The Future of Work?
The Robot Takeover
is Already Here
The machines that replace us do not have to have superintelligence to execute a takeover with overwhelming impacts. They must merely extend as they have been, rapidly becoming more invisible, autonomous and crucially instrumental in our essential systems.
It’s the Algorithm Age. In the next few years humans in most positions in the world of work will be nearly 100 percent replaced by or partnered with smart software and robots —’black box’ invisible algorithm-driven tools. Algorithms are driving the world. We are information. Everything is code. We are dependent upon and merging with our machines. Advancing the rights of the individual in this vast, complex network is difficult and crucial.
By Janna Anderson
I recently enjoyed the opportunity to give the final plenary talk at the World Future Society’s annual conference in San Francisco. The event featured marvelous speakers like Steve Jurvetson, one of the world’s top venture capitalists, John Hagel of Deloitte, Brian David Johnson of Intel and top futurist Paul Saffo.
It was an honor to get the chance to speak about “AI, Robotics and the Future of Work” in California, home to the most amazing innovations being made to advance our best and brightest future. Digital engineers and digital storytellers and the entrepreneurs who stand behind them are the primary movers continually shaping the possibilities for who we can be now and who we might become, imagining, inspiring and building the global future. That’s a heavy responsibility. It can’t be taken lightly.
I share this expanded version of my World Future talk, with its plea for a much-accelerated examination of the overwhelming impacts of the advance of algorithms to be undertaken as formally as possible right now.
Precarious times for traditional human ‘employment’ and the economy
Most people around the world today who have good Internet connectivity are already accomplishing up to 80 percent or more of their daily work with the help of algorithms — programmed assistance — on a networked computer. As anthropologist Amber Case pointed out a few years ago in a popular TED talk, we are all cyborgs now. When you assess the advances in algorithm-assisted work and the rapid advance of the robot takeover of driving, accounting and many other occupations you can clearly see that unless something changes we are in precarious times for human employment and the world’s economies.
A robot is generally defined as “a tool capable of carrying out a series of actions automatically.” There is no doubt that we are now in the midst of a “robot takeover” by mostly invisible autonomous algorithms in the machine-to-machine world and in our own algorithm-based work partnerships with Google, Facebook, Amazon, Salesforce, financial systems, you name it, the entire Web, really, and every magical digital tool we use.
Evolution in machine learning has come so far so fast that global business consultancy McKinsey predicts: “Three to five years out we expect to see far higher levels of artificial intelligence, as well as the development of distributed autonomous corporations. These self-motivating, self-contained agents, formed as corporations, will be able to carry out set objectives autonomously, without any direct human supervision.” Corporations with no human supervision? To learn more about them, search online to read the latest details about decentralized autonomous organizations (DAOs) or distributed autonomous corporations (DACs).
“Three to five years out we expect to see far higher levels of artificial intelligence, as well as the development of distributed autonomous corporations. These self-motivating, self-contained agents, formed as corporations, will be able to carry out set objectives autonomously, without any direct human supervision.”
— Dorian Pyle and Cristina San Jose in an article in McKinsey Quarterly
There is disagreement over how to define or categorize artificial intelligence, AI, and robots and how they fit how humans work, but our culture commonly sees AI as algorithms programmed to accomplish complex tasks much more quickly than humans. It is not super-smart right now, certainly not sentient, but getting things done and evolving all the time. As Eli Pariser (“Beware Online Filter Bubbles”), Frank Pasquale (“Black Box”) and others have pointed out the commonly used AI algorithms today already have significant impact, shaping how we think and what we know. Our smartest machines can even already learn on their own and create weird impressionist art.
Algorithms present overwhelming challenges as well as opportunities.
Throughout history people have partnered well with newly emerging labor-saving tools, and new technologies created more jobs than they displaced. Many people argue that today is no different (see futurist Paul Saffo’s contrarian point of view here and more essays in Pacific Standard’s “Future of Work” series here), but today’s advances will soon be capable of rapidly displacing millions of people globally, disrupting economies and governments.
Statistical indicators raise serious warning flags
McKinsey released another report in December 2015 in which it predicted that digitization could boost GDP by $2.2 trillion but, “Even as digitization creates opportunities for growth, it is likely to unleash economic dislocation. As digital technologies automate many of the tasks that humans are paid to do, the day-to-day nature of work will change in a majority of occupations. Companies will redefine many roles and business processes, affecting workers of all skill levels. Historical job-displacement rates could accelerate sharply over the next decade. The United States will need to adapt its institutions and training pathways to help workers acquire relevant skills and navigate this period of transition and churn.” (See a McKinsey chart from 2016 in which automation potential for 750 jobs is estimated: https://public.tableau.com/profile/mckinsey.analytics#!/vizhome/AutomationandUSjobs/AutomationPotentialandWagesforUSJobs)
Moshe Vardi, a professor of computer science at Rice University who has been one of the most vocal experts, says machines could replace more than half of human workers globally in the next 30 years. He bolsters his arguments with statistics. “As the decoupling data show,” he wrote in April 2016, “the U.S. economy has been performing quite poorly for the bottom 90 percent of Americans for the past 40 years. Technology is driving productivity improvements, which grow the economy. But the rising tide is not lifting all boats, and most people are not seeing any benefit from this growth. While the U.S. economy is still creating jobs, it is not creating enough of them. The labor force participation rate, which measures the active portion of the labor force, has been dropping since the late 1990s. While manufacturing output is at an all-time high, manufacturing employment is today lower than it was in the later 1940s. Wages for private nonsupervisory employees have stagnated since the late 1960s, and the wages-to-GDP ratio has been declining since 1970. Long-term unemployment is trending upwards, and inequality has become a global discussion topic, following the publication of Thomas Piketty’s 2014 book, Capital in the Twenty-First Century.”
Human-machine partnerships: How do they impact everyone?
There’s no reason not to take this potential threat seriously and prepare for the possibility. While the new trigger of concern over the future of employment is the advances we are making in our machine-human partnerships (the topic of this article) other overlapping drivers are involved, including life extension, global population growth, globalization (including the positive impacts of education on people in less-developed parts of the world and expected growth of the educated globally), skyrocketing healthcare/benefits costs for employers and many other factors.
Never has the debate about the future of humans in an automated world been so important. Vast amounts of human labor are already being achieved through symbiotic partnerships with algorithm-based, often autonomous and invisible systems online. We have even already become subservient to our machines in many ways, expecting them to just know what to do and do it for us. Now they are altering work at an accelerating rate.
Economists and entrepreneurs are beginning to express concerns over a future with far less work for humans. Never has the debate about the future of humans in an automated world been so important. Vast amounts of human labor are already being achieved through symbiotic partnerships with algorithm-based, often autonomous and invisible systems online. We have even already become subservient to our machines in many ways, expecting them to just know what to do and do it for us. Now they are altering work at an accelerating rate. And caution flags are being raised by the FTC and other organizations over the fact that we must blindly trust that those who write the programs that run our machines are working fairly, with an eye to all impacts and in our best interests.
