Posts in category: MIT

For engineers developing new materials or protective coatings, there are billions of different possibilities to sort through. Lab tests or even detailed computer simulations to determine their exact properties, such as toughness, can take hours, days, or more for each variation. Now, a new artificial intelligence-based approach developed at MIT could reduce that to a matter of milliseconds, making it practical to screen vast arrays of candidate materials.

The system, which MIT researchers hope could be used to develop stronger protective coatings or structural materials — for example, to protect aircraft or spacecraft from impacts — is described in a paper in the journal Matter, by MIT postdoc Chi-Hua Yu, civil and environmental engineering professor and department head Markus J. Buehler, and Yu-Chuan Hsu at the National Taiwan University.

The focus of this work was on predicting the way a material would break or fracture, by analyzing the propagation of cracks through the material’s molecular structure. Buehler and his colleagues have spent many years studying fractures and other failure modes in great detail, since understanding failure processes is key to developing robust, reliable materials. “One of the specialties of my lab is to use what we call molecular dynamics simulations, or basically atom-by-atom simulations” of such processes, Buehler says.

These simulations provide a chemically accurate description of how fracturing happens, he says. But it’s slow, because it requires solving equations of motion for every single atom. “It takes a lot of time to simulate these processes,” he says. The team decided to explore ways of streamlining that process, using a machine-learning system.

“We’re kind of taking a detour,” he says. “We’ve been asking, what if you had just the observation of how fracturing happens [in a given material], and let computers learn this relationship itself?” To do that, artificial intelligence (AI) systems need a variety of examples to use as a training set, to learn about the correlations between the material’s characteristics and its performance.

In this case, they were looking at a variety of composite, layered coatings made of crystalline materials. The variables included the composition of the layers and the relative orientations of their orderly crystal structures, and the way those materials each responded to fracturing, based on the molecular dynamics simulations. “We basically simulate, atom by atom, how materials break, and we record that information,” Buehler says.

The team used atom-by-atom simulations to determine how cracks propagate through different materials. This animation shows one such simulation, in which the crack propagates all the way through.

They painstakingly generated hundreds of such simulations, with a wide variety of structures, and subjected each one to many different simulated fractures. Then they fed large amounts of data about all these simulations into their AI system, to see if it could discover the underlying physical principles and predict the performance of a new material that was not part of the training set.

And it did. “That’s the really exciting thing,” Buehler says, “because the computer simulation through AI can do what normally takes a very long time using molecular dynamics, or using finite element simulations, which are another way that engineers solve this problem, and it’s very slow as well. So, this is a whole new way of simulating how materials fail.”

How materials fail is crucial information for any engineering project, Buehler emphasizes. Materials failures such as fractures are “one of the biggest reasons for losses in any industry. For inspecting planes or trains or cars, or for roads or infrastructure, or concrete, or steel corrosion, or to understand the fracture of biological tissues such as bone, the ability to simulate fracturing with AI, and doing that quickly and very efficiently, is a real game changer.”

The improvement in speed produced by using this method is remarkable. Hsu explains that “for single simulations in molecular dynamics, it has taken several hours to run the simulations, but in this artificial intelligence prediction, it only takes 10 milliseconds to go through all the predictions from the patterns, and show how a crack forms step by step.”

“Over the past 30 years or so there have been multiple approaches to model crack propagation in solids, but it remains a formidable and computationally expensive problem,” says Pradeep Guduru, a professor of engineering at Brown University, who was not involved in this work. “By shifting the computational expense to training a robust machine-learning algorithm, this new approach can potentially result in a quick and computationally inexpensive design tool, which is always desirable for practical applications.”

The method they developed is quite generalizable, Buehler says. “Even though in our paper we only applied it to one material with different crystal orientations, you can apply this methodology to much more complex materials.” And while they used data from atomistic simulations, the system could also be used to make predictions on the basis of experimental data such as images of a material undergoing fracturing.

“If we had a new material that we’ve never simulated before,” he says, “if we have a lot of images of the fracturing process, we can feed that data into the machine-learning model as well.” Whatever the input, simulated or experimental, the AI system essentially goes through the evolving process frame by frame, noting how each image differs from the one before in order to learn the underlying dynamics.

For example, as researchers make use of the new facilities in MIT.nano, the Institute’s facility dedicated to fabricating and testing materials at the nanoscale, vast amounts of new data about a variety of synthesized materials will be generated.

“As we have more and more high-throughput experimental techniques that can produce a lot of images very quickly, in an automated way, these kind of data sources can immediately be fed into the machine-learning model,” Buehler says. “We really think that the future will be one where we have a lot more integration between experiment and simulation, much more than we have in the past.”

The system could be applied not just to fracturing, as the team did in this initial demonstration, but to a wide variety of processes unfolding over time, he says, such as diffusion of one material into another, or corrosion processes. “Anytime where you have evolutions of physical fields, and we want to know how these fields evolve as a function of the microstructure,” he says, this method could be a boon.

The research was supported by the U.S. Office of Naval Research and the Army Research Office.

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Artificial intelligence could play a decisive role in stopping the Covid-19 pandemic. To give the technology a push, the MIT-IBM Watson AI Lab is funding 10 projects at MIT aimed at advancing AI’s transformative potential for society. The research will target the immediate public health and economic challenges of this moment. But it could have a lasting impact on how we evaluate and respond to risk long after the crisis has passed. The 10 research projects are highlighted below.

Early detection of sepsis in Covid-19 patients 

Sepsis is a deadly complication of Covid-19, the disease caused by the new coronavirus SARS-CoV-2. About 10 percent of Covid-19 patients get sick with sepsis within a week of showing symptoms, but only about half survive.

Identifying patients at risk for sepsis can lead to earlier, more aggressive treatment and a better chance of survival. Early detection can also help hospitals prioritize intensive-care resources for their sickest patients. In a project led by MIT Professor Daniela Rus, researchers will develop a machine learning system to analyze images of patients’ white blood cells for signs of an activated immune response against sepsis.

