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      in Data Management & Statistics

      A new state of the art for unsupervised vision

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      Labeling data can be a chore. It’s the main source of sustenance for computer-vision models; without it, they’d have a lot of difficulty identifying objects, people, and other important image characteristics. Yet producing just an hour of tagged and labeled data can take a whopping 800 hours of human time. Our high-fidelity understanding of the world develops as machines can better perceive and interact with our surroundings. But they need more help.

      Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Microsoft, and Cornell University have attempted to solve this problem plaguing vision models by creating “STEGO,” an algorithm that can jointly discover and segment objects without any human labels at all, down to the pixel.

      STEGO learns something called “semantic segmentation” — fancy speak for the process of assigning a label to every pixel in an image. Semantic segmentation is an important skill for today’s computer-vision systems because images can be cluttered with objects. Even more challenging is that these objects don’t always fit into literal boxes; algorithms tend to work better for discrete “things” like people and cars as opposed to “stuff” like vegetation, sky, and mashed potatoes. A previous system might simply perceive a nuanced scene of a dog playing in the park as just a dog, but by assigning every pixel of the image a label, STEGO can break the image into its main ingredients: a dog, sky, grass, and its owner.

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      A new state of the art for unsupervised computer vision

      Assigning every single pixel of the world a label is ambitious — especially without any kind of feedback from humans. The majority of algorithms today get their knowledge from mounds of labeled data, which can take painstaking human-hours to source. Just imagine the excitement of labeling every pixel of 100,000 images! To discover these objects without a human’s helpful guidance, STEGO looks for similar objects that appear throughout a dataset. It then associates these similar objects together to construct a consistent view of the world across all of the images it learns from.

      Seeing the world

      Machines that can “see” are crucial for a wide array of new and emerging technologies like self-driving cars and predictive modeling for medical diagnostics. Since STEGO can learn without labels, it can detect objects in many different domains, even those that humans don’t yet understand fully. 

      “If you’re looking at oncological scans, the surface of planets, or high-resolution biological images, it’s hard to know what objects to look for without expert knowledge. In emerging domains, sometimes even human experts don’t know what the right objects should be,” says Mark Hamilton, a PhD student in electrical engineering and computer science at MIT, research affiliate of MIT CSAIL, software engineer at Microsoft, and lead author on a new paper about STEGO. “In these types of situations where you want to design a method to operate at the boundaries of science, you can’t rely on humans to figure it out before machines do.”

      STEGO was tested on a slew of visual domains spanning general images, driving images, and high-altitude aerial photographs. In each domain, STEGO was able to identify and segment relevant objects that were closely aligned with human judgments. STEGO’s most diverse benchmark was the COCO-Stuff dataset, which is made up of diverse images from all over the world, from indoor scenes to people playing sports to trees and cows. In most cases, the previous state-of-the-art system could capture a low-resolution gist of a scene, but struggled on fine-grained details: A human was a blob, a motorcycle was captured as a person, and it couldn’t recognize any geese. On the same scenes, STEGO doubled the performance of previous systems and discovered concepts like animals, buildings, people, furniture, and many others.

      STEGO not only doubled the performance of prior systems on the COCO-Stuff benchmark, but made similar leaps forward in other visual domains. When applied to driverless car datasets, STEGO successfully segmented out roads, people, and street signs with much higher resolution and granularity than previous systems. On images from space, the system broke down every single square foot of the surface of the Earth into roads, vegetation, and buildings. 

      Connecting the pixels

      STEGO — which stands for “Self-supervised Transformer with Energy-based Graph Optimization” — builds on top of the DINO algorithm, which learned about the world through 14 million images from the ImageNet database. STEGO refines the DINO backbone through a learning process that mimics our own way of stitching together pieces of the world to make meaning. 

      For example, you might consider two images of dogs walking in the park. Even though they’re different dogs, with different owners, in different parks, STEGO can tell (without humans) how each scene’s objects relate to each other. The authors even probe STEGO’s mind to see how each little, brown, furry thing in the images are similar, and likewise with other shared objects like grass and people. By connecting objects across images, STEGO builds a consistent view of the word.

      “The idea is that these types of algorithms can find consistent groupings in a largely automated fashion so we don’t have to do that ourselves,” says Hamilton. “It might have taken years to understand complex visual datasets like biological imagery, but if we can avoid spending 1,000 hours combing through data and labeling it, we can find and discover new information that we might have missed. We hope this will help us understand the visual word in a more empirically grounded way.”

      Looking ahead

      Despite its improvements, STEGO still faces certain challenges. One is that labels can be arbitrary. For example, the labels of the COCO-Stuff dataset distinguish between “food-things” like bananas and chicken wings, and “food-stuff” like grits and pasta. STEGO doesn’t see much of a distinction there. In other cases, STEGO was confused by odd images — like one of a banana sitting on a phone receiver — where the receiver was labeled “foodstuff,” instead of “raw material.” 

