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    Roadmap details how to improve exoplanet exploration using the JWST

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    The launch of NASA’s James Webb Space Telescope (JWST) in 2021 kicked off an exciting new era for exoplanet research, especially for scientists looking at terrestrial planets orbiting stars other than our sun. But three years into the telescope’s mission, some scientists have run into challenges that have slowed down progress.In a recent paper published in Nature Astronomy, the TRAPPIST-1 JWST Community Initiative lays out a step-by-step roadmap to overcome the challenges they faced while studying the TRAPPIST-1 system by improving the efficiency of data gathering to benefit the astronomy community at large.“A whole community of experts came together to tackle these complex cross-disciplinary challenges to design the first multiyear observational strategy to give JWST a fighting chance at identifying habitable worlds over its lifetime,” says Julien de Wit, an associate professor in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) and one of the lead authors of the paper.Two-for-one dealLocated 41 light years from Earth, the TRAPPIST-1 system with its seven planets presents a unique opportunity to study a large system with multiple planets of different compositions, similar to our own solar system.“It’s a dream target: You have not one, but maybe three, planets in the habitable zone, so you have a way to actually compare in the same system,” says René Doyon from the Université de Montréal, who co-led the study with de Wit. “There are only a handful of well-characterized temperate rocky planets for which we can hope to detect their atmosphere, and most of them are within the TRAPPIST-1 system.”Astronomers like de Wit and Doyon study exoplanet atmospheres through a technique called transmission spectroscopy, where they look at the way starlight passes through a planet’s potential atmosphere to see what elements are present. Transmission spectra are collected when the planet passes in front of its host star.The planets within the TRAPPIST system have short orbital periods. As a result, their transits frequently overlap. Transit observation times are usually allotted in five-hour windows, and when scheduled properly, close to half of these can catch at least two transits. This “two-for-one” saves both time and money while doubling data collection.Stellar contaminationStars are not uniform; their surfaces can vary in temperature, creating spots that can be hotter or cooler. Molecules like water vapor can condense in cool spots and interfere with transmission spectra. Stellar information like this can be difficult to disentangle from the planetary signal and give false indications of a planet’s atmospheric composition, creating what’s known as “stellar contamination.” While it has often been ignored, the improved capabilities of the JWST have revealed the challenges stellar contamination introduces when studying planetary atmospheres.EAPS research scientist Ben Rackham ran into these challenges when they derailed his initial PhD research on small exoplanets using the Magellan Telescopes in Chile. He’s now seeing the same problem he first encountered as a graduate student repeating itself with the new JWST data.“As we predicted from that earlier work with data from ground-based telescopes, the very first spectral signatures we’re getting with JWST don’t really make any sense in terms of a planetary interpretation,” he says. “The features are not what we would expect to see, and they change from transit to transit.”Rackham and David Berardo, a postdoc in EAPS, have been working with de Wit on ways to correct for stellar contamination using two different methods: improving models of stellar spectra and using direct observations to derive corrections.“By observing a star as it rotates, we can use the sensitivity of JWST to get a clearer picture of what its surface looks like, allowing for a more accurate measuring of the atmosphere of planets that transit it,” says Berardo. This, combined with studying back-to-back transits as proposed in the roadmap, collects useful data on the star that can be used to filter out stellar contamination from both future studies and past ones.Beyond TRAPPIST-1The current roadmap was born from the efforts of the TRAPPIST JWST Community Initiative to bring together separate programs focused on individual planets, which prevented them from leveraging the optimal transit observation windows.“We understood early on that this effort would ‘take a village’ to avoid the efficiency traps of small observation programs,” says de Wit. “Our hope now is that a large-scale community effort guided by the roadmap can be initiated to yield deliverables at a timely pace.” De Wit hopes that it could result in identifying habitable, or inhabitable, worlds around TRAPPIST-1 within a decade.Both de Wit and Doyon believe that the TRAPPIST-1 system is the best place for conducting fundamental research on exoplanet atmospheres that will extend to studies in other systems. Doyon thinks that “the TRAPPIST-1 system will be useful not only for TRAPPIST-1 itself, but also to learn how to do very precise correction of stellar activity which will be beneficial to many other transmission spectroscopy programs also affected by stellar activity.”“We have within reach fundamental and transforming answers with a clear roadmap to them,” says de Wit. “We just need to follow it diligently.”  More

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      Study: When allocating scarce resources with AI, randomization can improve fairness