The job-deficit future is a threatening one because economic, social and political structures have long been tailored specifically to a world with human work.
- Humans with no economic means cannot support the work of governments and businesses.
- Humans without work are likely to have little or no means for satisfying basic needs and they are likely to suffer from a loss of self-worth and dignity in today’s cultures, which primarily measure adults’ identities and award status due to their work identities.
Humans were the first ‘computers’ — all were replaced by machines
Let’s time-travel briefly. The first known written reference to “computers” was in 1614, about 400 years ago, when the word was used to describe humans who do mathematical calculations. For hundreds of years human workers were the “computers,” conducting research and military intelligence.
The photo at left shows computers at Harvard in the late 1800s. It is Harvard’s first computer lab — people, paper and pencils, and maybe a slide rule or abacus. The people pictured are doing computation.
That work has — yes — seen a “robot takeover” in the past 70 years. In 1946 ENIAC, the first electronic general-purpose computer, was introduced. The media at that time called it a “giant brain.”
It weighed 30 tons, had 5 million soldered joints, was as large as a house and it was said to have dimmed the lights in Philadelphia each time it was turned on because it drew so much energy.
Today, just a few decades later, not much time at all in regard to human history, the phone you have in your pocket is magnitudes more powerful than ENIAC due to the massive and primarily peaceful and beneficial hardware and software “robot takeover” of critical work by digital machines.
In those few decades the evolution of algorithm-driven computing machines has created countless new opportunities and enhanced the work and the lives of billions of people. We are embracing them full force and rightfully so.
In March 2016, Google’s London-based DeepMind team’s AlphaGo defeated an expert human in the complicated game of Go. Team leader Demis Hassabis said the team’s goal is “solving intelligence and then using that to solve everything else,” adding, “you can think of AlphaGo as superhuman intuition instead of superhuman calculation.” Advances seem to be accelerating daily now. There are more than 1,000 new startups or research projects tied to evolving even-smarter systems.
Billions are being invested in advancing machine learning, neural networks and deep learning. IBM, Facebook, Microsoft, Wolfram, Google and others are deep-diving into them. It’s exciting to imagine how they will do good for the world. [Added note: At the January 2016 Consumer Electronics Show, Toyota announced that it has created a research institute focused on artificial intelligence; it hired an all-star team of AI experts to run it with a $1 billion starter budget.]
Deep learning is the science of creating algorithmic neural networks that can recognize patterns in data and classify and categorize them, the machines do it all on their own, enabling the examination and discovery of previously unmanageable and often unimagined data. Google alone is said to have at least 100 teams using these processes.
Nobel winners warned about the threat of a ‘cybernation’ in 1964
Jump backward in time again 50ish years to 1964, precisely 350 years after the first mention of computers but less than 20 years after ENIAC’s arrival.
In the mid-1960s an ad hoc committee of 35 scientists and social activists including Linus Pauling and several other Nobel Prize winners sent a letter to President Lyndon B. Johnson with a call to action. They warned that “the cybernation revolution” would create “a separate nation of the poor, the unskilled, the jobless.”
Is the digital takeover trending in that direction in 2015? Let’s look at some figures.
- The OECD reports that the gap between rich and poor is at a historically high level among many of its member states due to a drop in income for the bottom 40 percent of the population.
- 10.5% in the U.S. have given up looking for work or are working part-time but wish to work full-time as of June 2015.
- The share of U.S. economic output paid as wages stands at its lowest level since the government started keeping track in the mid‑20th century.
- 1 of 6 American men in the 25–54 age group are either not working or not looking for work; this number has doubled since the 1970s.
- More people are attending college but the real wages of recent college graduates have fallen by 7.7% since 2000. Economists at the University of Chicago have estimated that almost half of this decline is the result of businesses replacing workers with computers and software.
- In 1964, when LBJ received that protest letter, the U.S. telecom monopoly AT&T was worth $267 billion in today’s dollars and employed 758,611 people. As of July 2015, Google was worth $468 billion and employed fewer than 60,000 people. When Facebook acquired WhatsApp in 2014 for $19 billion the mobile-messaging startup had just 55 employees.
- The Bureau of Labor Statistics reports that just 5 percent of the jobs generated between 1993 and 2013 came from “high tech” — the computing, software and telecommunications — sectors.
In 1964, when LBJ received the protest letter, the U.S. telecom monopoly AT&T was worth $267 billion in today’s dollars and employed more than 758,000 (758,611) people. As of July 2015, Google was worth $468 billion and employed fewer than 60,000.
The most-populated occupations in the United States today — truck, bus or taxi driver, food and beverage worker, office clerk, retail salesperson and cashier — employ 21 million people. Experts say these are among the jobs most likely to see the most humans replaced in the near future. Algorithms have taken over most jobs in finance and accounting, Baxter the bot can flip burgers, sales bots are on the way and some people are even predicting that by 2030 it will be illegal for humans to drive. Manufacturing would have been at the top of the jobs hit list, but since 2000, the number of manufacturing jobs has fallen by almost 5 million, or about 30 percent, due to automation, the robot takeover. People have already written it off as the robots’ territory.
I haven’t even mentioned the ways in which the Internet of Things and machine-to-machine (M2M) systems are beginning to automate even more of everything globally. Leaders of the Internet Engineering Task Force are now looking at ways in which machine learning and artificial intelligence advances will impact all digital communications networks and increase the complexities of the systems they are evolving.
Nearly 100% soon to be autonomous only or by human-machine teams
So, yes, in just the next few decades work in most conventional settings will be nearly 100 percent accomplished by either 1) algorithm-driven software/machines in more of the solo or machine-to-machine interactions already accomplishing a great deal of work or 2) the sort of human-and-algorithm-based partnerships many are already experiencing daily through the use of the Internet and smart devices and machines.
In just the next few decades work in most conventional settings will be nearly 100 percent accomplished by either 1) algorithm-driven software/machines in more of the solo or machine-to-machine interactions already accomplishing a great deal of work or 2) human-and-algorithm-based partnerships.
Speculation about the future of work is making headlines everywhere in 2015. Why? Consider more automation trends. Cranes have reduced the longshore workforce to 10 percent of its size 60 years ago. Self-service gas pumps, airline reservation systems, ATMs and Netflix have mostly replaced gas station attendants, travel agents, bank tellers and video store employees. Accountants and auditors, medical and legal records professionals and financial and sports news reporters are being replaced by algorithms.
As they have throughout human history, people are using tools to be more efficient, replacing themselves where it is an advantage or leveraging it to allow them to hire cheap part-time labor. They are building complete business models —like those of Uber, Lyft and many others — on implementing algorithms to cut out the need for full-time employees.
