More stories

  • in

    Using generative AI to improve software testing

    Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application.

    Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms — especially in scenarios where real-world data are limited or sensitive.

    For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.

    The Synthetic Data Vault, or SDV, has been downloaded more than 1 million times, with more than 10,000 data scientists using the open-source library for generating synthetic tabular data. The founders — Principal Research Scientist Kalyan Veeramachaneni and alumna Neha Patki ’15, SM ’16 — believe the company’s success is due to SDV’s ability to revolutionize software testing.

    SDV goes viral

    In 2016, Veeramachaneni’s group in the Data to AI Lab unveiled a suite of open-source generative AI tools to help organizations create synthetic data that matched the statistical properties of real data.

    Companies can use synthetic data instead of sensitive information in programs while still preserving the statistical relationships between datapoints. Companies can also use synthetic data to run new software through simulations to see how it performs before releasing it to the public.

    Veeramachaneni’s group came across the problem because it was working with companies that wanted to share their data for research.

    “MIT helps you see all these different use cases,” Patki explains. “You work with finance companies and health care companies, and all those projects are useful to formulate solutions across industries.”

    In 2020, the researchers founded DataCebo to build more SDV features for larger organizations. Since then, the use cases have been as impressive as they’ve been varied.

    With DataCebo’s new flight simulator, for instance, airlines can plan for rare weather events in a way that would be impossible using only historic data. In another application, SDV users synthesized medical records to predict health outcomes for patients with cystic fibrosis. A team from Norway recently used SDV to create synthetic student data to evaluate whether various admissions policies were meritocratic and free from bias.

    In 2021, the data science platform Kaggle hosted a competition for data scientists that used SDV to create synthetic data sets to avoid using proprietary data. Roughly 30,000 data scientists participated, building solutions and predicting outcomes based on the company’s realistic data.

    And as DataCebo has grown, it’s stayed true to its MIT roots: All of the company’s current employees are MIT alumni.

    Supercharging software testing

    Although their open-source tools are being used for a variety of use cases, the company is focused on growing its traction in software testing.

    “You need data to test these software applications,” Veeramachaneni says. “Traditionally, developers manually write scripts to create synthetic data. With generative models, created using SDV, you can learn from a sample of data collected and then sample a large volume of synthetic data (which has the same properties as real data), or create specific scenarios and edge cases, and use the data to test your application.”

    For example, if a bank wanted to test a program designed to reject transfers from accounts with no money in them, it would have to simulate many accounts simultaneously transacting. Doing that with data created manually would take a lot of time. With DataCebo’s generative models, customers can create any edge case they want to test.

    “It’s common for industries to have data that is sensitive in some capacity,” Patki says. “Often when you’re in a domain with sensitive data you’re dealing with regulations, and even if there aren’t legal regulations, it’s in companies’ best interest to be diligent about who gets access to what at which time. So, synthetic data is always better from a privacy perspective.”

    Scaling synthetic data

    Veeramachaneni believes DataCebo is advancing the field of what it calls synthetic enterprise data, or data generated from user behavior on large companies’ software applications.

    “Enterprise data of this kind is complex, and there is no universal availability of it, unlike language data,” Veeramachaneni says. “When folks use our publicly available software and report back if works on a certain pattern, we learn a lot of these unique patterns, and it allows us to improve our algorithms. From one perspective, we are building a corpus of these complex patterns, which for language and images is readily available. “

    DataCebo also recently released features to improve SDV’s usefulness, including tools to assess the “realism” of the generated data, called the SDMetrics library as well as a way to compare models’ performances called SDGym.

    “It’s about ensuring organizations trust this new data,” Veeramachaneni says. “[Our tools offer] programmable synthetic data, which means we allow enterprises to insert their specific insight and intuition to build more transparent models.”

    As companies in every industry rush to adopt AI and other data science tools, DataCebo is ultimately helping them do so in a way that is more transparent and responsible.

    “In the next few years, synthetic data from generative models will transform all data work,” Veeramachaneni says. “We believe 90 percent of enterprise operations can be done with synthetic data.” More

  • in

    Startup accelerates progress toward light-speed computing

    Our ability to cram ever-smaller transistors onto a chip has enabled today’s age of ubiquitous computing. But that approach is finally running into limits, with some experts declaring an end to Moore’s Law and a related principle, known as Dennard’s Scaling.

    Those developments couldn’t be coming at a worse time. Demand for computing power has skyrocketed in recent years thanks in large part to the rise of artificial intelligence, and it shows no signs of slowing down.

    Now Lightmatter, a company founded by three MIT alumni, is continuing the remarkable progress of computing by rethinking the lifeblood of the chip. Instead of relying solely on electricity, the company also uses light for data processing and transport. The company’s first two products, a chip specializing in artificial intelligence operations and an interconnect that facilitates data transfer between chips, use both photons and electrons to drive more efficient operations.

    “The two problems we are solving are ‘How do chips talk?’ and ‘How do you do these [AI] calculations?’” Lightmatter co-founder and CEO Nicholas Harris PhD ’17 says. “With our first two products, Envise and Passage, we’re addressing both of those questions.”

    In a nod to the size of the problem and the demand for AI, Lightmatter raised just north of $300 million in 2023 at a valuation of $1.2 billion. Now the company is demonstrating its technology with some of the largest technology companies in the world in hopes of reducing the massive energy demand of data centers and AI models.

