AI simulation gives people a glimpse of their potential future self

Have you ever wanted to travel through time to see what your future self might be like? Now, thanks to the power of generative AI, you can.

Researchers from MIT and elsewhere created a system that enables users to have an online, text-based conversation with an AI-generated simulation of their potential future self.

Dubbed Future You, the system is aimed at helping young people improve their sense of future self-continuity, a psychological concept that describes how connected a person feels with their future self.

Research has shown that a stronger sense of future self-continuity can positively influence how people make long-term decisions, from one’s likelihood to contribute to financial savings to their focus on achieving academic success.

Future You utilizes a large language model that draws on information provided by the user to generate a relatable, virtual version of the individual at age 60. This simulated future self can answer questions about what someone’s life in the future could be like, as well as offer advice or insights on the path they could follow.

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In an initial user study, the researchers found that after interacting with Future You for about half an hour, people reported decreased anxiety and felt a stronger sense of connection with their future selves.

“We don’t have a real time machine yet, but AI can be a type of virtual time machine. We can use this simulation to help people think more about the consequences of the choices they are making today,” says Pat Pataranutaporn, a recent Media Lab doctoral graduate who is actively developing a program to advance human-AI interaction research at MIT, and co-lead author of a paper on Future You.

Pataranutaporn is joined on the paper by co-lead authors Kavin Winson, a researcher at KASIKORN Labs; and Peggy Yin, a Harvard University undergraduate; as well as Auttasak Lapapirojn and Pichayoot Ouppaphan of KASIKORN Labs; and senior authors Monchai Lertsutthiwong, head of AI research at the KASIKORN Business-Technology Group; Pattie Maes, the Germeshausen Professor of Media, Arts, and Sciences and head of the Fluid Interfaces group at MIT, and Hal Hershfield, professor of marketing, behavioral decision making, and psychology at the University of California at Los Angeles. The research will be presented at the IEEE Conference on Frontiers in Education.

A realistic simulation

Studies about conceptualizing one’s future self go back to at least the 1960s. One early method aimed at improving future self-continuity had people write letters to their future selves. More recently, researchers utilized virtual reality goggles to help people visualize future versions of themselves.

But none of these methods were very interactive, limiting the impact they could have on a user.

With the advent of generative AI and large language models like ChatGPT, the researchers saw an opportunity to make a simulated future self that could discuss someone’s actual goals and aspirations during a normal conversation.

“The system makes the simulation very realistic. Future You is much more detailed than what a person could come up with by just imagining their future selves,” says Maes.

Users begin by answering a series of questions about their current lives, things that are important to them, and goals for the future.

The AI system uses this information to create what the researchers call “future self memories” which provide a backstory the model pulls from when interacting with the user.

For instance, the chatbot could talk about the highlights of someone’s future career or answer questions about how the user overcame a particular challenge. This is possible because ChatGPT has been trained on extensive data involving people talking about their lives, careers, and good and bad experiences.

The user engages with the tool in two ways: through introspection, when they consider their life and goals as they construct their future selves, and retrospection, when they contemplate whether the simulation reflects who they see themselves becoming, says Yin.

“You can imagine Future You as a story search space. You have a chance to hear how some of your experiences, which may still be emotionally charged for you now, could be metabolized over the course of time,” she says.

To help people visualize their future selves, the system generates an age-progressed photo of the user. The chatbot is also designed to provide vivid answers using phrases like “when I was your age,” so the simulation feels more like an actual future version of the individual.

The ability to take advice from an older version of oneself, rather than a generic AI, can have a stronger positive impact on a user contemplating an uncertain future, Hershfield says.

“The interactive, vivid components of the platform give the user an anchor point and take something that could result in anxious rumination and make it more concrete and productive,” he adds.

But that realism could backfire if the simulation moves in a negative direction. To prevent this, they ensure Future You cautions users that it shows only one potential version of their future self, and they have the agency to change their lives. Providing alternate answers to the questionnaire yields a totally different conversation.

“This is not a prophesy, but rather a possibility,” Pataranutaporn says.