Here are more statistics indicating we could reach a crisis soon:
- Intuit estimates more than 40 percent of the workforce will consist of freelancers by 2020.
- The Bureau of Labor Statistics reports the number of “temporary-help services” workers has grown by 50 percent since 2010.
- Hilton, founded in 1919, has 530 hotels in 78 countries and employs 152,000 people. Airbnb, founded in 2008, has more than 1 million listings in 34,000 cities in 190 countries. It has just 800 employees.
- Uber has 160,000 independent contractors and just 2,000 employees — 1 in 80 of Uber’s workers receives full pay and benefits. And what happens to Uber’s thousands of contractors when it switches over to using autonomous cars?
Granted, there are many people who are happy to be self-employed or part-time workers, and many new companies are giving them that opportunity, at least for now. Algorithm-based online tools are allowing millions to live flexible lives while generating an income.
“Not only will there be fewer jobs for people doing manual work, the jobs of knowledge workers will also be replaced by computers. Almost every industry and profession will be impacted and this will create a new set of social problems — because most people can’t adapt to such dramatic change.”
— Vivek Wadhwa
In addition to Uber and Airbnb, there are so many more, including YouTube, eBay, Craigslist, Seamless, Thumbtack and TaskRabbit. The world of work is being rapidly transformed. But even these independent-contractor-friendly companies will be cutting as many humans out of the loop as possible in the future.
In its 2013 report Disruptive Technologies: Advances that will transform life, business and the global economy, McKinsey estimated that automation will have significant impact in the next few years in cutting into the $9 trillion paid globally to knowledge workers (27% of employment costs) and wrote: “Advances in artificial intelligence, machine learning, and natural user interfaces (e.g., voice recognition) are making it possible to automate many knowledge worker tasks that have long been regarded as impossible or impractical for machines to perform.”
Thus a chorus of top thinkers concerned about the quickly looming impact of all of this has emerged and grown in voice over the past year. Among them are Vivek Wadhwa, who wrote: “Not only will there be fewer jobs for people doing manual work, the jobs of knowledge workers will also be replaced by computers. Almost every industry and profession will be impacted and this will create a new set of social problems — because most people can’t adapt to such dramatic change.”
The movement toward offering blue-collar employees in more places in the U.S. a more-just $15 minimum wage may depress employment further, as the economics of running a business make more automation more attractive to more employers.
Gerald Huff, principal software engineer at Tesla Motors, wrote in a response to an Intelligence Squared debate in London, “Manufacturing is only about 9% of employment in the U.S. The vast majority of jobs now are in the retail, service and knowledge economy sectors. Until now, technology has been a complement to people in those sectors, but could not replace them. However, ubiquitous connectivity, advancing computer vision, learning, reasoning and language capabilities, and the vast digitization of economic activity are beginning to enable direct substitution for and elimination of human labor.”
And Vinod Khosla wrote, ““While the future is promising … the process of getting there raises all sorts of questions about the changing nature of work and the likely increase in income disparity. With less need for human labor and judgment, labor will be devalued relative to capital and even more so relative to ideas and machine learning technology. In an era of abundance and increasing income disparity we may need a version of capitalism that is focused on more than just efficient production and also places a greater prioritization on the less desirable side effects of capitalism.”
“With less need for human labor and judgment, labor will be devalued relative to capital and even more so relative to ideas and machine learning technology. In an era of abundance and increasing income disparity we may need a version of capitalism that is focused on more than just efficient production and also places a greater prioritization on the less desirable side effects of capitalism.”
— Vinod Khosla
Further evolution of artificial intelligence will bring much more significant impacts
Most people see themselves to be benefiting greatly from the algorithmic takeover of so much work, so that trend will continue, but it is important to recognize the significance of the unintended consequences that accompany it. We have achieved amazing algorithmic progress in only a few decades, even before the expected rapid evolution of neural networks and deep learning or the arrival of quantum computing. A massive takeover of blue-collar and white-collar work is soon to move at such a paradigm-shifting rate that we must quickly focus on identifying and implementing workable solutions to likely problems.
This trend is challenging not only humans’ work futures. The threat to work is a threat to local, national and global economies, and experts say autonomous algorithms also present a fuller existential threat. They pose so many dangers that within the past year:
- Elon Musk called advancements in algorithms “our greatest existential threat,” saying we are “summoning the demon.”
- Stephen Hawking said, “Full artificial intelligence could spell the end of the human race.”
- And Bill Gates said he is “concerned about superintelligence.”
But more about the threat of superintelligent future machines later. Let’s first look at how things get done today.
We are already facing fairly overwhelming and obvious challenges even before the true development of advanced artificial intelligence. We are finding that our accelerating dependence on algorithmic systems that are still fairly dumb raises new vulnerabilities daily, primarily due to human error and increasing complexity.
For a very small sample of the impact just look to July 8, 2015, when the world’s largest stock exchange was taken offline, one of the world’s largest airlines was grounded and the Wall Street Journal’s online site seemed to disappear due to what later were described as glitches. As we build layer upon layer of complexity in systems and networks we use to live and work and become more dependent upon automation it can magnify mistakes and leave us vulnerable to crippling attacks. Yet of course we will continue to count on algorithms more and more, adding further layers of complexity and more opportunity for errors and criminal acts to wreak havoc of some sort.
We are fully into the Algorithm Age now and benefiting from it in too many ways to be able to adequately express its full impact. We are so committed there’s no going back.
So what about the future of humans as machines evolve to do more tasks?
A much-discussed 2013 analysis of the likely future of work by researchers named Carl Frey and Michael Osbourne at Oxford University predicted that algorithm-based systems are likely to displace at least 47 percent of jobs in the U.S. by 2033, among the most endangered are accountants, actuaries and anyone who drives for a living.
The machines that replace us do not have to have superintelligence to execute a robot takeover with overwhelming impacts.
They merely need to extend in the ways in which they have already been insinuating themselves into human systems.
They are already quite a way along into the takeover.
Electronic Privacy Information Center now campaigning for ‘algorithmic transparency’
The advancement of the Internet of Things, robotics, materials science, energy storage-and-distribution and biotechnology, to name a few, will exponentially multiply this. The robot takeover is quickly becoming most prominently work accomplished by “black box” algorithms that are mostly invisible to most humans, with only a few humans controlling these systems. (Some invisible algorithm-based tech is being implanted in people today, with frightening results — see “I Want to Know What Code is Running Inside My Body.”)
This is raising more challenges all the time, but here we are addressing the alteration in human employment.
Software algorithms are already well established in operations of global financial markets and in compiling legal, medical and news reports. Algorithm-driven robots like those implemented by the manufacturing and shipping industries and the newly emerging autonomous vehicles will rapidly expand their roles.
In the “Digital Life in 2025” reports I worked on with colleagues for Elon University and Pew Research (released in 2014) we collected the opinions of thousands of technology experts.