    “We’re going to enable platforms on top of our interconnect technology that are made up of hundreds of thousands of next-generation compute units,” Harris says. “That simply wouldn’t be possible without the technology that we’re building.”

    From idea to $100K

    Prior to MIT, Harris worked at the semiconductor company Micron Technology, where he studied the fundamental devices behind integrated chips. The experience made him see how the traditional approach for improving computer performance — cramming more transistors onto each chip — was hitting its limits.

    “I saw how the roadmap for computing was slowing, and I wanted to figure out how I could continue it,” Harris says. “What approaches can augment computers? Quantum computing and photonics were two of those pathways.”

    Harris came to MIT to work on photonic quantum computing for his PhD under Dirk Englund, an associate professor in the Department of Electrical Engineering and Computer Science. As part of that work, he built silicon-based integrated photonic chips that could send and process information using light instead of electricity.

    The work led to dozens of patents and more than 80 research papers in prestigious journals like Nature. But another technology also caught Harris’s attention at MIT.

    “I remember walking down the hall and seeing students just piling out of these auditorium-sized classrooms, watching relayed live videos of lectures to see professors teach deep learning,” Harris recalls, referring to the artificial intelligence technique. “Everybody on campus knew that deep learning was going to be a huge deal, so I started learning more about it, and we realized that the systems I was building for photonic quantum computing could actually be leveraged to do deep learning.”

    Harris had planned to become a professor after his PhD, but he realized he could attract more funding and innovate more quickly through a startup, so he teamed up with Darius Bunandar PhD ’18, who was also studying in Englund’s lab, and Thomas Graham MBA ’18. The co-founders successfully launched into the startup world by winning the 2017 MIT $100K Entrepreneurship Competition.

    Seeing the light

    Lightmatter’s Envise chip takes the part of computing that electrons do well, like memory, and combines it with what light does well, like performing the massive matrix multiplications of deep-learning models.

    “With photonics, you can perform multiple calculations at the same time because the data is coming in on different colors of light,” Harris explains. “In one color, you could have a photo of a dog. In another color, you could have a photo of a cat. In another color, maybe a tree, and you could have all three of those operations going through the same optical computing unit, this matrix accelerator, at the same time. That drives up operations per area, and it reuses the hardware that’s there, driving up energy efficiency.”

    Passage takes advantage of light’s latency and bandwidth advantages to link processors in a manner similar to how fiber optic cables use light to send data over long distances. It also enables chips as big as entire wafers to act as a single processor. Sending information between chips is central to running the massive server farms that power cloud computing and run AI systems like ChatGPT.

    Both products are designed to bring energy efficiencies to computing, which Harris says are needed to keep up with rising demand without bringing huge increases in power consumption.

    “By 2040, some predict that around 80 percent of all energy usage on the planet will be devoted to data centers and computing, and AI is going to be a huge fraction of that,” Harris says. “When you look at computing deployments for training these large AI models, they’re headed toward using hundreds of megawatts. Their power usage is on the scale of cities.”

    Lightmatter is currently working with chipmakers and cloud service providers for mass deployment. Harris notes that because the company’s equipment runs on silicon, it can be produced by existing semiconductor fabrication facilities without massive changes in process.

    The ambitious plans are designed to open up a new path forward for computing that would have huge implications for the environment and economy.

    “We’re going to continue looking at all of the pieces of computers to figure out where light can accelerate them, make them more energy efficient, and faster, and we’re going to continue to replace those parts,” Harris says. “Right now, we’re focused on interconnect with Passage and on compute with Envise. But over time, we’re going to build out the next generation of computers, and it’s all going to be centered around light.” More

  • in

    “We offer another place for knowledge”

    In the Dzaleka Refugee Camp in Malawi, Jospin Hassan didn’t have access to the education opportunities he sought. So, he decided to create his own. 

    Hassan knew the booming fields of data science and artificial intelligence could bring job opportunities to his community and help solve local challenges. After earning a spot in the 2020-21 cohort of the Certificate Program in Computer and Data Science from MIT Refugee Action Hub (ReACT), Hassan started sharing MIT knowledge and skills with other motivated learners in Dzaleka.

    MIT ReACT is now Emerging Talent, part of the Jameel World Education Lab (J-WEL) at MIT Open Learning. Currently serving its fifth cohort of global learners, Emerging Talent’s year-long certificate program incorporates high-quality computer science and data analysis coursework from MITx, professional skill building, experiential learning, apprenticeship work, and opportunities for networking with MIT’s global community of innovators. Hassan’s cohort honed their leadership skills through interactive online workshops with J-WEL and the 10-week online MIT Innovation Leadership Bootcamp. 

    “My biggest takeaway was networking, collaboration, and learning from each other,” Hassan says.

    Today, Hassan’s organization ADAI Circle offers mentorship and education programs for youth and other job seekers in the Dzaleka Refugee Camp. The curriculum encourages hands-on learning and collaboration.

    Launched in 2020, ADAI Circle aims to foster job creation and reduce poverty in Malawi through technology and innovation. In addition to their classes in data science, AI, software development, and hardware design, their Innovation Hub offers internet access to anyone in need. 