Aiding self-development

To evaluate Future You, they conducted a user study with 344 individuals. Some users interacted with the system for 10-30 minutes, while others either interacted with a generic chatbot or only filled out surveys.

Participants who used Future You were able to build a closer relationship with their ideal future selves, based on a statistical analysis of their responses. These users also reported less anxiety about the future after their interactions. In addition, Future You users said the conversation felt sincere and that their values and beliefs seemed consistent in their simulated future identities.

“This work forges a new path by taking a well-established psychological technique to visualize times to come — an avatar of the future self — with cutting edge AI. This is exactly the type of work academics should be focusing on as technology to build virtual self models merges with large language models,” says Jeremy Bailenson, the Thomas More Storke Professor of Communication at Stanford University, who was not involved with this research.

Building off the results of this initial user study, the researchers continue to fine-tune the ways they establish context and prime users so they have conversations that help build a stronger sense of future self-continuity.

“We want to guide the user to talk about certain topics, rather than asking their future selves who the next president will be,” Pataranutaporn says.

They are also adding safeguards to prevent people from misusing the system. For instance, one could imagine a company creating a “future you” of a potential customer who achieves some great outcome in life because they purchased a particular product.

Moving forward, the researchers want to study specific applications of Future You, perhaps by enabling people to explore different careers or visualize how their everyday choices could impact climate change.

They are also gathering data from the Future You pilot to better understand how people use the system.

“We don’t want people to become dependent on this tool. Rather, we hope it is a meaningful experience that helps them see themselves and the world differently, and helps with self-development,” Maes says.

State of Supply Chain Sustainability report reveals growing investor pressure, challenges with emissions tracking

The MIT Center for Transportation and Logistics (MIT CTL) and the Council of Supply Chain Management Professionals (CSCMP) have released the 2024 State of Supply Chain Sustainability report, marking the fifth edition of this influential research. The report highlights how supply chain sustainability practices have evolved over the past five years, assessing their global implementation and implications for industries, professionals, and the environment.

This year’s report is based on four years of comprehensive international surveys with responses from over 7,000 supply chain professionals representing more than 80 countries, coupled with insights from executive interviews. It explores how external pressures on firms, such as the growing investor demand and climate regulations, are driving sustainability initiatives. However, it also reveals persistent gaps between companies’ sustainability goals and the actual investments required to achieve them.

“Over the past five years, we have seen supply chains face unprecedented global challenges. While companies have made strides, our analysis shows that many are still struggling to align their sustainability ambitions with real progress, particularly when it comes to tackling Scope 3 emissions,” says Josué Velázquez Martínez, MIT CTL research scientist and lead investigator. “Scope 3 emissions, which account for the vast majority of a company’s carbon footprint, remain a major hurdle due to the complexity of tracking emissions from indirect supply chain activities. The margin of error of the most common approach to estimate emissions are drastic, which disincentivizes companies to make more sustainable choices at the expense of investing in green alternatives.”

Among the key findings:

  • Increased pressure from investors: Over five years, pressure from investors to improve supply chain sustainability has grown by 25 percent, making it the fastest-growing driver of sustainability efforts.
  • Lack of readiness for net-zero goals: Although 67 percent of firms surveyed do not have a net-zero goal in place, those that do are often unprepared to meet them, especially when it comes to measuring and reducing Scope 3 emissions.
  • Company response to sustainability efforts in times of crisis: Companies react to different types of crises differently in regards to staying on track with their sustainable goals, whether it is a network disruption like the Covid-19 pandemic or economic turbulence.
  • Challenges with Scope 3 emissions: Despite significant efforts, Scope 3 emissions — which can account for up to 75 percent of a company’s total emissions — continue to be the most difficult to track and manage, due to the complexity of supplier networks and inconsistent data-sharing practices.

Mark Baxa, president and CEO of CSCMP, emphasized the importance of collaboration: “Businesses and consumers alike are putting pressure on us to source and supply products to live up to their social and environmental standards. The State of Supply Chain Sustainability 2024 provides a thorough analysis of our current understanding, along with valuable insights on how to improve our Scope 3 emissions accounting to have a greater impact on lowering our emissions.”

