Markus Andrews

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    When should someone trust an AI assistant’s predictions?

    In a busy hospital, a radiologist is using an artificial intelligence system to help her diagnose medical conditions based on patients’ X-ray images. Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI’s predictions?

    She doesn’t. Instead, she may rely on her expertise, a confidence level provided by the system itself, or an explanation of how the algorithm made its prediction — which may look convincing but still be wrong — to make an estimation.

    To help people better understand when to trust an AI “teammate,” MIT researchers created an onboarding technique that guides humans to develop a more accurate understanding of those situations in which a machine makes correct predictions and those in which it makes incorrect predictions.

    By showing people how the AI complements their abilities, the training technique could help humans make better decisions or come to conclusions faster when working with AI agents.

    “We propose a teaching phase where we gradually introduce the human to this AI model so they can, for themselves, see its weaknesses and strengths,” says Hussein Mozannar, a graduate student in the Social and Engineering Systems doctoral program within the Institute for Data, Systems, and Society (IDSS) who is also a researcher with the Clinical Machine Learning Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Medical Engineering and Science. “We do this by mimicking the way the human will interact with the AI in practice, but we intervene to give them feedback to help them understand each interaction they are making with the AI.”

    Mozannar wrote the paper with Arvind Satyanarayan, an assistant professor of computer science who leads the Visualization Group in CSAIL; and senior author David Sontag, an associate professor of electrical engineering and computer science at MIT and leader of the Clinical Machine Learning Group. The research will be presented at the Association for the Advancement of Artificial Intelligence in February.

    Mental models

    This work focuses on the mental models humans build about others. If the radiologist is not sure about a case, she may ask a colleague who is an expert in a certain area. From past experience and her knowledge of this colleague, she has a mental model of his strengths and weaknesses that she uses to assess his advice.

    Humans build the same kinds of mental models when they interact with AI agents, so it is important those models are accurate, Mozannar says. Cognitive science suggests that humans make decisions for complex tasks by remembering past interactions and experiences. So, the researchers designed an onboarding process that provides representative examples of the human and AI working together, which serve as reference points the human can draw on in the future. They began by creating an algorithm that can identify examples that will best teach the human about the AI.

    “We first learn a human expert’s biases and strengths, using observations of their past decisions unguided by AI,” Mozannar says. “We combine our knowledge about the human with what we know about the AI to see where it will be helpful for the human to rely on the AI. Then we obtain cases where we know the human should rely on the AI and similar cases where the human should not rely on the AI.”

    The researchers tested their onboarding technique on a passage-based question answering task: The user receives a written passage and a question whose answer is contained in the passage. The user then has to answer the question and can click a button to “let the AI answer.” The user can’t see the AI answer in advance, however, requiring them to rely on their mental model of the AI. The onboarding process they developed begins by showing these examples to the user, who tries to make a prediction with the help of the AI system. The human may be right or wrong, and the AI may be right or wrong, but in either case, after solving the example, the user sees the correct answer and an explanation for why the AI chose its prediction. To help the user generalize from the example, two contrasting examples are shown that explain why the AI got it right or wrong.

    For instance, perhaps the training question asks which of two plants is native to more continents, based on a convoluted paragraph from a botany textbook. The human can answer on her own or let the AI system answer. Then, she sees two follow-up examples that help her get a better sense of the AI’s abilities. Perhaps the AI is wrong on a follow-up question about fruits but right on a question about geology. In each example, the words the system used to make its prediction are highlighted. Seeing the highlighted words helps the human understand the limits of the AI agent, explains Mozannar.

    To help the user retain what they have learned, the user then writes down the rule she infers from this teaching example, such as “This AI is not good at predicting flowers.” She can then refer to these rules later when working with the agent in practice. These rules also constitute a formalization of the user’s mental model of the AI.

    The impact of teaching

    The researchers tested this teaching technique with three groups of participants. One group went through the entire onboarding technique, another group did not receive the follow-up comparison examples, and the baseline group didn’t receive any teaching but could see the AI’s answer in advance.

    “The participants who received teaching did just as well as the participants who didn’t receive teaching but could see the AI’s answer. So, the conclusion there is they are able to simulate the AI’s answer as well as if they had seen it,” Mozannar says.

    The researchers dug deeper into the data to see the rules individual participants wrote. They found that almost 50 percent of the people who received training wrote accurate lessons of the AI’s abilities. Those who had accurate lessons were right on 63 percent of the examples, whereas those who didn’t have accurate lessons were right on 54 percent. And those who didn’t receive teaching but could see the AI answers were right on 57 percent of the questions.

    “When teaching is successful, it has a significant impact. That is the takeaway here. When we are able to teach participants effectively, they are able to do better than if you actually gave them the answer,” he says.

    But the results also show there is still a gap. Only 50 percent of those who were trained built accurate mental models of the AI, and even those who did were only right 63 percent of the time. Even though they learned accurate lessons, they didn’t always follow their own rules, Mozannar says.

    That is one question that leaves the researchers scratching their heads — even if people know the AI should be right, why won’t they listen to their own mental model? They want to explore this question in the future, as well as refine the onboarding process to reduce the amount of time it takes. They are also interested in running user studies with more complex AI models, particularly in health care settings.

    “When humans collaborate with other humans, we rely heavily on knowing what our collaborators’ strengths and weaknesses are — it helps us know when (and when not) to lean on the other person for assistance. I’m glad to see this research applying that principle to humans and AI,” says Carrie Cai, a staff research scientist in the People + AI Research and Responsible AI groups at Google, who was not involved with this research. “Teaching users about an AI’s strengths and weaknesses is essential to producing positive human-AI joint outcomes.” 