    Doing something different in the community

    Hassan first had the idea for his organization in 2018 when he reached a barrier in his own education journey. There were several programs in the Dzaleka Refugee Camp teaching learners how to code websites and mobile apps, but Hassan felt that they were limited in scope. 

    “We had good devices and internet access,” he says, “but I wanted to learn something new.” 

    Teaming up with co-founder Patrick Byamasu, Hassan and Byamasu set their sights on the longevity of AI and how that might create more jobs for people in their community. “The world is changing every day, and data scientists are in a higher demand today in various companies,” Hassan says. “For this reason, I decided to expand and share the knowledge that I acquired with my fellow refugees and the surrounding villages.”

    ADAI Circle draws inspiration from Hassan’s own experience with MIT Emerging Talent coursework, community, and training opportunities. For example, the MIT Bootcamps model is now standard practice for ADAI Circle’s annual hackathon. Hassan first introduced the hackathon to ADAI Circle students as part of his final experiential learning project of the Emerging Talent certificate program. 

    ADAI Circle’s annual hackathon is now an interactive — and effective — way to select students who will most benefit from its programs. The local schools’ curricula, Hassan says, might not provide enough of an academic challenge. “We can’t teach everyone and accommodate everyone because there are a lot of schools,” Hassan says, “but we offer another place for knowledge.” 

    The hackathon helps students develop data science and robotics skills. Before they start coding, students have to convince ADAI Circle teachers that their designs are viable, answering questions like, “What problem are you solving?” and “How will this help the community?” A community-oriented mindset is just as important to the curriculum.

    In addition to the practical skills Hassan gained from Emerging Talent, he leveraged the program’s network to help his community. Thanks to a social media connection Hassan made with the nongovernmental organization Give Internet after one of Emerging Talent’s virtual events, Give Internet brought internet access to ADAI Circle.

    Bridging the AI gap to unmet communities

    In 2023, ADAI Circle connected with another MIT Open Learning program, Responsible AI for Social Empowerment and Education (RAISE), which led to a pilot test of a project-based AI curriculum for middle school students. The Responsible AI for Computational Action (RAICA) curriculum equipped ADAI Circle students with AI skills for chatbots and natural language processing. 

    “I liked that program because it was based on what we’re teaching at the center,” Hassan says, speaking of his organization’s mission of bridging the AI gap to reach unmet communities.

    The RAICA curriculum was designed by education experts at MIT Scheller Teacher Education Program (STEP Lab) and AI experts from MIT Personal Robots group and MIT App Inventor. ADAI Circle teachers gave detailed feedback about the pilot to the RAICA team. During weekly meetings with Glenda Stump, education research scientist for RAICA and J-WEL, and Angela Daniel, teacher development specialist for RAICA, the teachers discussed their experiences, prepared for upcoming lessons, and translated the learning materials in real time. 

    “We are trying to create a curriculum that’s accessible worldwide and to students who typically have little or no access to technology,” says Mary Cate Gustafson-Quiett, curriculum design manager at STEP Lab and project manager for RAICA. “Working with ADAI and students in a refugee camp challenged us to design in more culturally and technologically inclusive ways.”

    Gustafson-Quiett says the curriculum feedback from ADAI Circle helped inform how RAICA delivers teacher development resources to accommodate learning environments with limited internet access. “They also exposed places where our team’s western ideals, specifically around individualism, crept into activities in the lesson and contrasted with their more communal cultural beliefs,” she says.

    Eager to introduce more MIT-developed AI resources, Hassan also shared MIT RAISE’s Day of AI curricula with ADAI Circle teachers. The new ChatGPT module gave students the chance to level up their chatbot programming skills that they gained from the RAICA module. Some of the advanced students are taking initiative to use ChatGPT API to create their own projects in education.

    “We don’t want to tell them what to do, we want them to come up with their own ideas,” Hassan says.

    Although ADAI Circle faces many challenges, Hassan says his team is addressing them one by one. Last year, they didn’t have electricity in their Innovation Hub, but they solved that. This year, they achieved a stable internet connection that’s one of the fastest in Malawi. Next up, they are hoping to secure more devices for their students, create more jobs, and add additional hubs throughout the community. The work is never done, but Hassan is starting to see the impact that ADAI Circle is making. 

    “For those who want to learn data science, let’s let them learn,” Hassan says. More

  • in

    Putting AI into the hands of people with problems to solve

    As Media Lab students in 2010, Karthik Dinakar SM ’12, PhD ’17 and Birago Jones SM ’12 teamed up for a class project to build a tool that would help content moderation teams at companies like Twitter (now X) and YouTube. The project generated a huge amount of excitement, and the researchers were invited to give a demonstration at a cyberbullying summit at the White House — they just had to get the thing working.

    The day before the White House event, Dinakar spent hours trying to put together a working demo that could identify concerning posts on Twitter. Around 11 p.m., he called Jones to say he was giving up.

    Then Jones decided to look at the data. It turned out Dinakar’s model was flagging the right types of posts, but the posters were using teenage slang terms and other indirect language that Dinakar didn’t pick up on. The problem wasn’t the model; it was the disconnect between Dinakar and the teens he was trying to help.

    “We realized then, right before we got to the White House, that the people building these models should not be folks who are just machine-learning engineers,” Dinakar says. “They should be people who best understand their data.”

