Since the start of the crisis I’ve been regularly updating my LinkedIn feed with relevant insights about how this is unfolding. I’ve found a high level of engagement with this format rather than Twitter or indeed on this blog, so if you’d like to learn more about my thinking, please check my LinkedIn feed regularly.
Stay safe.
From the BBC comes this highly relevant article about the challenges of modern democracy. It highlights several flaws with the nature of Western governance and then highlights the key issue:
The time has come to face an inconvenient reality: that modern democracy – especially in wealthy countries – has enabled us to colonise the future. We treat the future like a distant colonial outpost devoid of people, where we can freely dump ecological degradation, technological risk, nuclear waste and public debt, and that we feel at liberty to plunder as we please.
The whole piece is worth reading.
The Vice Chairman of Korn Ferry and a McKinsey partner have published a short book that has studied the benefits of long term thinking. There’s an interview with the authors on the Wharton site that gives some context and one extract from this stands out:
Just beware of the trends going on in the world. Larry Fink, the CEO of BlackRock, which manages $6 trillion in assets, says that it would be key for CEOs to realize some of the changes going on in society. For example, [consider] this shift towards automation and artificial intelligence. A McKinsey study we cite in the book says that [those technologies] could displace 30% of American workers.
CEOs who want to survive in the long run, and want their companies to survive in the long run, have to be aware of what’s going on in society, and try to steer their companies to address some of these issues. If they do that, they’ll get the support of their investors, customers and employees.
As most of my updates now go to my clients rather than here on my blog, this post may seem out of place compared to previous writings. However I’ve become increasingly concerned about the failure of governments to understand the implications of the:
There are significantly more risks to consider in the years ahead, and these have severe implications for stability. Bain and Company has completed some good work on this recently, and a summary has just appeared on the HBR site. I don’t usually include large quotes here, but this piece of work is a concise summary that is hard to beat (the highlights are mine):
The benefits of automation, by contrast, will flow to about 20% of workers—primarily highly compensated, highly skilled workers—as well as to the owners of capital. The growing scarcity of highly-skilled workers may push their incomes even higher relative to less-skilled workers. As a result, automation has the potential to significantly increase income inequality.
The speed of change matters. A large transformation that unfolds at a slower pace allows economies the time to adjust and grow to reabsorb unemployed workers back into the labor force. However, our analysis shows that the automation of the U.S. service sector could eliminate jobs two to three times more rapidly than in previous periods of labor transformation in modern history.
Of course, the clear pattern of history is that creating more value with fewer resources has led to rising material wealth and prosperity for centuries. We see no reason to believe that this time will be different—eventually. But the time horizon for our analysis stretches only into the early 2030s. If the automation investment boom turns to bust in that time frame, as we expect, many societies will develop severe imbalances.
The coming decade will test leadership teams profoundly. There is no set formula for managing through significant economic upheaval, but companies can take many practical steps to assess how a vastly changed landscape might affect their business. Resilient organizations that can absorb shocks and change course quickly will have the best chance of thriving in the turbulent 2020s and beyond.
The full report from Bain is also well worth reading, and is available here.
After watching the sci-fi movie ‘Passengers,’ I became intrigued with the business models that support the plot of movies like this. In turn, this triggered a Guest Blog post with Scientific American, which you can read here.
This HBR article from a couple of years ago has some good techniques for helping make better bets about how the future might evolve for a specific outcomes. They would be useful when you’re at the pointy end of a scenario exercise, rather than at the start. The entire piece is a worthwhile read, and my three main relevant takeaways can be summarised as:
The smart, insightful and deep-thinking David Weinberger has published a must-read article on Wired about the implications of AI on the human concept of knowledge. Rather than paraphrase his excellent writing, I’m going to extract some of the key sections:
We are increasingly relying on machines that derive conclusions from models that they themselves have created, models that are often beyond human comprehension, models that “think” about the world differently than we do.
But this comes with a price. This infusion of alien intelligence is bringing into question the assumptions embedded in our long Western tradition. We thought knowledge was about finding the order hidden in the chaos. We thought it was about simplifying the world. It looks like we were wrong. Knowing the world may require giving up on understanding it.
If knowing has always entailed being able to explain and justify our true beliefs — Plato’s notion, which has persisted for over two thousand years — what are we to make of a new type of knowledge, in which that task of justification is not just difficult or daunting but impossible?
Even if the universe is governed by rules simple enough for us to understand them, the simplest of events in that universe is not understandable except through gross acts of simplification.
As this sinks in, we are beginning to undergo a paradigm shift in our pervasive, everyday idea not only of knowledge, but of how the world works. Where once we saw simple laws operating on relatively predictable data, we are now becoming acutely aware of the overwhelming complexity of even the simplest of situations. Where once the regularity of the movement of the heavenly bodies was our paradigm, and life’s constant unpredictable events were anomalies — mere “accidents,” a fine Aristotelian concept that differentiates them from a thing’s “essential” properties — now the contingency of all that happens is becoming our paradigmatic example.