Designing proteins to block SARS-CoV-2

Proteins are the basic building blocks of life, and with AI, researchers can explore and manipulate their structures to address longstanding problems. Take perishable food: The MIT-IBM Watson AI Lab recently used AI to discover that a silk protein made by honeybees could double as a coating for quick-to-rot foods to extend their shelf life.

In a related project led by MIT professors Benedetto Marelli and Markus Buehler, researchers will enlist the protein-folding method used in their honeybee-silk discovery to try to defeat the new coronavirus. Their goal is to design proteins able to block the virus from binding to human cells, and to synthesize and test their unique protein creations in the lab.

Saving lives while restarting the U.S. economy

Some states are reopening for business even as questions remain about how to protect those most vulnerable to the coronavirus. In a project led by MIT professors Daron Acemoglu, Simon Johnson and Asu Ozdaglar will model the effects of targeted lockdowns on the economy and public health.

In a recent working paper co-authored by Acemoglu, Victor Chernozhukov, Ivan Werning, and Michael Whinston, MIT economists analyzed the relative risk of infection, hospitalization, and death for different age groups. When they compared uniform lockdown policies against those targeted to protect seniors, they found that a targeted approach could save more lives. Building on this work, researchers will consider how antigen tests and contact tracing apps can further reduce public health risks.

Which materials make the best face masks?

Massachusetts and six other states have ordered residents to wear face masks in public to limit the spread of coronavirus. But apart from the coveted N95 mask, which traps 95 percent of airborne particles 300 nanometers or larger, the effectiveness of many masks remains unclear due to a lack of standardized methods to evaluate them.

In a project led by MIT Associate Professor Lydia Bourouiba, researchers are developing a rigorous set of methods to measure how well homemade and medical-grade masks do at blocking the tiny droplets of saliva and mucus expelled during normal breathing, coughs, or sneezes. The researchers will test materials worn alone and together, and in a variety of configurations and environmental conditions. Their methods and measurements will determine how well materials protect mask wearers and the people around them.

Treating Covid-19 with repurposed drugs

As Covid-19’s global death toll mounts, researchers are racing to find a cure among already-approved drugs. Machine learning can expedite screening by letting researchers quickly predict if promising candidates can hit their target.

In a project led by MIT Assistant Professor Rafael Gomez-Bombarelli, researchers will represent molecules in three dimensions to see if this added spatial information can help to identify drugs most likely to be effective against the disease. They will use NASA’s Ames and U.S. Department of Energy’s NSERC supercomputers to further speed the screening process.

A privacy-first approach to automated contact tracing

Smartphone data can help limit the spread of Covid-19 by identifying people who have come into contact with someone infected with the virus, and thus may have caught the infection themselves. But automated contact tracing also carries serious privacy risks.

In collaboration with MIT Lincoln Laboratory and others, MIT researchers Ronald Rivest and Daniel Weitzner will use encrypted Bluetooth data to ensure personally identifiable information remains anonymous and secure.

Overcoming manufacturing and supply hurdles to provide global access to a coronavirus vaccine

A vaccine against SARS-CoV-2 would be a crucial turning point in the fight against Covid-19. Yet, its potential impact will be determined by the ability to rapidly and equitably distribute billions of doses globally. This is an unprecedented challenge in biomanufacturing. 

In a project led by MIT professors Anthony Sinskey and Stacy Springs, researchers will build data-driven statistical models to evaluate tradeoffs in scaling the manufacture and supply of vaccine candidates. Questions include how much production capacity will need to be added, the impact of centralized versus distributed operations, and how to design strategies for fair vaccine distribution. The goal is to give decision-makers the evidence needed to cost-effectively achieve global access.

Leveraging electronic medical records to find a treatment for Covid-19

Developed as a treatment for Ebola, the anti-viral drug remdesivir is now in clinical trials in the United States as a treatment for Covid-19. Similar efforts to repurpose already-approved drugs to treat or prevent the disease are underway.

In a project led by MIT professors Roy Welsch and Stan Finkelstein, researchers will use statistics, machine learning, and simulated clinical drug trials to find and test already-approved drugs as potential therapeutics against Covid-19. Researchers will sift through millions of electronic health records and medical claims for signals indicating that drugs used to fight chronic conditions like hypertension, diabetes, and gastric influx might also work against Covid-19 and other diseases.

Finding better ways to treat Covid-19 patients on ventilators 

Troubled breathing from acute respiratory distress syndrome is one of the complications that brings Covid-19 patients to the ICU. There, life-saving machines help patients breathe by mechanically pumping oxygen into the lungs. But even as towns and cities lower their Covid-19 infections through social distancing, there remains a national shortage of mechanical ventilators and serious health risks of ventilation itself.

In collaboration with IBM researchers Zach Shahn and Daby Sow, MIT researchers Li-Wei Lehman and Roger Mark will develop an AI tool to help doctors find better ventilator settings for Covid-19 patients and decide how long to keep them on a machine. Shortened ventilator use can limit lung damage while freeing up machines for others. To build their models, researchers will draw on data from intensive-care patients with acute respiratory distress syndrome, as well as Covid-19 patients at a local Boston hospital.

Returning to normal via targeted lockdowns, personalized treatments, and mass testing

In a few short months, Covid-19 has devastated towns and cities around the world. Researchers are now piecing together the data to understand how government policies can limit new infections and deaths and how targeted policies might protect the most vulnerable.

In a project led by MIT Professor Dimitris Bertsimas, researchers will study the effects of lockdowns and other measures meant to reduce new infections and deaths and prevent the health-care system from being swamped. In a second phase of the project, they will develop machine learning models to predict how vulnerable a given patient is to Covid-19, and what personalized treatments might be most effective. They will also develop an inexpensive, spectroscopy-based test for Covid-19 that can deliver results in minutes and pave the way for mass testing. The project will draw on clinical data from four hospitals in the United States and Europe, including Codogno Hospital, which reported Italy’s first infection.