      For upcoming work, they’re planning to explore giving STEGO a bit more flexibility than just labeling pixels into a fixed number of classes as things in the real world can sometimes be multiple things at the same time (like “food”, “plant” and “fruit”). The authors hope this will give the algorithm room for uncertainty, trade-offs, and more abstract thinking.

      “In making a general tool for understanding potentially complicated datasets, we hope that this type of an algorithm can automate the scientific process of object discovery from images. There’s a lot of different domains where human labeling would be prohibitively expensive, or humans simply don’t even know the specific structure, like in certain biological and astrophysical domains. We hope that future work enables application to a very broad scope of datasets. Since you don’t need any human labels, we can now start to apply ML tools more broadly,” says Hamilton.

      “STEGO is simple, elegant, and very effective. I consider unsupervised segmentation to be a benchmark for progress in image understanding, and a very difficult problem. The research community has made terrific progress in unsupervised image understanding with the adoption of transformer architectures,” says Andrea Vedaldi, professor of computer vision and machine learning and a co-lead of the Visual Geometry Group at the engineering science department of the University of Oxford. “This research provides perhaps the most direct and effective demonstration of this progress on unsupervised segmentation.” 

      Hamilton wrote the paper alongside MIT CSAIL PhD student Zhoutong Zhang, Assistant Professor Bharath Hariharan of Cornell University, Associate Professor Noah Snavely of Cornell Tech, and MIT professor William T. Freeman. They will present the paper at the 2022 International Conference on Learning Representations (ICLR).  More

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      MIT Schwarzman College of Computing unveils Break Through Tech AI

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      Aimed at driving diversity and inclusion in artificial intelligence, the MIT Stephen A. Schwarzman College of Computing is launching Break Through Tech AI, a new program to bridge the talent gap for women and underrepresented genders in AI positions in industry.

      Break Through Tech AI will provide skills-based training, industry-relevant portfolios, and mentoring to qualified undergraduate students in the Greater Boston area in order to position them more competitively for careers in data science, machine learning, and artificial intelligence. The free, 18-month program will also provide each student with a stipend for participation to lower the barrier for those typically unable to engage in an unpaid, extra-curricular educational opportunity.

      “Helping position students from diverse backgrounds to succeed in fields such as data science, machine learning, and artificial intelligence is critical for our society’s future,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “We look forward to working with students from across the Greater Boston area to provide them with skills and mentorship to help them find careers in this competitive and growing industry.”

      The college is collaborating with Break Through Tech — a national initiative launched by Cornell Tech in 2016 to increase the number of women and underrepresented groups graduating with degrees in computing — to host and administer the program locally. In addition to Boston, the inaugural artificial intelligence and machine learning program will be offered in two other metropolitan areas — one based in New York hosted by Cornell Tech and another in Los Angeles hosted by the University of California at Los Angeles Samueli School of Engineering.

      “Break Through Tech’s success at diversifying who is pursuing computer science degrees and careers has transformed lives and the industry,” says Judith Spitz, executive director of Break Through Tech. “With our new collaborators, we can apply our impactful model to drive inclusion and diversity in artificial intelligence.”

      The new program will kick off this summer at MIT with an eight-week, skills-based online course and in-person lab experience that teaches industry-relevant tools to build real-world AI solutions. Students will learn how to analyze datasets and use several common machine learning libraries to build, train, and implement their own ML models in a business context.

      Following the summer course, students will be matched with machine-learning challenge projects for which they will convene monthly at MIT and work in teams to build solutions and collaborate with an industry advisor or mentor throughout the academic year, resulting in a portfolio of resume-quality work. The participants will also be paired with young professionals in the field to help build their network, prepare their portfolio, practice for interviews, and cultivate workplace skills.

      “Leveraging the college’s strong partnership with industry, Break Through AI will offer unique opportunities to students that will enhance their portfolio in machine learning and AI,” says Asu Ozdaglar, deputy dean of academics of the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science. Ozdaglar, who will be the MIT faculty director of Break Through Tech AI, adds: “The college is committed to making computing inclusive and accessible for all. We’re thrilled to host this program at MIT for the Greater Boston area and to do what we can to help increase diversity in computing fields.”

      Break Through Tech AI is part of the MIT Schwarzman College of Computing’s focus to advance diversity, equity, and inclusion in computing. The college aims to improve and create programs and activities that broaden participation in computing classes and degree programs, increase the diversity of top faculty candidates in computing fields, and ensure that faculty search and graduate admissions processes have diverse slates of candidates and interviews.

      “By engaging in activities like Break Through Tech AI that work to improve the climate for underrepresented groups, we’re taking an important step toward creating more welcoming environments where all members can innovate and thrive,” says Alana Anderson, assistant dean for diversity, equity and inclusion for the Schwarzman College of Computing. More

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      Does this artificial intelligence think like a human?

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      In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo.

      While tools exist to help experts make sense of a model’s reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns.

      Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model’s behavior. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a model’s reasoning matches that of a human.

      Shared Interest could help a user easily uncover concerning trends in a model’s decision-making — for example, perhaps the model often becomes confused by distracting, irrelevant features, like background objects in photos. Aggregating these insights could help the user quickly and quantitatively determine whether a model is trustworthy and ready to be deployed in a real-world situation.