- 83% said the Internet of Things, embedded machine-to-machine networks and wearable computing will progress significantly the next 10 years. There will be a global, immersive, invisible, ambient, networked computing environment built through the continued proliferation of programmed devices, databases and data centers. This will lead to the tagging, databasing and analytical mapping of physical and social realms that will be a boon to humanity in many regards.
- They added that as the Internet of Things centralizes information as it centralizes power and this will bring negative impacts along with the positives.
- Some warned that the monitoring of daily activities will magnify the level of profiling and targeting of individuals and amplify social, economic and political struggles. Some said “big data” will kill privacy and reduce individuals to numbers, often inaccurately, and enable social engineering and emotional manipulation. One person wrote: “The Internet of Things will demand and we will give willingly, our souls.”
- When asked about the most dangerous threats to the future of the open Internet they listed these four: commercial pressures, crackdowns by nation-states on freedoms, a loss of trust in online culture due to these and the downsides of algorithm-determined information flows.
- After we issued this report in July 2014 the Electronic Privacy Information Center began a new project to campaign for “algorithmic transparency.”
Some warned that monitoring of daily activities will magnify the level of profiling and targeting of individuals and amplify social, economic and political struggles. Some said “big data” will kill privacy and reduce individuals to numbers, often inaccurately, and enable social engineering and emotional manipulation. One person wrote: “The Internet of Things will demand and we will give willingly, our souls.”
‘Automation is Voldemort, the terrible force no one must name’
Among the statements made by survey respondents when they were asked if AI is likely to displace more jobs than it creates were these:
“The central question of 2025 will be, ‘What are people for in a world that does not need their labor and where only a minority are needed to guide the ’bot-based economy’?” — Stowe Boyd, lead researcher for GigaOm
“We are not creative enough to make meaningful jobs out of nothing — and that’s what we’ll be left with when we give all the skilled labor and unskilled labor to the machines.” — An anonymous college professor
“Technology will serve those offering the devices and controlling the algorithms.” — Law professor Greg Lastowka
“The technology may be ready but we are not — at least not yet.” — Geoff Livingston, president of Tenacity5 Media
“The problem with the Internet of Things is that the users are just another category of things.” — Electronic Privacy Information Center director Marc Rotenberg
“Automation is Voldemort, the terrible force no one must name… Humans will fall out of the system in droves. Consumer capitalism will tumble. In its place we’ll figure out how to share the value we create.” — Consultant Jerry Michalski
People are replacing people with algorithms where they can for good reason. Why would anyone settle for a human worker when fast, free, 24/7/365-working algorithms can be implemented?
Where do humans stack up in an algorithms-dominant work force?
“Humans Need Not Apply,” a short film made by CGP Grey in 2014, is a brief and intelligent introduction to the reasons humans are being displaced and will continue to be at a rapidly accelerating pace. The human brain is universally recognized as a marvel and a mystery and it may never quite be replicated in an artificial format — at least not by humans, and perhaps it should not be, because it can be glitchy and unreliable.
The human form is terribly flawed. It requires 8 to 12 hours of maintenance daily (including training, retraining, sleep/recharging, eating, exercising, cleaning/grooming and healthcare). It is easily damaged and can be difficult and sometimes impossible to repair or reprogram. It tires quickly, it is quirky, it is constantly distracted and it enters into an irreversible disintegration process within only a few years of its construction.
The machines that are now learning how to do work for us by studying aggregations of what we write on the Internet have been writing racist, sexist and snarky responses to queries because we do. We only have ourselves to blame. We can hope that the even-smarter algorithms that eventually write corrected algorithms will clean all of that mess up.
Humans can be crass, cruel and criminal. The latest evidence of this is seen in the fact that machines now learning how to speak, write and sort data by either being programmed by humans from a particular data set or by being asked to study humans’ aggregate input on the Internet have been found to often be racist, sexist and snarky. Their biases are our biases. As linguistics professor Jason Baldridge has noted, “Humans are biased and the biases we encode into machines are then scaled and automated.” Despite these human-based flaws the algorithms do lightning-fast work at a low-low price, so we will be sticking with them. We can hope that the even-smarter algorithms that eventually write corrected algorithms will clean all of that mess up.
“Humans are biased and the biases we encode into machines are then scaled and automated.”
— Jason Baldridge
It is quite clear by judging the advancing trends in self-driving vehicles, 3D printing, smartphones, drones and new types of work robots that smarter ’bots, sensors, embedded systems and other connectivity drivers will quickly slip into completely taking over or becoming even more vital in all human systems very soon.
To find more expert opinions about the future of work you can read the Millennium Project’s “Future of Work” survey results that share daunting-yet-necessary questions like this one: “Can we adapt our attitudes to work, business models, taxation and welfare fast enough to avoid mass civil unrest as machines replace people?”
So, where do we go from here?
Addressing challenges and putting solutions into action is a start
How do we deal with the prospect of a world with massive unemployment?
- We need to immediately identify and begin implementing workable solutions and concentrate more-significant resources on better understanding potential impacts of our ever-more-complex, fast-evolving human-machine systems. Whether you agree or not that human unemployment is going to reach a crisis state there’s no reason not to consider the possibilities and work for a better future. Be very suspicious of those who think we should ignore the issue.
- We also should be requiring all engineering and business school students to concentrate at least 10 percent of their course time on learning how to predict and respond to potential second- and third-order impacts of their work on social, economic and political forces, on humans and the Earth.
These are two steps to take more seriously and vigorously NOW.
How soon will algorithms impact workplaces and homes to such an extent that many millions more suffer from economic insecurity, raising pressures on individuals and potentially havoc with economies? This challenge is here. Now.
Millions of people today are doing vast amounts of unpaid or low-paid work online, often creating value and profits for others with no compensation. Some new media and sociology experts are calling for support for such workers — for instance Wikipedia editors — in a movement for “platform cooperativism” wherein digital workers self-organize to gain compensation and develop worker-owned platforms. In a 2014 essay Wikipedian Dorothy Howard suggested implementation of a method by which the most-engaged participants are rewarded for their contributions in a way similar to that in which YouTube allows people to profit for their labor. (Go here to view an excellent series of videos captured at a November 2015 Platform Cooperativism conference with top speakers and thinkers, including Yochai Benkler, Douglas Rushkoff, Richard Stallman, Frank Pasquale, Arun Sundararajan and Michel Bauwens. Read here about how the “sharing economy,” which some describe as the “gig economy,” is becoming a highly politicized issue in the 2016 presidential election.)
The scholars who see positive potential in a future in which many or most people have no jobs identify as “post-workists.” They discuss alternative “post-wage arrangements.” They say people have to overcome their dependence upon a job as dominant in determining their self-identity. The post-workists generally advocate that governments take a role in supplying some sort of assistance to the jobless. Many say the post-work world could free people to dedicate themselves to becoming volunteers, care-givers, craftspeople, artists and take on other creative tasks that make life sweet.