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For many of us, our microwaves and dishwashers aren’t the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way.

But we may soon be rethinking that, thanks to new research from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called “Sapple,” analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter.

Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used.

For example, for an elderly person living alone, learning appliance usage patterns could help their health-care professionals understand their ability to perform various activities of daily living, with the goal of eventually helping advise on healthy patterns. These can include personal hygiene, dressing, eating, maintaining continence, and mobility.

“This system uses passive sensing data, and does not require people to change the way they live,” says MIT PhD student Chen-Yu Hsu, the lead author on a new paper about Sapple. “It has potential to improve things like energy saving and efficiency, give us a better understanding of the daily activities of seniors living alone, and provide insight into the behavioral analytics for smart environments.”

Of the two sensors, the “location sensor” uses radio signals to sense placement, and covers around 40 feet, or enough to cover a typical one-bedroom apartment. A user can walk around their apartment to set up the sensor, which allows it to understand the physical boundaries, and then the sensor can limit itself to that specified area.

The team says the system could potentially be useful during the Covid-19 pandemic, where there’s an increasing interest in contactless sensing of health and behaviors. They can imagine using passive sensor data to free up the need for caregivers to visit higher-risk populations and minimize overall in-person contact.

Sapple comes from the team’s growing body of research focused on using wireless sensing to better understand our complex human bodies — such as an in-body “GPS” sensor with the goal of tracking tumors or dispensing drugs, a wireless smart-home system for monitoring diseases and helping the elderly “age in place,” and another system for measuring gait to help monitor and diagnose various ailments.

Previous work in learning appliance usage has looked at using energy data from a utility meter. But this approach makes it challenging to tease out details, as the energy data is a mix of multiple appliances’ patterns all added together.

Unsupervised approaches — those in which training data aren’t labeled — assume patterns of individual appliances are unknown. However, since the utility meter measures the total energy used by the home, it’s really hard to learn individual appliances or detect them effectively.

Sapple stays in the unsupervised realm: It doesn’t assume we know the patterns of individual appliances, but instead uses data from a second sensor to help learn appliance usage patterns with self-supervision. For example, the location sensor captures a person’s motion as they approach a microwave, put food in it, and turn it on. The model then analyzes the data, and learns when specific appliances are turned on, and what their locations are in a home.

In addition to health, Sapple could potentially help reduce our heavy imprint on the natural world. By analyzing appliance usage patterns within homes, the system could be used to encourage energy-saving behaviors and improve forecasting and delivery for utility companies.

The team notes that their system’s approach solves some of the issues that can be tricky for in-home sensors. For example, using the location data doesn’t always imply appliance usage, as people can be next to an appliance without using it. Also, many appliances like refrigerators cycle their power and create “background events,” and there could be location data from multiple people in a home, but not all of them are related to appliance usage. Sapple solves these problems by learning when the two sensor streams become related, and uses that to discover when appliances are turned on, and their locations.

“As indoor location-sensing starts to potentially become as common as Wi-Fi in the future, the hope is that our technology can be effortlessly applied to all places with utility meters,” says Hsu. “This could enable new applications for passive health sensing in the homes. Utility companies, for example, could reduce peak demands by providing personalized feedback, optimize energy generation and delivery, and ultimately improve energy efficiency.”

Hsu wrote the paper alongside CSAIL PhD students Abbas Zeitoun and Guang-He Lee, as well as MIT professors Dina Katabi and Tommi Jaakkola. They presented the paper virtually at the International Conference on Learning Representations.

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Most firetrucks come in red, but it’s not hard to picture one in blue. Computers aren’t nearly as creative.

Their understanding of the world is colored, often literally, by the data they’ve trained on. If all they’ve ever seen are pictures of red fire trucks, they have trouble drawing anything else. 

To give computer vision models a fuller, more imaginative view of the world, researchers have tried feeding them more varied images. Some have tried shooting objects from odd angles, and in unusual positions, to better convey their real-world complexity. Others have asked the models to generate pictures of their own, using a form of artificial intelligence called GANs, or generative adversarial networks. In both cases, the aim is to fill in the gaps of image datasets to better reflect the three-dimensional world and make face- and object-recognition models less biased.

In a new study at the International Conference on Learning Representations, MIT researchers propose a kind of creativity test to see how far GANs can go in riffing on a given image. They “steer” the model into the subject of the photo and ask it to draw objects and animals close up, in bright light, rotated in space, or in different colors.

The model’s creations vary in subtle, sometimes surprising ways. And those variations, it turns out, closely track how creative human photographers were in framing the scenes in front of their lens. Those biases are baked into the underlying dataset, and the steering method proposed in the study is meant to make those limitations visible. 

“Latent space is where the DNA of an image lies,” says study co-author Ali Jahanian, a research scientist at MIT. “We show that you can steer into this abstract space and control what properties you want the GAN to express — up to a point. We find that a GAN’s creativity is limited by the diversity of images it learns from.” Jahanian is joined on the study by co-author Lucy Chai, a PhD student at MIT, and senior author Phillip Isola, the Bonnie and Marty (1964) Tenenbaum CD Assistant Professor of Electrical Engineering and Computer Science.

The researchers applied their method to GANs that had already been trained on ImageNet’s 14 million photos. They then measured how far the models could go in transforming different classes of animals, objects, and scenes. The level of artistic risk-taking, they found, varied widely by the type of subject the GAN was trying to manipulate. 

For example, a rising hot air balloon generated more striking poses than, say, a rotated pizza. The same was true for zooming out on a Persian cat rather than a robin, with the cat melting into a pile of fur the farther it recedes from the viewer while the bird stays virtually unchanged. The model happily turned a car blue, and a jellyfish red, they found, but it refused to draw a goldfinch or firetruck in anything but their standard-issue colors. 