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      Organizations are increasingly utilizing machine-learning models to allocate scarce resources or opportunities. For instance, such models can help companies screen resumes to choose job interview candidates or aid hospitals in ranking kidney transplant patients based on their likelihood of survival.When deploying a model, users typically strive to ensure its predictions are fair by reducing bias. This often involves techniques like adjusting the features a model uses to make decisions or calibrating the scores it generates.However, researchers from MIT and Northeastern University argue that these fairness methods are not sufficient to address structural injustices and inherent uncertainties. In a new paper, they show how randomizing a model’s decisions in a structured way can improve fairness in certain situations.For example, if multiple companies use the same machine-learning model to rank job interview candidates deterministically — without any randomization — then one deserving individual could be the bottom-ranked candidate for every job, perhaps due to how the model weighs answers provided in an online form. Introducing randomization into a model’s decisions could prevent one worthy person or group from always being denied a scarce resource, like a job interview.Through their analysis, the researchers found that randomization can be especially beneficial when a model’s decisions involve uncertainty or when the same group consistently receives negative decisions.They present a framework one could use to introduce a specific amount of randomization into a model’s decisions by allocating resources through a weighted lottery. This method, which an individual can tailor to fit their situation, can improve fairness without hurting the efficiency or accuracy of a model.“Even if you could make fair predictions, should you be deciding these social allocations of scarce resources or opportunities strictly off scores or rankings? As things scale, and we see more and more opportunities being decided by these algorithms, the inherent uncertainties in these scores can be amplified. We show that fairness may require some sort of randomization,” says Shomik Jain, a graduate student in the Institute for Data, Systems, and Society (IDSS) and lead author of the paper.Jain is joined on the paper by Kathleen Creel, assistant professor of philosophy and computer science at Northeastern University; and senior author Ashia Wilson, the Lister Brothers Career Development Professor in the Department of Electrical Engineering and Computer Science and a principal investigator in the Laboratory for Information and Decision Systems (LIDS). The research will be presented at the International Conference on Machine Learning.Considering claimsThis work builds off a previous paper in which the researchers explored harms that can occur when one uses deterministic systems at scale. They found that using a machine-learning model to deterministically allocate resources can amplify inequalities that exist in training data, which can reinforce bias and systemic inequality. “Randomization is a very useful concept in statistics, and to our delight, satisfies the fairness demands coming from both a systemic and individual point of view,” Wilson says.In this paper, they explored the question of when randomization can improve fairness. They framed their analysis around the ideas of philosopher John Broome, who wrote about the value of using lotteries to award scarce resources in a way that honors all claims of individuals.A person’s claim to a scarce resource, like a kidney transplant, can stem from merit, deservingness, or need. For instance, everyone has a right to life, and their claims on a kidney transplant may stem from that right, Wilson explains.“When you acknowledge that people have different claims to these scarce resources, fairness is going to require that we respect all claims of individuals. If we always give someone with a stronger claim the resource, is that fair?” Jain says.That sort of deterministic allocation could cause systemic exclusion or exacerbate patterned inequality, which occurs when receiving one allocation increases an individual’s likelihood of receiving future allocations. In addition, machine-learning models can make mistakes, and a deterministic approach could cause the same mistake to be repeated.Randomization can overcome these problems, but that doesn’t mean all decisions a model makes should be randomized equally.Structured randomizationThe researchers use a weighted lottery to adjust the level of randomization based on the amount of uncertainty involved in the model’s decision-making. A decision that is less certain should incorporate more randomization.“In kidney allocation, usually the planning is around projected lifespan, and that is deeply uncertain. If two patients are only five years apart, it becomes a lot harder to measure. We want to leverage that level of uncertainty to tailor the randomization,” Wilson says.The researchers used statistical uncertainty quantification methods to determine how much randomization is needed in different situations. They show that calibrated randomization can lead to fairer outcomes for individuals without significantly affecting the utility, or effectiveness, of the model.“There is a balance to be had between overall utility and respecting the rights of the individuals who are receiving a scarce resource, but oftentimes the tradeoff is relatively small,” says Wilson.However, the researchers emphasize there are situations where randomizing decisions would not improve fairness and could harm individuals, such as in criminal justice contexts.But there could be other areas where randomization can improve fairness, such as college admissions, and the researchers plan to study other use cases in future work. They also want to explore how randomization can affect other factors, such as competition or prices, and how it could be used to improve the robustness of machine-learning models.“We are hoping our paper is a first move toward illustrating that there might be a benefit to randomization. We are offering randomization as a tool. How much you are going to want to do it is going to be up to all the stakeholders in the allocation to decide. And, of course, how they decide is another research question all together,” says Wilson. More

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      How to assess a general-purpose AI model’s reliability before it’s deployed