The report also underscores the importance of technological innovations, such as machine learning, advanced data analytics, and standardization to improve the accuracy of emissions tracking and help firms make data-driven sustainability decisions.

The 2024 State of Supply Chain Sustainability can be accessed online or in PDF format at sustainable.mit.edu.

The MIT CTL is a world leader in supply chain management research and education, with over 50 years of expertise. The center’s work spans industry partnerships, cutting-edge research, and the advancement of sustainable supply chain practices. CSCMP is the leading global association for supply chain professionals. Established in 1963, CSCMP provides its members with education, research, and networking opportunities to advance the field of supply chain management.

Aligning economic and regulatory frameworks for today’s nuclear reactor technology

Liam Hines ’22 didn’t move to Sarasota, Florida, until high school, but he’s a Floridian through and through. He jokes that he’s even got a floral shirt, what he calls a “Florida formal,” for every occasion.

Which is why it broke his heart when toxic red algae used to devastate the Sunshine State’s coastline, including at his favorite beach, Caspersen. The outbreak made headline news during his high school years, with the blooms destroying marine wildlife and adversely impacting the state’s tourism-driven economy.

In Florida, Hines says, environmental awareness is pretty high because everyday citizens are being directly impacted by climate change. After all, it’s hard not to worry when beautiful white sand beaches are covered in dead fish. Ongoing concerns about the climate cemented Hines’ resolve to pick a career that would have a strong “positive environmental impact.” He chose nuclear, as he saw it as “a green, low-carbon-emissions energy source with a pretty straightforward path to implementation.”

Liam Hines: Ensuring that nuclear policy keeps up with nuclear technology.

Undergraduate studies at MIT

Knowing he wanted a career in the sciences, Hines applied and got accepted to MIT for undergraduate studies in fall 2018. An orientation program hosted by the Department of Nuclear Science and Engineering (NSE) sold him on the idea of pursuing the field. “The department is just a really tight-knit community, and that really appealed to me,” Hines says.

During his undergraduate years, Hines realized he needed a job to pay part of his bills. “Instead of answering calls at the dorm front desk or working in the dining halls, I decided I’m going to become a licensed nuclear operator onsite,” he says. “Reactor operations offer so much hands-on experience with real nuclear systems. It doesn’t hurt that it pays better.” Becoming a licensed nuclear reactor operator is hard work, however, involving a year-long training process studying maintenance, operations, and equipment oversight. A bonus: The job, supervising the MIT Nuclear Reactor Laboratory, taught him the fundamentals of nuclear physics and engineering.

Always interested in research, Hines got an early start by exploring the regulatory challenges of advanced fusion systems. There have been questions related to licensing requirements and the safety consequences of the onsite radionuclide inventory. Hines’ undergraduate research work involved studying precedent for such fusion facilities and comparing them to experimental facilities such as Princeton University’s Tokamak Fusion Test Reactor.

Doctoral focus on legal and regulatory frameworks

When scientists want to make technologies as safe as possible, they have to do two things in concert: First they evaluate the safety of the technology, and then make sure legal and regulatory structures take into account the evolution of these advanced technologies. Hines is taking such a two-pronged approach to his doctoral work on nuclear fission systems.

Under the guidance of Professor Koroush Shirvan, Hines is conducting systems modeling of various reactor cores that include graphite, and simulating operations under long time spans. He then studies radionuclide transport from low-level waste facilities — the consequences of offsite storage after 50 or 100 or even 10,000 years of storage. The work has to make sure to hit safety and engineering margins, but also tread a fine line. “You want to make sure you’re not over-engineering systems and adding undue cost, but also making sure to assess the unique hazards of these advanced technologies as accurately as possible,” Hines says.

On a parallel track, under Professor Haruko Wainwright’s advisement, Hines is applying the current science on radionuclide geochemistry to track radionuclide wastes and map their profile for hazards. One of the challenges fission reactors face is that existing low-level waste regulations were fine-tuned to old reactors. Regulations have not kept up: “Now that we have new technologies with new wastes, some of the hazards of the new waste are completely missed by existing standards,” Hines says. He is working to seal these gaps.