      “In developing Shared Interest, our goal is to be able to scale up this analysis process so that you could understand on a more global level what your model’s behavior is,” says lead author Angie Boggust, a graduate student in the Visualization Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

      Boggust wrote the paper with her advisor, Arvind Satyanarayan, an assistant professor of computer science who leads the Visualization Group, as well as Benjamin Hoover and senior author Hendrik Strobelt, both of IBM Research. The paper will be presented at the Conference on Human Factors in Computing Systems.

      Boggust began working on this project during a summer internship at IBM, under the mentorship of Strobelt. After returning to MIT, Boggust and Satyanarayan expanded on the project and continued the collaboration with Strobelt and Hoover, who helped deploy the case studies that show how the technique could be used in practice.

      Human-AI alignment

      Shared Interest leverages popular techniques that show how a machine-learning model made a specific decision, known as saliency methods. If the model is classifying images, saliency methods highlight areas of an image that are important to the model when it made its decision. These areas are visualized as a type of heatmap, called a saliency map, that is often overlaid on the original image. If the model classified the image as a dog, and the dog’s head is highlighted, that means those pixels were important to the model when it decided the image contains a dog.

      Shared Interest works by comparing saliency methods to ground-truth data. In an image dataset, ground-truth data are typically human-generated annotations that surround the relevant parts of each image. In the previous example, the box would surround the entire dog in the photo. When evaluating an image classification model, Shared Interest compares the model-generated saliency data and the human-generated ground-truth data for the same image to see how well they align.

      The technique uses several metrics to quantify that alignment (or misalignment) and then sorts a particular decision into one of eight categories. The categories run the gamut from perfectly human-aligned (the model makes a correct prediction and the highlighted area in the saliency map is identical to the human-generated box) to completely distracted (the model makes an incorrect prediction and does not use any image features found in the human-generated box).

      “On one end of the spectrum, your model made the decision for the exact same reason a human did, and on the other end of the spectrum, your model and the human are making this decision for totally different reasons. By quantifying that for all the images in your dataset, you can use that quantification to sort through them,” Boggust explains.

      The technique works similarly with text-based data, where key words are highlighted instead of image regions.

      Rapid analysis

      The researchers used three case studies to show how Shared Interest could be useful to both nonexperts and machine-learning researchers.

      In the first case study, they used Shared Interest to help a dermatologist determine if he should trust a machine-learning model designed to help diagnose cancer from photos of skin lesions. Shared Interest enabled the dermatologist to quickly see examples of the model’s correct and incorrect predictions. Ultimately, the dermatologist decided he could not trust the model because it made too many predictions based on image artifacts, rather than actual lesions.

      “The value here is that using Shared Interest, we are able to see these patterns emerge in our model’s behavior. In about half an hour, the dermatologist was able to make a confident decision of whether or not to trust the model and whether or not to deploy it,” Boggust says.

      In the second case study, they worked with a machine-learning researcher to show how Shared Interest can evaluate a particular saliency method by revealing previously unknown pitfalls in the model. Their technique enabled the researcher to analyze thousands of correct and incorrect decisions in a fraction of the time required by typical manual methods.

      In the third case study, they used Shared Interest to dive deeper into a specific image classification example. By manipulating the ground-truth area of the image, they were able to conduct a what-if analysis to see which image features were most important for particular predictions.   

      The researchers were impressed by how well Shared Interest performed in these case studies, but Boggust cautions that the technique is only as good as the saliency methods it is based upon. If those techniques contain bias or are inaccurate, then Shared Interest will inherit those limitations.

      In the future, the researchers want to apply Shared Interest to different types of data, particularly tabular data which is used in medical records. They also want to use Shared Interest to help improve current saliency techniques. Boggust hopes this research inspires more work that seeks to quantify machine-learning model behavior in ways that make sense to humans.

      This work is funded, in part, by the MIT-IBM Watson AI Lab, the United States Air Force Research Laboratory, and the United States Air Force Artificial Intelligence Accelerator. More

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      Ocean vital signs

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      Without the ocean, the climate crisis would be even worse than it is. Each year, the ocean absorbs billions of tons of carbon from the atmosphere, preventing warming that greenhouse gas would otherwise cause. Scientists estimate about 25 to 30 percent of all carbon released into the atmosphere by both human and natural sources is absorbed by the ocean.

      “But there’s a lot of uncertainty in that number,” says Ryan Woosley, a marine chemist and a principal research scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at MIT. Different parts of the ocean take in different amounts of carbon depending on many factors, such as the season and the amount of mixing from storms. Current models of the carbon cycle don’t adequately capture this variation.

      To close the gap, Woosley and a team of other MIT scientists developed a research proposal for the MIT Climate Grand Challenges competition — an Institute-wide campaign to catalyze and fund innovative research addressing the climate crisis. The team’s proposal, “Ocean Vital Signs,” involves sending a fleet of sailing drones to cruise the oceans taking detailed measurements of how much carbon the ocean is really absorbing. Those data would be used to improve the precision of global carbon cycle models and improve researchers’ ability to verify emissions reductions claimed by countries.