This is bringing us to locate knowledge outside of our heads. We can only know what we know because we are deeply in league with alien tools of our own devising. Our mental stuff is not enough.
The world didn’t happen to be designed, by God or by coincidence, to be knowable by human brains. The nature of the world is closer to the way our network of computers and sensors represent it than how the human mind perceives it. Now that machines are acting independently, we are losing the illusion that the world just happens to be simple enough for us wee creatures to comprehend.
Late last week I spoke at a conference in New Zealand which had an unusual audience. It was made up of deep thinkers who deal regularly with ambiguity at the sharp end of policy. The Q&A session was fascinating, and a lot of attendees asked for more information. With this in mind, here’s a few bullet points that provide more context on some of the topics:
Practical Tips for Online Privacy
VUCA
Read/Viewing
This is my NBR column from Feb 2017:
In June last year a fascinating aerial battle took place. It didn’t take place in the actual sky but rather in the virtual one, which was appropriate considering it was a battle of man against machine.
The man in question wasn’t an ordinary pilot but a retired US Airforce pilot, Gene Lee, with combat experience in Iraq and a graduate of the US Fighter Weapons School. The machine he was battling was a simulated aircraft controlled by an artificial intelligence (AI).
What was surprising about the outcome was that the artifical AI emerged as the victor. What was more surprising was that the computer running the software wasn’t a multimillion dollar supercomputer but one that used about $35 worth of computing power.
Welcome to the fast-moving world of AI.
It’s an area that has attracted significant media focus, and justifiably so. Experts in the field see the deployment of AI as the dawn of a new age. Andrew Ng, chief scientist at Baidu Research, is one of the gurus in the field.
“AI is the new electricity,” he says. “Just as 100 years ago electricity transformed industry after industry, AI will now do the same.”
Most of the current applications of AI focus on recognising patterns. Software is “trained” with vast amounts of information, usually with help from people who have manually tagged the data. In this way, an AI may start with images that have been labelled as cars, then, through trial and error guided by programmers, eventually recognise images of cars without any intervention.
Extraordinary breakthroughs
This simple explanation of AI belies the extraordinary breakthroughs achieved with this approach and is illustrated by an experiment conducted by an English company called DeepMind.
In 2015, DeepMind revealed that its AI had learned how to play 1980s-era computer games without any instruction. Once it had learned the games, it could outperform any human player by astonishing margins.
This feat is a stark contrast to the battle waged almost two decades ago when an IBM computer beat Russian grandmaster Gary Kasparov at chess in the mid-1990s. To beat him, the computer relied on a virtual encyclopaedia of pre-programmed information about known moves. At no point did the machine learn how to play chess.
Winning simple computer games clearly wasn’t enough to prove the abilities of DeepMind, so a more challenging option was found in the game called Go. It’s an incredibly complex Asian board game with more possible moves than the total number of atoms in the visible universe.
To learn Go, the AI played itself more than a million times. To put this in perspective, if a person played 10 games a day every day for 60 years, they would only manage to play around 180,000 games.
Despite the bold predictions of expert Go players, when the tournament ended in 2015, it was the DeepMind AI that had beaten one of the world’s best players.
The ability to “learn” can be easily leveraged into the real world. While gaming applications may excite hard-core geeks, DeepMind’s power was unleashed on a more useful challenge last year – increasing energy efficiency in data centres.
By looking at the information about power consumption – such as temperature, server demand and cooling pump speeds – the AI reduced electricity requirements for a Google data centre by an astonishing 40%. This may seem esoteric but around the world data centres already use as much electricity as the entire UK.
Potential implications
Once you start to consider the power of AI, the feeling of astonishment evaporates and is replaced with an unsettling feeling about the potential implications. For example, at the end of last year a Japanese insurance company laid off a third of one of its departments when it announced plans to replace people with an IBM AI. In this example, only 34 people were made redundant but this trend is likely to accelerate.
At this stage, it’s useful to put this development in context and consider what jobs might be replaced by AI. Andrew Ng has a useful rule of thumb – “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”
What’s important about this quote is the term “near future.” Once you extend the timeline out longer, researchers have theorised that the implications of AI on the workforce are significant. One study published in 2015 estimated that across the OECD an average of 57% of jobs were at risk from automation.
This number has been disputed heavily since it was published but it doesn’t really matter what the exact percentage will be. What is important to keep in mind is that AI will change the nature of jobs forever, and it’s highly likely that work in the future will feature people working alongside machines. This will result in a more efficient workforce, which will in turn likely to lead to job losses.
However, it’s not just the workforce that could change. The potential for this technology dwarfs anything humans have ever invented, and, just like the splitting of the atom, the jury is out on how things will develop.
One of the world’s experts on existential threats to humanity – Nick Bostrom at Oxford University – surveyed the top 100 AI researchers. He asked them about the potential threat that AI poses to humanity, and responses were startling. More than half of them responded that they believed there is a substantial chance that the development of an artificial intelligence that matches the human mind won’t end up well for one of the groups involved. You don’t need to work alongside an AI to figure out which group.