    This research was supported, in part, by the National Science Foundation. More

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    The promise and pitfalls of artificial intelligence explored at TEDxMIT event

    Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT. 

    Attendees were entertained and challenged as they explored “the good and bad of computing,” explained CSAIL Director Professor Daniela Rus, who organized the event with John Werner, an MIT fellow and managing director of Link Ventures; MIT sophomore Lucy Zhao; and grad student Jessica Karaguesian. “As you listen to the talks today,” Rus told the audience, “consider how our world is made better by AI, and also our intrinsic responsibilities for ensuring that the technology is deployed for the greater good.”

    Rus mentioned some new capabilities that could be enabled by AI: an automated personal assistant that could monitor your sleep phases and wake you at the optimal time, as well as on-body sensors that monitor everything from your posture to your digestive system. “Intelligent assistance can help empower and augment our lives. But these intriguing possibilities should only be pursued if we can simultaneously resolve the challenges that these technologies bring,” said Rus. 

    The next speaker, CSAIL principal investigator and professor of electrical engineering and computer science Manolis Kellis, started off by suggesting what sounded like an unattainable goal — using AI to “put an end to evolution as we know it.” Looking at it from a computer science perspective, he said, what we call evolution is basically a brute force search. “You’re just exploring all of the search space, creating billions of copies of every one of your programs, and just letting them fight against each other. This is just brutal. And it’s also completely slow. It took us billions of years to get here.” Might it be possible, he asked, to speed up evolution and make it less messy?

    The answer, Kellis said, is that we can do better, and that we’re already doing better: “We’re not killing people like Sparta used to, throwing the weaklings off the mountain. We are truly saving diversity.”

    Knowledge, moreover, is now being widely shared, passed on “horizontally” through accessible information sources, he noted, rather than “vertically,” from parent to offspring. “I would like to argue that competition in the human species has been replaced by collaboration. Despite having a fixed cognitive hardware, we have software upgrades that are enabled by culture, by the 20 years that our children spend in school to fill their brains with everything that humanity has learned, regardless of which family came up with it. This is the secret of our great acceleration” — the fact that human advancement in recent centuries has vastly out-clipped evolution’s sluggish pace.

    The next step, Kellis said, is to harness insights about evolution in order to combat an individual’s genetic susceptibility to disease. “Our current approach is simply insufficient,” he added. “We’re treating manifestations of disease, not the causes of disease.” A key element in his lab’s ambitious strategy to transform medicine is to identify “the causal pathways through which genetic predisposition manifests. It’s only by understanding these pathways that we can truly manipulate disease causation and reverse the disease circuitry.” 

    Kellis was followed by Aleksander Madry, MIT professor of electrical engineering and computer science and CSAIL principal investigator, who told the crowd, “progress in AI is happening, and it’s happening fast.” Computer programs can routinely beat humans in games like chess, poker, and Go. So should we be worried about AI surpassing humans? 

    Madry, for one, is not afraid — or at least not yet. And some of that reassurance stems from research that has led him to the following conclusion: Despite its considerable success, AI, especially in the form of machine learning, is lazy. “Think about being lazy as this kind of smart student who doesn’t really want to study for an exam. Instead, what he does is just study all the past years’ exams and just look for patterns. Instead of trying to actually learn, he just tries to pass the test. And this is exactly the same way in which current AI is lazy.”

    A machine-learning model might recognize grazing sheep, for instance, simply by picking out pictures that have green grass in them. If a model is trained to identify fish from photos of anglers proudly displaying their catches, Madry explained, “the model figures out that if there’s a human holding something in the picture, I will just classify it as a fish.” The consequences can be more serious for an AI model intended to pick out malignant tumors. If the model is trained on images containing rulers that indicate the size of tumors, the model may end up selecting only those photos that have rulers in them.

    This leads to Madry’s biggest concerns about AI in its present form. “AI is beating us now,” he noted. “But the way it does it [involves] a little bit of cheating.” He fears that we will apply AI “in some way in which this mismatch between what the model actually does versus what we think it does will have some catastrophic consequences.” People relying on AI, especially in potentially life-or-death situations, need to be much more mindful of its current limitations, Madry cautioned.

    There were 10 speakers altogether, and the last to take the stage was MIT associate professor of electrical engineering and computer science and CSAIL principal investigator Marzyeh Ghassemi, who laid out her vision for how AI could best contribute to general health and well-being. But in order for that to happen, its models must be trained on accurate, diverse, and unbiased medical data.

    It’s important to focus on the data, Ghassemi stressed, because these models are learning from us. “Since our data is human-generated … a neural network is learning how to practice from a doctor. But doctors are human, and humans make mistakes. And if a human makes a mistake, and we train an AI from that, the AI will, too. Garbage in, garbage out. But it’s not like the garbage is distributed equally.”

    She pointed out that many subgroups receive worse care from medical practitioners, and members of these subgroups die from certain conditions at disproportionately high rates. This is an area, Ghassemi said, “where AI can actually help. This is something we can fix.” Her group is developing machine-learning models that are robust, private, and fair. What’s holding them back is neither algorithms nor GPUs. It’s data. Once we collect reliable data from diverse sources, Ghassemi added, we might start reaping the benefits that AI can bring to the realm of health care.

    In addition to CSAIL speakers, there were talks from members across MIT’s Institute for Data, Systems, and Society; the MIT Mobility Initiative; the MIT Media Lab; and the SENSEable City Lab.

    The proceedings concluded on that hopeful note. Rus and Werner then thanked everyone for coming. “Please continue to reflect about the good and bad of computing,” Rus urged. “And we look forward to seeing you back here in May for the next TEDxMIT event.”

