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

    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

    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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    Arvind, longtime MIT professor and prolific computer scientist, dies at 77

    Arvind Mithal, the Charles W. and Jennifer C. Johnson Professor in Computer Science and Engineering at MIT, head of the faculty of computer science in the Department of Electrical Engineering and Computer Science (EECS), and a pillar of the MIT community, died on June 17. Arvind, who went by the mononym, was 77 years old.A prolific researcher who led the Computation Structures Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Arvind served on the MIT faculty for nearly five decades.“He was beloved by countless people across the MIT community and around the world who were inspired by his intellectual brilliance and zest for life,” President Sally Kornbluth wrote in a letter to the MIT community today.As a scientist, Arvind was well known for important contributions to dataflow computing, which seeks to optimize the flow of data to take advantage of parallelism, achieving faster and more efficient computation.In the last 25 years, his research interests broadened to include developing techniques and tools for formal modeling, high-level synthesis, and formal verification of complex digital devices like microprocessors and hardware accelerators, as well as memory models and cache coherence protocols for parallel computing architectures and programming languages.Those who knew Arvind describe him as a rare individual whose interests and expertise ranged from high-level, theoretical formal systems all the way down through languages and compilers to the gates and structures of silicon hardware.The applications of Arvind’s work are far-reaching, from reducing the amount of energy and space required by data centers to streamlining the design of more efficient multicore computer chips.“Arvind was both a tremendous scholar in the fields of computer architecture and programming languages and a dedicated teacher, who brought systems-level thinking to our students. He was also an exceptional academic leader, often leading changes in curriculum and contributing to the Engineering Council in meaningful and impactful ways. I will greatly miss his sage advice and wisdom,” says Anantha Chandrakasan, chief innovation and strategy officer, dean of engineering, and the Vannevar Bush Professor of Electrical Engineering and Computer Science.“Arvind’s positive energy, together with his hearty laugh, brightened so many people’s lives. He was an enduring source of wise counsel for colleagues and for generations of students. With his deep commitment to academic excellence, he not only transformed research in computer architecture and parallel computing but also brought that commitment to his role as head of the computer science faculty in the EECS department. He left a lasting impact on all of us who had the privilege of working with him,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.Arvind developed an interest in parallel computing while he was a student at the Indian Institute of Technology in Kanpur, from which he received his bachelor’s degree in 1969. He earned a master’s degree and PhD in computer science in 1972 and 1973, respectively, from the University of Minnesota, where he studied operating systems and mathematical models of program behavior. He taught at the University of California at Irvine from 1974 to 1978 before joining the faculty at MIT.At MIT, Arvind’s group studied parallel computing and declarative programming languages, and he led the development of two parallel computing languages, Id and pH. He continued his work on these programming languages through the 1990s, publishing the book “Implicit Parallel Programming in pH” with co-author R.S. Nikhil in 2001, the culmination of more than 20 years of research.In addition to his research, Arvind was an important academic leader in EECS. He served as head of computer science faculty in the department and played a critical role in helping with the reorganization of EECS after the establishment of the MIT Schwarzman College of Computing.“Arvind was a force of nature, larger than life in every sense. His relentless positivity, unwavering optimism, boundless generosity, and exceptional strength as a researcher was truly inspiring and left a profound mark on all who had the privilege of knowing him. I feel enormous gratitude for the light he brought into our lives and his fundamental impact on our community,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and the director of CSAIL.His work on dataflow and parallel computing led to the Monsoon project in the late 1980s and early 1990s. Arvind’s group, in collaboration with Motorola, built 16 dataflow computing machines and developed their associated software. One Monsoon dataflow machine is now in the Computer History Museum in Mountain View, California.Arvind’s focus shifted in the 1990s when, as he explained in a 2012 interview for the Institute of Electrical and Electronics Engineers (IEEE), funding for research into parallel computing began to dry up.“Microprocessors were getting so much faster that people thought they didn’t need it,” he recalled.Instead, he began applying techniques his team had learned and developed for parallel programming to the principled design of digital hardware.In addition to mentoring students and junior colleagues at MIT, Arvind also advised universities and governments in many countries on research in parallel programming and semiconductor design.Based on his work on digital hardware design, Arvind founded Sandburst in 2000, a fabless manufacturing company for semiconductor chips. He served as the company’s president for two years before returning to the MIT faculty, while continuing as an advisor. Sandburst was later acquired by Broadcom.Arvind and his students also developed Bluespec, a programming language designed to automate the design of chips. Building off this work, he co-founded the startup Bluespec, Inc., in 2003, to develop practical tools that help engineers streamline device design.Over the past decade, he was dedicated to advancing undergraduate education at MIT by bringing modern design tools to courses 6.004 (Computation Structures) and 6.191 (Introduction to Deep Learning), and incorporating Minispec, a programming language that is closely related to Bluespec.Arvind was honored for these and other contributions to data flow and multithread computing, and the development of tools for the high-level synthesis of hardware, with membership in the National Academy of Engineering in 2008 and the American Academy of Arts and Sciences in 2012. He was also named a distinguished alumnus of IIT Kanpur, his undergraduate alma mater.“Arvind was more than a pillar of the EECS community and a titan of computer science; he was a beloved colleague and a treasured friend. Those of us with the remarkable good fortune to work and collaborate with Arvind are devastated by his sudden loss. His kindness and joviality were unwavering; his mentorship was thoughtful and well-considered; his guidance was priceless. We will miss Arvind deeply,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing and head of EECS.Among numerous other awards, including membership in the Indian National Academy of Sciences and fellowship in the Association for Computing Machinery and IEEE, he received the Harry H. Goode Memorial Award from IEEE in 2012, which honors significant contributions to theory or practice in the information processing field.A humble scientist, Arvind was the first to point out that these achievements were only possible because of his outstanding and brilliant collaborators. Chief among those collaborators were the undergraduate and graduate students he felt fortunate to work with at MIT. He maintained excellent relationships with them both professionally and personally, and valued these relationships more than the work they did together, according to family members.In summing up the key to his scientific success, Arvind put it this way in the 2012 IEEE interview: “Really, one has to do what one believes in. I think the level at which most of us work, it is not sustainable if you don’t enjoy it on a day-to-day basis. You can’t work on it just because of the results. You have to work on it because you say, ‘I have to know the answer to this,’” he said.He is survived by his wife, Gita Singh Mithal, their two sons Divakar ’01 and Prabhakar ’04, their wives Leena and Nisha, and two grandchildren, Maya and Vikram.  More