A philosophy-driven outlook

Hines is grateful for the dynamic learning environment at NSE. “A lot of the faculty have that go-getter attitude,” he points out, impressed by the entrepreneurial spirit on campus. “It’s made me confident to really tackle the things that I care about.”

An ethics class as an undergraduate made Hines realize there were discussions in class he could apply to the nuclear realm, especially when it came to teasing apart the implications of the technology — where the devices would be built and who they would serve. He eventually went on to double-major in NSE and philosophy.

The framework style of reading and reasoning involved in studying philosophy is particularly relevant in his current line of work, where he has to extract key points regarding nuclear regulatory issues. Much like philosophy discussions today that involve going over material that has been discussed for centuries and framing them through new perspectives, nuclear regulatory issues too need to take the long view.

“In philosophy, we have to insert ourselves into very large conversations. Similarly, in nuclear engineering, you have to understand how to take apart the discourse that’s most relevant to your research and frame it,” Hines says. This technique is especially necessary because most of the time the nuclear regulatory issues might seem like wading in the weeds of nitty-gritty technical matters, but they can have a huge impact on the public and public perception, Hines adds.

As for Florida, Hines visits every chance he can get. The red tide still surfaces but not as consistently as it once did. And since he started his job as a nuclear operator in his undergraduate days, Hines has progressed to senior reactor operator. This time around he gets to sign off on the checklists. “It’s much like when I was shift lead at Dunkin’ Donuts in high school,” Hines says, “everyone is kind of doing the same thing, but you get to be in charge for the afternoon.”

AI pareidolia: Can machines spot faces in inanimate objects?

In 1994, Florida jewelry designer Diana Duyser discovered what she believed to be the Virgin Mary’s image in a grilled cheese sandwich, which she preserved and later auctioned for $28,000. But how much do we really understand about pareidolia, the phenomenon of seeing faces and patterns in objects when they aren’t really there? 

A new study from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) delves into this phenomenon, introducing an extensive, human-labeled dataset of 5,000 pareidolic images, far surpassing previous collections. Using this dataset, the team discovered several surprising results about the differences between human and machine perception, and how the ability to see faces in a slice of toast might have saved your distant relatives’ lives.

“Face pareidolia has long fascinated psychologists, but it’s been largely unexplored in the computer vision community,” says Mark Hamilton, MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead researcher on the work. “We wanted to create a resource that could help us understand how both humans and AI systems process these illusory faces.”

So what did all of these fake faces reveal? For one, AI models don’t seem to recognize pareidolic faces like we do. Surprisingly, the team found that it wasn’t until they trained algorithms to recognize animal faces that they became significantly better at detecting pareidolic faces. This unexpected connection hints at a possible evolutionary link between our ability to spot animal faces — crucial for survival — and our tendency to see faces in inanimate objects. “A result like this seems to suggest that pareidolia might not arise from human social behavior, but from something deeper: like quickly spotting a lurking tiger, or identifying which way a deer is looking so our primordial ancestors could hunt,” says Hamilton.

A row of five photos of animal faces atop five photos of inanimate objects that look like faces

Another intriguing discovery is what the researchers call the “Goldilocks Zone of Pareidolia,” a class of images where pareidolia is most likely to occur. “There’s a specific range of visual complexity where both humans and machines are most likely to perceive faces in non-face objects,” William T. Freeman, MIT professor of electrical engineering and computer science and principal investigator of the project says. “Too simple, and there’s not enough detail to form a face. Too complex, and it becomes visual noise.”

To uncover this, the team developed an equation that models how people and algorithms detect illusory faces.  When analyzing this equation, they found a clear “pareidolic peak” where the likelihood of seeing faces is highest, corresponding to images that have “just the right amount” of complexity. This predicted “Goldilocks zone” was then validated in tests with both real human subjects and AI face detection systems.