    The exact theme of the spring 2022 gathering will have something to do with “superpowers.” But — if December’s mind-bending presentations were any indication — the May offering is almost certain to give its attendees plenty to think about. And maybe provide the inspiration for a startup or two. More

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    Physics and the machine-learning “black box”

    Machine-learning algorithms are often referred to as a “black box.” Once data are put into an algorithm, it’s not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the “black box” problem, through a combination of data science and physics-based engineering.

    In class 2.C161 (Physical Systems Modeling and Design Using Machine Learning), Professor George Barbastathis demonstrates how mechanical engineers can use their unique knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

    “I wanted to take 2.C161 because machine-learning models are usually a “black box,” but this class taught us how to construct a system model that is informed by physics so we can peek inside,” explains Crystal Owens, a mechanical engineering graduate student who took the course in spring 2021.

    As chair of the Committee on the Strategic Integration of Data Science into Mechanical Engineering, Barbastathis has had many conversations with mechanical engineering students, researchers, and faculty to better understand the challenges and successes they’ve had using machine learning in their work.

    “One comment we heard frequently was that these colleagues can see the value of data science methods for problems they are facing in their mechanical engineering-centric research; yet they are lacking the tools to make the most out of it,” says Barbastathis. “Mechanical, civil, electrical, and other types of engineers want a fundamental understanding of data principles without having to convert themselves to being full-time data scientists or AI researchers.”

    Additionally, as mechanical engineering students move on from MIT to their careers, many will need to manage data scientists on their teams someday. Barbastathis hopes to set these students up for success with class 2.C161.

    Bridging MechE and the MIT Schwartzman College of Computing

    Class 2.C161 is part of the MIT Schwartzman College of Computing “Computing Core.” The goal of these classes is to connect data science and physics-based engineering disciplines, like mechanical engineering. Students take the course alongside 6.C402 (Modeling with Machine Learning: from Algorithms to Applications), taught by professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola.

    The two classes are taught concurrently during the semester, exposing students to both fundamentals in machine learning and domain-specific applications in mechanical engineering.

    In 2.C161, Barbastathis highlights how complementary physics-based engineering and data science are. Physical laws present a number of ambiguities and unknowns, ranging from temperature and humidity to electromagnetic forces. Data science can be used to predict these physical phenomena. Meanwhile, having an understanding of physical systems helps ensure the resulting output of an algorithm is accurate and explainable.

    “What’s needed is a deeper combined understanding of the associated physical phenomena and the principles of data science, machine learning in particular, to close the gap,” adds Barbastathis. “By combining data with physical principles, the new revolution in physics-based engineering is relatively immune to the “black box” problem facing other types of machine learning.”

    Equipped with a working knowledge of machine-learning topics covered in class 6.C402 and a deeper understanding of how to pair data science with physics, students are charged with developing a final project that solves for an actual physical system.

    Developing solutions for real-world physical systems

    For their final project, students in 2.C161 are asked to identify a real-world problem that requires data science to address the ambiguity inherent in physical systems. After obtaining all relevant data, students are asked to select a machine-learning method, implement their chosen solution, and present and critique the results.

    Topics this past semester ranged from weather forecasting to the flow of gas in combustion engines, with two student teams drawing inspiration from the ongoing Covid-19 pandemic.

    Owens and her teammates, fellow graduate students Arun Krishnadas and Joshua David John Rathinaraj, set out to develop a model for the Covid-19 vaccine rollout.

    “We developed a method of combining a neural network with a susceptible-infected-recovered (SIR) epidemiological model to create a physics-informed prediction system for the spread of Covid-19 after vaccinations started,” explains Owens.

    The team accounted for various unknowns including population mobility, weather, and political climate. This combined approach resulted in a prediction of Covid-19’s spread during the vaccine rollout that was more reliable than using either the SIR model or a neural network alone.

    Another team, including graduate student Yiwen Hu, developed a model to predict mutation rates in Covid-19, a topic that became all too pertinent as the delta variant began its global spread.

    “We used machine learning to predict the time-series-based mutation rate of Covid-19, and then incorporated that as an independent parameter into the prediction of pandemic dynamics to see if it could help us better predict the trend of the Covid-19 pandemic,” says Hu.

    Hu, who had previously conducted research into how vibrations on coronavirus protein spikes affect infection rates, hopes to apply the physics-based machine-learning approaches he learned in 2.C161 to his research on de novo protein design.

    Whatever the physical system students addressed in their final projects, Barbastathis was careful to stress one unifying goal: the need to assess ethical implications in data science. While more traditional computing methods like face or voice recognition have proven to be rife with ethical issues, there is an opportunity to combine physical systems with machine learning in a fair, ethical way.

    “We must ensure that collection and use of data are carried out equitably and inclusively, respecting the diversity in our society and avoiding well-known problems that computer scientists in the past have run into,” says Barbastathis.

    Barbastathis hopes that by encouraging mechanical engineering students to be both ethics-literate and well-versed in data science, they can move on to develop reliable, ethically sound solutions and predictions for physical-based engineering challenges. More

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    Tackling hard computational problems

    The notion that some computational problems in math and computer science can be hard should come as no surprise. There is, in fact, an entire class of problems deemed impossible to solve algorithmically. Just below this class lie slightly “easier” problems that are less well-understood — and may be impossible, too.

    David Gamarnik, professor of operations research at the MIT Sloan School of Management and the Institute for Data, Systems, and Society, is focusing his attention on the latter, less-studied category of problems, which are more relevant to the everyday world because they involve randomness — an integral feature of natural systems. He and his colleagues have developed a potent tool for analyzing these problems called the overlap gap property (or OGP). Gamarnik described the new methodology in a recent paper in the Proceedings of the National Academy of Sciences.