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      Foundation models are massive deep-learning models that have been pretrained on an enormous amount of general-purpose, unlabeled data. They can be applied to a variety of tasks, like generating images or answering customer questions.But these models, which serve as the backbone for powerful artificial intelligence tools like ChatGPT and DALL-E, can offer up incorrect or misleading information. In a safety-critical situation, such as a pedestrian approaching a self-driving car, these mistakes could have serious consequences.To help prevent such mistakes, researchers from MIT and the MIT-IBM Watson AI Lab developed a technique to estimate the reliability of foundation models before they are deployed to a specific task.They do this by considering a set of foundation models that are slightly different from one another. Then they use their algorithm to assess the consistency of the representations each model learns about the same test data point. If the representations are consistent, it means the model is reliable.When they compared their technique to state-of-the-art baseline methods, it was better at capturing the reliability of foundation models on a variety of downstream classification tasks.Someone could use this technique to decide if a model should be applied in a certain setting, without the need to test it on a real-world dataset. This could be especially useful when datasets may not be accessible due to privacy concerns, like in health care settings. In addition, the technique could be used to rank models based on reliability scores, enabling a user to select the best one for their task.“All models can be wrong, but models that know when they are wrong are more useful. The problem of quantifying uncertainty or reliability is more challenging for these foundation models because their abstract representations are difficult to compare. Our method allows one to quantify how reliable a representation model is for any given input data,” says senior author Navid Azizan, the Esther and Harold E. Edgerton Assistant Professor in the MIT Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), and a member of the Laboratory for Information and Decision Systems (LIDS).He is joined on a paper about the work by lead author Young-Jin Park, a LIDS graduate student; Hao Wang, a research scientist at the MIT-IBM Watson AI Lab; and Shervin Ardeshir, a senior research scientist at Netflix. The paper will be presented at the Conference on Uncertainty in Artificial Intelligence.Measuring consensusTraditional machine-learning models are trained to perform a specific task. These models typically make a concrete prediction based on an input. For instance, the model might tell you whether a certain image contains a cat or a dog. In this case, assessing reliability could be a matter of looking at the final prediction to see if the model is right.But foundation models are different. The model is pretrained using general data, in a setting where its creators don’t know all downstream tasks it will be applied to. Users adapt it to their specific tasks after it has already been trained.Unlike traditional machine-learning models, foundation models don’t give concrete outputs like “cat” or “dog” labels. Instead, they generate an abstract representation based on an input data point.To assess the reliability of a foundation model, the researchers used an ensemble approach by training several models which share many properties but are slightly different from one another.“Our idea is like measuring the consensus. If all those foundation models are giving consistent representations for any data in our dataset, then we can say this model is reliable,” Park says.But they ran into a problem: How could they compare abstract representations?“These models just output a vector, comprised of some numbers, so we can’t compare them easily,” he adds.They solved this problem using an idea called neighborhood consistency.For their approach, the researchers prepare a set of reliable reference points to test on the ensemble of models. Then, for each model, they investigate the reference points located near that model’s representation of the test point.By looking at the consistency of neighboring points, they can estimate the reliability of the models.Aligning the representationsFoundation models map data points to what is known as a representation space. One way to think about this space is as a sphere. Each model maps similar data points to the same part of its sphere, so images of cats go in one place and images of dogs go in another.But each model would map animals differently in its own sphere, so while cats may be grouped near the South Pole of one sphere, another model could map cats somewhere in the Northern Hemisphere.The researchers use the neighboring points like anchors to align those spheres so they can make the representations comparable. If a data point’s neighbors are consistent across multiple representations, then one should be confident about the reliability of the model’s output for that point.When they tested this approach on a wide range of classification tasks, they found that it was much more consistent than baselines. Plus, it wasn’t tripped up by challenging test points that caused other methods to fail.Moreover, their approach can be used to assess reliability for any input data, so one could evaluate how well a model works for a particular type of individual, such as a patient with certain characteristics.“Even if the models all have average performance overall, from an individual point of view, you’d prefer the one that works best for that individual,” Wang says.However, one limitation comes from the fact that they must train an ensemble of foundation models, which is computationally expensive. In the future, they plan to find more efficient ways to build multiple models, perhaps by using small perturbations of a single model.“With the current trend of using foundational models for their embeddings to support various downstream tasks — from fine-tuning to retrieval augmented generation — the topic of quantifying uncertainty at the representation level is increasingly important, but challenging, as embeddings on their own have no grounding. What matters instead is how embeddings of different inputs are related to one another, an idea that this work neatly captures through the proposed neighborhood consistency score,” says Marco Pavone, an associate professor in the Department of Aeronautics and Astronautics at Stanford University, who was not involved with this work. “This is a promising step towards high quality uncertainty quantifications for embedding models, and I’m excited to see future extensions which can operate without requiring model-ensembling to really enable this approach to scale to foundation-size models.”This work is funded, in part, by the MIT-IBM Watson AI Lab, MathWorks, and Amazon. More

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      Machine learning and the microscope