The GANs also seemed astonishingly attuned to some landscapes. When the researchers bumped up the brightness on a set of mountain photos, the model whimsically added fiery eruptions to the volcano, but not a geologically older, dormant relative in the Alps. It’s as if the GANs picked up on the lighting changes as day slips into night, but seemed to understand that only volcanos grow brighter at night.

The study is a reminder of just how deeply the outputs of deep learning models hinge on their data inputs, researchers say. GANs have caught the attention of intelligence researchers for their ability to extrapolate from data, and visualize the world in new and inventive ways. 

They can take a headshot and transform it into a Renaissance-style portrait or favorite celebrity. But though GANs are capable of learning surprising details on their own, like how to divide a landscape into clouds and trees, or generate images that stick in people’s minds, they are still mostly slaves to data. Their creations reflect the biases of thousands of photographers, both in what they’ve chosen to shoot and how they framed their subject.

“What I like about this work is it’s poking at representations the GAN has learned, and pushing it to reveal why it made those decisions,” says Jaakko Lehtinen, a professor at Finland’s Aaalto University and a research scientist at NVIDIA who was not involved in the study. “GANs are incredible, and can learn all kinds of things about the physical world, but they still can’t represent images in physically meaningful ways, as humans can.”

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This is part 3 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu. 

Modern technology affects different workers in different ways. In some white-collar jobs — designer, engineer — people become more productive with sophisticated software at their side. In other cases, forms of automation, from robots to phone-answering systems, have simply replaced factory workers, receptionists, and many other kinds of employees.

Now a new study co-authored by an MIT economist suggests automation has a bigger impact on the labor market and income inequality than previous research would indicate — and identifies the year 1987 as a key inflection point in this process, the moment when jobs lost to automation stopped being replaced by an equal number of similar workplace opportunities.

“Automation is critical for understanding inequality dynamics,” says MIT economist Daron Acemoglu, co-author of a newly published paper detailing the findings.

Within industries adopting automation, the study shows, the average “displacement” (or job loss) from 1947-1987 was 17 percent of jobs, while the average “reinstatement” (new opportunities) was 19 percent. But from 1987-2016, displacement was 16 percent, while reinstatement was just 10 percent. In short, those factory positions or phone-answering jobs are not coming back.

“A lot of the new job opportunities that technology brought from the 1960s to the 1980s benefitted low-skill workers,” Acemoglu adds. “But from the 1980s, and especially in the 1990s and 2000s, there’s a double whammy for low-skill workers: They’re hurt by displacement, and the new tasks that are coming, are coming slower and benefitting high-skill workers.”

The new paper, “Unpacking Skill Bias: Automation and New Tasks,” will appear in the May issue of the American Economic Association: Papers and Proceedings. The authors are Acemoglu, who is an Institute Professor at MIT, and Pascual Restrepo PhD ’16, an assistant professor of economics at Boston University.

Low-skill workers: Moving backward

The new paper is one of several studies Acemoglu and Restrepo have conducted recently examining the effects of robots and automation in the workplace. In a just-published paper, they concluded that across the U.S. from 1993 to 2007, each new robot replaced 3.3 jobs.

In still another new paper, Acemoglu and Restrepo examined French industry from 2010 to 2015. They found that firms that quickly adopted robots became more productive and hired more workers, while their competitors fell behind and shed workers — with jobs again being reduced overall.

In the current study, Acemoglu and Restrepo construct a model of technology’s effects on the labor market, while testing the model’s strength by using empirical data from 44 relevant industries. (The study uses U.S. Census statistics on employment and wages, as well as economic data from the Bureau of Economic Analysis and the Bureau of Labor Studies, among other sources.)

The result is an alternative to the standard economic modeling in the field, which has emphasized the idea of “skill-biased” technological change — meaning that technology tends to benefit select high-skilled workers more than low-skill workers, helping the wages of high-skilled workers more, while the value of other workers stagnates. Think again of highly trained engineers who use new software to finish more projects more quickly: They become more productive and valuable, while workers lacking synergy with new technology are comparatively less valued.  

However, Acemoglu and Restrepo think even this scenario, with the prosperity gap it implies, is still too benign. Where automation occurs, lower-skill workers are not just failing to make gains; they are actively pushed backward financially. Moreover,  Acemoglu and Restrepo note, the standard model of skill-biased change does not fully account for this dynamic; it estimates that productivity gains and real (inflation-adjusted) wages of workers should be higher than they actually are.

More specifically, the standard model implies an estimate of about 2 percent annual growth in productivity since 1963, whereas annual productivity gains have been about 1.2 percent; it also estimates wage growth for low-skill workers of about 1 percent per year, whereas real wages for low-skill workers have actually dropped since the 1970s.

“Productivity growth has been lackluster, and real wages have fallen,” Acemoglu says. “Automation accounts for both of those.” Moreover, he adds, “Demand for skills has gone down almost exclusely in industries that have seen a lot of automation.”

Why “so-so technologies” are so, so bad

Indeed, Acemoglu says, automation is a special case within the larger set of technological changes in the workplace. As he puts it, automation “is different than garden-variety skill-biased technological change,” because it can replace jobs without adding much productivity to the economy.

Think of a self-checkout system in your supermarket or pharmacy: It reduces labor costs without making the task more efficient. The difference is the work is done by you, not paid employees. These kinds of systems are what Acemoglu and Restrepo have termed “so-so technologies,” because of the minimal value they offer.

“So-so technologies are not really doing a fantastic job, nobody’s enthusiastic about going one-by-one through their items at checkout, and nobody likes it when the airline they’re calling puts them through automated menus,” Acemoglu says. “So-so technologies are cost-saving devices for firms that just reduce their costs a little bit but don’t increase productivity by much. They create the usual displacement effect but don’t benefit other workers that much, and firms have no reason to hire more workers or pay other workers more.”