      “If we start to enact mitigation strategies—either through removing CO2 from the atmosphere or reducing emissions — we need to know where CO2 is going in order to know how effective they are,” says Woosley. Without more precise models there’s no way to confirm whether observed carbon reductions were thanks to policy and people, or thanks to the ocean.

      “So that’s the trillion-dollar question,” says Woosley. “If countries are spending all this money to reduce emissions, is it enough to matter?”

      In February, the team’s Climate Grand Challenges proposal was named one of 27 finalists out of the almost 100 entries submitted. From among this list of finalists, MIT will announce in April the selection of five flagship projects to receive further funding and support.

      Woosley is leading the team along with Christopher Hill, a principal research engineer in EAPS. The team includes physical and chemical oceanographers, marine microbiologists, biogeochemists, and experts in computational modeling from across the department, in addition to collaborators from the Media Lab and the departments of Mathematics, Aeronautics and Astronautics, and Electrical Engineering and Computer Science.

      Today, data on the flux of carbon dioxide between the air and the oceans are collected in a piecemeal way. Research ships intermittently cruise out to gather data. Some commercial ships are also fitted with sensors. But these present a limited view of the entire ocean, and include biases. For instance, commercial ships usually avoid storms, which can increase the turnover of water exposed to the atmosphere and cause a substantial increase in the amount of carbon absorbed by the ocean.

      “It’s very difficult for us to get to it and measure that,” says Woosley. “But these drones can.”

      If funded, the team’s project would begin by deploying a few drones in a small area to test the technology. The wind-powered drones — made by a California-based company called Saildrone — would autonomously navigate through an area, collecting data on air-sea carbon dioxide flux continuously with solar-powered sensors. This would then scale up to more than 5,000 drone-days’ worth of observations, spread over five years, and in all five ocean basins.

      Those data would be used to feed neural networks to create more precise maps of how much carbon is absorbed by the oceans, shrinking the uncertainties involved in the models. These models would continue to be verified and improved by new data. “The better the models are, the more we can rely on them,” says Woosley. “But we will always need measurements to verify the models.”

      Improved carbon cycle models are relevant beyond climate warming as well. “CO2 is involved in so much of how the world works,” says Woosley. “We’re made of carbon, and all the other organisms and ecosystems are as well. What does the perturbation to the carbon cycle do to these ecosystems?”

      One of the best understood impacts is ocean acidification. Carbon absorbed by the ocean reacts to form an acid. A more acidic ocean can have dire impacts on marine organisms like coral and oysters, whose calcium carbonate shells and skeletons can dissolve in the lower pH. Since the Industrial Revolution, the ocean has become about 30 percent more acidic on average.

      “So while it’s great for us that the oceans have been taking up the CO2, it’s not great for the oceans,” says Woosley. “Knowing how this uptake affects the health of the ocean is important as well.” More

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      System helps severely motor-impaired individuals type more quickly and accurately

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      In 1995, French fashion magazine editor Jean-Dominique Bauby suffered a seizure while driving a car, which left him with a condition known as locked-in syndrome, a neurological disease in which the patient is completely paralyzed and can only move muscles that control the eyes.

      Bauby, who had signed a book contract shortly before his accident, wrote the memoir “The Diving Bell and the Butterfly” using a dictation system in which his speech therapist recited the alphabet and he would blink when she said the correct letter. They wrote the 130-page book one blink at a time.

      Technology has come a long way since Bauby’s accident. Many individuals with severe motor impairments caused by locked-in syndrome, cerebral palsy, amyotrophic lateral sclerosis, or other conditions can communicate using computer interfaces where they select letters or words in an onscreen grid by activating a single switch, often by pressing a button, releasing a puff of air, or blinking.

      But these row-column scanning systems are very rigid, and, similar to the technique used by Bauby’s speech therapist, they highlight each option one at a time, making them frustratingly slow for some users. And they are not suitable for tasks where options can’t be arranged in a grid, like drawing, browsing the web, or gaming.

      A more flexible system being developed by researchers at MIT places individual selection indicators next to each option on a computer screen. The indicators can be placed anywhere — next to anything someone might click with a mouse — so a user does not need to cycle through a grid of choices to make selections. The system, called Nomon, incorporates probabilistic reasoning to learn how users make selections, and then adjusts the interface to improve their speed and accuracy.

      Participants in a user study were able to type faster using Nomon than with a row-column scanning system. The users also performed better on a picture selection task, demonstrating how Nomon could be used for more than typing.

      “It is so cool and exciting to be able to develop software that has the potential to really help people. Being able to find those signals and turn them into communication as we are used to it is a really interesting problem,” says senior author Tamara Broderick, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.

      Joining Broderick on the paper are lead author Nicholas Bonaker, an EECS graduate student; Emli-Mari Nel, head of innovation and machine learning at Averly and a visiting lecturer at the University of Witwatersrand in South Africa; and Keith Vertanen, an associate professor at Michigan Tech. The research is being presented at the ACM Conference on Human Factors in Computing Systems.

