms, droughts, tornadoes, blizzards, Spring floods, flash floods, rising rivers, rising sea levels, coastal storm surges, wildfires, diseases like Zika or malaria that have spread from the tropics – or earthquakes, just for good measure (even though those aren’t related to climate change).”

I don’t know about you, but I know of no such fortunate place. Everyone is vulnerable in some way, and those who are smug and self-righteous today (like members of the Texas delegation were after Superstorm Sandy) may find themselves as the next supplicants.

So, with a steadily rising incidence of extreme weather (and climate related) events inflicting steadily higher amounts of damage, most of us will be randomly impoverished sooner or later. Which brings me back to my first point: We will have to learn to rely on ourselves.

What Can We Do?

Breaking down what we can do in general terms, without knowing in advance what disasters we might encounter, divides into two parts: How do we recover once we are hit? And, how do we reduce our future vulnerability?

Both questions share a partial answer: Foresight can drastically reduce eventual costs.

Let me go back to Puerto Rico to serve as an example of how foresight can reduce costs in recovery.

Almost all of Puerto Rico’s electric grid was knocked out by Maria. It would take years, and many billions of dollars, to rebuild it. However, why reproduce what is fundamentally an early 20th century solution in a 21st Century world?

Instead of stringing all that wire, and rebuilding large-scale, centrally-located (and therefore vulnerable), electric power generating stations, build a distributed renewable energy system, with rooftop solar panels, community windmills, and individual building and community power storage batteries. This would:

  • fix Puerto Rico’s need for electricity more quickly, plus isolated communities wouldn’t have to wait for power cables to reach them;
  • be cheaper in terms of capital cost than rebuilding a traditional grid;
  • produce cheaper electricity in the long run; and
  • be more resilient in the event of a future hurricane or extreme weather event because power generation would be distributed.

Meanwhile, governments at all levels should look at the kinds of disasters that have struck, or that may strike, communities within their jurisdictions, and develop and stockpile up-to-date, smart solutions to such problems, as well as identify resources and suppliers who can quickly produce the needed components to remedy a disaster. And, in particular, as new, smarter technologies emerge that can enable us to do things we need done, but faster, cheaper, and more effectively, we swap out old ways of doing things with new ones.

Then, when a disaster does hit, the government involved pulls the relevant solution off the shelf, looks at how it might need to be adapted or updated to the specific circumstance, then pushes the “GO” button to implement it.

The cost of this kind of disaster-recovery planning is tiny compared to trying to reproduce an aging infrastructure from scratch.

Making Infrastructure More Robust Reduces Vulnerability

Foresight in planning a more robust infrastructure pays off big-time:

“In 2009, social scientists Andrew Healy and Neil Malhotra pointed out that the federal government can invest disaster money either before a crisis — in disaster preparedness such as equipment to protect against flooding — or afterward — in disaster relief such as direct payments to victims. … The results, based on data from 1988 to 2004, are dramatic: The researchers found that within one presidential election cycle, voters reward presidents for spending on relief, but not for spending on preparedness.

“It’s unfortunate that we reward post-disaster spending, since it’s smarter to invest in preparedness. Healy and Malhotra found that spending roughly $1 on preparedness is worth the same as spending about $15 on relief, in terms of actual disaster management.[1]

The bolded emphasis is mine.

Since we know there will be more extreme weather events, it makes a lot of sense to exercise foresight now in the form of researching, planning, and executing investments into more robust infrastructure, and accepting the relatively minor tax increases that this would require. This is particularly so as governments in virtually all developed countries have chronically under-invested in infrastructure for the last 40 years or more. So, we can plan and replace aging infrastructure now, and make ourselves more resilient to future disasters – or we can sit passively by and wait until we are in desperate need, let events impoverish us by destroying the things we own, and watch our taxes skyrocket as we spend $15 where we could have spent $1.

All it takes is political will, a willingness to spend a little bit of money now rather than massive amounts of money later, and foresight.

Unfortunately, this means we are facing a real challenge to see if humans truly are intelligent, or have barely enough brains to get in out of the rain once we’re getting wet.

And One More Thing…

If we assume that humanity is smart enough to invest in its own future, then one of the best things we could do to reduce future vulnerability is to reduce the extent to which climate will change.

We are going to experience significant changes in climate. That’s already baked into the Earth’s future by our past behavior. We will see at least a 2o (Celsius) increase in global temperatures because of all the greenhouse gases (GHGs) we have already dumped into the atmosphere.

But we are steadily making things even worse for ourselves. Not only do we continue to dump CO2 and other GHGs into the atmosphere, but we are doing so at an accelerating rate! Not only are we not getting better at what we do, we are actually getting worse, and faster, despite the clear evidence in front of us that it may ruin us.

Those who oppose this kind of action talk about how much it will cost. There are two counters to this. First, it may actually save us money. Renewable energy is now, in many places, cheaper than fossil fuels, and prices continue to fall. In the case of solar energy, prices are dropping by an estimated 26% with every doubling of capacity – an astonishing rate of change. So it is clear that if we think carefully, plan it wisely, and do it sensibly, we can actually save money by reducing GHGs in many, perhaps most, situations.

My second counter-argument is more big-picture: How much will we spend to recover from disasters like Hurricanes Harvey, Irma, and Maria, plus all the other extreme weather events ahead of us, compared to how much it will cost us to change our behavior? I don’t know of anyone who has performed this kind or scale of calculation, but I would bet that, using Monte Carlo (probabilistic) modelling of future events, versus the pretty well-defined economics of significantly curtailing or eliminating GHG emissions, we would find that we will spend less changing our behavior than picking up the pieces in the wake of future disasters.

We cannot duck out of paying for the problems ahead. The question really comes down to whether we want to pay more for an awful, impoverished future, or think clearly, plan wisely, invest less money now, and enjoy a more livable future.

This is the choice we will be forced to make.

© Copyright, IF Research, October 2017.

[1] “Disaster Politics Can Get in the Way of Disaster Preparedness”, Olive Roeder & Andrea Jones-Rooy, 31 Aug. 2017, fivethirtyeight.com website, fivethirtyeight.com/features/disaster-politics-can-get-in-the-way-of-disaster-preparedness/

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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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