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The work of the future

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2 years ago

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The work of the future


Editor’s Note: In 2020, an MIT Task Force produced a comprehensive report on the Work of the Future. Since then, the global pandemic has had a significant effect on work and businesses, providing the impetus for The Work of the Future, by the same authors. The book, from which the following excerpt is adapted, will be published by MIT Press on January 25, 2022.

A decade ago, powerful mobile phones were still a novelty, driverless cars were never seen on public roadways, and computers did not listen to conversations or respond to spoken questions. The possibility of robots taking jobs seemed far off, save for an assembly line or two. But as the emerging capabilities of robotics and artificial intelligence began capturing headlines and the popular imagination, researchers and commentators began warning that jobs long thought to be immune to automation—those demanding expertise, judgment, creativity, and seasoned experience—might soon be better accomplished by machines. Citizens of industrialized countries took notice, reacting with mounting trepidation. 

Our research did not confirm the dystopian vision of robots ushering workers off factory floors or AI rendering human expertise and judgment superfluous. But it did uncover something equally pernicious: amid a technological ecosystem delivering rising productivity and an economy generating plenty of jobs (at least until the covid-19 crisis), we found a labor market in which the fruits are so unequally distributed, so skewed toward the top, that the majority of workers have tasted only a tiny morsel of a vast harvest. 

For most US workers, the trajectory of productivity growth diverged from the trajectory of wage growth four decades ago. This decoupling had baleful economic and social consequences: low-paid, insecure jobs held by non-college-educated workers; low participation rates in the labor force; weak upward mobility across generations; and festering racial disparities in earnings and employment that have not substantially improved in decades. While new technologies have contributed to these poor results, these outcomes were not an inevitable consequence of technological change, or of globalization, or of market forces. Similar pressures from digitalization and globalization affected most industrialized countries, yet their labor markets fared better. 

We know that history and economics show no intrinsic conflict among technological change, full employment, and rising earnings. The dynamic interplay of task automation, innovation, and new work creation, while always disruptive, is a primary wellspring of rising productivity. Innovation improves the quantity, quality, and variety of work that a worker can accomplish in a given time. This rising productivity, in turn, enables improving living standards and the flourishing of human endeavors. 

When innovation fails to drive opportunity, however, it generates a fear of the future: the suspicion that technological progress, even if it makes the country wealthier, will threaten numerous livelihoods. This fear exacts a high price: political and regional divisions, distrust of institutions, and mistrust of innovation itself. In US politics, a growing gulf between the “haves” and the “have-nots” has driven a deepening national schism over how society should respond to the needs of those at the bottom of the economic ladder. 

The central challenge ahead—indeed, the work of the future—is to advance labor market opportunity to meet, complement, and shape technological innovation. This drive will require innovating in our labor market institutions by modernizing the laws, policies, norms, organizations, and enterprises that set the “rules of the game.” 

The labor market impacts of technologies like AI and robotics are taking years to unfold. But we have no time to spare in preparing for them. If those technologies are deployed in the labor institutions of today, which were designed for the last century, we will see effects similar to those manifested in recent decades: downward pressure on wages and benefits, and an increasingly bifurcated labor market. 

Building a future of work that harvests the dividends of rapidly advancing automation and ever more powerful computers can deliver opportunity and economic security for workers. To do that, we must foster institutional innovations that complement technological change.

The central challenge ahead—indeed, the work of the future—is to advance labor market opportunity to meet, complement, and shape technological innovation.

Long before the pandemic disruption, our research on the work of the future showed how many in our country are failing to thrive in a labor market that generates plenty of jobs but little economic security. The effects of the pandemic have made it even more viscerally and publicly clear: despite the official designation of many low-paid workers as “essential,” they endured the riskiest working conditions in the face of covid-19, since most of their jobs cannot be done remotely. 

Some forecast that robots will soon take over those roles, though few have to date. Others see the indispensable role of human flexibility, since it is human, not machine, adaptability that has allowed us to reorganize work on the fly during the pandemic. Still others see covid-19 as an automation-forcing event—a catalytic force that will pull technologies from the future into the present as we learn to deploy machines in jobs that humans cannot safely perform. However it plays out, the effects of covid-19 on technology and work will last long beyond the pandemic, instigating changes that may look quite unlike what anyone envisioned in 2018. 