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    Researchers use large language models to help robots navigate

    Someday, you may want your home robot to carry a load of dirty clothes downstairs and deposit them in the washing machine in the far-left corner of the basement. The robot will need to combine your instructions with its visual observations to determine the steps it should take to complete this task.For an AI agent, this is easier said than done. Current approaches often utilize multiple hand-crafted machine-learning models to tackle different parts of the task, which require a great deal of human effort and expertise to build. These methods, which use visual representations to directly make navigation decisions, demand massive amounts of visual data for training, which are often hard to come by.To overcome these challenges, researchers from MIT and the MIT-IBM Watson AI Lab devised a navigation method that converts visual representations into pieces of language, which are then fed into one large language model that achieves all parts of the multistep navigation task.Rather than encoding visual features from images of a robot’s surroundings as visual representations, which is computationally intensive, their method creates text captions that describe the robot’s point-of-view. A large language model uses the captions to predict the actions a robot should take to fulfill a user’s language-based instructions.Because their method utilizes purely language-based representations, they can use a large language model to efficiently generate a huge amount of synthetic training data.While this approach does not outperform techniques that use visual features, it performs well in situations that lack enough visual data for training. The researchers found that combining their language-based inputs with visual signals leads to better navigation performance.“By purely using language as the perceptual representation, ours is a more straightforward approach. Since all the inputs can be encoded as language, we can generate a human-understandable trajectory,” says Bowen Pan, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this approach.Pan’s co-authors include his advisor, Aude Oliva, director of strategic industry engagement at the MIT Schwarzman College of Computing, MIT director of the MIT-IBM Watson AI Lab, and a senior research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL); Philip Isola, an associate professor of EECS and a member of CSAIL; senior author Yoon Kim, an assistant professor of EECS and a member of CSAIL; and others at the MIT-IBM Watson AI Lab and Dartmouth College. The research will be presented at the Conference of the North American Chapter of the Association for Computational Linguistics.Solving a vision problem with languageSince large language models are the most powerful machine-learning models available, the researchers sought to incorporate them into the complex task known as vision-and-language navigation, Pan says.But such models take text-based inputs and can’t process visual data from a robot’s camera. So, the team needed to find a way to use language instead.Their technique utilizes a simple captioning model to obtain text descriptions of a robot’s visual observations. These captions are combined with language-based instructions and fed into a large language model, which decides what navigation step the robot should take next.The large language model outputs a caption of the scene the robot should see after completing that step. This is used to update the trajectory history so the robot can keep track of where it has been.The model repeats these processes to generate a trajectory that guides the robot to its goal, one step at a time.To streamline the process, the researchers designed templates so observation information is presented to the model in a standard form — as a series of choices the robot can make based on its surroundings.For instance, a caption might say “to your 30-degree left is a door with a potted plant beside it, to your back is a small office with a desk and a computer,” etc. The model chooses whether the robot should move toward the door or the office.“One of the biggest challenges was figuring out how to encode this kind of information into language in a proper way to make the agent understand what the task is and how they should respond,” Pan says.Advantages of languageWhen they tested this approach, while it could not outperform vision-based techniques, they found that it offered several advantages.First, because text requires fewer computational resources to synthesize than complex image data, their method can be used to rapidly generate synthetic training data. In one test, they generated 10,000 synthetic trajectories based on 10 real-world, visual trajectories.The technique can also bridge the gap that can prevent an agent trained with a simulated environment from performing well in the real world. This gap often occurs because computer-generated images can appear quite different from real-world scenes due to elements like lighting or color. But language that describes a synthetic versus a real image would be much harder to tell apart, Pan says. Also, the representations their model uses are easier for a human to understand because they are written in natural language.