    P ≠ NP

    Fifty years ago, the most famous problem in theoretical computer science was formulated. Labeled “P ≠ NP,” it asks if problems involving vast datasets exist for which an answer can be verified relatively quickly, but whose solution — even if worked out on the fastest available computers — would take an absurdly long time.

    The P ≠ NP conjecture is still unproven, yet most computer scientists believe that many familiar problems — including, for instance, the traveling salesman problem — fall into this impossibly hard category. The challenge in the salesman example is to find the shortest route, in terms of distance or time, through N different cities. The task is easily managed when N=4, because there are only six possible routes to consider. But for 30 cities, there are more than 1030 possible routes, and the numbers rise dramatically from there. The biggest difficulty comes in designing an algorithm that quickly solves the problem in all cases, for all integer values of N. Computer scientists are confident, based on algorithmic complexity theory, that no such algorithm exists, thus affirming that P ≠ NP.

    There are many other examples of intractable problems like this. Suppose, for instance, you have a giant table of numbers with thousands of rows and thousands of columns. Can you find, among all possible combinations, the precise arrangement of 10 rows and 10 columns such that its 100 entries will have the highest sum attainable? “We call them optimization tasks,” Gamarnik says, “because you’re always trying to find the biggest or best — the biggest sum of numbers, the best route through cities, and so forth.”

    Computer scientists have long recognized that you can’t create a fast algorithm that can, in all cases, efficiently solve problems like the saga of the traveling salesman. “Such a thing is likely impossible for reasons that are well-understood,” Gamarnik notes. “But in real life, nature doesn’t generate problems from an adversarial perspective. It’s not trying to thwart you with the most challenging, hand-picked problem conceivable.” In fact, people normally encounter problems under more random, less contrived circumstances, and those are the problems the OGP is intended to address.

    Peaks and valleys

    To understand what the OGP is all about, it might first be instructive to see how the idea arose. Since the 1970s, physicists have been studying spin glasses — materials with properties of both liquids and solids that have unusual magnetic behaviors. Research into spin glasses has given rise to a general theory of complex systems that’s relevant to problems in physics, math, computer science, materials science, and other fields. (This work earned Giorgio Parisi a 2021 Nobel Prize in Physics.)

    One vexing issue physicists have wrestled with is trying to predict the energy states, and particularly the lowest energy configurations, of different spin glass structures. The situation is sometimes depicted by a “landscape” of countless mountain peaks separated by valleys, where the goal is to identify the highest peak. In this case, the highest peak actually represents the lowest energy state (though one could flip the picture around and instead look for the deepest hole). This turns out to be an optimization problem similar in form to the traveling salesman’s dilemma, Gamarnik explains: “You’ve got this huge collection of mountains, and the only way to find the highest appears to be by climbing up each one” — a Sisyphean chore comparable to finding a needle in a haystack.

    Physicists have shown that you can simplify this picture, and take a step toward a solution, by slicing the mountains at a certain, predetermined elevation and ignoring everything below that cutoff level. You’d then be left with a collection of peaks protruding above a uniform layer of clouds, with each point on those peaks representing a potential solution to the original problem.

    In a 2014 paper, Gamarnik and his coauthors noticed something that had previously been overlooked. In some cases, they realized, the diameter of each peak will be much smaller than the distances between different peaks. Consequently, if one were to pick any two points on this sprawling landscape — any two possible “solutions” — they would either be very close (if they came from the same peak) or very far apart (if drawn from different peaks). In other words, there would be a telltale “gap” in these distances — either small or large, but nothing in-between. A system in this state, Gamarnik and colleagues proposed, is characterized by the OGP.

    “We discovered that all known problems of a random nature that are algorithmically hard have a version of this property” — namely, that the mountain diameter in the schematic model is much smaller than the space between mountains, Gamarnik asserts. “This provides a more precise measure of algorithmic hardness.”

    Unlocking the secrets of algorithmic complexity

    The emergence of the OGP can help researchers assess the difficulty of creating fast algorithms to tackle particular problems. And it has already enabled them “to mathematically [and] rigorously rule out a large class of algorithms as potential contenders,” Gamarnik says. “We’ve learned, specifically, that stable algorithms — those whose output won’t change much if the input only changes a little — will fail at solving this type of optimization problem.” This negative result applies not only to conventional computers but also to quantum computers and, specifically, to so-called “quantum approximation optimization algorithms” (QAOAs), which some investigators had hoped could solve these same optimization problems. Now, owing to Gamarnik and his co-authors’ findings, those hopes have been moderated by the recognition that many layers of operations would be required for QAOA-type algorithms to succeed, which could be technically challenging.

    “Whether that’s good news or bad news depends on your perspective,” he says. “I think it’s good news in the sense that it helps us unlock the secrets of algorithmic complexity and enhances our knowledge as to what is in the realm of possibility and what is not. It’s bad news in the sense that it tells us that these problems are hard, even if nature produces them, and even if they’re generated in a random way.” The news is not really surprising, he adds. “Many of us expected it all along, but we now we have a more solid basis upon which to make this claim.”

    That still leaves researchers light-years away from being able to prove the nonexistence of fast algorithms that could solve these optimization problems in random settings. Having such a proof would provide a definitive answer to the P ≠ NP problem. “If we could show that we can’t have an algorithm that works most of the time,” he says, “that would tell us we certainly can’t have an algorithm that works all the time.”

    Predicting how long it will take before the P ≠ NP problem is resolved appears to be an intractable problem in itself. It’s likely there will be many more peaks to climb, and valleys to traverse, before researchers gain a clearer perspective on the situation. More

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    Understanding air pollution from space

    Climate change and air pollution are interlocking crises that threaten human health. Reducing emissions of some air pollutants can help achieve climate goals, and some climate mitigation efforts can in turn improve air quality.