To be sure, not all automation resembles self-checkout systems, which were not around in 1987. Automation at that time consisted more of printed office records being converted into databases, or machinery being added to sectors like textiles and furniture-making. Robots became more commonly added to heavy industrial manufacturing in the 1990s. Automation is a suite of technologies, continuing today with software and AI, which are inherently worker-displacing.

“Displacement is really the center of our theory,” Acemoglu says. “And it has grimmer implications, because wage inequality is associated with disruptive changes for workers. It’s a much more Luddite explanation.”

After all, the Luddites — British textile mill workers who destroyed machinery in the 1810s — may be synonymous with technophobia, but their actions were motivated by economic concerns; they knew machines were replacing their jobs. That same displacement continues today, although, Acemoglu contends, the net negative consequences of technology on jobs is not inevitable. We could, perhaps, find more ways to produce job-enhancing technologies, rather than job-replacing innovations.

“It’s not all doom and gloom,” says Acemoglu. “There is nothing that says technology is all bad for workers. It is the choice we make about the direction to develop technology that is critical.”

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This is part 2 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu. 

Overall, adding robots to manufacturing reduces jobs — by more than three per robot, in fact. But a new study co-authored by an MIT professor reveals an important pattern: Firms that move quickly to use robots tend to add workers to their payroll, while industry job losses are more concentrated in firms that make this change more slowly.

The study, by MIT economist Daron Acemoglu, examines the introduction of robots to French manufacturing in recent decades, illuminating the business dynamics and labor implications in granular detail.

“When you look at use of robots at the firm level, it is really interesting because there is an additional dimension,” says Acemoglu. “We know firms are adopting robots in order to reduce their costs, so it is quite plausible that firms adopting robots early are going to expand at the expense of their competitors whose costs are not going down. And that’s exactly what we find.”

Indeed, as the study shows, a 20 percentage point increase in robot use in manufacturing from 2010 to 2015 led to a 3.2 percent decline in industry-wide employment. And yet, for firms adopting robots during that timespan, employee hours worked rose by 10.9 percent, and wages rose modestly as well.

A new paper detailing the study, “Competing with Robots: Firm-Level Evidence from France,” will appear in the May issue of the American Economic Association: Papers and Proceedings. The authors are Acemoglu, who is an Institute Professor at MIT; Clair Lelarge, a senior research economist at the Banque de France and the Center for Economic Policy Research; and Pascual Restrepo Phd ’16, an assistant professor of economics at Boston University.

A French robot census

To conduct the study, the scholars examined 55,390 French manufacturing firms, of which 598 purchased robots during the period from 2010 to 2015. The study uses data provided by France’s Ministry of Industry, client data from French robot suppliers, customs data about imported robots, and firm-level financial data concerning sales, employment, and wages, among other things.

The 598 firms that did purchase robots, while comprising just 1 percent of manufacturing firms, accounted for about 20 percent of manufacturing production during that five-year period.

“Our paper is unique in that we have an almost comprehensive [view] of robot adoption,” Acemoglu says.

The manufacturing industries most heavily adding robots to their production lines in France were pharmaceutical companies, chemicals and plastic manufacturers, food and beverage producers, metal and machinery manufacturers, and automakers.

The industries investing least in robots from 2010 to 2015 included paper and printing, textiles and apparel manufacturing, appliance manufacturers, furniture makers, and minerals companies.

The firms that did add robots to their manufacturing processes became more productive and profitable, and the use of automation lowered their labor share — the part of their income going to workers — between roughly 4 and 6 percentage points. However, because their investments in technology fueled more growth and more market share, they added more workers overall.

By contrast, the firms that did not add robots saw no change in the labor share, and for every 10 percentage point increase in robot adoption by their competitors, these firms saw their own employment drop 2.5 percent. Essentially, the firms not investing in technology were losing ground to their competitors.

This dynamic — job growth at robot-adopting firms, but job losses overall — fits with another finding Acemoglu and Restrepo made in a separate paper about the effects of robots on employment in the U.S. There, the economists found that each robot added to the work force essentially eliminated 3.3 jobs nationally.

“Looking at the result, you might think [at first] it’s the opposite of the U.S. result, where the robot adoption goes hand in hand with destruction of jobs, whereas in France, robot-adopting firms are expanding their employment,” Acemoglu says. “But that’s only because they’re expanding at the expense of their competitors. What we show is that when we add the indirect effect on those competitors, the overall effect is negative and comparable to what we find the in the U.S.”

Superstar firms and the labor share issue

The competitive dynamics the researchers found in France resemble those in another high-profile piece of economics research recently published by MIT professors. In a recent paper, MIT economists David Autor and John Van Reenen, along with three co-authors, published evidence indicating the decline in the labor share in the U.S. as a whole was driven by gains made by “superstar firms,” which find ways to lower their labor share and gain market power.

While those elite firms may hire more workers and even pay relatively well as they grow, labor share declines in their industries, overall.

“It’s very complementary,” Acemoglu observes about the work of Autor and Van Reenen. However, he notes, “A slight difference is that superstar firms [in the work of Autor and Van Reenen, in the U.S.] could come from many different sources. By having this individual firm-level technology data, we are able to show that a lot of this is about automation.”

So, while economists have offered many possible explanations for the decline of the labor share generally — including technology, tax policy, changes in labor market institutions, and more — Acemoglu suspects technology, and automation specifically, is the prime candidate, certainly in France.

“A big part of the [economic] literature now on technology, globalization, labor market institutions, is turning to the question of what explains the decline in the labor share,” Acemoglu says. “Many of those are reasonably interesting hypotheses, but in France it’s only the firms that adopt robots — and they are very large firms — that are reducing their labor share, and that’s what accounts for the entirety of the decline in the labor share in French manufacturing. This really emphasizes that automation, and in particular robots, is a critical part in understanding what’s going on.”

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This is part 1 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu.  