      On the clock

      In the Nomon interface, a small analog clock is placed next to every option the user can select. (A gnomon is the part of a sundial that casts a shadow.) The user looks at one option and then clicks their switch when that clock’s hand passes a red “noon” line. After each click, the system changes the phases of the clocks to separate the most probable next targets. The user clicks repeatedly until their target is selected.

      When used as a keyboard, Nomon’s machine-learning algorithms try to guess the next word based on previous words and each new letter as the user makes selections.

      Broderick developed a simplified version of Nomon several years ago but decided to revisit it to make the system easier for motor-impaired individuals to use. She enlisted the help of then-undergraduate Bonaker to redesign the interface.

      They first consulted nonprofit organizations that work with motor-impaired individuals, as well as a motor-impaired switch user, to gather feedback on the Nomon design.

      Then they designed a user study that would better represent the abilities of motor-impaired individuals. They wanted to make sure to thoroughly vet the system before using much of the valuable time of motor-impaired users, so they first tested on non-switch users, Broderick explains.

      Switching up the switch

      To gather more representative data, Bonaker devised a webcam-based switch that was harder to use than simply clicking a key. The non-switch users had to lean their bodies to one side of the screen and then back to the other side to register a click.

      “And they have to do this at precisely the right time, so it really slows them down. We did some empirical studies which showed that they were much closer to the response times of motor-impaired individuals,” Broderick says.

      They ran a 10-session user study with 13 non-switch participants and one single-switch user with an advanced form of spinal muscular dystrophy. In the first nine sessions, participants used Nomon and a row-column scanning interface for 20 minutes each to perform text entry, and in the 10th session they used the two systems for a picture selection task.

      Non-switch users typed 15 percent faster using Nomon, while the motor-impaired user typed even faster than the non-switch users. When typing unfamiliar words, the users were 20 percent faster overall and made half as many errors. In their final session, they were able to complete the picture selection task 36 percent faster using Nomon.

      “Nomon is much more forgiving than row-column scanning. With row-column scanning, even if you are just slightly off, now you’ve chosen B instead of A and that’s an error,” Broderick says.

      Adapting to noisy clicks

      With its probabilistic reasoning, Nomon incorporates everything it knows about where a user is likely to click to make the process faster, easier, and less error-prone. For instance, if the user selects “Q,” Nomon will make it as easy as possible for the user to select “U” next.

      Nomon also learns how a user clicks. So, if the user always clicks a little after the clock’s hand strikes noon, the system adapts to that in real time. It also adapts to noisiness. If a user’s click is often off the mark, the system requires extra clicks to ensure accuracy.

      This probabilistic reasoning makes Nomon powerful but also requires a higher click-load than row-column scanning systems. Clicking multiple times can be a trying task for severely motor-impaired users.

      Broderick hopes to reduce the click-load by incorporating gaze tracking into Nomon, which would give the system more robust information about what a user might choose next based on which part of the screen they are looking at. The researchers also want to find a better way to automatically adjust the clock speeds to help users be more accurate and efficient.

      They are working on a new series of studies in which they plan to partner with more motor-impaired users.

      “So far, the feedback from motor-impaired users has been invaluable to us; we’re very grateful to the motor-impaired user who commented on our initial interface and the separate motor-impaired user who participated in our study. We’re currently extending our study to work with a bigger and more diverse group of our target population. With their help, we’re already making further improvements to our interface and working to better understand the performance of Nomon,” she says.

      “Nonspeaking individuals with motor disabilities are currently not provided with efficient communication solutions for interacting with either speaking partners or computer systems. This ‘communication gap’ is a known unresolved problem in human-computer interaction, and so far there are no good solutions. This paper demonstrates that a highly creative approach underpinned by a statistical model can provide tangible performance gains to the users who need it the most: nonspeaking individuals reliant on a single switch to communicate,” says Per Ola Kristensson, professor of interactive systems engineering at Cambridge University, who was not involved with this research. “The paper also demonstrates the value of complementing insights from computational experiments with the involvement of end-users and other stakeholders in the design process. I find this a highly creative and important paper in an area where it is notoriously difficult to make significant progress.”

      This research was supported, in part, by the Seth Teller Memorial Fund to Advanced Technology for People with Disabilities, a Peter J. Eloranta Summer Undergraduate Research Fellowship, the MIT Quest for Intelligence, and the National Science Foundation. More

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      Generating new molecules with graph grammar

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      Chemical engineers and materials scientists are constantly looking for the next revolutionary material, chemical, and drug. The rise of machine-learning approaches is expediting the discovery process, which could otherwise take years. “Ideally, the goal is to train a machine-learning model on a few existing chemical samples and then allow it to produce as many manufacturable molecules of the same class as possible, with predictable physical properties,” says Wojciech Matusik, professor of electrical engineering and computer science at MIT. “If you have all these components, you can build new molecules with optimal properties, and you also know how to synthesize them. That’s the overall vision that people in that space want to achieve”

      However, current techniques, mainly deep learning, require extensive datasets for training models, and many class-specific chemical datasets contain a handful of example compounds, limiting their ability to generalize and generate physical molecules that could be created in the real world.