    One part of MIT Professor Arlene Fiore’s research program is to investigate the fundamental science in understanding air pollutants — how long they persist and move through our environment to affect air quality.

    “We need to understand the conditions under which pollutants, such as ozone, form. How much ozone is formed locally and how much is transported long distances?” says Fiore, who notes that Asian air pollution can be transported across the Pacific Ocean to North America. “We need to think about processes spanning local to global dimensions.”

    Fiore, the Peter H. Stone and Paola Malanotte Stone Professor in Earth, Atmospheric and Planetary Sciences, analyzes data from on-the-ground readings and from satellites, along with models, to better understand the chemistry and behavior of air pollutants — which ultimately can inform mitigation strategies and policy setting.

    A global concern

    At the United Nations’ most recent climate change conference, COP26, air quality management was a topic discussed over two days of presentations.

    “Breathing is vital. It’s life. But for the vast majority of people on this planet right now, the air that they breathe is not giving life, but cutting it short,” said Sarah Vogel, senior vice president for health at the Environmental Defense Fund, at the COP26 session.

    “We need to confront this twin challenge now through both a climate and clean air lens, of targeting those pollutants that warm both the air and harm our health.”

    Earlier this year, the World Health Organization (WHO) updated its global air quality guidelines it had issued 15 years earlier for six key pollutants including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). The new guidelines are more stringent based on what the WHO stated is the “quality and quantity of evidence” of how these pollutants affect human health. WHO estimates that roughly 7 million premature deaths are attributable to the joint effects of air pollution.

    “We’ve had all these health-motivated reductions of aerosol and ozone precursor emissions. What are the implications for the climate system, both locally but also around the globe? How does air quality respond to climate change? We study these two-way interactions between air pollution and the climate system,” says Fiore.

    But fundamental science is still required to understand how gases, such as ozone and nitrogen dioxide, linger and move throughout the troposphere — the lowermost layer of our atmosphere, containing the air we breathe.

    “We care about ozone in the air we’re breathing where we live at the Earth’s surface,” says Fiore. “Ozone reacts with biological tissue, and can be damaging to plants and human lungs. Even if you’re a healthy adult, if you’re out running hard during an ozone smog event, you might feel an extra weight on your lungs.”

    Telltale signs from space

    Ozone is not emitted directly, but instead forms through chemical reactions catalyzed by radiation from the sun interacting with nitrogen oxides — pollutants released in large part from burning fossil fuels—and volatile organic compounds. However, current satellite instruments cannot sense ground-level ozone.

    “We can’t retrieve surface- or even near-surface ozone from space,” says Fiore of the satellite data, “although the anticipated launch of a new instrument looks promising for new advances in retrieving lower-tropospheric ozone”. Instead, scientists can look at signatures from other gas emissions to get a sense of ozone formation. “Nitrogen dioxide and formaldehyde are a heavy focus of our research because they serve as proxies for two of the key ingredients that go on to form ozone in the atmosphere.”

    To understand ozone formation via these precursor pollutants, scientists have gathered data for more than two decades using spectrometer instruments aboard satellites that measure sunlight in ultraviolet and visible wavelengths that interact with these pollutants in the Earth’s atmosphere — known as solar backscatter radiation.

    Satellites, such as NASA’s Aura, carry instruments like the Ozone Monitoring Instrument (OMI). OMI, along with European-launched satellites such as the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY), and the newest generation TROPOspheric Monitoring instrument (TROPOMI), all orbit the Earth, collecting data during daylight hours when sunlight is interacting with the atmosphere over a particular location.

    In a recent paper from Fiore’s group, former graduate student Xiaomeng Jin (now a postdoc at the University of California at Berkeley), demonstrated that she could bring together and “beat down the noise in the data,” as Fiore says, to identify trends in ozone formation chemistry over several U.S. metropolitan areas that “are consistent with our on-the-ground understanding from in situ ozone measurements.”

    “This finding implies that we can use these records to learn about changes in surface ozone chemistry in places where we lack on-the-ground monitoring,” says Fiore. Extracting these signals by stringing together satellite data — OMI, GOME, and SCIAMACHY — to produce a two-decade record required reconciling the instruments’ differing orbit days, times, and fields of view on the ground, or spatial resolutions. 

    Currently, spectrometer instruments aboard satellites are retrieving data once per day. However, newer instruments, such as the Geostationary Environment Monitoring Spectrometer launched in February 2020 by the National Institute of Environmental Research in the Ministry of Environment of South Korea, will monitor a particular region continuously, providing much more data in real time.

    Over North America, the Tropospheric Emissions: Monitoring of Pollution Search (TEMPO) collaboration between NASA and the Smithsonian Astrophysical Observatory, led by Kelly Chance of Harvard University, will provide not only a stationary view of the atmospheric chemistry over the continent, but also a finer-resolution view — with the instrument recording pollution data from only a few square miles per pixel (with an anticipated launch in 2022).

    “What we’re very excited about is the opportunity to have continuous coverage where we get hourly measurements that allow us to follow pollution from morning rush hour through the course of the day and see how plumes of pollution are evolving in real time,” says Fiore.

    Data for the people

    Providing Earth-observing data to people in addition to scientists — namely environmental managers, city planners, and other government officials — is the goal for the NASA Health and Air Quality Applied Sciences Team (HAQAST).

    Since 2016, Fiore has been part of HAQAST, including collaborative “tiger teams” — projects that bring together scientists, nongovernment entities, and government officials — to bring data to bear on real issues.