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      With recent advances in imaging, genomics and other technologies, the life sciences are awash in data. If a biologist is studying cells taken from the brain tissue of Alzheimer’s patients, for example, there could be any number of characteristics they want to investigate — a cell’s type, the genes it’s expressing, its location within the tissue, or more. However, while cells can now be probed experimentally using different kinds of measurements simultaneously, when it comes to analyzing the data, scientists usually can only work with one type of measurement at a time.Working with “multimodal” data, as it’s called, requires new computational tools, which is where Xinyi Zhang comes in.The fourth-year MIT PhD student is bridging machine learning and biology to understand fundamental biological principles, especially in areas where conventional methods have hit limitations. Working in the lab of MIT Professor Caroline Uhler in the Department of Electrical Engineering and Computer Science, the Laboratory for Information and Decision Systems, and the Institute for Data, Systems, and Society, and collaborating with researchers at the Eric and Wendy Schmidt Center at the Broad Institute and elsewhere, Zhang has led multiple efforts to build computational frameworks and principles for understanding the regulatory mechanisms of cells.“All of these are small steps toward the end goal of trying to answer how cells work, how tissues and organs work, why they have disease, and why they can sometimes be cured and sometimes not,” Zhang says.The activities Zhang pursues in her down time are no less ambitious. The list of hobbies she has taken up at the Institute include sailing, skiing, ice skating, rock climbing, performing with MIT’s Concert Choir, and flying single-engine planes. (She earned her pilot’s license in November 2022.)“I guess I like to go to places I’ve never been and do things I haven’t done before,” she says with signature understatement.Uhler, her advisor, says that Zhang’s quiet humility leads to a surprise “in every conversation.”“Every time, you learn something like, ‘Okay, so now she’s learning to fly,’” Uhler says. “It’s just amazing. Anything she does, she does for the right reasons. She wants to be good at the things she cares about, which I think is really exciting.”Zhang first became interested in biology as a high school student in Hangzhou, China. She liked that her teachers couldn’t answer her questions in biology class, which led her to see it as the “most interesting” topic to study.Her interest in biology eventually turned into an interest in bioengineering. After her parents, who were middle school teachers, suggested studying in the United States, she majored in the latter alongside electrical engineering and computer science as an undergraduate at the University of California at Berkeley.Zhang was ready to dive straight into MIT’s EECS PhD program after graduating in 2020, but the Covid-19 pandemic delayed her first year. Despite that, in December 2022, she, Uhler, and two other co-authors published a paper in Nature Communications.The groundwork for the paper was laid by Xiao Wang, one of the co-authors. She had previously done work with the Broad Institute in developing a form of spatial cell analysis that combined multiple forms of cell imaging and gene expression for the same cell while also mapping out the cell’s place in the tissue sample it came from — something that had never been done before.This innovation had many potential applications, including enabling new ways of tracking the progression of various diseases, but there was no way to analyze all the multimodal data the method produced. In came Zhang, who became interested in designing a computational method that could.The team focused on chromatin staining as their imaging method of choice, which is relatively cheap but still reveals a great deal of information about cells. The next step was integrating the spatial analysis techniques developed by Wang, and to do that, Zhang began designing an autoencoder.Autoencoders are a type of neural network that typically encodes and shrinks large amounts of high-dimensional data, then expand the transformed data back to its original size. In this case, Zhang’s autoencoder did the reverse, taking the input data and making it higher-dimensional. This allowed them to combine data from different animals and remove technical variations that were not due to meaningful biological differences.In the paper, they used this technology, abbreviated as STACI, to identify how cells and tissues reveal the progression of Alzheimer’s disease when observed under a number of spatial and imaging techniques. The model can also be used to analyze any number of diseases, Zhang says.Given unlimited time and resources, her dream would be to build a fully complete model of human life. Unfortunately, both time and resources are limited. Her ambition isn’t, however, and she says she wants to keep applying her skills to solve the “most challenging questions that we don’t have the tools to answer.”She’s currently working on wrapping up a couple of projects, one focused on studying neurodegeneration by analyzing frontal cortex imaging and another on predicting protein images from protein sequences and chromatin imaging.“There are still many unanswered questions,” she says. “I want to pick questions that are biologically meaningful, that help us understand things we didn’t know before.” More

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      When to trust an AI model

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      Because machine-learning models can give false predictions, researchers often equip them with the ability to tell a user how confident they are about a certain decision. This is especially important in high-stake settings, such as when models are used to help identify disease in medical images or filter job applications.But a model’s uncertainty quantifications are only useful if they are accurate. If a model says it is 49 percent confident that a medical image shows a pleural effusion, then 49 percent of the time, the model should be right.MIT researchers have introduced a new approach that can improve uncertainty estimates in machine-learning models. Their method not only generates more accurate uncertainty estimates than other techniques, but does so more efficiently.In addition, because the technique is scalable, it can be applied to huge deep-learning models that are increasingly being deployed in health care and other safety-critical situations.This technique could give end users, many of whom lack machine-learning expertise, better information they can use to determine whether to trust a model’s predictions or if the model should be deployed for a particular task.“It is easy to see these models perform really well in scenarios where they are very good, and then assume they will be just as good in other scenarios. This makes it especially important to push this kind of work that seeks to better calibrate the uncertainty of these models to make sure they align with human notions of uncertainty,” says lead author Nathan Ng, a graduate student at the University of Toronto who is a visiting student at MIT.Ng wrote the paper with Roger Grosse, an assistant professor of computer science at the University of Toronto; and senior author Marzyeh Ghassemi, an associate professor in the Department of Electrical Engineering and Computer Science and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems. The research will be presented at the International Conference on Machine Learning.Quantifying uncertaintyUncertainty quantification methods often require complex statistical calculations that don’t scale well to machine-learning models with millions of parameters. These methods also require users to make assumptions about the model and data used to train it.The MIT researchers took a different approach. They use what is known as the minimum description length principle (MDL), which does not require the assumptions that can hamper the accuracy of other methods. MDL is used to better quantify and calibrate uncertainty for test points the model has been asked to label.The technique the researchers developed, known as IF-COMP, makes MDL fast enough to use with the kinds of large deep-learning models deployed in many real-world settings.MDL involves considering all possible labels a model could give a test point. If there are many alternative labels for this point that fit well, its confidence in the label it chose should decrease accordingly.“One way to understand how confident a model is would be to tell it some counterfactual information and see how likely it is to believe you,” Ng says.For example, consider a model that says a medical image shows a pleural effusion. If the researchers tell the model this image shows an edema, and it is willing to update its belief, then the model should be less confident in its original decision.With MDL, if a model is confident when it labels a datapoint, it should use a very short code to describe that point. If it is uncertain about its decision because the point could have many other labels, it uses a longer code to capture these possibilities.The amount of code used to label a datapoint is known as stochastic data complexity. If the researchers ask the model how willing it is to update its belief about a datapoint given contrary evidence, the stochastic data complexity should decrease if the model is confident.But testing each datapoint using MDL would require an enormous amount of computation.Speeding up the processWith IF-COMP, the researchers developed an approximation technique that can accurately estimate stochastic data complexity using a special function, known as an influence function. They also employed a statistical technique called temperature-scaling, which improves the calibration of the model’s outputs. This combination of influence functions and temperature-scaling enables high-quality approximations of the stochastic data complexity.In the end, IF-COMP can efficiently produce well-calibrated uncertainty quantifications that reflect a model’s true confidence. The technique can also determine whether the model has mislabeled certain data points or reveal which data points are outliers.The researchers tested their system on these three tasks and found that it was faster and more accurate than other methods.“It is really important to have some certainty that a model is well-calibrated, and there is a growing need to detect when a specific prediction doesn’t look quite right. Auditing tools are becoming more necessary in machine-learning problems as we use large amounts of unexamined data to make models that will be applied to human-facing problems,” Ghassemi says.IF-COMP is model-agnostic, so it can provide accurate uncertainty quantifications for many types of machine-learning models. This could enable it to be deployed in a wider range of real-world settings, ultimately helping more practitioners make better decisions.“People need to understand that these systems are very fallible and can make things up as they go. A model may look like it is highly confident, but there are a ton of different things it is willing to believe given evidence to the contrary,” Ng says.In the future, the researchers are interested in applying their approach to large language models and studying other potential use cases for the minimum description length principle.  More