    The insight led the researchers to develop point-and-click tools that allow nonexperts to build machine-learning models. Those tools became the basis for Pienso, which today is helping people build large language models for detecting misinformation, human trafficking, weapons sales, and more, without writing any code.

    “These kinds of applications are important to us because our roots are in cyberbullying and understanding how to use AI for things that really help humanity,” says Jones.

    As for the early version of the system shown at the White House, the founders ended up collaborating with students at nearby schools in Cambridge, Massachusetts, to let them train the models.

    “The models those kids trained were so much better and nuanced than anything I could’ve ever come up with,” Dinakar says. “Birago and I had this big ‘Aha!’ moment where we realized empowering domain experts — which is different from democratizing AI — was the best path forward.”

    A project with purpose

    Jones and Dinakar met as graduate students in the Software Agents research group of the MIT Media Lab. Their work on what became Pienso started in Course 6.864 (Natural Language Processing) and continued until they earned their master’s degrees in 2012.

    It turned out 2010 wasn’t the last time the founders were invited to the White House to demo their project. The work generated a lot of enthusiasm, but the founders worked on Pienso part time until 2016, when Dinakar finished his PhD at MIT and deep learning began to explode in popularity.

    “We’re still connected to many people around campus,” Dinakar says. “The exposure we had at MIT, the melding of human and computer interfaces, widened our understanding. Our philosophy at Pienso couldn’t be possible without the vibrancy of MIT’s campus.”

    The founders also credit MIT’s Industrial Liaison Program (ILP) and Startup Accelerator (STEX) for connecting them to early partners.

    One early partner was SkyUK. The company’s customer success team used Pienso to build models to understand their customer’s most common problems. Today those models are helping to process half a million customer calls a day, and the founders say they have saved the company over £7 million pounds to date by shortening the length of calls into the company’s call center.

    “The difference between democratizing AI and empowering people with AI comes down to who understands the data best — you or a doctor or a journalist or someone who works with customers every day?” Jones says. “Those are the people who should be creating the models. That’s how you get insights out of your data.”

    In 2020, just as Covid-19 outbreaks began in the U.S., government officials contacted the founders to use their tool to better understand the emerging disease. Pienso helped experts in virology and infectious disease set up machine-learning models to mine thousands of research articles about coronaviruses. Dinakar says they later learned the work helped the government identify and strengthen critical supply chains for drugs, including the popular antiviral remdesivir.

    “Those compounds were surfaced by a team that did not know deep learning but was able to use our platform,” Dinakar says.

    Building a better AI future

    Because Pienso can run on internal servers and cloud infrastructure, the founders say it offers an alternative for businesses being forced to donate their data by using services offered by other AI companies.

    “The Pienso interface is a series of web apps stitched together,” Dinakar explains. “You can think of it like an Adobe Photoshop for large language models, but in the web. You can point and import data without writing a line of code. You can refine the data, prepare it for deep learning, analyze it, give it structure if it’s not labeled or annotated, and you can walk away with fine-tuned, large language model in a matter of 25 minutes.”

    Earlier this year, Pienso announced a partnership with GraphCore, which provides a faster, more efficient computing platform for machine learning. The founders say the partnership will further lower barriers to leveraging AI by dramatically reducing latency.

    “If you’re building an interactive AI platform, users aren’t going to have a cup of coffee every time they click a button,” Dinakar says. “It needs to be fast and responsive.”

    The founders believe their solution is enabling a future where more effective AI models are developed for specific use cases by the people who are most familiar with the problems they are trying to solve.

    “No one model can do everything,” Dinakar says. “Everyone’s application is different, their needs are different, their data is different. It’s highly unlikely that one model will do everything for you. It’s about bringing a garden of models together and allowing them to collaborate with each other and orchestrating them in a way that makes sense — and the people doing that orchestration should be the people who understand the data best.” More

  • in

    Automated method helps researchers quantify uncertainty in their predictions

    Pollsters trying to predict presidential election results and physicists searching for distant exoplanets have at least one thing in common: They often use a tried-and-true scientific technique called Bayesian inference.

    Bayesian inference allows these scientists to effectively estimate some unknown parameter — like the winner of an election — from data such as poll results. But Bayesian inference can be slow, sometimes consuming weeks or even months of computation time or requiring a researcher to spend hours deriving tedious equations by hand. 

    Researchers from MIT and elsewhere have introduced an optimization technique that speeds things up without requiring a scientist to do a lot of additional work. Their method can achieve more accurate results faster than another popular approach for accelerating Bayesian inference.

    Using this new automated technique, a scientist could simply input their model and then the optimization method does all the calculations under the hood to provide an approximation of some unknown parameter. The method also offers reliable uncertainty estimates that can help a researcher understand when to trust its predictions.

    This versatile technique could be applied to a wide array of scientific quandaries that incorporate Bayesian inference. For instance, it could be used by economists studying the impact of microcredit loans in developing nations or sports analysts using a model to rank top tennis players.

    “When you actually dig into what people are doing in the social sciences, physics, chemistry, or biology, they are often using a lot of the same tools under the hood. There are so many Bayesian analyses out there. If we can build a really great tool that makes these researchers lives easier, then we can really make a difference to a lot of people in many different research areas,” says senior author Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.

