In this Q&A with Michael Osborne, we delve into the historical context of AI and the impact automation will have on the future of work. Michael Osborne is the Dyson Associate Professor in Machine Learning at the University of Oxford. He is a faculty member of the Oxford-Man Institute of Quantitative Finance and the co-director of the Oxford Martin Programme on Technology and Employment.
Let’s start with your background.
I’m an engineer by background with a focus on machine learning. My academic career to date has mainly focused on designing algorithms that, in one form or another, automate human work. At heart, they wrest decision-making away from people. Such algorithms can operate well beyond human capacity, examining, for example, billions of data points apiece in search of an anomalous signal.
This technical background, in time, offered a segue to my current work, exploring the societal consequences of automating the preserve of human activity. Around 2013, I connected with the economist Carl Frey, which led in turn to a joint paper and a renewed focus — using machine learning itself as a tool to understand machine learning as a crucial driver in industry and society at large.
Can you provide some context on the history of labor and its relationship with automation?
Our assertion that work is increasingly vulnerable to automation draws fierce pushback: that is, the historical antecedents to our claim have primarily been proven false. Any labor decline from breakthroughs in automation has been consistently offset with a range of new employment. So what’s different this time?
We believe the profound wave of machine learning currently sweeping through society will replace cognitive work, much as the Industrial Revolution of the 18th and 19th centuries replaced its manual analog. As machines grow increasingly adept at automating cognitive labor, the human share of labor correspondingly declines. The present shift need not replicate the historical pattern, in which humans redistributed to other work. As others have noted, a better analogy invokes not humans themselves, but their equestrian companion.
Imagine, if you will, that you are a horse in the early 1900s. Despite breathtaking revolutions in technology over the previous hundred years (e.g., the telegraph overtaking the Pony Express, and railroads cannibalizing horse-powered travel), you might be feeling pretty happy about your prospects. In fact, the US horse population continued to increase approximately sixfold between 1840 and 1900. Your confidence in future job opportunities might begin to seem like an idee fixe: equine labor is in some fundamental way resistant to automation.
Such confidence would soon crumble under its own weight. By 1950, the US equine population declined to 10% of its 1900 level. Society had crossed a rubicon of sorts, beyond which machines could outdo horses in every relevant dimension.
Our work examines how this scenario might unfold with human instead of equine labor. Humans, for example, may do better at very high-level emotional interactions. Yet it seems unlikely that such a skill (or others like it) will find sufficient demand to maintain full employment. This isn’t a conclusive prediction, but rather a plausible outcome worthy of our attention.
Assuming machines really do crowd out human labor, what aspects of work are at risk?
This is a vital question. Absent the worst-case scenario, even moderate perturbations in the labor market can lead to a major upheaval in society. Our work suggests the automation burden rests most heavily upon the shoulders of the least skilled‚ a tragic outcome considering the difficulty of retraining.
We contend that new jobs will emerge from the dust of automation, but they might be a shadow of their former selves. 21st-century work, by and large, may not match the skill mix and volume for a healthy replacement rate. In the absence of decisive education reform, a growing list of occupations (e.g., truck drivers, auditors, clerks in various retail situations‚ to name just a few) will fail to keep up.
The workforce dislocation might permanently disenfranchise a meaningful swath of society, setting them adrift in an economy without the demand for their time and skill. This stands as one of the key points we hope to convey to policymakers: these trends in automation pose a real risk to already widening wealth inequality.
In the coming decades, which are the jobs more immune to automation?
We found three loose groupings of skills that offer some degree of protection from automation. The first of these is creativity. The ability to generate novel ideas still remains generally out of reach for machines. The second is social intelligence. While algorithms can interact with humans via chatbots, for example, they still fall short at higher-level social functions (e.g., negotiation or persuasion). The final of these three guardrails, so to speak, centers on manual dexterity‚ unstructured physical interaction in the world. This is fairly difficult to automate even today. The upshot of our work suggests that jobs without at least one the above bottlenecks faces a material risk of automation.