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      MIT ARCLab announces winners of inaugural Prize for AI Innovation in Space

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      Satellite density in Earth’s orbit has increased exponentially in recent years, with lower costs of small satellites allowing governments, researchers, and private companies to launch and operate some 2,877 satellites into orbit in 2023 alone. This includes increased geostationary Earth orbit (GEO) satellite activity, which brings technologies with global-scale impact, from broadband internet to climate surveillance. Along with the manifold benefits of these satellite-enabled technologies, however, come increased safety and security risks, as well as environmental concerns. More accurate and efficient methods of monitoring and modeling satellite behavior are urgently needed to prevent collisions and other disasters.To address this challenge, the MIT Astrodynamics, Space Robotic, and Controls Laboratory (ARCLab) launched the MIT ARCLab Prize for AI Innovation in Space: a first-of-its-kind competition asking contestants to harness AI to characterize satellites’ patterns of life (PoLs) — the long-term behavioral narrative of a satellite in orbit — using purely passively collected information. Following the call for participants last fall, 126 teams used machine learning to create algorithms to label and time-stamp the behavioral modes of GEO satellites over a six-month period, competing for accuracy and efficiency.With support from the U.S. Department of the Air Force-MIT AI Accelerator, the challenge offers a total of $25,000. A team of judges from ARCLab and MIT Lincoln Laboratory evaluated the submissions based on clarity, novelty, technical depth, and reproducibility, assigning each entry a score out of 100 points. Now the judges have announced the winners and runners-up:First prize: David Baldsiefen — Team Hawaii2024With a winning score of 96, Baldsiefen will be awarded $10,000 and is invited to join the ARCLab team in presenting at a poster session at the Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference in Hawaii this fall. One evaluator noted, “Clear and concise report, with very good ideas such as the label encoding of the localizer. Decisions on the architectures and the feature engineering are well reasoned. The code provided is also well documented and structured, allowing an easy reproducibility of the experimentation.”Second prize: Binh Tran, Christopher Yeung, Kurtis Johnson, Nathan Metzger — Team Millennial-IUPWith a score of 94.2, Y, Millennial-IUP will be awarded $5,000 and will also join the ARCLab team at the AMOS conference. One evaluator said, “The models chosen were sensible and justified, they made impressive efforts in efficiency gains… They used physics to inform their models and this appeared to be reproducible. Overall it was an easy to follow, concise report without much jargon.”Third Prize: Isaac Haik and Francois Porcher — Team QR_IsWith a score of 94, Haik and Porcher will share the third prize of $3,000 and will also be invited to the AMOS conference with the ARCLab team. One evaluator noted, “This informative and interesting report describes the combination of ML and signal processing techniques in a compelling way, assisted by informative plots, tables, and sequence diagrams. The author identifies and describes a modular approach to class detection and their assessment of feature utility, which they correctly identify is not evenly useful across classes… Any lack of mission expertise is made up for by a clear and detailed discussion of the benefits and pitfalls of the methods they used and discussion of what they learned.”The fourth- through seventh-place scoring teams will each receive $1,000 and a certificate of excellence.“The goal of this competition was to foster an interdisciplinary approach to problem-solving in the space domain by inviting AI development experts to apply their skills in this new context of orbital capacity. And all of our winning teams really delivered — they brought technical skill, novel approaches, and expertise to a very impressive round of submissions.” says Professor Richard Linares, who heads ARCLab.Active modeling with passive dataThroughout a GEO satellite’s time in orbit, operators issue commands to place them in various behavioral modes—station-keeping, longitudinal shifts, end-of-life behaviors, and so on. Satellite Patterns of Life (PoLs) describe on-orbit behavior composed of sequences of both natural and non-natural behavior modes.ARCLab has developed a groundbreaking benchmarking tool for geosynchronous satellite pattern-of-life characterization and created the Satellite Pattern-of-Life Identification Dataset (SPLID), comprising real and synthetic space object data. The challenge participants used this tool to create algorithms that use AI to map out the on-orbit behaviors of a satellite.The goal of the MIT ARCLab Prize for AI Innovation in Space is to encourage technologists and enthusiasts to bring innovation and new skills sets to well-established challenges in aerospace. The team aims to hold the competition in 2025 and 2026 to explore other topics and invite experts in AI to apply their skills to new challenges.  More