    Broderick is joined on the paper by co-lead authors Ryan Giordano, an assistant professor of statistics at the University of California at Berkeley; and Martin Ingram, a data scientist at the AI company KONUX. The paper was recently published in the Journal of Machine Learning Research.

    Faster results

    When researchers seek a faster form of Bayesian inference, they often turn to a technique called automatic differentiation variational inference (ADVI), which is often both fast to run and easy to use.

    But Broderick and her collaborators have found a number of practical issues with ADVI. It has to solve an optimization problem and can do so only approximately. So, ADVI can still require a lot of computation time and user effort to determine whether the approximate solution is good enough. And once it arrives at a solution, it tends to provide poor uncertainty estimates.

    Rather than reinventing the wheel, the team took many ideas from ADVI but turned them around to create a technique called deterministic ADVI (DADVI) that doesn’t have these downsides.

    With DADVI, it is very clear when the optimization is finished, so a user won’t need to spend extra computation time to ensure that the best solution has been found. DADVI also permits the incorporation of more powerful optimization methods that give it an additional speed and performance boost.

    Once it reaches a result, DADVI is set up to allow the use of uncertainty corrections. These corrections make its uncertainty estimates much more accurate than those of ADVI.

    DADVI also enables the user to clearly see how much error they have incurred in the approximation to the optimization problem. This prevents a user from needlessly running the optimization again and again with more and more resources to try and reduce the error.

    “We wanted to see if we could live up to the promise of black-box inference in the sense of, once the user makes their model, they can just run Bayesian inference and don’t have to derive everything by hand, they don’t need to figure out when to stop their algorithm, and they have a sense of how accurate their approximate solution is,” Broderick says.

    Defying conventional wisdom

    DADVI can be more effective than ADVI because it uses an efficient approximation method, called sample average approximation, which estimates an unknown quantity by taking a series of exact steps.

    Because the steps along the way are exact, it is clear when the objective has been reached. Plus, getting to that objective typically requires fewer steps.

    Often, researchers expect sample average approximation to be more computationally intensive than a more popular method, known as stochastic gradient, which is used by ADVI. But Broderick and her collaborators showed that, in many applications, this is not the case.

    “A lot of problems really do have special structure, and you can be so much more efficient and get better performance by taking advantage of that special structure. That is something we have really seen in this paper,” she adds.

    They tested DADVI on a number of real-world models and datasets, including a model used by economists to evaluate the effectiveness of microcredit loans and one used in ecology to determine whether a species is present at a particular site.

    Across the board, they found that DADVI can estimate unknown parameters faster and more reliably than other methods, and achieves as good or better accuracy than ADVI. Because it is easier to use than other techniques, DADVI could offer a boost to scientists in a wide variety of fields.

    In the future, the researchers want to dig deeper into correction methods for uncertainty estimates so they can better understand why these corrections can produce such accurate uncertainties, and when they could fall short.

    “In applied statistics, we often have to use approximate algorithms for problems that are too complex or high-dimensional to allow exact solutions to be computed in reasonable time. This new paper offers an interesting set of theory and empirical results that point to an improvement in a popular existing approximate algorithm for Bayesian inference,” says Andrew Gelman ’85, ’86, a professor of statistics and political science at Columbia University, who was not involved with the study. “As one of the team involved in the creation of that earlier work, I’m happy to see our algorithm superseded by something more stable.”

    This research was supported by a National Science Foundation CAREER Award and the U.S. Office of Naval Research.  More

  • in

    Six MIT students selected as spring 2024 MIT-Pillar AI Collective Fellows

    The MIT-Pillar AI Collective has announced six fellows for the spring 2024 semester. With support from the program, the graduate students, who are in their final year of a master’s or PhD program, will conduct research in the areas of AI, machine learning, and data science with the aim of commercializing their innovations.

    Launched by MIT’s School of Engineering and Pillar VC in 2022, the MIT-Pillar AI Collective supports faculty, postdocs, and students conducting research on AI, machine learning, and data science. Supported by a gift from Pillar VC and administered by the MIT Deshpande Center for Technological Innovation, the mission of the program is to advance research toward commercialization.

    The spring 2024 MIT-Pillar AI Collective Fellows are:

    Yasmeen AlFaraj

    Yasmeen AlFaraj is a PhD candidate in chemistry whose interest is in the application of data science and machine learning to soft materials design to enable next-generation, sustainable plastics, rubber, and composite materials. More specifically, she is applying machine learning to the design of novel molecular additives to enable the low-cost manufacturing of chemically deconstructable thermosets and composites. AlFaraj’s work has led to the discovery of scalable, translatable new materials that could address thermoset plastic waste. As a Pillar Fellow, she will pursue bringing this technology to market, initially focusing on wind turbine blade manufacturing and conformal coatings. Through the Deshpande Center for Technological Innovation, AlFaraj serves as a lead for a team developing a spinout focused on recyclable versions of existing high-performance thermosets by incorporating small quantities of a degradable co-monomer. In addition, she participated in the National Science Foundation Innovation Corps program and recently graduated from the Clean Tech Open, where she focused on enhancing her business plan, analyzing potential markets, ensuring a complete IP portfolio, and connecting with potential funders. AlFaraj earned a BS in chemistry from University of California at Berkeley.