All commonly proposed solutions to mass unemployment would likely require a heavy tax on the owners of the most capital or some other massive change that would tap into their financial gains to support the health of the public they serve. The creators of capital do not tend to willingly give up their profits to governments. One reason is greed, but there are others. Governments tend to be inefficient systems for wealth redistribution and government welfare programs are often ineffective. In addition, channeling funds into handouts can handicap business innovators’ ability to remain competitive and continue to evolve optimally.
Post-workists are concerned about providing for people who do not have the means to pay for food, shelter, education and other crucial necessities, and they refer to people in this precarious position as the “precariat.” Three regulations-rooted ideas that are commonly suggested by post-workists and others (but seen by most economic realists as highly unlikely remedies because in the current political atmosphere they will be difficult if not impossible to get the backing to implement and sustain) are:
- The provision of a basic income to all or to all who are unemployed. Researcher Martin Ford, author of “Rise of the Robots” and “Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future,” says this basic stipend should be incentive-based, tied to attaining education, performing community service and so forth.
- The establishment of government work programs (along the lines of World War II’s WPA) in which people are employed to perform public service.
- Government requirements forcing companies to employ a standardized number of humans.
The “universal basic income” is not a new idea, in fact in the 1960s economist Milton Friedman, Richard Nixon and George McGovern each expressed support for a guaranteed annual income. It would require a heavy tax on the owners of the most capital. Some communities in various places outside the U.S. have been experimenting with a basic income. Robert Reich made a video briefly explaining the concept of a universal basic income and why he considers it to be important.
All commonly proposed solutions to mass unemployment would likely require a heavy tax on the owners of the most capital or some other massive change that would tap into their financial gains to support the health of the public they serve. The creators of capital do not tend to willingly give up their profits to governments. One reason is greed, but there are others. Governments tend to be inefficient systems for wealth redistribution and government welfare programs are often ineffective. In addition, channeling funds into handouts can handicap business innovators’ ability to remain competitive and continue to evolve optimally.
Marina Gorbis and Devin Fidler of The Institute for the Future wrote in April 2016: “The design of ‘Positive Platforms’ — platforms that not only maximize profits but also provide dignified and sustainable livelihoods for those who work on the, plus enrich society as a whole — is one of the most urgent tasks we are facing today.” They advocate eight principles for creating on-demand platforms for better work futures. They said, “If we don’t do this, we de facto cede many key social choices about how we work, what is fair compensation, who owns our work products, data, and reputations to platform creators who are likely flying blind. We embed values into our technologies, and today such values are reflections of Silicon Valley’s techno-centric ethos and funding models.”
Economic inequality is already a grave problem; it may get far worse
Some big thinkers are saying disruptive digital ingenuity will save the day because it is in the process of flipping the world from an era of scarcity to a time of abundance. They predict unlimited energy, food, clean water and human life extension. They say that programmed intelligence will act as our digital agents in the creation and sharing of products and knowledge and everything will turn out just fine.
There are many who disagree with this vision, primarily due to already-existing concerns about fast-rising economic inequality and the fact that the people who live a life of abundance today are not making enough of an effort to share what they could with those who do not. Why should anyone believe that they will in the future?
The companies in the Silicon Valley are now estimated to have a market value of $3 trillion. Anyone who has seen the homeless people in San Jose and the long lines of economically challenged people standing in line for a free meal in downtown San Francisco has a hard time believing that profit-driven capitalists who live and die by the shareholder value theory even while they disparage it will take the action necessary to make a better future than the one we perceive to be ahead.
In 2016 the companies in the Silicon Valley were estimated to have a market value of $3 trillion. Anyone who has seen the homeless people in San Jose and the long lines of economically challenged people standing in line for a free meal in downtown San Francisco has a hard time believing that profit-driven capitalists who live and die by the shareholder value theory even while they disparage it will take the action necessary to make a better future than the one we perceive to be ahead. (Read “Dispossessed in the Land of Dreams,” a New Republic piece by Monica Potts.)
Technology innovators must do more and better strategic thinking and acting to deal with the accelerating impacts of algorithms. While it is too soon to tell for sure, it appears that they are. A few weeks ago in an “Open Letter on the Digital Economy” economist/authors Eric Brynjolfsson and Andrew McAfee (“The Second Machine Age”), venture capitalist Steve Jurvetson and dozens of others recommended several initial suggested steps. They urged the following: a set of public policy changes; a mobilization of business leaders to develop new models and approaches to “inclusive prosperity”; and more research into implications of the digital revolution, with “increased efforts to develop long-term solutions that go beyond current thinking.”
It remains to be seen if there will be any high-impact and sustained follow-through by these powerful people and the others who can make a difference.
Remember the “Triple Threat” letter warning LBJ back in 1964? How much impact did it have on where we stand today as a “cybernation”?
It is crucial that the proposals to find solutions that are led by digital technologies business leaders and economists turn out to be much more than political maneuvering on the part of the technology sector’s corporate community to keep critics at bay.
Some hope can be found in the uptick in conference-based Future of Work conversations such as SVForum’s “Innovation for Jobs,” and, especially, tech publisher Tim O’Reilly’s “Next Economy: What’s the Future of Work?” O’Reilly and others have started a vigorous conversation since August 2015 in essays being posted at the WTF Economy site on Medium. See O’Reilly’s intriguing discussion of part-time work’s changing paradigm in the Algorithm Age here and an argument for creating a “safety net in a multi-employer world” in a response by Steven Hill of the New America Foundation here.
One thing is certain: Employment, as it is currently defined, is already extremely unstable and today many of the people who live a life of abundance are not making nearly enough of an effort yet to fully share what they could with those who do not.
More potential measures: Change work hours, overhaul education
What about other commonly proposed solutions for coping with mass unemployment? Some people propose a redefinition of the parameters of work for full-time employees, a 30-hour week or flexible, reduced hours in some format.
Google co-founder Larry Page recently joined the voices already expressing support for the idea of a four-day workweek to open up more jobs. How many places now operate with a four-day workweek?
Because education is lagging behind where it should be in the digital age, many people are proposing a complete overhaul of public education. We need to prepare humans to be capable of partnering effectively with the autonomous work systems we are evolving. Today mostly 1800s and 1900s education methods are being used to teach people to work in the 2000s. A reinvention will require a vast paradigm shift, the implementation of education algorithms paired with great teachers, the funding of inspirational, imaginative people tasked to develop the tools of change.
Comfortable with what they already know, most entrenched academic administrators and funders are reinforcing the tyranny of the status quo when it comes to education systems. Their students and the students’ parents, all accustomed to operating in the familiar old paradigm thus not yet making demands to force change, are paying more all the time for less than optimal results. Most formal education systems are treading water in the knowledge pools of the past; they are not teaching the skills, the tools or the character traits truly needed in the Algorithm Age.