    For example, in 2017, Fiore led a tiger team that provided guidance to state air management agencies on how satellite data can be incorporated into state implementation plans (SIPs). “Submission of a SIP is required for any state with a region in non-attainment of U.S. National Ambient Air Quality Standards to demonstrate their approach to achieving compliance with the standard,” says Fiore. “What we found is that small tweaks in, for example, the metrics we use to convey the science findings, can go a long way to making the science more usable, especially when there are detailed policy frameworks in place that must be followed.”

    Now, in 2021, Fiore is part of two tiger teams announced by HAQAST in late September. One team is looking at data to address environmental justice issues, by providing data to assess communities disproportionately affected by environmental health risks. Such information can be used to estimate the benefits of governmental investments in environmental improvements for disproportionately burdened communities. The other team is looking at urban emissions of nitrogen oxides to try to better quantify and communicate uncertainties in the estimates of anthropogenic sources of pollution.

    “For our HAQAST work, we’re looking at not just the estimate of the exposure to air pollutants, or in other words their concentrations,” says Fiore, “but how confident are we in our exposure estimates, which in turn affect our understanding of the public health burden due to exposure. We have stakeholder partners at the New York Department of Health who will pair exposure datasets with health data to help prioritize decisions around public health.

    “I enjoy working with stakeholders who have questions that require science to answer and can make a difference in their decisions.” Fiore says. More

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    Reducing food waste to increase access to affordable foods

    About a third of the world’s food supply never gets eaten. That means the water, labor, energy, and fertilizer that went into growing, processing, and distributing the food is wasted.

    On the other end of the supply chain are cash-strapped consumers, who have been further distressed in recent years by factors like the Covid-19 pandemic and inflation.

    Spoiler Alert, a company founded by two MIT alumni, is helping companies bridge the gap between food waste and food insecurity with a platform connecting major food and beverage brands with discount grocers, retailers, and nonprofits. The platform helps brands discount or donate excess and short-dated inventory days, weeks, and months before it expires.

    “There is a tremendous amount of underutilized data that exists in the manufacturing and distribution space that results in good food going to waste,” says Ricky Ashenfelter MBA ’15, who co-founded the company with Emily Malina MBA ’15.

    Spoiler Alert helps brands manage distressed inventory data, create offers for potential buyers, and review and accept bids. The platform is designed to work with companies’ existing inventory and fulfillment systems, using automation and pricing intelligence to further streamline sales.

    “At a high level, we’re a waste-prevention software built for sales and supply-chain teams,” Ashenfelter says. “You can think of it as a private [business-to-business] eBay of sorts.”

    Spoiler Alert is working with global companies like Nestle, Kraft Heinz, and Danone, as well as discount grocers like the United Grocery Outlet and Misfits Market. Those brands are already using the platform to reduce food waste and get more food on people’s tables.

    “Project Drawdown [a nonprofit working on climate solutions] has identified food waste as the number one priority to address the global climate crisis, so these types of corporate initiatives can be really powerful from an environmental standpoint,” Ashenfelter says, noting the nonprofit estimates food waste accounts for 8 percent of global greenhouse gas emissions. “Contrast that with growing levels of food insecurity and folks not being able to access affordable nutrition, and you start to see how tackling supply-chain inefficiency can have a dramatic impact from both an environmental and a social lens. That’s what motivates us.”

    Untapped data for change

    Ashenfelter came to MIT’s Sloan School of Management after several years in sustainability software and management consulting within the retail and consumer products industries.

    “I was really attracted to transitioning into something much more entrepreneurial, and to leverage not only Sloan’s focus on entrepreneurship, but also the broader MIT ecosystem’s focus on technology, entrepreneurship, clean tech innovation, and other themes along that front,” he says.

    Ashenfelter met Malina at one of Sloan’s admitted students events in 2013, and the founders soon set out to use data to decrease food waste.

    “For us, the idea was clear: How do we better leverage data to manage excess and short-dated inventory?” Ashenfelter says. “How we go about that has evolved over the last six years, but it’s all rooted in solving an enormous climate problem, solving a major food insecurity problem, and from a capitalistic standpoint, helping businesses cut costs and generate revenue from otherwise wasted products.”

    The founders spent many hours in the Martin Trust Center for MIT Entrepreneurship with support from the Sloan Sustainability Initiative, and used Spoiler Alert as a case study in nearly every class they took, thinking through product development, sales, marketing, pricing, and more through their coursework.

    “We brought our idea into just about every action learning class that we could at Sloan and MIT,” Ashenfelter says.

    They also participated in the MIT $100K Entrepreneurship Competition and received support from the Venture Mentoring Service and the IDEAS Global Challenge program.

    Upon graduation, the founders initially began building a platform to facilitate donations of excess inventory, but soon learned big companies’ processes for discounting that inventory were also highly manual. Today, more than 90 percent of Spoiler Alert’s transaction volume is discounted, with the remainder donated.

    Different teams within an organization can upload excess inventory reports to Spoiler Alert’s system, eliminating the need to manually aggregate datasets and preparing what the industry refers to as “blowout lists” to sell. Spoiler Alert uses machine-learning-based tools to help both parties with pricing and negotiations to close deals more quickly.

    “Companies are taking pretty manual and slow approaches to deciding [what to do with excess inventory],” Ashenfelter says. “And when you have slow decision-making, you’re losing days or even weeks of shelf life on that product. That can be the difference between selling product versus donating, and donating versus dumping.”

    Once a deal has been made, Spoiler Alert automatically generates the forms and workflows needed by fulfillment teams to get the product out the door. The relationships companies build on the platform are also a major driver for cutting down waste.

    “We’re providing suppliers with the ability to control where their discounted and donated product ends up,” Ashenfelter says. “That’s really powerful because it allows these CPG brands to ensure that this product is, in many cases, getting to affordable nutrition outlets in underserved communities.”

    Ashenfelter says the majority of inventory goes to regional and national discount grocers, supplemented with extensive purchasing from local and nonprofit grocery chains.