Other forces have also roiled the 2018 visions of the future, including the rupture between the world’s two largest economies and a surge of political turmoil and economic populism that culminated in the violent attack on the US Capitol in the wake of the 2020 election of President Joe Biden. These pressures are reshaping alliances, breaking apart and reorganizing global business relationships, and spurring new forms of cyberwarfare, including disinformation, industrial-scale espionage, and electronic compromising of critical infrastructure. The US and China had friction before, but nothing like the fracture that is now occurring. What began as a trade war has morphed into a technology war. China’s whole-of-government approach to tackling major industrial and technological goals poses a competitive challenge for Western economies, which typically take a decentralized, often business-led approach. It remains to be seen whether China’s focus on government-driven domination of data accumulation yields technological advances beyond creating powerful tools for monitoring and controlling its own population. 

The clash with China is rippling through the economy and threatens to hinder innovation, which increasingly emerges from countries around the world as researchers collaborate across borders and time zones. How can we make sure that technological advances, whenever they come, yield prosperity that is widely shared? How can the US and its workers continue to play a leading role in inventing and shaping the technologies and reaping the benefits? 

No compelling historical or contemporary evidence suggests that technological advances are driving us toward a jobless future. On the contrary, we anticipate that in the next two decades, industrialized countries will have more job openings than workers to fill them, and that robotics and automation will play an increasingly crucial role in closing these gaps.

Spectacular advances in computing and communications, robotics and AI, are reshaping industries as diverse as insurance, retail, health care, manufacturing, logistics, and transportation. But we observe substantial time lags, often on the scale of decades, from the birth of an invention to its broad commercialization, assimilation into business processes, widespread adoption, and impacts on the workforce. Novel industrial robots have been slow to move into small and medium-­sized firms, for example, and autonomous vehicles have yet to be deployed on a large scale. Indeed, the most profound labor market effects of new technology that we found were due less to robotics and AI than to the continuing diffusion of decades-old (though much improved) technologies like the internet, mobile and cloud computing, and mobile phones. 

This time scale of technological change provides the opportunity to craft policies, develop skills, and foment investments to constructively shape the trajectory of change toward the greatest social and economic benefit.

What will be required to reshape and refocus the institutions and policies of the US to create the shared prosperity that is possible if we are willing to make the necessary changes?

We begin by looking at how workers are trained to make their way in a fast-­changing economy. Enabling workers to remain productive in a continuously evolving workplace requires empowering them to learn new skills at all stages of life—in primary and secondary schools, in vocational and college programs, and in ongoing adult training programs. The distinctive US system for worker training has shortcomings, but it also has unique virtues. For example, it offers numerous points of entry for workers who may want to reshape their career paths or need to find new work after a layoff. We argue that the US must invest in existing educational and training institutions and innovate to create new training models to make ongoing skills development accessible, engaging, and cost-effective. 

But even well-trained and motivated workers need and deserve a sense of basic security. Rising labor productivity has not translated into broad increases in incomes because labor market institutions and policies have fallen into disrepair. 

Peer nations from Sweden to Germany to Canada have faced the same economic, technological, and global forces as the US, and have enjoyed equally strong economic growth, but have delivered better results for their workers. What set the US apart are specific institutional changes and policy choices that failed to blunt, and in some cases magnified, the consequences of these pressures on the labor market. 

The US has allowed traditional channels for the workers’ voice to atrophy without fostering new institutions or buttressing existing ones. It has permitted the federal minimum wage to recede to near irrelevance, lowering the floor under the labor market for low-paid workers. It has embraced a policy-driven expansion of free trade with the developing world, Mexico and China in particular, that has raised aggregate national income, and yet it has failed to address the resulting employment losses and the retraining needs of workers displaced by these policies. 

No evidence suggests that this strategy of embracing growth while ignoring the plight of rank-and-file workers has paid off for the US. US leadership in growth and innovation is long-standing—the country led the world throughout the 20th century, and particularly in the several decades immediately after World War II—while the labor market maladies documented here are recent. These failures do not inevitably follow from innovation or constitute costs worth paying to gain the other economic benefits that they ostensibly deliver. We can do better. 

Recognizing the centrality of good jobs to human welfare and the centrality of innovation to the creation of good jobs leads us to ask how we can leverage investments in innovation to drive job creation, speed growth, and meet rising competitive challenges. 

Investments in innovation expand the economic pie, which is crucial to meeting challenges posed by a globalized economy marked by fierce technological competition. Throughout our studies, we found technologies that were direct results of US federal investment in research and development over the past century and longer: the internet, advanced semiconductors, AI, robotics, and autonomous vehicles, to name but a few. These new goods and services generate new industries and occupations that demand new skills and offer new earnings opportunities. The US has a stellar record of supporting innovations that inventors, entrepreneurs, and creative capital deploy to support and create new businesses. 