Experts expect that none of this will happen soon enough in education’s currently stagnant state. Comfortable with what they already know, most entrenched academic administrators and funders are reinforcing the tyranny of the status quo when it comes to education systems. Their students and the students’ parents, all accustomed to operating in the familiar old paradigm thus not yet making demands to force change, are paying more all the time for less than optimal results. Most formal education systems are treading water in the knowledge pools of the past; many experts say they are not teaching the skills, the tools or the character traits truly needed in the Algorithm Age.
In a 2012 study Elon and Pew asked experts to speculate about the future of young adults in 2020. They said education systems are not preparing people for this new work world and said the skills young people should be learning to be prepared for a career in 2020 include:
- The ability to concentrate, to focus deeply.
- The ability to distinguish between the “noise” and the message in the ever-growing sea of information.
- The ability to do public problem solving through cooperative work.
- The ability to search effectively for information and to be able to discern the quality and veracity of the information one finds and then communicate these findings well.
- Synthesizing skills (being able to bring together details from many sources).
- The capability to be futures-minded through formal education in the practices of horizon-scanning, trends analysis and strategic foresight.
What about identity — who am I without a job?
It’s not just education that is in need of an overhaul. A primary concern in this future is the reinvention of humans’ own perceptions of human value.
Self-identity, group-identity and self-worth tend to be tied to one’s work.
Engineer Hod Lipson of Creative Machines Lab told Technology Review, “We have a new problem to innovate around: How do you keep people engaged when AI can do most things better than most people? I don’t know what the solution is, but it’s a new kind of grand challenge for engineers.”
While some experts predict that freeing people from the drudgery of work will allow their creativity and ingenuity to bloom, some predict that a post-work society will generate in many malaise, depression, a lack of purpose and waves of apathetic surrender leading to increased problems with addiction or violent behavior.
How will people discover their talents, fully develop their potential or self-actualize in a rewarding way if they face barriers caused by economic insecurity and a lack of self-esteem and if they perceive little incentive to develop themselves? Efforts to predict the best practices in a world with no work must include suggestions for a paradigm shift in self-perception or new ways and new worlds in which people might find satisfying, rewarding work.
Studies have shown that people who had a life of employment that came to an early end often become depressed and express a feeling of irrepressible loss. They often choose to sleep in, watch television or play games and kill time rather than becoming active participants in culture in various ways.
How will people discover their talents, fully develop their potential or self-actualize in a rewarding way if they face barriers caused by economic insecurity and a lack of self-esteem and if they perceive little incentive to develop themselves? Some people may find their way; many will need help. Efforts to predict the best practices in a world with no work must include suggestions for a paradigm shift in self-perception or new ways and new worlds in which people might find satisfying, rewarding work.
One answer could be finding gainful employment in augmented-reality and virtual worlds
In our 2014 Future of the Internet survey we asked experts a question about what new developments the gigabit Internet might bring, if and when we get vastly enhanced upload and download speeds online globally. This report was titled “Killer Apps in the Gigabit Age.” Many people predicted that advances in augmented reality and virtual reality will be significant by 2025 if infrastructure investments are made. Among the key themes emerging were that there will be enhanced collaboration through telepresence, augmented reality will extend people’s sense and understanding of their real-life surroundings, and virtual reality will make simulated environments richer spaces in which to spend time. They said the connection between humans and technology will tighten as machines gather, assess and display real-time personalized information in an always-on environment, impacting everything.
One solution to the jobs problem can be found in advancements in augmented-reality tools and immersive virtual-reality worlds. AR and VR tools can be tasked to purposefully help people “race with the robots,” provide previously unimagined and retro employment opportunities and enhance humans’ capabilities for living a satisfyingly productive life.
Augmented-reality — AR — wearables, embedded devices and real-world information overlays such as those being designed by Osterhout Design Group can equip human workers to be paired with AI in ways that make us higher-value digital-age employees.
We can also create virtual geographies in which there are many rewarding employment possibilities. Alternate virtual-reality worlds can be created in which people without a traditional job in “real life” can go to work every day to earn a salary and self-esteem.
We could even create a 20th-century Earth “mirror world,” a replica in VR in which people could go to “work” every day in any time period past or future, for instance the 1980s, to earn a salary and self-esteem. There are also negatives to a future in which people are fully invested in VR worlds
— for instance how do people stay in good physical health when their minds are working but their real-world bodies aren’t?
With developments by Oculus, Microsoft, Samsung and others, VR worlds are poised to soon become much more immersive and user-friendly. It remains to be seen if commercial interests will develop VR that is uplifting, educational and allows for self-actualization in a mature sense. These worlds could be set in any time, any place. (Read here about ways in which the open Web is being developed as a VR platform.)
We could even create a 20th-century Earth “mirror world,” a replica in VR in which people could go to “work” every day in any time period past or future, for instance the 1980s. There are also negatives to a future in which people are fully invested in VR worlds — for instance how do people stay in good physical health when their minds are working but their real-world bodies aren’t?
Preimagining likely problems in order to work toward best practices
Thinking through potential future “what-if?” scenarios is important. What are the second- and third-order effects in a world with fewer jobs and more people all the time? When jobs are taken over by technology that trumps people what happens to the companies that depend upon people’s purchasing power? Imagine how stock markets will perform and local and global economies will respond when the headlines quarter after quarter report lower and lower earnings as the middle-class consumer base becomes jobless? What happens to the communities and public infrastructure dependent upon tax revenues from those people and companies?
Will we see more people joining the creative economy successfully? Will the newly emerging software solutions that allow individuals to work as independent contractors help them survive corporate downsizing? Will growing ranks of unemployed “have-nots” possibly become disillusioned and angry and rise up to challenge the “haves”?
One thing is certain: Life is being redefined.
Jobs are the means by which economies and tax-supported social systems survive, the means by which people gain access to food and shelter and the means for them to build self-esteem and lead a fulfilling life.
The overlapping impacts of the algorithm-driven on-demand economy, 3D printing and other accelerating changes in human systems require us to place more focus on strategic foresight.
Strategists often image “what if” scenarios to help them think through potential futures. Let’s look at two possible scenarios out of scores of them for the future of jobs.
Scenario One:
Hiring practices may force humans to be more robot-like
When the algorithms are doing the hiring and executing data-based annual evaluations will you be deemed to be a valuable employee? Josh Bersin and many others have been writing recently about the “Datafication of HR.” And this summer Time magazine has been reporting about a move in hiring from considering IQ and EQ concerns to determining what it calls the XQ or “X Quotient” — one way to describe a person’s capability for “execution intelligence” or effectiveness in getting a job done efficiently with no muss or fuss.
Many formulas are emerging — algorithms written to judge people as workers. The growing, $2 billion “people analytics” industry is now being used in some regard by most Fortune 500 companies, and even flight attendants and fast-food workers are being put through algorithmic inventories to determine their XQ.