“If the agent fails to reach its goal, we can more easily determine where it failed and why it failed. Maybe the history information is not clear enough or the observation ignores some important details,” Pan says.In addition, their method could be applied more easily to varied tasks and environments because it uses only one type of input. As long as data can be encoded as language, they can use the same model without making any modifications.But one disadvantage is that their method naturally loses some information that would be captured by vision-based models, such as depth information.However, the researchers were surprised to see that combining language-based representations with vision-based methods improves an agent’s ability to navigate.“Maybe this means that language can capture some higher-level information than cannot be captured with pure vision features,” he says.This is one area the researchers want to continue exploring. They also want to develop a navigation-oriented captioner that could boost the method’s performance. In addition, they want to probe the ability of large language models to exhibit spatial awareness and see how this could aid language-based navigation.This research is funded, in part, by the MIT-IBM Watson AI Lab. More

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    A technique for more effective multipurpose robots

    Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver. To do that, you would need an enormous amount of data demonstrating tool use.Existing robotic datasets vary widely in modality — some include color images while others are composed of tactile imprints, for instance. Data could also be collected in different domains, like simulation or human demos. And each dataset may capture a unique task and environment.It is difficult to efficiently incorporate data from so many sources in one machine-learning model, so many methods use just one type of data to train a robot. But robots trained this way, with a relatively small amount of task-specific data, are often unable to perform new tasks in unfamiliar environments.In an effort to train better multipurpose robots, MIT researchers developed a technique to combine multiple sources of data across domains, modalities, and tasks using a type of generative AI known as diffusion models.They train a separate diffusion model to learn a strategy, or policy, for completing one task using one specific dataset. Then they combine the policies learned by the diffusion models into a general policy that enables a robot to perform multiple tasks in various settings.In simulations and real-world experiments, this training approach enabled a robot to perform multiple tool-use tasks and adapt to new tasks it did not see during training. The method, known as Policy Composition (PoCo), led to a 20 percent improvement in task performance when compared to baseline techniques.“Addressing heterogeneity in robotic datasets is like a chicken-egg problem. If we want to use a lot of data to train general robot policies, then we first need deployable robots to get all this data. I think that leveraging all the heterogeneous data available, similar to what researchers have done with ChatGPT, is an important step for the robotics field,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on PoCo.     Wang’s coauthors include Jialiang Zhao, a mechanical engineering graduate student; Yilun Du, an EECS graduate student; Edward Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, and a member of CSAIL. The research will be presented at the Robotics: Science and Systems Conference.Combining disparate datasetsA robotic policy is a machine-learning model that takes inputs and uses them to perform an action. One way to think about a policy is as a strategy. In the case of a robotic arm, that strategy might be a trajectory, or a series of poses that move the arm so it picks up a hammer and uses it to pound a nail.Datasets used to learn robotic policies are typically small and focused on one particular task and environment, like packing items into boxes in a warehouse.“Every single robotic warehouse is generating terabytes of data, but it only belongs to that specific robot installation working on those packages. It is not ideal if you want to use all of these data to train a general machine,” Wang says.The MIT researchers developed a technique that can take a series of smaller datasets, like those gathered from many robotic warehouses, learn separate policies from each one, and combine the policies in a way that enables a robot to generalize to many tasks.They represent each policy using a type of generative AI model known as a diffusion model. Diffusion models, often used for image generation, learn to create new data samples that resemble samples in a training dataset by iteratively refining their output.But rather than teaching a diffusion model to generate images, the researchers teach it to generate a trajectory for a robot. They do this by adding noise to the trajectories in a training dataset. The diffusion model gradually removes the noise and refines its output into a trajectory.This technique, known as Diffusion Policy, was previously introduced by researchers at MIT, Columbia University, and the Toyota Research Institute. PoCo builds off this Diffusion Policy work. The team trains each diffusion model with a different type of dataset, such as one with human video demonstrations and another gleaned from teleoperation of a robotic arm.Then the researchers perform a weighted combination of the individual policies learned by all the diffusion models, iteratively refining the output so the combined policy satisfies the objectives of each individual policy.Greater than the sum of its parts“One of the benefits of this approach is that we can combine policies to get the best of both worlds. For instance, a policy trained on real-world data might be able to achieve more dexterity, while a policy trained on simulation might be able to achieve more generalization,” Wang says.