Adopting new technology creates winners and losers and will continue to do so. Seeking input from all stakeholders—including workers, businesses, investors, educational and nonprofit organizations, and government—can minimize the harms and maximize the benefits to individuals and communities and help ensure that the labor market of the future offers advantages, opportunity, and a measure of economic security to all.

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Why I became a TechTrekker

Published

9 months ago

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22 August 2023

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group jumps into the air with snowy mountains in the background


My senior spring in high school, I decided to defer my MIT enrollment by a year. I had always planned to take a gap year, but after receiving the silver tube in the mail and seeing all my college-bound friends plan out their classes and dorm decor, I got cold feet. Every time I mentioned my plans, I was met with questions like “But what about school?” and “MIT is cool with this?”

Yeah. MIT totally is. Postponing your MIT start date is as simple as clicking a checkbox. 

Sofia Pronina (right) was among those who hiked to the Katla Glacier during this year’s TechTrek to Iceland.

COURTESY PHOTO

Now, having finished my first year of classes, I’m really grateful that I stuck with my decision to delay MIT, as I realized that having a full year of unstructured time is a gift. I could let my creative juices run. Pick up hobbies for fun. Do cool things like work at an AI startup and teach myself how to create latte art. My favorite part of the year, however, was backpacking across Europe. I traveled through Austria, Slovakia, Russia, Spain, France, the UK, Greece, Italy, Germany, Poland, Romania, and Hungary. 

Moreover, despite my fear that I’d be losing a valuable year, traveling turned out to be the most productive thing I could have done with my time. I got to explore different cultures, meet new people from all over the world, and gain unique perspectives that I couldn’t have gotten otherwise. My travels throughout Europe allowed me to leave my comfort zone and expand my understanding of the greater human experience. 

“In Iceland there’s less focus on hustle culture, and this relaxed approach to work-life balance ends up fostering creativity. This was a wild revelation to a bunch of MIT students.”

When I became a full-time student last fall, I realized that StartLabs, the premier undergraduate entrepreneurship club on campus, gives MIT undergrads a similar opportunity to expand their horizons and experience new things. I immediately signed up. At StartLabs, we host fireside chats and ideathons throughout the year. But our flagship event is our annual TechTrek over spring break. In previous years, StartLabs has gone on TechTrek trips to Germany, Switzerland, and Israel. On these fully funded trips, StartLabs members have visited and collaborated with industry leaders, incubators, startups, and academic institutions. They take these treks both to connect with the global startup sphere and to build closer relationships within the club itself.

Most important, however, the process of organizing the TechTrek is itself an expedited introduction to entrepreneurship. The trip is entirely planned by StartLabs members; we figure out travel logistics, find sponsors, and then discover ways to optimize our funding. 

two students soaking in a hot spring in Iceland

COURTESY PHOTO

In organizing this year’s trip to Iceland, we had to learn how to delegate roles to all the planners and how to maintain morale when making this trip a reality seemed to be an impossible task. We woke up extra early to take 6 a.m. calls with Icelandic founders and sponsors. We came up with options for different levels of sponsorship, used pattern recognition to deduce the email addresses of hundreds of potential contacts at organizations we wanted to visit, and all got scrappy with utilizing our LinkedIn connections.

And as any good entrepreneur must, we had to learn how to be lean and maximize our resources. To stretch our food budget, we planned all our incubator and company visits around lunchtime in hopes of getting fed, played human Tetris as we fit 16 people into a six-person Airbnb, and emailed grocery stores to get their nearly expired foods for a discount. We even made a deal with the local bus company to give us free tickets in exchange for a story post on our Instagram account. 

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The Download: spying keyboard software, and why boring AI is best

Published

9 months ago

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22 August 2023

By

🧠


This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How ubiquitous keyboard software puts hundreds of millions of Chinese users at risk

For millions of Chinese people, the first software they download onto devices is always the same: a keyboard app. Yet few of them are aware that it may make everything they type vulnerable to spying eyes. 

QWERTY keyboards are inefficient as many Chinese characters share the same latinized spelling. As a result, many switch to smart, localized keyboard apps to save time and frustration. Today, over 800 million Chinese people use third-party keyboard apps on their PCs, laptops, and mobile phones. 

But a recent report by the Citizen Lab, a University of Toronto–affiliated research group, revealed that Sogou, one of the most popular Chinese keyboard apps, had a massive security loophole. Read the full story. 

—Zeyi Yang

Why we should all be rooting for boring AI

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. It hopes they could improve intelligence and operational planning. 

