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AI and the Future of Work: What to Expect

MIT conference convenes AI and automation experts to consider the technology’s effects on jobs and industries

4 min read
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The robots have come for our jobs. This is the fear that artificial intelligence increasingly stokes with both the tech and policy elite and the public at large. But how worried should we really be?

To consider what impact AI will have on employment, a conference at MIT titled The Future of Work is convening this week, bringing together some leading thinkers and analysts. In advance of the conference, IEEE Spectrum talked with R. David Edelman, director of MIT’s Project on Technology, Economy & National Security about his take on AI’s coming role.

Edelman says he’s lately seen AI-related worry (mixed with economic anxiety) ramp up in much the same way that cybersecurity worries began ratcheting up ten or fifteen years ago.

“Increasingly, issues like the implications of AI on the future of work are table stakes for understanding our economic future and our ability to deliver prosperity for Americans,” he says. “That’s why you’re seeing broad interest, not just from the Department of Labor, not just from the Council of Economic Advisors, but across the government and, in turn, across society.”

Before coming to MIT, Edelman worked in the White House from 2010-’17 under a number of titles, including Special Assistant to the President on Economic and Technology Policy. Edelman also organizes a related conference in the spring at MIT, the MIT AI Policy Congress.

At this week’s Future of Work conference though, Edelman say he’ll be keeping his ears open for a number of issues that he thinks are not quite on everyone’s radar yet. But they may be soon.

For starters, Edelman says, not enough attention in mainstream conversations concerns the boundary between AI-controlled systems and human-controlled ones.

“We need to figure out when people are comfortable handing decisions over to robots, and when they’re not,” he says. “There is a yawning gap between the percentage of Americans who are willing to turn their lives over to autopilot on a plane, and the percentage of Americans willing to turn their lives over to autopilot on a Tesla.”

Which, to be clear, Edelman is not saying represents any sort of unfounded fear. Just that public discussion over self-driving or self-piloting systems is very either/or. Either a self-driving system is seen as 100 percent reliable for all situations, or it’s seen as immature and never to be used.

Second, not enough attention has yet been devoted to the question of metrics we can put in place to understand when an AI system has earned public trust and when it has not.

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AI systems are, Edelman points out, only as reliable as the data that created them. So questions about racial and socioeconomic bias in, for instance, AI hiring algorithms are entirely appropriate.“Claims about AI-driven hiring are careening evermore quickly forward,” Edelman says. “There seems to be a disconnect. I’m eager to know, Are we in a place where we need to pump the brakes on AI-influenced hiring? Or do we have some models in a technical or legal context that can give us the confidence we lack today that these systems won’t create a second source of bias?”

A third area of the conversation that Edelman says deserves more media and policy attention is the question of what industries does AI threaten most. While there’s been discussion about jobs that have been put in the AI cross hairs, less discussed, he says, is the bias inherent in the question itself.

A 2017 study by Yale University and the University of Oxford’s Future of Humanity Institute surveyed AI experts for their predictions about, in part, the gravest threats AI poses to jobs and economic prosperity. Edelman points out that the industry professionals surveyed all tipped their hands a bit in the survey: The very last profession AI researchers said would ever be automated was—surprise, surprise—AI researchers.

“Everyone believes that their job will be the last job to be automated, because it’s too complex for machines to possibly master,” Edelman says.

“It’s time we make sure we’re appropriately challenging this consensus that the only sort of preparation we need to do is for the lowest-wage and lowest-skilled jobs,” he says. “Because it may well be that what we think of as good middle-income jobs, maybe even requiring some education, might be displaced or have major skills within them displaced.”

Last is the belief that AI’s effect on industries will be to eliminate jobs and only to eliminate jobs. When, Edelman says, the evidence suggests any such threats could be more nuanced.

AI may indeed eliminate some categories of jobs but may also spawn hybrid jobs that incorporate the new technology into an old format. As was the case with the rollout of electricity at the turn of the 20th century, new fields of study spring up too. Electrical engineers weren’t really needed before electricity became something more than a parlor curiosity, after all. Could AI engineering one day be a field unto its own? (With, surely, its own categories of jobs and academic fields of study and professional membership organizations?)

“We should be doing the hard and technical and often unsexy and unglamorous work of designing systems to earn our trust,” he says. “Humanity has been spectacularly unsuccessful in placing technological genies back in bottles. … We’re at the vanguard of a revolution in teaching technology how to play nice with humans. But that’s gonna be a lot of work. Because it’s got a lot to learn.”

This post was updated on 26 November 2019. 

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An IBM Quantum Computer Will Soon Pass the 1,000-Qubit Mark

The Condor processor is just one quantum-computing advance slated for 2023

4 min read
This photo shows a woman working on a piece of apparatus that is suspended from the ceiling of the laboratory.

A researcher at IBM’s Thomas J. Watson Research Center examines some of the quantum hardware being constructed there.

Connie Zhou/IBM

IBM’s Condor, the world’s first universal quantum computer with more than 1,000 qubits, is set to debut in 2023. The year is also expected to see IBM launch Heron, the first of a new flock of modular quantum processors that the company says may help it produce quantum computers with more than 4,000 qubits by 2025.

This article is part of our special report Top Tech 2023.

While quantum computers can, in theory, quickly find answers to problems that classical computers would take eons to solve, today’s quantum hardware is still short on qubits, limiting its usefulness. Entanglement and other quantum states necessary for quantum computation are infamously fragile, being susceptible to heat and other disturbances, which makes scaling up the number of qubits a huge technical challenge.

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