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      “They can see themselves shaping the world they live in”

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      During the journey from the suburbs to the city, the tree canopy often dwindles down as skyscrapers rise up. A group of New England Innovation Academy students wondered why that is.“Our friend Victoria noticed that where we live in Marlborough there are lots of trees in our own backyards. But if you drive just 30 minutes to Boston, there are almost no trees,” said high school junior Ileana Fournier. “We were struck by that duality.”This inspired Fournier and her classmates Victoria Leeth and Jessie Magenyi to prototype a mobile app that illustrates Massachusetts deforestation trends for Day of AI, a free, hands-on curriculum developed by the MIT Responsible AI for Social Empowerment and Education (RAISE) initiative, headquartered in the MIT Media Lab and in collaboration with the MIT Schwarzman College of Computing and MIT Open Learning. They were among a group of 20 students from New England Innovation Academy who shared their projects during the 2024 Day of AI global celebration hosted with the Museum of Science.The Day of AI curriculum introduces K-12 students to artificial intelligence. Now in its third year, Day of AI enables students to improve their communities and collaborate on larger global challenges using AI. Fournier, Leeth, and Magenyi’s TreeSavers app falls under the Telling Climate Stories with Data module, one of four new climate-change-focused lessons.“We want you to be able to express yourselves creatively to use AI to solve problems with critical-thinking skills,” Cynthia Breazeal, director of MIT RAISE, dean for digital learning at MIT Open Learning, and professor of media arts and sciences, said during this year’s Day of AI global celebration at the Museum of Science. “We want you to have an ethical and responsible way to think about this really powerful, cool, and exciting technology.”Moving from understanding to actionDay of AI invites students to examine the intersection of AI and various disciplines, such as history, civics, computer science, math, and climate change. With the curriculum available year-round, more than 10,000 educators across 114 countries have brought Day of AI activities to their classrooms and homes.The curriculum gives students the agency to evaluate local issues and invent meaningful solutions. “We’re thinking about how to create tools that will allow kids to have direct access to data and have a personal connection that intersects with their lived experiences,” Robert Parks, curriculum developer at MIT RAISE, said at the Day of AI global celebration.Before this year, first-year Jeremie Kwapong said he knew very little about AI. “I was very intrigued,” he said. “I started to experiment with ChatGPT to see how it reacts. How close can I get this to human emotion? What is AI’s knowledge compared to a human’s knowledge?”In addition to helping students spark an interest in AI literacy, teachers around the world have told MIT RAISE that they want to use data science lessons to engage students in conversations about climate change. Therefore, Day of AI’s new hands-on projects use weather and climate change to show students why it’s important to develop a critical understanding of dataset design and collection when observing the world around them.“There is a lag between cause and effect in everyday lives,” said Parks. “Our goal is to demystify that, and allow kids to access data so they can see a long view of things.”Tools like MIT App Inventor — which allows anyone to create a mobile application — help students make sense of what they can learn from data. Fournier, Leeth, and Magenyi programmed TreeSavers in App Inventor to chart regional deforestation rates across Massachusetts, identify ongoing trends through statistical models, and predict environmental impact. The students put that “long view” of climate change into practice when developing TreeSavers’ interactive maps. Users can toggle between Massachusetts’s current tree cover, historical data, and future high-risk areas.Although AI provides fast answers, it doesn’t necessarily offer equitable solutions, said David Sittenfeld, director of the Center for the Environment at the Museum of Science. The Day of AI curriculum asks students to make decisions on sourcing data, ensuring unbiased data, and thinking responsibly about how findings could be used.“There’s an ethical concern about tracking people’s data,” said Ethan Jorda, a New England Innovation Academy student. His group used open-source data to program an app that helps users track and reduce their carbon footprint.Christine Cunningham, senior vice president of STEM Learning at the Museum of Science, believes students are prepared to use AI responsibly to make the world a better place. “They can see themselves shaping the world they live in,” said Cunningham. “Moving through from understanding to action, kids will never look at a bridge or a piece of plastic lying on the ground in the same way again.”Deepening collaboration on earth and beyondThe 2024 Day of AI speakers emphasized collaborative problem solving at the local, national, and global levels.“Through different ideas and different perspectives, we’re going to get better solutions,” said Cunningham. “How do we start young enough that every child has a chance to both understand the world around them but also to move toward shaping the future?”Presenters from MIT, the Museum of Science, and NASA approached this question with a common goal — expanding STEM education to learners of all ages and backgrounds.“We have been delighted to collaborate with the MIT RAISE team to bring this year’s Day of AI celebration to the Museum of Science,” says Meg Rosenburg, manager of operations at the Museum of Science Centers for Public Science Learning. “This opportunity to highlight the new climate modules for the curriculum not only perfectly aligns with the museum’s goals to focus on climate and active hope throughout our Year of the Earthshot initiative, but it has also allowed us to bring our teams together and grow a relationship that we are very excited to build upon in the future.”Rachel Connolly, systems integration and analysis lead for NASA’s Science Activation Program, showed the power of collaboration with the example of how human comprehension of Saturn’s appearance has evolved. From Galileo’s early telescope to the Cassini space probe, modern imaging of Saturn represents 400 years of science, technology, and math working together to further knowledge.“Technologies, and the engineers who built them, advance the questions we’re able to ask and therefore what we’re able to understand,” said Connolly, research scientist at MIT Media Lab.New England Innovation Academy students saw an opportunity for collaboration a little closer to home. Emmett Buck-Thompson, Jeff Cheng, and Max Hunt envisioned a social media app to connect volunteers with local charities. Their project was inspired by Buck-Thompson’s father’s difficulties finding volunteering opportunities, Hunt’s role as the president of the school’s Community Impact Club, and Cheng’s aspiration to reduce screen time for social media users. Using MIT App Inventor, ​their combined ideas led to a prototype with the potential to make a real-world impact in their community.The Day of AI curriculum teaches the mechanics of AI, ethical considerations and responsible uses, and interdisciplinary applications for different fields. It also empowers students to become creative problem solvers and engaged citizens in their communities and online. From supporting volunteer efforts to encouraging action for the state’s forests to tackling the global challenge of climate change, today’s students are becoming tomorrow’s leaders with Day of AI.“We want to empower you to know that this is a tool you can use to make your community better, to help people around you with this technology,” said Breazeal.Other Day of AI speakers included Tim Ritchie, president of the Museum of Science; Michael Lawrence Evans, program director of the Boston Mayor’s Office of New Urban Mechanics; Dava Newman, director of the MIT Media Lab; and Natalie Lao, executive director of the App Inventor Foundation. More