But those might not be the right use cases, writes our senior AI reporter Melissa Heikkila. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases. 

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. The DoD’s best bet is to apply generative AI to more mundane things like Excel, email, or word processing. Read the full story. 

This story is from The Algorithm, Melissa’s weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The ice cores that will let us look 1.5 million years into the past

To better understand the role atmospheric carbon dioxide plays in Earth’s climate cycles, scientists have long turned to ice cores drilled in Antarctica, where snow layers accumulate and compact over hundreds of thousands of years, trapping samples of ancient air in a lattice of bubbles that serve as tiny time capsules. 

By analyzing those cores, scientists can connect greenhouse-gas concentrations with temperatures going back 800,000 years. Now, a new European-led initiative hopes to eventually retrieve the oldest core yet, dating back 1.5 million years. But that impressive feat is still only the first step. Once they’ve done that, they’ll have to figure out how they’re going to extract the air from the ice. Read the full story.

—Christian Elliott

This story is from the latest edition of our print magazine, set to go live tomorrow. Subscribe today for as low as $8/month to ensure you receive full access to the new Ethics issue and in-depth stories on experimental drugs, AI assisted warfare, microfinance, and more.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 How AI got dragged into the culture wars
Fears about ‘woke’ AI fundamentally misunderstand how it works. Yet they’re gaining traction. (The Guardian) 
+ Why it’s impossible to build an unbiased AI language model. (MIT Technology Review)
 
2 Researchers are racing to understand a new coronavirus variant 
It’s unlikely to be cause for concern, but it shows this virus still has plenty of tricks up its sleeve. (Nature)
+ Covid hasn’t entirely gone away—here’s where we stand. (MIT Technology Review)
+ Why we can’t afford to stop monitoring it. (Ars Technica)
 
3 How Hilary became such a monster storm
Much of it is down to unusually hot sea surface temperatures. (Wired $)
+ The era of simultaneous climate disasters is here to stay. (Axios)
+ People are donning cooling vests so they can work through the heat. (Wired $)
 
4 Brain privacy is set to become important 
Scientists are getting better at decoding our brain data. It’s surely only a matter of time before others want a peek. (The Atlantic $)
+ How your brain data could be used against you. (MIT Technology Review)
 
5 How Nvidia built such a big competitive advantage in AI chips
Today it accounts for 70% of all AI chip sales—and an even greater share for training generative models. (NYT $)
+ The chips it’s selling to China are less effective due to US export controls. (Ars Technica)
+ These simple design rules could turn the chip industry on its head. (MIT Technology Review)
 
6 Inside the complex world of dissociative identity disorder on TikTok 
Reducing stigma is great, but doctors fear people are self-diagnosing or even imitating the disorder. (The Verge)
 
7 What TikTok might have to give up to keep operating in the US
This shows just how hollow the authorities’ purported data-collection concerns really are. (Forbes)
 
8 Soldiers in Ukraine are playing World of Tanks on their phones
It’s eerily similar to the war they are themselves fighting, but they say it helps them to dissociate from the horror. (NYT $)
 
9 Conspiracy theorists are sharing mad ideas on what causes wildfires
But it’s all just a convoluted way to try to avoid having to tackle climate change. (Slate $)
 
10 Christie’s accidentally leaked the location of tons of valuable art 🖼📍
Seemingly thanks to the metadata that often automatically attaches to smartphone photos. (WP $)

Quote of the day

“Is it going to take people dying for something to move forward?”

—An anonymous air traffic controller warns that staffing shortages in their industry, plus other factors, are starting to threaten passenger safety, the New York Times reports.

The big story

Inside effective altruism, where the far future counts a lot more than the present

" "

VICTOR KERLOW

October 2022

Since its birth in the late 2000s, effective altruism has aimed to answer the question “How can those with means have the most impact on the world in a quantifiable way?”—and supplied methods for calculating the answer.

It’s no surprise that effective altruisms’ ideas have long faced criticism for reflecting white Western saviorism, alongside an avoidance of structural problems in favor of abstract math. And as believers pour even greater amounts of money into the movement’s increasingly sci-fi ideals, such charges are only intensifying. Read the full story.

—Rebecca Ackermann

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Watch Andrew Scott’s electrifying reading of the 1965 commencement address ‘Choose One of Five’ by Edith Sampson.
+ Here’s how Metallica makes sure its live performances ROCK. ($)
+ Cannot deal with this utterly ludicrous wooden vehicle. 
+ Learn about a weird and wonderful new instrument called a harpejji.