If we look at these trends and imagine a scenario about future employment we see that algorithms are already taking over much of the human work of hiring humans. And, unless they are programmed to seek out currently undervalued and difficult-to-track factors, they may tend to find that the more robot-like a human is the best she or he will be at doing most jobs. Algorithms could most likely hire the most-robot-like humans and humans could begin to pattern their behavior to compete by being more robot-like.
Algorithms are taking over much of the human work of hiring humans. And, unless they are programmed to seek out currently undervalued and difficult-to-track factors, they may tend to find that the more robot-like a human is the best she or he will be at doing most jobs. So, it could be that the robots are most likely to hire the most robotic humans.
The tests for the measuring of execution intelligence are programmed by people who rely on analytics performed by algorithms. With algorithms upon algorithms in the mix that makes and measures the results, who are the great deciders in hiring? More and more it is algorithms deciding today, at least in the first sorting, who gets the job.
Algorithms are taking over much of the human work of hiring humans. And, unless they are programmed to seek out currently undervalued and difficult-to-track factors, they may tend to find that the more robot-like a human is the best she or he will be at doing most jobs. So, it could be that the robots are most likely to hire the most robotic humans.
In that sort of environment, perhaps in order to position themselves to be attractive job candidates humans may, to use a “Star Trek” analogy, be likely evolve to be more like the emotionless Spock and less like Jim, Uhura, Bones or Scotty.
“We seem now to be attempting a fusion with robots in order to brute-force our brains into a persistent state of ‘peak productivity.’ Not only is this questionable from a scientific perspective, but just because we can does not mean we should.”
— Andrew Smart
In fact, scientists are already making progress in the advancement of performance-enhancing “brain apps” that people might soon feel they have to implement to be competitive, and while experiments are still in early stages many also expect in the future people will take drugs or begin to adapt to some sort of biochemical methods to advance beyond their standard human operational capabilities.
Author Andrew Smart writes, “We seem now to be attempting a fusion with robots in order to brute-force our brains into a persistent state of ‘peak productivity.’ Not only is this questionable from a scientific perspective, but just because we can does not mean we should.”
In this sort of environment, humans may, to use a “2001” analogy become more like Hal 9000.
Most humans today see the world as a place of positive evolution in spite of but also because of fallible humans. We believe that the world is a better place with a diverse workforce. And we know that beautiful things can emerge from human error, but the algorithms that write the future algorithms may not see it that way.
Scenario Two: Women are ‘downsized’ to the extreme
The 2015 film “Advantageous” shows a fairly near future that’s pretty much like today except for the fact that automation has taken over so much work that nearly everyone is what one character describes as “greedy or desperate.”
The few jobs still available for humans are generally awarded only to men because it is believed that unemployed women are less of a threat to turn into a violent mob than a large contingent of unemployed men.
In a scenario that considers a future with low employment, high economic insecurity and overpopulation, women — who are seen to be less likely than men to be a threat to society if they are unemployed — do not have work or motherhood to sustain themselves or give their lives meaning.
Perhaps if more women in this scenario had been involved in all of the decision-making in the decades leading up to this fictional society’s debacle this dystopian world might not have been the outcome.
We must more vigorously challenge the people at the technology companies that are imagining and building our futures to go into overdrive immediately in the pursuit of more-inclusive workplaces that implement diversity in decision-making and in the programming of the smart machines that are taking over the world. It is crucial to our positive survival.
Let that be a lesson to the testosterone-dominant organizations plotting the courses of our evolution.
We must challenge the people at the technology companies that are imagining and building our futures to go into overdrive immediately in the pursuit of more-inclusive workplaces that implement diversity in decision-making and in the programming of the smart machines that are taking over the world. It is crucial to our positive survival.
Images we see in entertainment often become the realities we live with
That’s a look at two possible negative scenarios. Scenarios not only help us anticipate and work to achieve the future we want. The scenarios we are constantly presented with in popular culture, in films, books, music, can heavily influence the future we build.
We need to embed more positive and mixed-outcome futures imagery in the storytelling we do today about the possible world of tomorrow. Most futuristic fiction in our culture is dystopian drama. It shows us problems with no solutions or problems with unrealistic heroes-save-the-day outcomes.
It is dangerous to prime people to expect only the worst because it can prevail over them, preconditioning them to surrender to a negative future, making them passive participants who allow such outcomes because these are the future images they have repeatedly had embedded in their minds.
The very influential popular-film versions of complicated futuristic stories are generally not nuanced. They do not present many of the complexities of potential futures, and — outside of the highly influential “Star Trek” — they do not help us also foresee some realistic and positive potential outcomes.
To see real proof of the positive impacts achievable by presenting positive futures in a culture’s fiction check out the History Channel documentary “How William Shatner Changed the World.” In it, inventors and researchers share how the first “Star Trek” TV series inspired the invention of the cell phone, robotic surgery and the search for intelligent life.
Dystopian scenarios like the ones the general public sees in most movies and television series about the future have some value in warning about potential problems and spurring discussion, but it is dangerous to prime people to expect only the worst because it can prevail over them, preconditioning them to surrender to a negative future, making them passive participants who allow such outcomes because these are the future images they have repeatedly had embedded in their minds.
Our smart machines are and will be reading and viewing the content we create as they learn to work with and for us. If our algorithmic deciders ultimately base their future decisions about the value of humans on Hollywood’s version of human behavior or the evidence of human behavior they see in our news media reports they will have no choice but to let us go because we are obviously a grave threat to Earth’s existence.
We need to create and popularize through storytelling more realistic images of what a positive or mixed future can look like so we have those images in our minds to work toward. Science fiction writers have been doing this to a certain extent for some time, but the vividness of visual storytelling seen in video games, television and films sticks with and reaches a much larger audience, and these genres are becoming more powerful as virtual reality applications begin to immerse humans further in imagery.
The creative people in the video game, film and television industries must get to work creating realistic futures scenarios in which people find success in overcoming dangerous challenges and build inclusive tech-based societies much as they have taken on the job of working to advocate civil rights, for instance in portraying the positives of gender rights.
Remember, too, that our smart machines are and will be reading and viewing the content we create to learn to work with and for us. (Yes, they are already reading print content and studying and learning from images.) If our robot workers, algorithmic deciders, ultimately base their future decisions about the value of humans on Hollywood’s version of human behavior they will have no choice but to let us go because we are such a grave threat to Earth’s existence.
The Algorithm Age challenge: Invisible complexity
We are rather haphazardly weaving layers of complexity as we evolve ourselves and our tools in the Algorithm Age, creating complex systems in which operations disappear into a black-box setting, a realm in which profit/efficiency are the goal and in which there is little or no consistently capable human oversight or enough care taken in the quick-turnover evolving of the systems.