In many parts of the U.S., robots have been replacing workers over the last few decades. But to what extent, really? Some technologists have forecast that automation will lead to a future without work, while other observers have been more skeptical about such scenarios.

Now a study co-authored by an MIT professor puts firm numbers on the trend, finding a very real impact — although one that falls well short of a robot takeover. The study also finds that in the U.S., the impact of robots varies widely by industry and region, and may play a notable role in exacerbating income inequality.

“We find fairly major negative employment effects,” MIT economist Daron Acemoglu says, although he notes that the impact of the trend can be overstated.

From 1990 to 2007, the study shows, adding one additional robot per 1,000 workers reduced the national employment-to-population ratio by about 0.2 percent, with some areas of the U.S. affected far more than others.

This means each additional robot added in manufacturing replaced about 3.3 workers nationally, on average.

That increased use of robots in the workplace also lowered wages by roughly 0.4 percent during the same time period.

“We find negative wage effects, that workers are losing in terms of real wages in more affected areas, because robots are pretty good at competing against them,” Acemoglu says.

The paper, “Robots and Jobs: Evidence from U.S. Labor Markets,” appears in advance online form in the Journal of Political Economy. The authors are Acemoglu and Pascual Restrepo PhD ’16, an assistant professor of economics at Boston University.

Displaced in Detroit

To conduct the study, Acemoglu and Restrepo used data on 19 industries, compiled by the International Federation of Robotics (IFR), a Frankfurt-based industry group that keeps detailed statistics on robot deployments worldwide. The scholars combined that with U.S.-based data on population, employment, business, and wages, from the U.S. Census Bureau, the Bureau of Economic Analysis, and the Bureau of Labor Statistics, among other sources.

The researchers also compared robot deployment in the U.S. to that of other countries, finding it lags behind that of Europe. From 1993 to 2007, U.S. firms actually did introduce almost exactly one new robot per 1,000 workers; in Europe, firms introduced 1.6 new robots per 1,000 workers.

“Even though the U.S. is a technologically very advanced economy, in terms of industrial robots’ production and usage and innovation, it’s behind many other advanced economies,” Acemoglu says.

In the U.S., four manufacturing industries account for 70 percent of robots: automakers (38 percent of robots in use), electronics (15 percent), the plastics and chemical industry (10 percent), and metals manufacturers (7 percent).

Across the U.S., the study analyzed the impact of robots in 722 commuting zones in the continental U.S. — essentially metropolitan areas — and found considerable geographic variation in how intensively robots are utilized.

Given industry trends in robot deployment, the area of the country most affected is the seat of the automobile industry. Michigan has the highest concentration of robots in the workplace, with employment in Detroit, Lansing, and Saginaw affected more than anywhere else in the country.

“Different industries have different footprints in different places in the U.S.,” Acemoglu observes. “The place where the robot issue is most apparent is Detroit. Whatever happens to automobile manufacturing has a much greater impact on the Detroit area [than elsewhere].”

In commuting zones where robots were added to the workforce, each robot replaces about 6.6 jobs locally, the researchers found. However, in a subtle twist, adding robots in manufacturing benefits people in other industries and other areas of the country — by lowering the cost of goods, among other things. These national economic benefits are the reason the researchers calculated that adding one robot replaces 3.3 jobs for the country as a whole.

The inequality issue

In conducting the study, Acemoglu and Restrepo went to considerable lengths to see if the employment trends in robot-heavy areas might have been caused by other factors, such as trade policy, but they found no complicating empirical effects.

The study does suggest, however, that robots have a direct influence on income inequality. The manufacturing jobs they replace come from parts of the workforce without many other good employment options; as a result, there is a direct connection between automation in robot-using industries and sagging incomes among blue-collar workers.

“There are major distributional implications,” Acemoglu says. When robots are added to manufacturing plants, “The burden falls on the low-skill and especially middle-skill workers. That’s really an important part of our overall research [on robots], that automation actually is a much bigger part of the technological factors that have contributed to rising inequality over the last 30 years.”

So while claims about machines wiping out human work entirely may be overstated, the research by Acemoglu and Restrepo shows that the robot effect is a very real one in manufacturing, with significant social implications.

“It certainly won’t give any support to those who think robots are going to take all of our jobs,” Acemoglu says. “But it does imply that automation is a real force to be grappled with.”

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On April 27, the National Academy of Sciences elected 120 new members and 26 international associates, including three professors from MIT — Abhijit Banerjee, Bonnie Berger, and Roger Summons — recognizing their “distinguished and continuing achievements in original research.” Current membership totals 2,403 active members and 501 international associates, including 190 Nobel Prize recipients.

The National Academy of Sciences is a private, nonprofit institution for scientific advancement established in 1863 by congressional charter and signed into law by President Abraham Lincoln. Together, with the National Academy of Engineering and the National Academy of Medicine, the 157-year-old society provides science, engineering, and health policy advice to the federal government and other organizations.

Abhijit Banerjee is the Ford Foundation International Professor of Economics, and in 2003 cofounded, with Esther Duflo and Sendhil Mullainathan, the Abdul Latif Jameel Poverty Action Lab (J-PAL). Banerjee’s groundbreaking research focuses on development economics and the alleviation of global poverty, work for which he shared the 2019 Nobel Prize in Economic Sciences.

He continues to serve as a director of J-PAL; he is also a past president of the Bureau for Research and Economic Analysis of Development, a research associate of the National Bureau of Economic Research, a Center for Economic and Policy Research research fellow, an international research fellow of the Kiel Institute, and a fellow of the American Academy of Arts and Sciences and the Econometric Society. He has been a Guggenheim fellow, an Alfred P. Sloan fellow, and a winner of the Infosys Prize.   