    “Everything we do is oriented around helping sell as much product as possible to a reputable set of buyers at the most fair, equitable prices possible,” Ashenfelter says.

    Scaling for impact

    The pandemic has disrupted many aspects of the food supply chains. But Ashenfelter says it has also accelerated the adoption of digital solutions that can better manage such volatility.

    When Campbell began using Spoiler Alert’s system in 2019, for instance, it achieved a 36 percent increase in discount sales and a 27 percent increase in donations over the first five months.

    Ashenfelter says the results have proven that companies’ sustainability targets can go hand in hand with initiatives that boost their bottom lines. In fact, because Spoiler Alert focuses so much on the untapped revenue associated with food waste, many customers don’t even realize Spoiler Alert is a sustainability company until after they’ve signed on.

    “What’s neat about this program is that it becomes an incredibly powerful case study internally for how sustainability and operational outcomes aren’t in conflict and can drive both business results as well as overall environmental impact,” Ashenfelter says.

    Going forward, Spoiler Alert will continue building out algorithmic solutions that could further cut down on waste internationally and across a wider array of products.

    “At every step in our process, we’re collecting a tremendous amount of data in terms of what is and isn’t selling, at what price point, to which buyers, out of which geographies, and with how much remaining shelf life,” Ashenfelter explains. “We are only starting to scratch the surface in terms of bringing our recommendations engine to life for our suppliers and buyers. Ultimately our goal is to power the waste-free economy, and rooted in that is making better decisions faster, in collaboration with a growing ecosystem of supply chain partners, and with as little manual intervention as possible.” More

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    Meet the 2021-22 Accenture Fellows

    Launched in October of 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways in which industry and technology come together to spur innovation. The five-year initiative aims to achieve its mission through research, education, and fellowships. To that end, Accenture has once again awarded five annual fellowships to MIT graduate students working on research in industry and technology convergence who are underrepresented, including by race, ethnicity, and gender.

    This year’s Accenture Fellows work across disciplines including robotics, manufacturing, artificial intelligence, and biomedicine. Their research covers a wide array of subjects, including: advancing manufacturing through computational design, with the potential to benefit global vaccine production; designing low-energy robotics for both consumer electronics and the aerospace industry; developing robotics and machine learning systems that may aid the elderly in their homes; and creating ingestible biomedical devices that can help gather medical data from inside a patient’s body.

    Student nominations from each unit within the School of Engineering, as well as from the four other MIT schools and the MIT Schwarzman College of Computing, were invited as part of the application process. Five exceptional students were selected as fellows in the initiative’s second year.

    Xinming (Lily) Liu is a PhD student in operations research at MIT Sloan School of Management. Her work is focused on behavioral and data-driven operations for social good, incorporating human behaviors into traditional optimization models, designing incentives, and analyzing real-world data. Her current research looks at the convergence of social media, digital platforms, and agriculture, with particular attention to expanding technological equity and economic opportunity in developing countries. Liu earned her BS from Cornell University, with a double major in operations research and computer science.

    Caris Moses is a PhD student in electrical engineering and computer science specializing inartificial intelligence. Moses’ research focuses on using machine learning, optimization, and electromechanical engineering to build robotics systems that are robust, flexible, intelligent, and can learn on the job. The technology she is developing holds promise for industries including flexible, small-batch manufacturing; robots to assist the elderly in their households; and warehouse management and fulfillment. Moses earned her BS in mechanical engineering from Cornell University and her MS in computer science from Northeastern University.

    Sergio Rodriguez Aponte is a PhD student in biological engineering. He is working on the convergence of computational design and manufacturing practices, which have the potential to impact industries such as biopharmaceuticals, food, and wellness/nutrition. His current research aims to develop strategies for applying computational tools, such as multiscale modeling and machine learning, to the design and production of manufacturable and accessible vaccine candidates that could eventually be available globally. Rodriguez Aponte earned his BS in industrial biotechnology from the University of Puerto Rico at Mayaguez.

    Soumya Sudhakar SM ’20 is a PhD student in aeronautics and astronautics. Her work is focused on theco-design of new algorithms and integrated circuits for autonomous low-energy robotics that could have novel applications in aerospace and consumer electronics. Her contributions bring together the emerging robotics industry, integrated circuits industry, aerospace industry, and consumer electronics industry. Sudhakar earned her BSE in mechanical and aerospace engineering from Princeton University and her MS in aeronautics and astronautics from MIT.

    So-Yoon Yang is a PhD student in electrical engineering and computer science. Her work on the development of low-power, wireless, ingestible biomedical devices for health care is at the intersection of the medical device, integrated circuit, artificial intelligence, and pharmaceutical fields. Currently, the majority of wireless biomedical devices can only provide a limited range of medical data measured from outside the body. Ingestible devices hold promise for the next generation of personal health care because they do not require surgical implantation, can be useful for detecting physiological and pathophysiological signals, and can also function as therapeutic alternatives when treatment cannot be done externally. Yang earned her BS in electrical and computer engineering from Seoul National University in South Korea and her MS in electrical engineering from Caltech. More

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    Q&A: Cathy Wu on developing algorithms to safely integrate robots into our world

    Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Data, Systems, and Society. As an undergraduate, Wu won MIT’s toughest robotics competition, and as a graduate student took the University of California at Berkeley’s first-ever course on deep reinforcement learning. Now back at MIT, she’s working to improve the flow of robots in Amazon warehouses under the Science Hub, a new collaboration between the tech giant and the MIT Schwarzman College of Computing. Outside of the lab and classroom, Wu can be found running, drawing, pouring lattes at home, and watching YouTube videos on math and infrastructure via 3Blue1Brown and Practical Engineering. She recently took a break from all of that to talk about her work.