3 photos of clouds above 3 photos of a fruit tart. The left photo of each is “Too Simple” to perceive a face; the middle photo is “Just Right,” and the last photo is “Too Complex"

This new dataset, “Faces in Things,” dwarfs those of previous studies that typically used only 20-30 stimuli. This scale allowed the researchers to explore how state-of-the-art face detection algorithms behaved after fine-tuning on pareidolic faces, showing that not only could these algorithms be edited to detect these faces, but that they could also act as a silicon stand-in for our own brain, allowing the team to ask and answer questions about the origins of pareidolic face detection that are impossible to ask in humans. 

To build this dataset, the team curated approximately 20,000 candidate images from the LAION-5B dataset, which were then meticulously labeled and judged by human annotators. This process involved drawing bounding boxes around perceived faces and answering detailed questions about each face, such as the perceived emotion, age, and whether the face was accidental or intentional. “Gathering and annotating thousands of images was a monumental task,” says Hamilton. “Much of the dataset owes its existence to my mom,” a retired banker, “who spent countless hours lovingly labeling images for our analysis.”

The study also has potential applications in improving face detection systems by reducing false positives, which could have implications for fields like self-driving cars, human-computer interaction, and robotics. The dataset and models could also help areas like product design, where understanding and controlling pareidolia could create better products. “Imagine being able to automatically tweak the design of a car or a child’s toy so it looks friendlier, or ensuring a medical device doesn’t inadvertently appear threatening,” says Hamilton.

“It’s fascinating how humans instinctively interpret inanimate objects with human-like traits. For instance, when you glance at an electrical socket, you might immediately envision it singing, and you can even imagine how it would ‘move its lips.’ Algorithms, however, don’t naturally recognize these cartoonish faces in the same way we do,” says Hamilton. “This raises intriguing questions: What accounts for this difference between human perception and algorithmic interpretation? Is pareidolia beneficial or detrimental? Why don’t algorithms experience this effect as we do? These questions sparked our investigation, as this classic psychological phenomenon in humans had not been thoroughly explored in algorithms.”

As the researchers prepare to share their dataset with the scientific community, they’re already looking ahead. Future work may involve training vision-language models to understand and describe pareidolic faces, potentially leading to AI systems that can engage with visual stimuli in more human-like ways.

“This is a delightful paper! It is fun to read and it makes me think. Hamilton et al. propose a tantalizing question: Why do we see faces in things?” says Pietro Perona, the Allen E. Puckett Professor of Electrical Engineering at Caltech, who was not involved in the work. “As they point out, learning from examples, including animal faces, goes only half-way to explaining the phenomenon. I bet that thinking about this question will teach us something important about how our visual system generalizes beyond the training it receives through life.”

Hamilton and Freeman’s co-authors include Simon Stent, staff research scientist at the Toyota Research Institute; Ruth Rosenholtz, principal research scientist in the Department of Brain and Cognitive Sciences, NVIDIA research scientist, and former CSAIL member; and CSAIL affiliates postdoc Vasha DuTell, Anne Harrington MEng ’23, and Research Scientist Jennifer Corbett. Their work was supported, in part, by the National Science Foundation and the CSAIL MEnTorEd Opportunities in Research (METEOR) Fellowship, while being sponsored by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator. The MIT SuperCloud and Lincoln Laboratory Supercomputing Center provided HPC resources for the researchers’ results.

This work is being presented this week at the European Conference on Computer Vision.

Helping robots zero in on the objects that matter

Imagine having to straighten up a messy kitchen, starting with a counter littered with sauce packets. If your goal is to wipe the counter clean, you might sweep up the packets as a group. If, however, you wanted to first pick out the mustard packets before throwing the rest away, you would sort more discriminately, by sauce type. And if, among the mustards, you had a hankering for Grey Poupon, finding this specific brand would entail a more careful search.

MIT engineers have developed a method that enables robots to make similarly intuitive, task-relevant decisions.

The team’s new approach, named Clio, enables a robot to identify the parts of a scene that matter, given the tasks at hand. With Clio, a robot takes in a list of tasks described in natural language and, based on those tasks, it then determines the level of granularity required to interpret its surroundings and “remember” only the parts of a scene that are relevant.