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      MIT researchers introduce generative AI for databases

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      A new tool makes it easier for database users to perform complicated statistical analyses of tabular data without the need to know what is going on behind the scenes.GenSQL, a generative AI system for databases, could help users make predictions, detect anomalies, guess missing values, fix errors, or generate synthetic data with just a few keystrokes.For instance, if the system were used to analyze medical data from a patient who has always had high blood pressure, it could catch a blood pressure reading that is low for that particular patient but would otherwise be in the normal range.GenSQL automatically integrates a tabular dataset and a generative probabilistic AI model, which can account for uncertainty and adjust their decision-making based on new data.Moreover, GenSQL can be used to produce and analyze synthetic data that mimic the real data in a database. This could be especially useful in situations where sensitive data cannot be shared, such as patient health records, or when real data are sparse.This new tool is built on top of SQL, a programming language for database creation and manipulation that was introduced in the late 1970s and is used by millions of developers worldwide.“Historically, SQL taught the business world what a computer could do. They didn’t have to write custom programs, they just had to ask questions of a database in high-level language. We think that, when we move from just querying data to asking questions of models and data, we are going to need an analogous language that teaches people the coherent questions you can ask a computer that has a probabilistic model of the data,” says Vikash Mansinghka ’05, MEng ’09, PhD ’09, senior author of a paper introducing GenSQL and a principal research scientist and leader of the Probabilistic Computing Project in the MIT Department of Brain and Cognitive Sciences.When the researchers compared GenSQL to popular, AI-based approaches for data analysis, they found that it was not only faster but also produced more accurate results. Importantly, the probabilistic models used by GenSQL are explainable, so users can read and edit them.“Looking at the data and trying to find some meaningful patterns by just using some simple statistical rules might miss important interactions. You really want to capture the correlations and the dependencies of the variables, which can be quite complicated, in a model. With GenSQL, we want to enable a large set of users to query their data and their model without having to know all the details,” adds lead author Mathieu Huot, a research scientist in the Department of Brain and Cognitive Sciences and member of the Probabilistic Computing Project.They are joined on the paper by Matin Ghavami and Alexander Lew, MIT graduate students; Cameron Freer, a research scientist; Ulrich Schaechtel and Zane Shelby of Digital Garage; Martin Rinard, an MIT professor in the Department of Electrical Engineering and Computer Science and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Feras Saad ’15, MEng ’16, PhD ’22, an assistant professor at Carnegie Mellon University. The research was recently presented at the ACM Conference on Programming Language Design and Implementation.Combining models and databasesSQL, which stands for structured query language, is a programming language for storing and manipulating information in a database. In SQL, people can ask questions about data using keywords, such as by summing, filtering, or grouping database records.However, querying a model can provide deeper insights, since models can capture what data imply for an individual. For instance, a female developer who wonders if she is underpaid is likely more interested in what salary data mean for her individually than in trends from database records.The researchers noticed that SQL didn’t provide an effective way to incorporate probabilistic AI models, but at the same time, approaches that use probabilistic models to make inferences didn’t support complex database queries.They built GenSQL to fill this gap, enabling someone to query both a dataset and a probabilistic model using a straightforward yet powerful formal programming language.A GenSQL user uploads their data and probabilistic model, which the system automatically integrates. Then, she can run queries on data that also get input from the probabilistic model running behind the scenes. This not only enables more complex queries but can also provide more accurate answers.For instance, a query in GenSQL might be something like, “How likely is it that a developer from Seattle knows the programming language Rust?” Just looking at a correlation between columns in a database might miss subtle dependencies. Incorporating a probabilistic model can capture more complex interactions.   Plus, the probabilistic models GenSQL utilizes are auditable, so people can see which data the model uses for decision-making. In addition, these models provide measures of calibrated uncertainty along with each answer.For instance, with this calibrated uncertainty, if one queries the model for predicted outcomes of different cancer treatments for a patient from a minority group that is underrepresented in the dataset, GenSQL would tell the user that it is uncertain, and how uncertain it is, rather than overconfidently advocating for the wrong treatment.Faster and more accurate resultsTo evaluate GenSQL, the researchers compared their system to popular baseline methods that use neural networks. GenSQL was between 1.7 and 6.8 times faster than these approaches, executing most queries in a few milliseconds while providing more accurate results.They also applied GenSQL in two case studies: one in which the system identified mislabeled clinical trial data and the other in which it generated accurate synthetic data that captured complex relationships in genomics.Next, the researchers want to apply GenSQL more broadly to conduct largescale modeling of human populations. With GenSQL, they can generate synthetic data to draw inferences about things like health and salary while controlling what information is used in the analysis.They also want to make GenSQL easier to use and more powerful by adding new optimizations and automation to the system. In the long run, the researchers want to enable users to make natural language queries in GenSQL. Their goal is to eventually develop a ChatGPT-like AI expert one could talk to about any database, which grounds its answers using GenSQL queries.   This research is funded, in part, by the Defense Advanced Research Projects Agency (DARPA), Google, and the Siegel Family Foundation. More