    Ruben Castro Ornelas

    Ruben Castro Ornelas is a PhD student in mechanical engineering who is passionate about the future of multipurpose robots and designing the hardware to use them with AI control solutions. Combining his expertise in programming, embedded systems, machine design, reinforcement learning, and AI, he designed a dexterous robotic hand capable of carrying out useful everyday tasks without sacrificing size, durability, complexity, or simulatability. Ornelas’s innovative design holds significant commercial potential in domestic, industrial, and health-care applications because it could be adapted to hold everything from kitchenware to delicate objects. As a Pillar Fellow, he will focus on identifying potential commercial markets, determining the optimal approach for business-to-business sales, and identifying critical advisors. Ornelas served as co-director of StartLabs, an undergraduate entrepreneurship club at MIT, where he earned an BS in mechanical engineering.

    Keeley Erhardt

    Keeley Erhardt is a PhD candidate in media arts and sciences whose research interests lie in the transformative potential of AI in network analysis, particularly for entity correlation and hidden link detection within and across domains. She has designed machine learning algorithms to identify and track temporal correlations and hidden signals in large-scale networks, uncovering online influence campaigns originating from multiple countries. She has similarly demonstrated the use of graph neural networks to identify coordinated cryptocurrency accounts by analyzing financial time series data and transaction dynamics. As a Pillar Fellow, Erhardt will pursue the potential commercial applications of her work, such as detecting fraud, propaganda, money laundering, and other covert activity in the finance, energy, and national security sectors. She has had internships at Google, Facebook, and Apple and held software engineering roles at multiple tech unicorns. Erhardt earned an MEng in electrical engineering and computer science and a BS in computer science, both from MIT.

    Vineet Jagadeesan Nair

    Vineet Jagadeesan Nair is a PhD candidate in mechanical engineering whose research focuses on modeling power grids and designing electricity markets to integrate renewables, batteries, and electric vehicles. He is broadly interested in developing computational tools to tackle climate change. As a Pillar Fellow, Nair will explore the application of machine learning and data science to power systems. Specifically, he will experiment with approaches to improve the accuracy of forecasting electricity demand and supply with high spatial-temporal resolution. In collaboration with Project Tapestry @ Google X, he is also working on fusing physics-informed machine learning with conventional numerical methods to increase the speed and accuracy of high-fidelity simulations. Nair’s work could help realize future grids with high penetrations of renewables and other clean, distributed energy resources. Outside academics, Nair is active in entrepreneurship, most recently helping to organize the 2023 MIT Global Startup Workshop in Greece. He earned an MS in computational science and engineering from MIT, an MPhil in energy technologies from Cambridge University as a Gates Scholar, and a BS in mechanical engineering and a BA in economics from University of California at Berkeley.

    Mahdi Ramadan

    Mahdi Ramadan is a PhD candidate in brain and cognitive sciences whose research interests lie at the intersection of cognitive science, computational modeling, and neural technologies. His work uses novel unsupervised methods for learning and generating interpretable representations of neural dynamics, capitalizing on recent advances in AI, specifically contrastive and geometric deep learning techniques capable of uncovering the latent dynamics underlying neural processes with high fidelity. As a Pillar Fellow, he will leverage these methods to gain a better understanding of dynamical models of muscle signals for generative motor control. By supplementing current spinal prosthetics with generative AI motor models that can streamline, speed up, and correct limb muscle activations in real time, as well as potentially using multimodal vision-language models to infer the patients’ high-level intentions, Ramadan aspires to build truly scalable, accessible, and capable commercial neuroprosthetics. Ramadan’s entrepreneurial experience includes being the co-founder of UltraNeuro, a neurotechnology startup, and co-founder of Presizely, a computer vision startup. He earned a BS in neurobiology from University of Washington.

    Rui (Raymond) Zhou

    Rui (Raymond) Zhou is a PhD candidate in mechanical engineering whose research focuses on multimodal AI for engineering design. As a Pillar Fellow, he will advance models that could enable designers to translate information in any modality or combination of modalities into comprehensive 2D and 3D designs, including parametric data, component visuals, assembly graphs, and sketches. These models could also optimize existing human designs to accomplish goals such as improving ergonomics or reducing drag coefficient. Ultimately, Zhou aims to translate his work into a software-as-a-service platform that redefines product design across various sectors, from automotive to consumer electronics. His efforts have the potential to not only accelerate the design process but also reduce costs, opening the door to unprecedented levels of customization, idea generation, and rapid prototyping. Beyond his academic pursuits, Zhou founded UrsaTech, a startup that integrates AI into education and engineering design. He earned a BS in electrical engineering and computer sciences from University of California at Berkeley. More

  • in

    How symmetry can come to the aid of machine learning

    Behrooz Tahmasebi — an MIT PhD student in the Department of Electrical Engineering and Computer Science (EECS) and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL) — was taking a mathematics course on differential equations in late 2021 when a glimmer of inspiration struck. In that class, he learned for the first time about Weyl’s law, which had been formulated 110 years earlier by the German mathematician Hermann Weyl. Tahmasebi realized it might have some relevance to the computer science problem he was then wrestling with, even though the connection appeared — on the surface — to be thin, at best. Weyl’s law, he says, provides a formula that measures the complexity of the spectral information, or data, contained within the fundamental frequencies of a drum head or guitar string.