In real experiments ranging from a cluttered cubicle to a five-story building on MIT’s campus, the team used Clio to automatically segment a scene at different levels of granularity, based on a set of tasks specified in natural-language prompts such as “move rack of magazines” and “get first aid kit.”

The team also ran Clio in real-time on a quadruped robot. As the robot explored an office building, Clio identified and mapped only those parts of the scene that related to the robot’s tasks (such as retrieving a dog toy while ignoring piles of office supplies), allowing the robot to grasp the objects of interest.

Clio is named after the Greek muse of history, for its ability to identify and remember only the elements that matter for a given task. The researchers envision that Clio would be useful in many situations and environments in which a robot would have to quickly survey and make sense of its surroundings in the context of its given task.

“Search and rescue is the motivating application for this work, but Clio can also power domestic robots and robots working on a factory floor alongside humans,” says Luca Carlone, associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), principal investigator in the Laboratory for Information and Decision Systems (LIDS), and director of the MIT SPARK Laboratory. “It’s really about helping the robot understand the environment and what it has to remember in order to carry out its mission.”

The team details their results in a study appearing today in the journal Robotics and Automation Letters. Carlone’s co-authors include members of the SPARK Lab: Dominic Maggio, Yun Chang, Nathan Hughes, and Lukas Schmid; and members of MIT Lincoln Laboratory: Matthew Trang, Dan Griffith, Carlyn Dougherty, and Eric Cristofalo.

Open fields

Huge advances in the fields of computer vision and natural language processing have enabled robots to identify objects in their surroundings. But until recently, robots were only able to do so in “closed-set” scenarios, where they are programmed to work in a carefully curated and controlled environment, with a finite number of objects that the robot has been pretrained to recognize.

In recent years, researchers have taken a more “open” approach to enable robots to recognize objects in more realistic settings. In the field of open-set recognition, researchers have leveraged deep-learning tools to build neural networks that can process billions of images from the internet, along with each image’s associated text (such as a friend’s Facebook picture of a dog, captioned “Meet my new puppy!”).

From millions of image-text pairs, a neural network learns from, then identifies, those segments in a scene that are characteristic of certain terms, such as a dog. A robot can then apply that neural network to spot a dog in a totally new scene.

But a challenge still remains as to how to parse a scene in a useful way that is relevant for a particular task.

“Typical methods will pick some arbitrary, fixed level of granularity for determining how to fuse segments of a scene into what you can consider as one ‘object,’” Maggio says. “However, the granularity of what you call an ‘object’ is actually related to what the robot has to do. If that granularity is fixed without considering the tasks, then the robot may end up with a map that isn’t useful for its tasks.”

Information bottleneck

With Clio, the MIT team aimed to enable robots to interpret their surroundings with a level of granularity that can be automatically tuned to the tasks at hand.

For instance, given a task of moving a stack of books to a shelf, the robot should be able to  determine that the entire stack of books is the task-relevant object. Likewise, if the task were to move only the green book from the rest of the stack, the robot should distinguish the green book as a single target object and disregard the rest of the scene — including the other books in the stack.

The team’s approach combines state-of-the-art computer vision and large language models comprising neural networks that make connections among millions of open-source images and semantic text. They also incorporate mapping tools that automatically split an image into many small segments, which can be fed into the neural network to determine if certain segments are semantically similar. The researchers then leverage an idea from classic information theory called the “information bottleneck,” which they use to compress a number of image segments in a way that picks out and stores segments that are semantically most relevant to a given task.

“For example, say there is a pile of books in the scene and my task is just to get the green book. In that case we push all this information about the scene through this bottleneck and end up with a cluster of segments that represent the green book,” Maggio explains. “All the other segments that are not relevant just get grouped in a cluster which we can simply remove. And we’re left with an object at the right granularity that is needed to support my task.”

The researchers demonstrated Clio in different real-world environments.