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Why we should all be rooting for boring AI

Published

9 months ago

on

22 August 2023

By

Why we should all be rooting for boring AI


This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

I’m back from a wholesome week off picking blueberries in a forest. So this story we published last week about the messy ethics of AI in warfare is just the antidote, bringing my blood pressure right back up again. 

Arthur Holland Michel does a great job looking at the complicated and nuanced ethical questions around warfare and the military’s increasing use of artificial-intelligence tools. There are myriad ways AI could fail catastrophically or be abused in conflict situations, and there don’t seem to be any real rules constraining it yet. Holland Michel’s story illustrates how little there is to hold people accountable when things go wrong.  

Last year I wrote about how the war in Ukraine kick-started a new boom in business for defense AI startups. The latest hype cycle has only added to that, as companies—and now the military too—race to embed generative AI in products and services. 

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. 

The department sees tons of potential to “improve intelligence, operational planning, and administrative and business processes.” 

But Holland Michel’s story highlights why the first two use cases might be a bad idea. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases.  

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. Everyone agrees that humans should make the final call, but that is made harder by technology that acts unpredictably, especially in fast-moving conflict situations. 

Some worry that the people lowest on the hierarchy will pay the highest price when things go wrong: “In the event of an accident—regardless of whether the human was wrong, the computer was wrong, or they were wrong together—the person who made the ‘decision’ will absorb the blame and protect everyone else along the chain of command from the full impact of accountability,” Holland Michel writes. 

The only ones who seem likely to face no consequences when AI fails in war are the companies supplying the technology.

It helps companies when the rules the US has set to govern AI in warfare are mere recommendations, not laws. That makes it really hard to hold anyone accountable. Even the AI Act, the EU’s sweeping upcoming regulation for high-risk AI systems, exempts military uses, which arguably are the highest-risk applications of them all. 

While everyone is looking for exciting new uses for generative AI, I personally can’t wait for it to become boring. 

Amid early signs that people are starting to lose interest in the technology, companies might find that these sorts of tools are better suited for mundane, low-risk applications than solving humanity’s biggest problems.

Applying AI in, for example, productivity software such as Excel, email, or word processing might not be the sexiest idea, but compared to warfare it’s a relatively low-stakes application, and simple enough to have the potential to actually work as advertised. It could help us do the tedious bits of our jobs faster and better.

Boring AI is unlikely to break as easily and, most important, won’t kill anyone. Hopefully, soon we’ll forget we’re interacting with AI at all. (It wasn’t that long ago when machine translation was an exciting new thing in AI. Now most people don’t even think about its role in powering Google Translate.) 

That’s why I’m more confident that organizations like the DoD will find success applying generative AI in administrative and business processes. 

Boring AI is not morally complex. It’s not magic. But it works. 

Deeper Learning

AI isn’t great at decoding human emotions. So why are regulators targeting the tech?

Amid all the chatter about ChatGPT, artificial general intelligence, and the prospect of robots taking people’s jobs, regulators in the EU and the US have been ramping up warnings against AI and emotion recognition. Emotion recognition is the attempt to identify a person’s feelings or state of mind using AI analysis of video, facial images, or audio recordings. 

But why is this a top concern? Western regulators are particularly concerned about China’s use of the technology, and its potential to enable social control. And there’s also evidence that it simply does not work properly. Tate Ryan-Mosley dissected the thorny questions around the technology in last week’s edition of The Technocrat, our weekly newsletter on tech policy.

Bits and Bytes

Meta is preparing to launch free code-generating software
A version of its new LLaMA 2 language model that is able to generate programming code will pose a stiff challenge to similar proprietary code-generating programs from rivals such as OpenAI, Microsoft, and Google. The open-source program is called Code Llama, and its launch is imminent, according to The Information. (The Information) 

OpenAI is testing GPT-4 for content moderation
Using the language model to moderate online content could really help alleviate the mental toll content moderation takes on humans. OpenAI says it’s seen some promising first results, although the tech does not outperform highly trained humans. A lot of big, open questions remain, such as whether the tool can be attuned to different cultures and pick up context and nuance. (OpenAI)

Google is working on an AI assistant that offers life advice
The generative AI tools could function as a life coach, offering up ideas, planning instructions, and tutoring tips. (The New York Times)

Two tech luminaries have quit their jobs to build AI systems inspired by bees
Sakana, a new AI research lab, draws inspiration from the animal kingdom. Founded by two prominent industry researchers and former Googlers, the company plans to make multiple smaller AI models that work together, the idea being that a “swarm” of programs could be as powerful as a single large AI model. (Bloomberg)

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