    Tahmasebi was, at the same time, thinking about measuring the complexity of the input data to a neural network, wondering whether that complexity could be reduced by taking into account some of the symmetries inherent to the dataset. Such a reduction, in turn, could facilitate — as well as speed up — machine learning processes.

    Weyl’s law, conceived about a century before the boom in machine learning, had traditionally been applied to very different physical situations — such as those concerning the vibrations of a string or the spectrum of electromagnetic (black-body) radiation given off by a heated object. Nevertheless, Tahmasebi believed that a customized version of that law might help with the machine learning problem he was pursuing. And if the approach panned out, the payoff could be considerable.

    He spoke with his advisor, Stefanie Jegelka — an associate professor in EECS and affiliate of CSAIL and the MIT Institute for Data, Systems, and Society — who believed the idea was definitely worth looking into. As Tahmasebi saw it, Weyl’s law had to do with gauging the complexity of data, and so did this project. But Weyl’s law, in its original form, said nothing about symmetry.

    He and Jegelka have now succeeded in modifying Weyl’s law so that symmetry can be factored into the assessment of a dataset’s complexity. “To the best of my knowledge,” Tahmasebi says, “this is the first time Weyl’s law has been used to determine how machine learning can be enhanced by symmetry.”

    The paper he and Jegelka wrote earned a “Spotlight” designation when it was presented at the December 2023 conference on Neural Information Processing Systems — widely regarded as the world’s top conference on machine learning.

    This work, comments Soledad Villar, an applied mathematician at Johns Hopkins University, “shows that models that satisfy the symmetries of the problem are not only correct but also can produce predictions with smaller errors, using a small amount of training points. [This] is especially important in scientific domains, like computational chemistry, where training data can be scarce.”

    In their paper, Tahmasebi and Jegelka explored the ways in which symmetries, or so-called “invariances,” could benefit machine learning. Suppose, for example, the goal of a particular computer run is to pick out every image that contains the numeral 3. That task can be a lot easier, and go a lot quicker, if the algorithm can identify the 3 regardless of where it is placed in the box — whether it’s exactly in the center or off to the side — and whether it is pointed right-side up, upside down, or oriented at a random angle. An algorithm equipped with the latter capability can take advantage of the symmetries of translation and rotations, meaning that a 3, or any other object, is not changed in itself by altering its position or by rotating it around an arbitrary axis. It is said to be invariant to those shifts. The same logic can be applied to algorithms charged with identifying dogs or cats. A dog is a dog is a dog, one might say, irrespective of how it is embedded within an image. 

    The point of the entire exercise, the authors explain, is to exploit a dataset’s intrinsic symmetries in order to reduce the complexity of machine learning tasks. That, in turn, can lead to a reduction in the amount of data needed for learning. Concretely, the new work answers the question: How many fewer data are needed to train a machine learning model if the data contain symmetries?

    There are two ways of achieving a gain, or benefit, by capitalizing on the symmetries present. The first has to do with the size of the sample to be looked at. Let’s imagine that you are charged, for instance, with analyzing an image that has mirror symmetry — the right side being an exact replica, or mirror image, of the left. In that case, you don’t have to look at every pixel; you can get all the information you need from half of the image — a factor of two improvement. If, on the other hand, the image can be partitioned into 10 identical parts, you can get a factor of 10 improvement. This kind of boosting effect is linear.

    To take another example, imagine you are sifting through a dataset, trying to find sequences of blocks that have seven different colors — black, blue, green, purple, red, white, and yellow. Your job becomes much easier if you don’t care about the order in which the blocks are arranged. If the order mattered, there would be 5,040 different combinations to look for. But if all you care about are sequences of blocks in which all seven colors appear, then you have reduced the number of things — or sequences — you are searching for from 5,040 to just one.

    Tahmasebi and Jegelka discovered that it is possible to achieve a different kind of gain — one that is exponential — that can be reaped for symmetries that operate over many dimensions. This advantage is related to the notion that the complexity of a learning task grows exponentially with the dimensionality of the data space. Making use of a multidimensional symmetry can therefore yield a disproportionately large return. “This is a new contribution that is basically telling us that symmetries of higher dimension are more important because they can give us an exponential gain,” Tahmasebi says. 

    The NeurIPS 2023 paper that he wrote with Jegelka contains two theorems that were proved mathematically. “The first theorem shows that an improvement in sample complexity is achievable with the general algorithm we provide,” Tahmasebi says. The second theorem complements the first, he added, “showing that this is the best possible gain you can get; nothing else is achievable.”

    He and Jegelka have provided a formula that predicts the gain one can obtain from a particular symmetry in a given application. A virtue of this formula is its generality, Tahmasebi notes. “It works for any symmetry and any input space.” It works not only for symmetries that are known today, but it could also be applied in the future to symmetries that are yet to be discovered. The latter prospect is not too farfetched to consider, given that the search for new symmetries has long been a major thrust in physics. That suggests that, as more symmetries are found, the methodology introduced by Tahmasebi and Jegelka should only get better over time.