    Q: What put you on the path to robotics and self-driving cars?

    A: My parents always wanted a doctor in the family. However, I’m bad at following instructions and became the wrong kind of doctor! Inspired by my physics and computer science classes in high school, I decided to study engineering. I wanted to help as many people as a medical doctor could.

    At MIT, I looked for applications in energy, education, and agriculture, but the self-driving car was the first to grab me. It has yet to let go! Ninety-four percent of serious car crashes are caused by human error and could potentially be prevented by self-driving cars. Autonomous vehicles could also ease traffic congestion, save energy, and improve mobility.

    I first learned about self-driving cars from Seth Teller during his guest lecture for the course Mobile Autonomous Systems Lab (MASLAB), in which MIT undergraduates compete to build the best full-functioning robot from scratch. Our ball-fetching bot, Putzputz, won first place. From there, I took more classes in machine learning, computer vision, and transportation, and joined Teller’s lab. I also competed in several mobility-related hackathons, including one sponsored by Hubway, now known as Blue Bike.

    Q: You’ve explored ways to help humans and autonomous vehicles interact more smoothly. What makes this problem so hard?

    A: Both systems are highly complex, and our classical modeling tools are woefully insufficient. Integrating autonomous vehicles into our existing mobility systems is a huge undertaking. For example, we don’t know whether autonomous vehicles will cut energy use by 40 percent, or double it. We need more powerful tools to cut through the uncertainty. My PhD thesis at Berkeley tried to do this. I developed scalable optimization methods in the areas of robot control, state estimation, and system design. These methods could help decision-makers anticipate future scenarios and design better systems to accommodate both humans and robots.

    Q: How is deep reinforcement learning, combining deep and reinforcement learning algorithms, changing robotics?

    A: I took John Schulman and Pieter Abbeel’s reinforcement learning class at Berkeley in 2015 shortly after Deepmind published their breakthrough paper in Nature. They had trained an agent via deep learning and reinforcement learning to play “Space Invaders” and a suite of Atari games at superhuman levels. That created quite some buzz. A year later, I started to incorporate reinforcement learning into problems involving mixed traffic systems, in which only some cars are automated. I realized that classical control techniques couldn’t handle the complex nonlinear control problems I was formulating.

    Deep RL is now mainstream but it’s by no means pervasive in robotics, which still relies heavily on classical model-based control and planning methods. Deep learning continues to be important for processing raw sensor data like camera images and radio waves, and reinforcement learning is gradually being incorporated. I see traffic systems as gigantic multi-robot systems. I’m excited for an upcoming collaboration with Utah’s Department of Transportation to apply reinforcement learning to coordinate cars with traffic signals, reducing congestion and thus carbon emissions.

    Q: You’ve talked about the MIT course, 6.007 (Signals and Systems), and its impact on you. What about it spoke to you?

    A: The mindset. That problems that look messy can be analyzed with common, and sometimes simple, tools. Signals are transformed by systems in various ways, but what do these abstract terms mean, anyway? A mechanical system can take a signal like gears turning at some speed and transform it into a lever turning at another speed. A digital system can take binary digits and turn them into other binary digits or a string of letters or an image. Financial systems can take news and transform it via millions of trading decisions into stock prices. People take in signals every day through advertisements, job offers, gossip, and so on, and translate them into actions that in turn influence society and other people. This humble class on signals and systems linked mechanical, digital, and societal systems and showed me how foundational tools can cut through the noise.

    Q: In your project with Amazon you’re training warehouse robots to pick up, sort, and deliver goods. What are the technical challenges?

    A: This project involves assigning robots to a given task and routing them there. [Professor] Cynthia Barnhart’s team is focused on task assignment, and mine, on path planning. Both problems are considered combinatorial optimization problems because the solution involves a combination of choices. As the number of tasks and robots increases, the number of possible solutions grows exponentially. It’s called the curse of dimensionality. Both problems are what we call NP Hard; there may not be an efficient algorithm to solve them. Our goal is to devise a shortcut.

    Routing a single robot for a single task isn’t difficult. It’s like using Google Maps to find the shortest path home. It can be solved efficiently with several algorithms, including Dijkstra’s. But warehouses resemble small cities with hundreds of robots. When traffic jams occur, customers can’t get their packages as quickly. Our goal is to develop algorithms that find the most efficient paths for all of the robots.

    Q: Are there other applications?

    A: Yes. The algorithms we test in Amazon warehouses might one day help to ease congestion in real cities. Other potential applications include controlling planes on runways, swarms of drones in the air, and even characters in video games. These algorithms could also be used for other robotic planning tasks like scheduling and routing.

    Q: AI is evolving rapidly. Where do you hope to see the big breakthroughs coming?

    A: I’d like to see deep learning and deep RL used to solve societal problems involving mobility, infrastructure, social media, health care, and education. Deep RL now has a toehold in robotics and industrial applications like chip design, but we still need to be careful in applying it to systems with humans in the loop. Ultimately, we want to design systems for people. Currently, we simply don’t have the right tools.

    Q: What worries you most about AI taking on more and more specialized tasks?

    A: AI has the potential for tremendous good, but it could also help to accelerate the widening gap between the haves and the have-nots. Our political and regulatory systems could help to integrate AI into society and minimize job losses and income inequality, but I worry that they’re not equipped yet to handle the firehose of AI.

    Q: What’s the last great book you read?

    A: “How to Avoid a Climate Disaster,” by Bill Gates. I absolutely loved the way that Gates was able to take an overwhelmingly complex topic and distill it down into words that everyone can understand. His optimism inspires me to keep pushing on applications of AI and robotics to help avoid a climate disaster. More