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      Fotini Christia named director of the Institute for Data, Systems, and Society

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      Fotini Christia, the Ford International Professor of Social Sciences in the Department of Political Science, has been named the new director of the Institute for Data, Systems, and Society (IDSS), effective July 1.“Fotini is well-positioned to guide IDSS into the next chapter. With her tenure as the director of the Sociotechnical Systems Research Center and as an associate director of IDSS since 2020, she has actively forged connections between the social sciences, data science, and computation,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “I eagerly anticipate the ways in which she will advance and champion IDSS in alignment with the spirit and mission of the Schwarzman College of Computing.”“Fotini’s profound expertise as a social scientist and her adept use of data science, computational tools, and novel methodologies to grasp the dynamics of societal evolution across diverse fields, makes her a natural fit to lead IDSS,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science.Christia’s research has focused on issues of conflict and cooperation in the Muslim world, for which she has conducted fieldwork in Afghanistan, Bosnia, Iraq, the Palestinian Territories, and Yemen, among others. More recently, her research has been directed at examining how to effectively integrate artificial intelligence tools in public policy.She was appointed the director of the Sociotechnical Systems Research Center (SSRC) and an associate director of IDSS in October 2020. SSRC, an interdisciplinary center housed within IDSS in the MIT Schwarzman College of Computing, focuses on the study of high-impact, complex societal challenges that shape our world.As part of IDSS, she is co-organizer of a cross-disciplinary research effort, the Initiative on Combatting Systemic Racism. Bringing together faculty and researchers from all of MIT’s five schools and the college, the initiative builds on extensive social science literature on systemic racism and uses big data to develop and harness computational tools that can help effect structural and normative change toward racial equity across housing, health care, policing, and social media. Christia is also chair of IDSS’s doctoral program in Social and Engineering Systems.Christia is the author of “Alliance Formation in Civil War” (Cambridge University Press, 2012), which was awarded the Luebbert Award for Best Book in Comparative Politics, the Lepgold Prize for Best Book in International Relations, and a Distinguished Book Award from the International Studies Association. She is co-editor with Graeme Blair (University of California, Los Angeles) and Jeremy Weinstein (incoming dean at Harvard Kennedy School) of “Crime, Insecurity, and Community Policing: Experiments on Building Trust,” forthcoming in August 2024 with Cambridge University Press.Her research has also appeared in Science, Nature Human Behavior, Review of Economic Studies, American Economic Journal: Applied Economics, NeurIPs, Communications Medicine, IEEE Transactions on Network Science and Engineering, American Political Science Review, and Annual Review of Political Science, among other journals. Her opinion pieces have been published in Foreign Affairs, The New York Times, The Washington Post, and The Boston Globe, among other outlets.A native of Greece, where she grew up in the port city of Salonika, Christia moved to the United States to attend college at Columbia University. She graduated magna cum laude in 2001 with a joint BA in economics–operations research and an MA in international affairs. She joined the MIT faculty in 2008 after receiving her PhD in public policy from Harvard University.Christia succeeds Noelle Selin, a professor in IDSS and the Department of Earth, Atmospheric, and Planetary Sciences. Selin has led IDSS as interim director for the 2023-24 academic year since July 2023, following Professor Martin Wainwright.“I am incredibly grateful to Noelle for serving as interim director this year. Her contributions in this role, as well as her time leading the Technology and Policy Program, have been invaluable. I’m delighted she will remain part of the IDSS community as a faculty member,” says Huttenlocher. More