      Now, a new paper from researchers at MIT and IBM tackles this problem using a generative graph model to build new synthesizable molecules within the same chemical class as their training data. To do this, they treat the formation of atoms and chemical bonds as a graph and develop a graph grammar — a linguistics analogy of systems and structures for word ordering — that contains a sequence of rules for building molecules, such as monomers and polymers. Using the grammar and production rules that were inferred from the training set, the model can not only reverse engineer its examples, but can create new compounds in a systematic and data-efficient way. “We basically built a language for creating molecules,” says Matusik “This grammar essentially is the generative model.”

      Matusik’s co-authors include MIT graduate students Minghao Guo, who is the lead author, and Beichen Li as well as Veronika Thost, Payal Das, and Jie Chen, research staff members with IBM Research. Matusik, Thost, and Chen are affiliated with the MIT-IBM Watson AI Lab. Their method, which they’ve called data-efficient graph grammar (DEG), will be presented at the International Conference on Learning Representations.

      “We want to use this grammar representation for monomer and polymer generation, because this grammar is explainable and expressive,” says Guo. “With only a few number of the production rules, we can generate many kinds of structures.”

      A molecular structure can be thought of as a symbolic representation in a graph — a string of atoms (nodes) joined together by chemical bonds (edges). In this method, the researchers allow the model to take the chemical structure and collapse a substructure of the molecule down to one node; this may be two atoms connected by a bond, a short sequence of bonded atoms, or a ring of atoms. This is done repeatedly, creating the production rules as it goes, until a single node remains. The rules and grammar then could be applied in the reverse order to recreate the training set from scratch or combined in different combinations to produce new molecules of the same chemical class.

      “Existing graph generation methods would produce one node or one edge sequentially at a time, but we are looking at higher-level structures and, specifically, exploiting chemistry knowledge, so that we don’t treat the individual atoms and bonds as the unit. This simplifies the generation process and also makes it more data-efficient to learn,” says Chen.

      Further, the researchers optimized the technique so that the bottom-up grammar was relatively simple and straightforward, such that it fabricated molecules that could be made.

      “If we switch the order of applying these production rules, we would get another molecule; what’s more, we can enumerate all the possibilities and generate tons of them,” says Chen. “Some of these molecules are valid and some of them not, so the learning of the grammar itself is actually to figure out a minimal collection of production rules, such that the percentage of molecules that can actually be synthesized is maximized.” While the researchers concentrated on three training sets of less than 33 samples each — acrylates, chain extenders, and isocyanates — they note that the process could be applied to any chemical class.

      To see how their method performed, the researchers tested DEG against other state-of-the-art models and techniques, looking at percentages of chemically valid and unique molecules, diversity of those created, success rate of retrosynthesis, and percentage of molecules belonging to the training data’s monomer class.

      “We clearly show that, for the synthesizability and membership, our algorithm outperforms all the existing methods by a very large margin, while it’s comparable for some other widely-used metrics,” says Guo. Further, “what is amazing about our algorithm is that we only need about 0.15 percent of the original dataset to achieve very similar results compared to state-of-the-art approaches that train on tens of thousands of samples. Our algorithm can specifically handle the problem of data sparsity.”

      In the immediate future, the team plans to address scaling up this grammar learning process to be able to generate large graphs, as well as produce and identify chemicals with desired properties.

      Down the road, the researchers see many applications for the DEG method, as it’s adaptable beyond generating new chemical structures, the team points out. A graph is a very flexible representation, and many entities can be symbolized in this form — robots, vehicles, buildings, and electronic circuits, for example. “Essentially, our goal is to build up our grammar, so that our graphic representation can be widely used across many different domains,” says Guo, as “DEG can automate the design of novel entities and structures,” says Chen.

      This research was supported, in part, by the MIT-IBM Watson AI Lab and Evonik. More

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      in Data Management & Statistics

      Fighting discrimination in mortgage lending

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      Although the U.S. Equal Credit Opportunity Act prohibits discrimination in mortgage lending, biases still impact many borrowers. One 2021 Journal of Financial Economics study found that borrowers from minority groups were charged interest rates that were nearly 8 percent higher and were rejected for loans 14 percent more often than those from privileged groups.

      When these biases bleed into machine-learning models that lenders use to streamline decision-making, they can have far-reaching consequences for housing fairness and even contribute to widening the racial wealth gap.

      If a model is trained on an unfair dataset, such as one in which a higher proportion of Black borrowers were denied loans versus white borrowers with the same income, credit score, etc., those biases will affect the model’s predictions when it is applied to real situations. To stem the spread of mortgage lending discrimination, MIT researchers created a process that removes bias in data that are used to train these machine-learning models.