Banerjee’s scholarship, in collaboration with fellow NAS member and MIT Professor Esther Duflo, emphasizes the importance of field work in antipoverty initiatives, in order to recreate the precision of randomized controlled trials (RCTs) and laboratory-style data within the complexity of ever-evolving social realities. The resulting RCT evidence reveals which poverty interventions really work, enabling governments, non-governmental organizations, donors, and the private sector to plan effective programs and policies for poverty alleviation. When Banerjee began his career, development economics was considered marginal in economic studies, a view that Banerjee’s work and high-profile achievements have helped to correct.

Bonnie Berger is the Simons Professor of Mathematics and holds a joint appointment in the Department of Electrical Engineering and Computer Science. She is the head of the Computation and Biology group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). She is also a faculty member of the Harvard-MIT Program in Health Sciences and Technology and an associate member of the Broad Institute of MIT and Harvard .

After beginning her career working in algorithms at MIT, Berger was one of the pioneer researchers in the area of computational molecular biology and, together with the many students she has mentored, has been instrumental in defining the field. Her work addresses biological and biomedical questions by using computation in support of or in place of laboratory procedures, with a goal being to get more accurate answers at a greatly reduced cost. Combining genomic and health-related data from millions of patients will empower unprecedented insights into human health and disease risk. Berger transforms and creates techniques from algorithmic thinking to provide novel computational methods and software to enable biomedical data sharing and analysis at scale.

Berger is an elected fellow of the American Academy of Arts and Sciences, Association for Computing Machinery, International Society for Computational Biology (ISCB), American Institute of Medical and Biological Engineering, and American Mathematical Society. Recently she was recognized by ISCB with their Accomplishments by a Senior Scientist Award. She received the NIH Margaret Pittman Director’s Award, the SIAM Sonya Kovalevsky Lecture Prize, and an honorary doctorate from EPFL. Earlier in her career, she received an NSF Career Award, the Biophysical Society’s Dayhoff Award, and recognition as MIT Technology Review magazine’s inaugural TR100 top young innovators. She serves as vice president of ISCB, head of the steering committee for Research in Computational Molecular Biology, and member-at-large of the Section on Mathematics at American Association for the Advancement of Science (AAAS), as well as on multiple advisory committees and editorial boards.

Roger Summons is the Schlumberger Professor of Geobiology in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at MIT.  

Working at the intersection of biogeochemistry, geobiology, and astrobiology, Summons’ work examines the origins and co-evolution of Earth’s early life and the environment, beginning with the first geological and geochemical records and microbially dominated ecosystems. As an investigator in the Simons Collaboration on the Origins of Life, he’s particularly focused on lipid chemistry of microbes important to understating Earth through deep time, organic and isotopic indicators of climate change, and biomarkers in sediments and petroleum. 

Summons applies findings from this research to understanding life on Earth and the search for it elsewhere in the universe, recently on Mars. As such, he has served on three committees of the National Research Council: Committee on Origin and Evolution of Life, the Committee on Limits of Life, and the Committee on Mars Astrobiology. As an emeritus member of the NASA Astrobiology Institute (NAI) Executive Council and the head of the MIT team of NAI called the Foundations of Complex Life: Evolution, Preservation, and Detection on Earth and Beyond, Summons helped integrate this research with international science communities. Here, his group investigated factors that led to the evolution of complex life by examining processes and conditions that preserve biological signatures. More recently, Summons has contributed to Mars rover missions Curiosity and Perseverance, providing expertise on the preservation of organic matter from different environments on Earth and the red planet.

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As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models. 

It’s so simple that they unveiled it in a tweet last month: Train the model, prune its weakest connections, retrain the model at its fast, early training rate, and repeat, until the model is as tiny as you want. 

“That’s it,” says Alex Renda, a PhD student at MIT. “The standard things people do to prune their models are crazy complicated.” 

Renda discussed the technique when the International Conference of Learning Representations (ICLR) convened remotely this month. Renda is a co-author of the work with Jonathan Frankle, a fellow PhD student in MIT’s Department of Electrical Engineering and Computer Science (EECS), and Michael Carbin, an assistant professor of electrical engineering and computer science — all members of the Computer Science and Artificial Science Laboratory.  

The search for a better compression technique grew out of Frankle and Carbin’s award-winning Lottery Ticket Hypothesis paper at ICLR last year. They showed that a deep neural network could perform with only one-tenth the number of connections if the right subnetwork was found early in training. Their revelation came as demand for computing power and energy to train ever larger deep learning models was increasing exponentially, a trend that continues to this day. Costs of that growth include a rise in planet-warming carbon emissions and a potential drop in innovation as researchers not affiliated with big tech companies compete for scarce computing resources. Everyday users are affected, too. Big AI models eat up mobile-phone bandwidth and battery power.

But at a colleague’s suggestion, Frankle decided to see what lessons it might hold for pruning, a set of techniques for reducing the size of a neural network by removing unnecessary connections or neurons. Pruning algorithms had been around for decades, but the field saw a resurgence after the breakout success of neural networks at classifying images in the ImageNet competition. As models got bigger, with researchers adding on layers of artificial neurons to boost performance, others proposed techniques for whittling them down. 

Song Han, now an assistant professor at MIT, was one pioneer. Building on a series of influential papers, Han unveiled a pruning algorithm he called AMC, or AutoML for model compression, that’s still the industry standard. Under Han’s technique, redundant neurons and connections are automatically removed, and the model is retrained to restore its initial accuracy. 

In response to Han’s work, Frankle recently suggested in an unpublished paper that results could be further improved by rewinding the smaller, pruned model to its initial parameters, or weights, and retraining the smaller model at its faster, initial rate. 

In the current ICLR study, the researchers realized that the model could simply be rewound to its early training rate without fiddling with any parameters. In any pruning regimen, the tinier a model gets, the less accurate it becomes. But when the researchers compared this new method to Han’s AMC or Frankle’s weight-rewinding methods, it performed better no matter how much the model shrank. 