We are heading into an age in which the executions of many billions, even trillions, of daily split-second processes — networked and not — create such an extreme level of complexity it could easily tip into or be tipped into chaos.
We aren’t nurturing and training enough humans who are capable of the technical skills combined with the foresight to be managing development of these complex systems. Most change is driven by economics. If automation will make things run more efficiently and save money or raise earnings people will invest in its implementation.
Deep learning is advancing rapidly, pushing us toward a more machine-driven near-future. Quantum computing breakthroughs indicate that it could arrive soon to magnify the trends toward algorithm-driven everything.
We aren’t nurturing and training enough humans who are capable of the technical skills combined with the foresight to be managing development of these complex systems. Most change is driven by economics. If automation will make things run more efficiently and save money or raise earnings people will invest in its implementation. Deep learning is advancing rapidly, pushing us toward a more machine-driven near-future. Quantum computing breakthroughs indicate that it could arrive soon to magnify the trends toward algorithm-driven everything.
The accelerating speed of technological advancement in the race toward riches and global domination amplifies the potential for negative outcomes that may outweigh the positives in the end.
As UK Guardian reporter Tim Adams, who interviewed more than a dozen of Google’s top futures-thinkers this summer, noted, Google’s honestly earnest employees are “guided by the thrills of problem-solving logic, optimized by data and funded by billions. One of the downsides… is that while the perceived benefits those solutions can provoke change on a vast scale, they also invariably mean the law of unintended consequences is multiplied by many orders of magnitude. Human costs are not part of the algorithm.”
Never has the debate about the future of humans in an automated world been so intense. Some people who pooh-pooh a “robot takeover” have taken a great amount of pleasure in declaring that there is and will be slow progress in perfecting AI, pointing out the humorous pratfalls seen in DARPA’s annual Robotics Challenge, but the nightmare-evil-robot scenarios that are easiest to depict in entertainment are not the ones with which we must be concerned.
It is that which we cannot see that we should question, challenge and even fear the most.
We have to do much better at working together at pre-imagining and even sometimes pre-testing solutions for the looming implications of the invisible, networked algorithms that we are allowing to insinuate themselves into every nook and cranny of our lives.
It is that which we cannot see that we should question, challenge and even fear the most. Algorithms are the future of economics, politics and society. We are information. Everything is code. We are becoming dependent upon and even merging with our machines in the Algorithm Age. Advancing the rights of the individual in this vast, complex network is difficult and crucial.
Algorithms are the now and the future
Algorithms are the future of economics, politics and society. We are information. Everything is code. We are becoming dependent upon and even merging with our machines in the Algorithm Age. Advancing the rights of the individual in this vast, complex network is difficult and crucial.
- How are we and our brains changing as we converge with information in cyberspace?
- How will we live and work in a future that will definitely be very different from today?
- Is our growing development of machine intelligence a threat to our very existence?
In 2015, the Future of Life Institute awarded 37 grants totaling $7 million to researchers whose proposed projects are aimed at “keeping AI robust and beneficial.” In addition to various proposals to, for instance, investigate AI cyber security and the deployment of AI-based weapons systems, futurist Nick Bostrom won by far the largest grant, $1.5 million to start the Strategic Research Center for Artificial Intelligence at Oxford and Cambridge. All of these projects are an important step in trying to anticipate change and work for best results. But $7 million for 37 projects is not nearly enough.
New industry-based efforts launched in 2016 to share information and begin assessing the impacts of algorithms included OpenAI and the Partnership on AI, originally formed as an alliance of Amazon, DeepMind, Facebook, Google, IBM and Microsoft.
Some expert techno-optimists like Ray Kurzweil say we must and will become one with our machines, merging our human-substrate software with algorithm-driven hardware in an act to achieve eternal survival. How peaceful and ethical could such cybersuperhumans be in the future? Possibly very — if these humans have somehow shed the selfish survival instincts with which they are now both blessed and cursed.
Certainly any version of that future will be better if we invest much more in preparing well for it. Why aren’t university curriculums — especially those for business and engineering majors — requiring more instruction in scenarios thinking, trends analysis and strategic planning? The smart institutions are soft-pedaling history courses and teaching “chronology” — understanding your discipline over the arc of time, mostly concentrating on today’s trends and where they may lead and what to do to best prepare for the future we can see looming ahead.
It is vital cooperate globally in a new examination of systems science
In addition to all of the above suggestions, we must address the chaos we are creating as we evolve the ways in which we operate in the world. The largest concern in systems is how we bring humans together to agree on shared values that cultivate harmony so they can work together to build the best future possible.
Enhanced global cooperation is necessary to ensure a more universal understanding of where we are and where we may go wrong and how we can go right. We need to work together toward a more formalized investment of the best and brightest in well-funded and trusted and heeded global foresight units to be at the forefront. As a I and a group of contributors to the Millennium Project’s 2014 State of the Future report wrote, decision-making is based on beliefs about the future and, “Humanity needs a global, multifaceted, general long-view of the future with long-range goals to help it make better decisions.”
We need to further develop systems science focused on finding the best weave of closed and open system synergies — both types have flaws in flows. How do we work to partner with and within our systems and enhance, globally, autonomous, human and shared work processes?
There must be a refocusing on things such as cybernetics and control theory. A new understanding of best processes in designing and evolving effective systems is needed at this time of accelerating change.
It isn’t just profit motives driving the algorithmic takeover forward. We see ways in which we may overcome diseases such as cancer and Alzheimer’s, make new high-impact improvements in finding enough food and water to serve human needs, lift billions more out of poverty, raise human potential with opportunities and possibly prevent existential risks through better decision-making.
As New York Times technology journalist John Markoff observes in his new book “Machines of Loving Grace,” we are in a quest for “common ground between humans and robots” as we evolve together.
We can’t allow ourselves to maintain the status quo. We are on the verge of even more immersive and mesmerizing, life-enhancing, rights-threatening, criminal- and terrorist-enabling, politically challenging, disruptive networked communications. Such complexity challenges our ability to detect, project and respond to issues. We must invest far more fully in the careful study of the emerging and likely future impacts of the Algorithm Age.
Everyone who has a voice in technological evolution has to come through for us today to create our best tomorrow. We can’t allow ourselves to maintain the status quo. We are on the verge of even more immersive and mesmerizing, life-enhancing, rights-threatening, criminal- and terrorist-enabling, politically challenging, disruptive networked communications. Such complexity challenges our ability to detect, project and respond to issues. We must invest far more fully in the careful study of the emerging and likely future impacts of the Algorithm Age.
Let’s at least get to work figuring out how we might create ways in which all of us can live happily and well in a world with much less “work” as it is defined in 2015 terms.
Janna Anderson, a professor of communications at Elon University and director of its Imagining the Internet Center, is also a contract researcher for the Internet, Science and Technology Project at Pew Research.
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