      While other methods try to tackle this bias, the researchers’ technique is new in the mortgage lending domain because it can remove bias from a dataset that has multiple sensitive attributes, such as race and ethnicity, as well as several “sensitive” options for each attribute, such as Black or white, and Hispanic or Latino or non-Hispanic or Latino. Sensitive attributes and options are features that distinguish a privileged group from an underprivileged group.

      The researchers used their technique, which they call DualFair, to train a machine-learning classifier that makes fair predictions of whether borrowers will receive a mortgage loan. When they applied it to mortgage lending data from several U.S. states, their method significantly reduced the discrimination in the predictions while maintaining high accuracy.

      “As Sikh Americans, we deal with bias on a frequent basis and we think it is unacceptable to see that transform to algorithms in real-world applications. For things like mortgage lending and financial systems, it is very important that bias not infiltrate these systems because it can emphasize the gaps that are already in place against certain groups,” says Jashandeep Singh, a senior at Floyd Buchanan High School and co-lead author of the paper with his twin brother, Arashdeep. The Singh brothers were recently accepted into MIT.

      Joining Arashdeep and Jashandeep Singh on the paper are MIT sophomore Ariba Khan and senior author Amar Gupta, a researcher in the Computer Science and Artificial Intelligence Laboratory at MIT, who studies the use of evolving technology to address inequity and other societal issues. The research was recently published online and will appear in a special issue of Machine Learning and Knowledge Extraction.

      Double take

      DualFair tackles two types of bias in a mortgage lending dataset — label bias and selection bias. Label bias occurs when the balance of favorable or unfavorable outcomes for a particular group is unfair. (Black applicants are denied loans more frequently than they should be.) Selection bias is created when data are not representative of the larger population. (The dataset only includes individuals from one neighborhood where incomes are historically low.)

      The DualFair process eliminates label bias by subdividing a dataset into the largest number of subgroups based on combinations of sensitive attributes and options, such as white men who are not Hispanic or Latino, Black women who are Hispanic or Latino, etc.

      By breaking down the dataset into as many subgroups as possible, DualFair can simultaneously address discrimination based on multiple attributes.

      “Researchers have mostly tried to classify biased cases as binary so far. There are multiple parameters to bias, and these multiple parameters have their own impact in different cases. They are not equally weighed. Our method is able to calibrate it much better,” says Gupta.

      After the subgroups have been generated, DualFair evens out the number of borrowers in each subgroup by duplicating individuals from minority groups and deleting individuals from the majority group. DualFair then balances the proportion of loan acceptances and rejections in each subgroup so they match the median in the original dataset before recombining the subgroups.

      DualFair then eliminates selection bias by iterating on each data point to see if discrimination is present. For instance, if an individual is a non-Hispanic or Latino Black woman who was rejected for a loan, the system will adjust her race, ethnicity, and gender one at a time to see if the outcome changes. If this borrower is granted a loan when her race is changed to white, DualFair considers that data point biased and removes it from the dataset.

      Fairness vs. accuracy

      To test DualFair, the researchers used the publicly available Home Mortgage Disclosure Act dataset, which spans 88 percent of all mortgage loans in the U.S. in 2019, and includes 21 features, including race, sex, and ethnicity. They used DualFair to “de-bias” the entire dataset and smaller datasets for six states, and then trained a machine-learning model to predict loan acceptances and rejections.

      After applying DualFair, the fairness of predictions increased while the accuracy level remained high across all states. They used an existing fairness metric known as average odds difference, but it can only measure fairness in one sensitive attribute at a time.

      So, they created their own fairness metric, called alternate world index, that considers bias from multiple sensitive attributes and options as a whole. Using this metric, they found that DualFair increased fairness in predictions for four of the six states while maintaining high accuracy.

      “It is the common belief that if you want to be accurate, you have to give up on fairness, or if you want to be fair, you have to give up on accuracy. We show that we can make strides toward lessening that gap,” Khan says.

      The researchers now want to apply their method to de-bias different types of datasets, such as those that capture health care outcomes, car insurance rates, or job applications. They also plan to address limitations of DualFair, including its instability when there are small amounts of data with multiple sensitive attributes and options.

      While this is only a first step, the researchers are hopeful their work can someday have an impact on mitigating bias in lending and beyond.

      “Technology, very bluntly, works only for a certain group of people. In the mortgage loan domain in particular, African American women have been historically discriminated against. We feel passionate about making sure that systemic racism does not extend to algorithmic models. There is no point in making an algorithm that can automate a process if it doesn’t work for everyone equally,” says Khan.

      This research is supported, in part, by the FinTech@CSAIL initiative. More

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      in Data Management & Statistics

      Security tool guarantees privacy in surveillance footage

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      Surveillance cameras have an identity problem, fueled by an inherent tension between utility and privacy. As these powerful little devices have cropped up seemingly everywhere, the use of machine learning tools has automated video content analysis at a massive scale — but with increasing mass surveillance, there are currently no legally enforceable rules to limit privacy invasions. 