    According to Haggai Maron, a computer scientist at Technion (the Israel Institute of Technology) and NVIDIA who was not involved in the work, the approach presented in the paper “diverges substantially from related previous works, adopting a geometric perspective and employing tools from differential geometry. This theoretical contribution lends mathematical support to the emerging subfield of ‘Geometric Deep Learning,’ which has applications in graph learning, 3D data, and more. The paper helps establish a theoretical basis to guide further developments in this rapidly expanding research area.” More

  • in

    Generating the policy of tomorrow

    As first-year students in the Social and Engineering Systems (SES) doctoral program within the MIT Institute for Data, Systems, and Society (IDSS), Eric Liu and Ashely Peake share an interest in investigating housing inequality issues.

    They also share a desire to dive head-first into their research.

    “In the first year of your PhD, you’re taking classes and still getting adjusted, but we came in very eager to start doing research,” Liu says.

    Liu, Peake, and many others found an opportunity to do hands-on research on real-world problems at the MIT Policy Hackathon, an initiative organized by students in IDSS, including the Technology and Policy Program (TPP). The weekend-long, interdisciplinary event — now in its sixth year — continues to gather hundreds of participants from around the globe to explore potential solutions to some of society’s greatest challenges.

    This year’s theme, “Hack-GPT: Generating the Policy of Tomorrow,” sought to capitalize on the popularity of generative AI (like the chatbot ChatGPT) and the ways it is changing how we think about technical and policy-based challenges, according to Dansil Green, a second-year TPP master’s student and co-chair of the event.

    “We encouraged our teams to utilize and cite these tools, thinking about the implications that generative AI tools have on their different challenge categories,” Green says.

    After 2022’s hybrid event, this year’s organizers pivoted back to a virtual-only approach, allowing them to increase the overall number of participants in addition to increasing the number of teams per challenge by 20 percent.

    “Virtual allows you to reach more people — we had a high number of international participants this year — and it helps reduce some of the costs,” Green says. “I think going forward we are going to try and switch back and forth between virtual and in-person because there are different benefits to each.”

    “When the magic hits”

    Liu and Peake competed in the housing challenge category, where they could gain research experience in their actual field of study. 

    “While I am doing housing research, I haven’t necessarily had a lot of opportunities to work with actual housing data before,” says Peake, who recently joined the SES doctoral program after completing an undergraduate degree in applied math last year. “It was a really good experience to get involved with an actual data problem, working closer with Eric, who’s also in my lab group, in addition to meeting people from MIT and around the world who are interested in tackling similar questions and seeing how they think about things differently.”

    Joined by Adrian Butterton, a Boston-based paralegal, as well as Hudson Yuen and Ian Chan, two software engineers from Canada, Liu and Peake formed what would end up being the winning team in their category: “Team Ctrl+Alt+Defeat.” They quickly began organizing a plan to address the eviction crisis in the United States.

    “I think we were kind of surprised by the scope of the question,” Peake laughs. “In the end, I think having such a large scope motivated us to think about it in a more realistic kind of way — how could we come up with a solution that was adaptable and therefore could be replicated to tackle different kinds of problems.”

    Watching the challenge on the livestream together on campus, Liu says they immediately went to work, and could not believe how quickly things came together.

    “We got our challenge description in the evening, came out to the purple common area in the IDSS building and literally it took maybe an hour and we drafted up the entire project from start to finish,” Liu says. “Then our software engineer partners had a dashboard built by 1 a.m. — I feel like the hackathon really promotes that really fast dynamic work stream.”

    “People always talk about the grind or applying for funding — but when that magic hits, it just reminds you of the part of research that people don’t talk about, and it was really a great experience to have,” Liu adds.

    A fresh perspective

    “We’ve organized hackathons internally at our company and they are great for fostering innovation and creativity,” says Letizia Bordoli, senior AI product manager at Veridos, a German-based identity solutions company that provided this year’s challenge in Data Systems for Human Rights. “It is a great opportunity to connect with talented individuals and explore new ideas and solutions that we might not have thought about.”

    The challenge provided by Veridos was focused on finding innovative solutions to universal birth registration, something Bordoli says only benefited from the fact that the hackathon participants were from all over the world.

    “Many had local and firsthand knowledge about certain realities and challenges [posed by the lack of] birth registration,” Bordoli says. “It brings fresh perspectives to existing challenges, and it gave us an energy boost to try to bring innovative solutions that we may not have considered before.”

    New frontiers

    Alongside the housing and data systems for human rights challenges was a challenge in health, as well as a first-time opportunity to tackle an aerospace challenge in the area of space for environmental justice.

    “Space can be a very hard challenge category to do data-wise since a lot of data is proprietary, so this really developed over the last few months with us having to think about how we could do more with open-source data,” Green explains. “But I am glad we went the environmental route because it opened the challenge up to not only space enthusiasts, but also environment and climate people.”

    One of the participants to tackle this new challenge category was Yassine Elhallaoui, a system test engineer from Norway who specializes in AI solutions and has 16 years of experience working in the oil and gas fields. Elhallaoui was a member of Team EcoEquity, which proposed an increase in policies supporting the use of satellite data to ensure proper evaluation and increase water resiliency for vulnerable communities.

    “The hackathons I have participated in in the past were more technical,” Elhallaoui says. “Starting with [MIT Science and Technology Policy Institute Director Kristen Kulinowski’s] workshop about policy writers and the solutions they came up with, and the analysis they had to do … it really changed my perspective on what a hackathon can do.”

    “A policy hackathon is something that can make real changes in the world,” she adds. More