How far along is the AI research community in tackling these bottlenecks? And which of the three do you think will be the first to succumb to machines?
If I were to rely merely on technical progress, I think we’ll see advances in manipulation first, social intelligence second, and creativity third. Advances in robotics continue to enable improved object manipulation in obstructed environments. As it relates to social intelligence, we’ve seen the reemergence of chatbots and algorithms with meaningful marks on the Turing Test. Finally, creativity itself has found expression in machines over the past couple of years, such as the DeepDream algorithms that can’t in a number of artistic styles.
While research continues to shatter our expectations of the possible, the technologies with the most immediate impact trace back to older work. In terms of jobs, cutting-edge research matters less than the evolving nature of work itself: what matters more is the means by which jobs, and by extension industry, can be remodeled to exploit state-of-the-art machines. It’s less about new technology, and more a question of redesigning jobs to suit the technology already at hand.
Can you elaborate?
Consider the typing pool of the 1950s, in which groups of workers were arrayed to take dictation and other miscellaneous tasks. These occupations now seem but a distant memory. You might attribute the demise of the typing pool to the invention of the word processor, but word processors alone were insufficient as a drop-in replacement.
Firms eventually realized that while typing pools covered a wide range of tasks, their cost outweighed the benefit of the alternative (that is, whittling down the task of handling documents to a degree that employees could manage themselves). This key re-architecture made the typing pool obsolete.
Which industries and which categories of labor will experience the biggest impact of automation?
In a paper published in 2013, we described a novel approach to estimating the probability of computerisation for 702 occupations using a Gaussian process classifier. Our work drew heavily from O*NET data from the Department of Labor and involved some degree of hand labeling. In the final analysis, we found that 47% of the US labor market faces the risk of automation.
Over a twenty-year horizon, we found the accommodation and food services industries to be particularly high-risk; 87% of their current employment faces the real threat of automation. As an example, restaurants like Chili’s are replacing some of the tasks performed by their waitstaff with tablets. At the same time, travel booking websites like Airbnb portend profound shifts in the accommodations space. In the UK alone, we’ve seen employment for travel agents drop by 50% in the last decade or so.
In the case of the transportation and warehousing industry, 75% of employment is at risk — from forklift operators to hospital porters.
The transportation of goods already commonly occurs in highly structured environments. For example, Amazon recently acquired Kiva systems, which astutely recognized that their robots don’t need to fully solve the SLAM (simultaneous localization and mapping) problem.
Instead, the robots can make effective use of barcodes strategically placed on the warehouse floor for guidance, leaving humans the more complex task of removing items from shelves. The robot, for its part, simply moves the entire shelving unit as required. These robots reduce, but don’t fully replace human labor. But like a thousand cuts, such reductions add up over time.
If the biggest impact will be for jobs that are more amenable to being restructured, how do we gauge the restructurability of a given task?
Toward this end, we’re exploring automation around primary healthcare delivery in the UK. Automation in healthcare is both urgent and complicated: rising costs combined with the specter of budget cuts in the UK demand some degree of automation. Our work includes ethnographic surveys and other primary research. In interviewing front-line staff, we seek to understand their views and interactions with technology, as well as opportunities for efficiency.
Assessing the restructurability of a job requires a narrow aperture and a nuanced understanding of the given occupation. That said, even if a task can be automated, following through requires navigating a web of stakeholders and norms. In the case of healthcare, for example, GP and patient associations chafe at certain kinds of automation. The barriers, in other words, are many.
What are the most exciting directions you expect your research and that of your peers to take in the next five or so years?
Within machine learning, most recent advances have occurred within supervised learning tasks, requiring algorithms to be explicitly taught (structured) tasks. I expect that in the next five or so years, we’ll begin to make more progress on the more challenging problems within unsupervised learning, in which an agent must infer properties of the world from raw observations of it; and in active and reinforcement learning, in which an agent is able to request new data so as to optimally inform itself about the world. These latter modes are much more closely akin to how humans learn and offer the most exciting prospects for artificial learning agents.