      Security cameras can do a lot — they’ve become smarter and supremely more competent than their ghosts of grainy pictures past, the ofttimes “hero tool” in crime media. (“See that little blurry blue blob in the right hand corner of that densely populated corner — we got him!”) Now, video surveillance can help health officials measure the fraction of people wearing masks, enable transportation departments to monitor the density and flow of vehicles, bikes, and pedestrians, and provide businesses with a better understanding of shopping behaviors. But why has privacy remained a weak afterthought? 

      The status quo is to retrofit video with blurred faces or black boxes. Not only does this prevent analysts from asking some genuine queries (e.g., Are people wearing masks?), it also doesn’t always work; the system may miss some faces and leave them unblurred for the world to see. Dissatisfied with this status quo, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with other institutions, came up with a system to better guarantee privacy in video footage from surveillance cameras. Called “Privid,” the system lets analysts submit video data queries, and adds a little bit of noise (extra data) to the end result to ensure that an individual can’t be identified. The system builds on a formal definition of privacy — “differential privacy” — which allows access to aggregate statistics about private data without revealing personally identifiable information.

      Typically, analysts would just have access to the entire video to do whatever they want with it, but Privid makes sure the video isn’t a free buffet. Honest analysts can get access to the information they need, but that access is restrictive enough that malicious analysts can’t do too much with it. To enable this, rather than running the code over the entire video in one shot, Privid breaks the video into small pieces and runs processing code over each chunk. Instead of getting results back from each piece, the segments are aggregated, and that additional noise is added. (There’s also information on the error bound you’re going to get on your result — maybe a 2 percent error margin, given the extra noisy data added). 

      For example, the code might output the number of people observed in each video chunk, and the aggregation might be the “sum,” to count the total number of people wearing face coverings, or the “average” to estimate the density of crowds. 

      Privid allows analysts to use their own deep neural networks that are commonplace for video analytics today. This gives analysts the flexibility to ask questions that the designers of Privid did not anticipate. Across a variety of videos and queries, Privid was accurate within 79 to 99 percent of a non-private system.

      “We’re at a stage right now where cameras are practically ubiquitous. If there’s a camera on every street corner, every place you go, and if someone could actually process all of those videos in aggregate, you can imagine that entity building a very precise timeline of when and where a person has gone,” says MIT CSAIL PhD student ​​Frank Cangialosi, the lead author on a paper about Privid. “People are already worried about location privacy with GPS — video data in aggregate could capture not only your location history, but also moods, behaviors, and more at each location.” 

      Privid introduces a new notion of “duration-based privacy,” which decouples the definition of privacy from its enforcement — with obfuscation, if your privacy goal is to protect all people, the enforcement mechanism needs to do some work to find the people to protect, which it may or may not do perfectly. With this mechanism, you don’t need to fully specify everything, and you’re not hiding more information than you need to. 

      Let’s say we have a video overlooking a street. Two analysts, Alice and Bob, both claim they want to count the number of people that pass by each hour, so they submit a video processing module and ask for a sum aggregation.

      The first analyst is the city planning department, which hopes to use this information to understand footfall patterns and plan sidewalks for the city. Their model counts people and outputs this count for each video chunk.

      The other analyst is malicious. They hope to identify every time “Charlie” passes by the camera. Their model only looks for Charlie’s face, and outputs a large number if Charlie is present (i.e., the “signal” they’re trying to extract), or zero otherwise. Their hope is that the sum will be non-zero if Charlie was present. 

      From Privid’s perspective, these two queries look identical. It’s hard to reliably determine what their models might be doing internally, or what the analyst hopes to use the data for. This is where the noise comes in. Privid executes both of the queries, and adds the same amount of noise for each. In the first case, because Alice was counting all people, this noise will only have a small impact on the result, but likely won’t impact the usefulness. 

      In the second case, since Bob was looking for a specific signal (Charlie was only visible for a few chunks), the noise is enough to prevent them from knowing if Charlie was there or not. If they see a non-zero result, it might be because Charlie was actually there, or because the model outputs “zero,” but the noise made it non-zero. Privid didn’t need to know anything about when or where Charlie appeared, the system just needed to know a rough upper bound on how long Charlie might appear for, which is easier to specify than figuring out the exact locations, which prior methods rely on. 

      The challenge is determining how much noise to add — Privid wants to add just enough to hide everyone, but not so much that it would be useless for analysts. Adding noise to the data and insisting on queries over time windows means that your result isn’t going to be as accurate as it could be, but the results are still useful while providing better privacy. 

      Cangialosi wrote the paper with Princeton PhD student Neil Agarwal, MIT CSAIL PhD student Venkat Arun, assistant professor at the University of Chicago Junchen Jiang, assistant professor at Rutgers University and former MIT CSAIL postdoc Srinivas Narayana, associate professor at Rutgers University Anand Sarwate, and assistant professor at Princeton University and Ravi Netravali SM ’15, PhD ’18. Cangialosi will present the paper at the USENIX Symposium on Networked Systems Design and Implementation Conference in April in Renton, Washington. 

      This work was partially supported by a Sloan Research Fellowship and National Science Foundation grants. More