The thesis is simple – Darwinian theory applied to the biological world leads to the dominance of one species over another. If humans create a machine intelligence, probably the first thing it would do is re-programme itself become smarter. In the blink of an evolutionary eye, people could become subservient to machines with intelligence levels that were impossible to comprehend.
The exact timeframe for this scenario is hotly debated, but the same experts polled by Bostrom thought that there was a high chance of machines having human-level intelligence this century – perhaps as early as 2050.
To paraphrase a well-worn cliché, we will live in interesting times.
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Read more: https://www.nbr.co.nz/opinion/keeping-eye-artificial-intelligence
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This is my NBR column from December 2016:
Since the invention of the first “horseless buggy” in 1891, there haven’t been many significant changes to the basic of the car. There have been incremental improvements to the platform – such as better engines, increased safety and more comfort – but the core has remained unchanged. A driver from 1920 would be able to adapt to a modern car and the reverse would also apply.
While a driver from the 1920s would be able to drive a car, a mechanic from the same era would no longer recognise the key components. Today’s new cars are equipped with collision avoidance sensors, traction control, ABS, air bags, reversing cameras, engine computers and media players. This technology means that new vehicles contain more software than a modern passenger aircraft and a laptop is more useful than a wrench when tinkering under the hood.
While this may be startling to some people, it pales into insignificance compared to what’s about to happen to the car when driverless vehicles become mainstream.
Since their first significant debut in 2004, driverless cars have evolved quickly. They have now been demonstrated in a range of situations, with manufacturers posting videos online showing just how well their machines work (usually in near-perfect conditions).
These advances have been enabled by developments in sensors, cameras and computing power. On their own, each of these required technologies was prohibitively expensive only a decade ago. Fast forward to now, however, and the cost has fallen to the point where it’s feasible to bundle them into a car.
For example, one of the key components is a device called a LIDAR which creates a millimetre accurate map of the world around the car. Early versions of LIDAR systems fitted on a car cost $75,000. Just last week one manufacturer announced a version with similar capabilities that would cost about $50.
Implications for ownership
While a lot of attention is on the technology in the car, most astute analysts are focused on the second and third tier implications of driverless vehicles. This is the most interesting part of the discussion because cars are ubiquitous in most urban environments, and a change in their form and function has massive implications.
The most significant implication will concern the very notion of car ownership.
A car is one of the most expensive assets in a household but at the same time it’s also one of the least used. Most a car’s life is spent stationary, though the cost of ownership is justified through what it creates.
In modern society a car creates access to opportunity, and for cities without an efficient mass transit system, car ownership is the way people access opportunity.
However, the notion of car ownership is being questioned in some cities and people have calculated that using a car-sharing service is cheaper than owning a car in some situations. Driverless cars are the next evolution of on-demand mobility without requiring ownership.
The most likely scenario to emerge in cities is that private car ownership will dwindle, and the demand for mobility will be met by fleets of vehicles available on demand and tailored to your requirements.
For example, a two-seater car could take you to a meeting, while a people carrier may stop past your house in the morning to collect your kids and take them to school.
Eliminating road congestion
Once you have a network of fleets running in a city, and every car is sending data about its state, it then becomes possible to optimise roads in a way that’s simply not possible now. When you know exactly how many cars are on the road at any one time and where they are going, you can start to organise their routes in such a way that eliminates congestion.
Another implication of driverless cars is the remodelling of city streets to remove carparks – cars without drivers never need to be parked for hours on the kerbside.
The biggest benefit of driverless cars is likely to be the near elimination of road accidents. A car that’s operated by a computer will never get distracted by phone calls or fall asleep at the wheel. Some researchers have predicted that driverless cars have the potential to reduce road deaths by up to 90%.
Regulating for driverless cars is one of the biggest hurdles to their adoption, and for this reason uptake on private roads (which are free of regulation) has already begun.
To illustrate, some Australian mines have operated driverless trucks since 2008, and since their introduction productivity has increased and accidents have decreased. In New Zealand one of the first significant pilots of driverless vehicles will take place in 2017 when Christchurch airport will introduce a driverless shuttle bus on its private roads.
In the next few years the workforce will start to be impacted by this technology, with truck drivers likely to be affected first. Already a delivery truck owned by an Uber subsidiary has driven almost two hundred kilometres across the US on interstate highways in self driving mode. This has profound implications for the three million truck drivers employed in the US and the industries that support them.
The next decade will be a transition period where driverless vehicles start to become commonplace in some situations. They’re unlikely to be widespread in cities as many experts believe that there are very hard problems that still need to be solved. For this reason it won’t be until after 2025 that we’re likely to see a dramatic change in the transportation fleet.
What makes this timeframe interesting, is that unlike many technology driven changes that have slowly changed business, this one is clear to see. Organisations that have the foresight to leverage insights about the changes created by driverless cars will do extremely well. Those that don’t will end up like the horseless buggy.
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Read more: https://www.nbr.co.nz/opinion/fast-forward%C2%A0normalisation-driverless-cars-not-so-far
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