    With policy composition, researchers are able to combine datasets from multiple sources so they can teach a robot to effectively use a wide range of tools, like a hammer, screwdriver, or this spatula.Image: Courtesy of the researchers

    Because the policies are trained separately, one could mix and match diffusion policies to achieve better results for a certain task. A user could also add data in a new modality or domain by training an additional Diffusion Policy with that dataset, rather than starting the entire process from scratch.

    The policy composition technique the researchers developed can be used to effectively teach a robot to use tools even when objects are placed around it to try and distract it from its task, as seen here.Image: Courtesy of the researchers

    The researchers tested PoCo in simulation and on real robotic arms that performed a variety of tools tasks, such as using a hammer to pound a nail and flipping an object with a spatula. PoCo led to a 20 percent improvement in task performance compared to baseline methods.“The striking thing was that when we finished tuning and visualized it, we can clearly see that the composed trajectory looks much better than either one of them individually,” Wang says.In the future, the researchers want to apply this technique to long-horizon tasks where a robot would pick up one tool, use it, then switch to another tool. They also want to incorporate larger robotics datasets to improve performance.“We will need all three kinds of data to succeed for robotics: internet data, simulation data, and real robot data. How to combine them effectively will be the million-dollar question. PoCo is a solid step on the right track,” says Jim Fan, senior research scientist at NVIDIA and leader of the AI Agents Initiative, who was not involved with this work.This research is funded, in part, by Amazon, the Singapore Defense Science and Technology Agency, the U.S. National Science Foundation, and the Toyota Research Institute. More

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    Looking for a specific action in a video? This AI-based method can find it for you