“What we thought would be a really no-nonsense experiment would be to run Clio in my apartment, where I didn’t do any cleaning beforehand,” Maggio says.

The team drew up a list of natural-language tasks, such as “move pile of clothes” and then applied Clio to images of Maggio’s cluttered apartment. In these cases, Clio was able to quickly segment scenes of the apartment and feed the segments through the Information Bottleneck algorithm to identify those segments that made up the pile of clothes.

They also ran Clio on Boston Dynamic’s quadruped robot, Spot. They gave the robot a list of tasks to complete, and as the robot explored and mapped the inside of an office building, Clio ran in real-time on an on-board computer mounted to Spot, to pick out segments in the mapped scenes that visually relate to the given task. The method generated an overlaying map showing just the target objects, which the robot then used to approach the identified objects and physically complete the task.

“Running Clio in real-time was a big accomplishment for the team,” Maggio says. “A lot of prior work can take several hours to run.”

Going forward, the team plans to adapt Clio to be able to handle higher-level tasks and build upon recent advances in photorealistic visual scene representations.

“We’re still giving Clio tasks that are somewhat specific, like ‘find deck of cards,’” Maggio says. “For search and rescue, you need to give it more high-level tasks, like ‘find survivors,’ or ‘get power back on.’ So, we want to get to a more human-level understanding of how to accomplish more complex tasks.”

This research was supported, in part, by the U.S. National Science Foundation, the Swiss National Science Foundation, MIT Lincoln Laboratory, the U.S. Office of Naval Research, and the U.S. Army Research Lab Distributed and Collaborative Intelligent Systems and Technology Collaborative Research Alliance.

Where flood policy helps most — and where it could do more

Flooding, including the devastation caused recently by Hurricane Helene, is responsible for $5 billion in annual damages in the U.S. That’s more than any other type of weather-related extreme event.

To address the problem, the federal government instituted a program in 1990 that helps reduce flood insurance costs in communities enacting measures to better handle flooding. If, say, a town preserves open space as a buffer against coastal flooding, or develops better stormwater management, area policy owners get discounts on their premiums. Studies show the program works well: It has reduced overall flood damage in participating communities.

However, a new study led by an MIT researcher shows that the effects of the program differ greatly from place to place. For instance, higher-population communities, which likely have more means to introduce flood defenses, benefit more than smaller communities, to the tune of about $4,000 per insured household.

“When we evaluate it, the effects of the same policy vary widely among different types of communities,” says study co-author Lidia Cano Pecharromán, a PhD candidate in MIT’s Department of Urban Studies and Planning.

Referring to climate and environmental justice concerns, she adds: “It’s important to understand not just if a policy is effective, but who is benefitting, so that we can make necessary adjustments and reach all the targets we want to reach.”

The paper, “Exposing Disparities in Flood Adaptation for Equitable Future Interventions in the USA,” is published today in Nature Communications. The authors are Cano Pecharromán and ChangHoon Hahn, an associate research scholar at Princeton University.

Able to afford help

The program in question was developed by the Federal Emergency Management Agency (FEMA), which has a division, the Flood Insurance Mitigation Administration, focusing on this issue. In 1990, FEMA initiated the National Flood Insurance Program’s Community Rating System, which incentivizes communities to enact measures that help prevent or reduce flooding.

Communities can engage in a broad set of related activities, including floodplain mapping, preservation of open spaces, stormwater management activities, creating flood warning systems, or even developing public information and participation programs. In exchange, area residents receive a discount on their flood insurance premium rates.

To conduct the study, the researchers examined 2.5 million flood insurance claims filed with FEMA since then. They also examined U.S. Census Bureau data to analyze demographic and economic data about communities, and incorporated flood risk data from the First Street Foundation.

By comparing over 1,500 communities in the FEMA program, the researchers were able to quantify its different relative effects — depending on community characteristics such as population, race, income or flood risk. For instance, higher-income communities seem better able to make more flood-control and mitigation investments, earning better FEMA ratings and, ultimately, enacting more effective measures.