It’s unclear why the pruning technique works as well as it does. The researchers say they will leave that question for others to answer. As for those who wish to try it, the algorithm is as easy to implement as other pruning methods, without time-consuming tuning, the researchers say. 

“It’s the pruning algorithm from the ‘Book,’” says Frankle. “It’s clear, generic, and drop-dead simple.”

Han, for his part, has now partly shifted focus from compression AI models to channeling AI to design small, efficient models from the start. His newest method, Once for All, also debuts at ICLR. Of the new learning rate method, he says: “I’m happy to see new pruning and retraining techniques evolve, giving more people access to high-performing AI applications.” 

Support for the study came from the Defense Advanced Research Projects Agency, Google, MIT-IBM Watson AI Lab, MIT Quest for Intelligence, and the U.S. Office of Naval Research.

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Driven by an innate curiosity, children pick up new skills as they explore the world and learn from their experience. Computers, by contrast, often get stuck when thrown into new environments.

To get around this, engineers have tried encoding simple forms of curiosity into their algorithms with the hope that an agent pushed to explore will learn about its environment more effectively. An agent with a child’s curiosity might go from learning to pick up, manipulate, and throw objects to understanding the pull of gravity, a realization that could dramatically accelerate its ability to learn many other things. 

Engineers have discovered many ways of encoding curious exploration into machine learning algorithms. A research team at MIT wondered if a computer could do better, based on a long history of enlisting computers in the search for new algorithms. 

In recent years, the design of deep neural networks, algorithms that search for solutions by adjusting numeric parameters, has been automated with software like Google’s AutoML and auto-sklearn in Python. That’s made it easier for non-experts to develop AI applications. But while deep nets excel at specific tasks, they have trouble generalizing to new situations. Algorithms expressed in code, in a high-level programming language, by contrast, have the capacity to transfer knowledge across different tasks and environments. 

“Algorithms designed by humans are very general,” says study co-author Ferran Alet, a graduate student in MIT’s Department of Electrical Engineering and Computer Science and Computer Science and Artificial Intelligence Laboratory (CSAIL). “We were inspired to use AI to find algorithms with curiosity strategies that can adapt to a range of environments.”

The researchers created a “meta-learning” algorithm that generated 52,000 exploration algorithms. They found that the top two were entirely new — seemingly too obvious or counterintuitive for a human to have proposed. Both algorithms generated exploration behavior that substantially improved learning in a range of simulated tasks, from navigating a two-dimensional grid based on images to making a robotic ant walk. Because the meta-learning process generates high-level computer code as output, both algorithms can be dissected to peer inside their decision-making processes.

The paper’s senior authors are Leslie Kaelbling and Tomás Lozano-Pérez, both professors of computer science and electrical engineering at MIT. The work will be presented at the virtual International Conference on Learning Representations later this month. 

The paper received praise from researchers not involved in the work. “The use of program search to discover a better intrinsic reward is very creative,” says Quoc Le, a principal scientist at Google who has helped pioneer computer-aided design of deep learning models. “I like this idea a lot, especially since the programs are interpretable.”

The researchers compare their automated algorithm design process to writing sentences with a limited number of words. They started by choosing a set of basic building blocks to define their exploration algorithms. After studying other curiosity algorithms for inspiration, they picked nearly three dozen high-level operations, including basic programs and deep learning models, to guide the agent to do things like remember previous inputs, compare current and past inputs, and use learning methods to change its own modules. The computer then combined up to seven operations at a time to create computation graphs describing 52,000 algorithms. 

Even with a fast computer, testing them all would have taken decades. So, instead, the researchers limited their search by first ruling out algorithms predicted to perform poorly, based on their code structure alone. Then, they tested their most promising candidates on a basic grid-navigation task requiring substantial exploration but minimal computation. If the candidate did well, its performance became the new benchmark, eliminating even more candidates. 

Four machines searched over 10 hours to find the best algorithms. More than 99 percent were junk, but about a hundred were sensible, high-performing algorithms. Remarkably, the top 16 were both novel and useful, performing as well as, or better than, human-designed algorithms at a range of other virtual tasks, from landing a moon rover to raising a robotic arm and moving an ant-like robot in a physical simulation. 

All 16 algorithms shared two basic exploration functions. 

In the first, the agent is rewarded for visiting new places where it has a greater chance of making a new kind of move. In the second, the agent is also rewarded for visiting new places, but in a more nuanced way: One neural network learns to predict the future state while a second recalls the past, and then tries to predict the present by predicting the past from the future. If this prediction is erroneous it rewards itself, as it is a sign that it discovered something it didn’t know before. The second algorithm was so counterintuitive it took the researchers time to figure out. 

“Our biases often prevent us from trying very novel ideas,” says Alet. “But computers don’t care. They try, and see what works, and sometimes we get great unexpected results.”

More researchers are turning to machine learning to design better machine learning algorithms, a field known as AutoML. At Google, Le and his colleagues recently unveiled a new algorithm-discovery tool called Auto-ML Zero. (Its name is a play on Google’s AutoML software for customizing deep net architectures for a given application, and Google DeepMind’s Alpha Zero, the program that can learn to play different board games by playing millions of games against itself.) 

Their method searches through a space of algorithms made up of simpler primitive operations. But rather than look for an exploration strategy, their goal is to discover algorithms for classifying images. Both studies show the potential for humans to use machine-learning methods themselves to create novel, high-performing machine-learning algorithms.

“The algorithms we generated could be read and interpreted by humans, but to actually understand the code we had to reason through each variable and operation and how they evolve with time,” says study co-author Martin Schneider, a graduate student at MIT. “It’s an interesting open challenge to design algorithms and workflows that leverage the computer’s ability to evaluate lots of algorithms and our human ability to explain and improve on those ideas.” 

The research received support from the U.S. National Science Foundation, Air Force Office of Scientific Research, Office of Naval Research, Honda Research Institute, SUTD Temasek Laboratories, and MIT Quest for Intelligence.

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