    The internet is awash in instructional videos that can teach curious viewers everything from cooking the perfect pancake to performing a life-saving Heimlich maneuver.But pinpointing when and where a particular action happens in a long video can be tedious. To streamline the process, scientists are trying to teach computers to perform this task. Ideally, a user could just describe the action they’re looking for, and an AI model would skip to its location in the video.However, teaching machine-learning models to do this usually requires a great deal of expensive video data that have been painstakingly hand-labeled.A new, more efficient approach from researchers at MIT and the MIT-IBM Watson AI Lab trains a model to perform this task, known as spatio-temporal grounding, using only videos and their automatically generated transcripts.The researchers teach a model to understand an unlabeled video in two distinct ways: by looking at small details to figure out where objects are located (spatial information) and looking at the bigger picture to understand when the action occurs (temporal information).Compared to other AI approaches, their method more accurately identifies actions in longer videos with multiple activities. Interestingly, they found that simultaneously training on spatial and temporal information makes a model better at identifying each individually.In addition to streamlining online learning and virtual training processes, this technique could also be useful in health care settings by rapidly finding key moments in videos of diagnostic procedures, for example.“We disentangle the challenge of trying to encode spatial and temporal information all at once and instead think about it like two experts working on their own, which turns out to be a more explicit way to encode the information. Our model, which combines these two separate branches, leads to the best performance,” says Brian Chen, lead author of a paper on this technique.Chen, a 2023 graduate of Columbia University who conducted this research while a visiting student at the MIT-IBM Watson AI Lab, is joined on the paper by James Glass, senior research scientist, member of the MIT-IBM Watson AI Lab, and head of the Spoken Language Systems Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL); Hilde Kuehne, a member of the MIT-IBM Watson AI Lab who is also affiliated with Goethe University Frankfurt; and others at MIT, Goethe University, the MIT-IBM Watson AI Lab, and Quality Match GmbH. The research will be presented at the Conference on Computer Vision and Pattern Recognition.Global and local learningResearchers usually teach models to perform spatio-temporal grounding using videos in which humans have annotated the start and end times of particular tasks.Not only is generating these data expensive, but it can be difficult for humans to figure out exactly what to label. If the action is “cooking a pancake,” does that action start when the chef begins mixing the batter or when she pours it into the pan?“This time, the task may be about cooking, but next time, it might be about fixing a car. There are so many different domains for people to annotate. But if we can learn everything without labels, it is a more general solution,” Chen says.For their approach, the researchers use unlabeled instructional videos and accompanying text transcripts from a website like YouTube as training data. These don’t need any special preparation.They split the training process into two pieces. For one, they teach a machine-learning model to look at the entire video to understand what actions happen at certain times. This high-level information is called a global representation.For the second, they teach the model to focus on a specific region in parts of the video where action is happening. In a large kitchen, for instance, the model might only need to focus on the wooden spoon a chef is using to mix pancake batter, rather than the entire counter. This fine-grained information is called a local representation.The researchers incorporate an additional component into their framework to mitigate misalignments that occur between narration and video. Perhaps the chef talks about cooking the pancake first and performs the action later.To develop a more realistic solution, the researchers focused on uncut videos that are several minutes long. In contrast, most AI techniques train using few-second clips that someone trimmed to show only one action.A new benchmarkBut when they came to evaluate their approach, the researchers couldn’t find an effective benchmark for testing a model on these longer, uncut videos — so they created one.To build their benchmark dataset, the researchers devised a new annotation technique that works well for identifying multistep actions. They had users mark the intersection of objects, like the point where a knife edge cuts a tomato, rather than drawing a box around important objects.“This is more clearly defined and speeds up the annotation process, which reduces the human labor and cost,” Chen says.Plus, having multiple people do point annotation on the same video can better capture actions that occur over time, like the flow of milk being poured. All annotators won’t mark the exact same point in the flow of liquid.When they used this benchmark to test their approach, the researchers found that it was more accurate at pinpointing actions than other AI techniques.Their method was also better at focusing on human-object interactions. For instance, if the action is “serving a pancake,” many other approaches might focus only on key objects, like a stack of pancakes sitting on a counter. Instead, their method focuses on the actual moment when the chef flips a pancake onto a plate.Next, the researchers plan to enhance their approach so models can automatically detect when text and narration are not aligned, and switch focus from one modality to the other. They also want to extend their framework to audio data, since there are usually strong correlations between actions and the sounds objects make.“AI research has made incredible progress towards creating models like ChatGPT that understand images. But our progress on understanding video is far behind. This work represents a significant step forward in that direction,” says Kate Saenko, a professor in the Department of Computer Science at Boston University who was not involved with this work.This research is funded, in part, by the MIT-IBM Watson AI Lab. More

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    Janabel Xia: Algorithms, dance rhythms, and the drive to succeed

    Senior math major Janabel Xia is a study of a person in constant motion.When she isn’t sorting algorithms and improving traffic control systems for driverless vehicles, she’s dancing as a member of at least four dance clubs. She’s joined several social justice organizations, worked on cryptography and web authentication technology, and created a polling app that allows users to vote anonymously.In her final semester, she’s putting the pedal to the metal, with a green light to lessen the carbon footprint of urban transportation by using sensors at traffic light intersections.First stepsGrowing up in Lexington, Massachusetts, Janabel has been competing on math teams since elementary school. On her math team, which met early mornings before the start of school, she discovered a love of problem-solving that challenged her more than her classroom “plug-and-chug exercises.”At Lexington High School, she was math team captain, a two-time Math Olympiad attendee, and a silver medalist for Team USA at the European Girls’ Mathematical Olympiad.As a math major, she studies combinatorics and theoretical computer science, including theoretical and applied cryptography. In her sophomore year, she was a researcher in the Cryptography and Information Security Group at the MIT Computer Science and Artificial Intelligence Laboratory, where she conducted cryptanalysis research under Professor Vinod Vaikuntanathan.Part of her interests in cryptography stem from the beauty of the underlying mathematics itself — the field feels like clever engineering with mathematical tools. But another part of her interest in cryptography stems from its political dimensions, including its potential to fundamentally change existing power structures and governance. Xia and students at the University of California at Berkeley and Stanford University created zkPoll, a private polling app written with the Circom programming language, that allows users to create polls for specific sets of people, while generating a zero-knowledge proof that keeps personal information hidden to decrease negative voting influences from public perception.Her participation in the PKG Center’s Active Community Engagement Freshman Pre-Orientation Program introduced her to local community organizations focusing on food security, housing for formerly incarcerated individuals, and access to health care. She is also part of Reading for Revolution, a student book club that discusses race, class, and working-class movements within MIT and the Greater Boston area.Xia’s educational journey led to her ongoing pursuit of combining mathematical and computational methods in areas adjacent to urban planning.  “When I realized how much planning was concerned with social justice as it was concerned with design, I became more attracted to the field.”Going on autopilotShe took classes with the Department of Urban Studies and Planning and is currently working on an Undergraduate Research Opportunities Program (UROP) project with Professor Cathy Wu in the Institute for Data, Systems, and Society.Recent work on eco-driving by Wu and doctoral student Vindula Jayawardana investigated semi-autonomous vehicles that communicate with sensors localized at traffic intersections, which in theory could reduce carbon emissions by up to 21 percent.Xia aims to optimize the implementation scheme for these sensors at traffic intersections, considering a graded scheme where perhaps only 20 percent of all sensors are initially installed, and more sensors get added in waves. She wants to maximize the emission reduction rates at each step of the process, as well as ensure there is no unnecessary installation and de-installation of such sensors.  Dance numbersMeanwhile, Xia has been a member of MIT’s Fixation, Ridonkulous, and MissBehavior groups, and as a traditional Chinese dance choreographer for the MIT Asian Dance Team. A dancer since she was 3, Xia started with Chinese traditional dance, and later added ballet and jazz. Because she is as much of a dancer as a researcher, she has figured out how to make her schedule work.“Production weeks are always madness, with dancers running straight from class to dress rehearsals and shows all evening and coming back early next morning to take down lights and roll up marley [material that covers the stage floor],” she says. “As busy as it keeps me, I couldn’t have survived MIT without dance. I love the discipline, creativity, and most importantly the teamwork that dance demands of us. I really love the dance community here with my whole heart. These friends have inspired me and given me the love to power me through MIT.”Xia lives with her fellow Dance Team members at the off-campus Women’s Independent Living Group (WILG).  “I really value WILG’s culture of independence, both in lifestyle — cooking, cleaning up after yourself, managing house facilities, etc. — and thought — questioning norms, staying away from status games, finding new passions.”In addition to her UROP, she’s wrapping up some graduation requirements, finishing up a research paper on sorting algorithms from her summer at the University of Minnesota Duluth Research Experience for Undergraduates in combinatorics, and deciding between PhD programs in math and computer science.  “My biggest goal right now is to figure out how to combine my interests in mathematics and urban studies, and more broadly connect technical perspectives with human-centered work in a way that feels right to me,” she says.“Overall, MIT has given me so many avenues to explore that I would have never thought about before coming here, for which I’m infinitely grateful. Every time I find something new, it’s hard for me not to find it cool. There’s just so much out there to learn about. While it can feel overwhelming at times, I hope to continue that learning and exploration for the rest of my life.” More

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    Fostering research, careers, and community in materials science

    Gabrielle Wood, a junior at Howard University majoring in chemical engineering, is on a mission to improve the sustainability and life cycles of natural resources and materials. Her work in the Materials Initiative for Comprehensive Research Opportunity (MICRO) program has given her hands-on experience with many different aspects of research, including MATLAB programming, experimental design, data analysis, figure-making, and scientific writing.Wood is also one of 10 undergraduates from 10 universities around the United States to participate in the first MICRO Summit earlier this year. The internship program, developed by the MIT Department of Materials Science and Engineering (DMSE), first launched in fall 2021. Now in its third year, the program continues to grow, providing even more opportunities for non-MIT undergraduate students — including the MICRO Summit and the program’s expansion to include Northwestern University.“I think one of the most valuable aspects of the MICRO program is the ability to do research long term with an experienced professor in materials science and engineering,” says Wood. “My school has limited opportunities for undergraduate research in sustainable polymers, so the MICRO program allowed me to gain valuable experience in this field, which I would not otherwise have.”Like Wood, Griheydi Garcia, a senior chemistry major at Manhattan College, values the exposure to materials science, especially since she is not able to learn as much about it at her home institution.“I learned a lot about crystallography and defects in materials through the MICRO curriculum, especially through videos,” says Garcia. “The research itself is very valuable, as well, because we get to apply what we’ve learned through the videos in the research we do remotely.”Expanding research opportunitiesFrom the beginning, the MICRO program was designed as a fully remote, rigorous education and mentoring program targeted toward students from underserved backgrounds interested in pursuing graduate school in materials science or related fields. Interns are matched with faculty to work on their specific research interests.Jessica Sandland ’99, PhD ’05, principal lecturer in DMSE and co-founder of MICRO, says that research projects for the interns are designed to be work that they can do remotely, such as developing a machine-learning algorithm or a data analysis approach.“It’s important to note that it’s not just about what the program and faculty are bringing to the student interns,” says Sandland, a member of the MIT Digital Learning Lab, a joint program between MIT Open Learning and the Institute’s academic departments. “The students are doing real research and work, and creating things of real value. It’s very much an exchange.”Cécile Chazot PhD ’22, now an assistant professor of materials science and engineering at Northwestern University, had helped to establish MICRO at MIT from the very beginning. Once at Northwestern, she quickly realized that expanding MICRO to Northwestern would offer even more research opportunities to interns than by relying on MIT alone — leveraging the university’s strong materials science and engineering department, as well as offering resources for biomaterials research through Northwestern’s medical school. The program received funding from 3M and officially launched at Northwestern in fall 2023. Approximately half of the MICRO interns are now in the program with MIT and half are with Northwestern. Wood and Garcia both participate in the program via Northwestern.“By expanding to another school, we’ve been able to have interns work with a much broader range of research projects,” says Chazot. “It has become easier for us to place students with faculty and research that match their interests.”Building communityThe MICRO program received a Higher Education Innovation grant from the Abdul Latif Jameel World Education Lab, part of MIT Open Learning, to develop an in-person summit. In January 2024, interns visited MIT for three days of presentations, workshops, and campus tours — including a tour of the MIT.nano building — as well as various community-building activities.“A big part of MICRO is the community,” says Chazot. “A highlight of the summit was just seeing the students come together.”The summit also included panel discussions that allowed interns to gain insights and advice from graduate students and professionals. The graduate panel discussion included MIT graduate students Sam Figueroa (mechanical engineering), Isabella Caruso (DMSE), and Eliana Feygin (DMSE). The career panel was led by Chazot and included Jatin Patil PhD ’23, head of product at SiTration; Maureen Reitman ’90, ScD ’93, group vice president and principal engineer at Exponent; Lucas Caretta PhD ’19, assistant professor of engineering at Brown University; Raquel D’Oyen ’90, who holds a PhD from Northwestern University and is a senior engineer at Raytheon; and Ashley Kaiser MS ’19, PhD ’21, senior process engineer at 6K.Students also had an opportunity to share their work with each other through research presentations. Their presentations covered a wide range of topics, including: developing a computer program to calculate solubility parameters for polymers used in textile manufacturing; performing a life-cycle analysis of a photonic chip and evaluating its environmental impact in comparison to a standard silicon microchip; and applying machine learning algorithms to scanning transmission electron microscopy images of CrSBr, a two-dimensional magnetic material. “The summit was wonderful and the best academic experience I have had as a first-year college student,” says MICRO intern Gabriella La Cour, who is pursuing a major in chemistry and dual degree biomedical engineering at Spelman College and participates in MICRO through MIT. “I got to meet so many students who were all in grades above me … and I learned a little about how to navigate college as an upperclassman.” “I actually have an extremely close friendship with one of the students, and we keep in touch regularly,” adds La Cour. “Professor Chazot gave valuable advice about applications and recommendation letters that will be useful when I apply to REUs [Research Experiences for Undergraduates] and graduate schools.”Looking to the future, MICRO organizers hope to continue to grow the program’s reach.“We would love to see other schools taking on this model,” says Sandland. “There are a lot of opportunities out there. The more departments, research groups, and mentors that get involved with this program, the more impact it can have.” More