“You see some positive effects for low-income communities, but as the risks go up, these disappear, while only high-income communities continue seeing these positive effects,” says Cano Pecharromán. “They are likely able to afford measures that handle a higher risk indices for flooding.”

Similarly, the researchers found, communities with higher overall levels of education fare better from the flood-insurance program, with about $2,000 more in savings per individual policy than communities with lower levels of education. One way or another, communities with more assets in the first place — size, wealth, education — are better able to deploy or hire the civic and technical expertise necessary to enact more best practices against flood damage.

And even among lower-income communities in the program, communities with less population diversity see greater effectiveness from their flood program activities, realizing a gain of about $6,000 per household compared to communities where racial and ethnic minorities are predominant.

“These are substantial effects, and we should consider these things when making decisions and reviewing if our climate adaptation policies work,” Cano Pecharromán says.

An even larger number of communities is not in the FEMA program at all. The study identified 14,729 unique U.S. communities with flood issues. Many of those are likely lacking the capacity to engage on flooding issues the way even the lower-ranked communities within the FEMA program have at least taken some action so far.

“If we are able to consider all the communities that are not in the program because they can’t afford to do the basics, we would likely see that the effects are even larger among different communities,” Cano Pecharromán says.

Getting communities started

To make the program more effective for more people, Cano Pecharromán suggests that the federal government should consider how to help communities enact flood-control and mitigation measures in the first place.

“When we set out these kinds of policies, we need to consider how certain types of communities might need help with implementation,” she says.

Methodologically, the researchers arrived at their conclusions using an advanced statistical approach that Hahn, who is an astrophysicist by training, has applied to the study of dark energy and galaxies. Instead of finding one “average treatment effect” of the FEMA program across all participating communities, they quantified the program’s impact while subdividing the set of participating set of communities according to their characteristics.

“We are able to calculate the causal effect of [the program], not as an average, which can hide these inequalities, but at every given level of the specific characteristic of communities we’re looking at, different levels of income, different levels of education, and more,” Cano Pecharromán says.

Government officials have seen Cano Pecharromán present the preliminary findings at meetings, and expressed interest in the results. Currently, she is also working on a follow-up study, which aims to pinpoint which types of local flood-mitigation programs provide the biggest benefits for local communities.

Support for the research was provided, in part, by the La Caixa Foundation, the MIT Martin Family Society of Fellows for Sustainability, and the AI Accelerator program of the Schmidt Futures Foundation.

MIT launches new Music Technology and Computation Graduate Program

A new, multidisciplinary MIT graduate program in music technology and computation will feature faculty, labs, and curricula from across the Institute.

The program is a collaboration between the Music and Theater Arts Section in the School of Humanities, Arts, and Social Sciences (SHASS); Department of Electrical Engineering and Computer Science (EECS) in the School of Engineering; and the MIT Schwarzman College of Computing.

“The launch of a new graduate program in music technology strikes me as both a necessary and a provocative gesture — an important leap in an era being rapidly redefined by exponential growth in computation, artificial intelligence, and human-computer interactions of every conceivable kind,” says Jay Scheib,​​ head of the MIT Music and Theater Arts Section and the Class of 1949 Professor.

“Music plays an elegant role at the fore of a remarkable convergence of art and technology,” adds Scheib. “It’s the right time to launch this program and if not at MIT, then where?”

MIT’s practitioners define music technology as the field of scientific inquiry where they study, discover, and develop new computational approaches to music that include music information retrieval; artificial intelligence; machine learning; generative algorithms; interaction and performance systems; digital instrument design; conceptual and perceptual modeling of music; acoustics; audio signal processing; and software development for creative expression and music applications.

Eran Egozy, professor of the practice in music technology and one of the program leads, says MIT’s focus is technical research in music technology that always centers the humanistic and artistic aspects of making music.

“There are so many MIT students who are fabulous musicians,” says Egozy. “We’ll approach music technology as computer scientists, mathematicians, and musicians.”

With the launch of this new program — an offering alongside those available in MIT’s Media Lab and elsewhere — Egozy sees MIT becoming the obvious destination for students interested in music and computation study, preparing high-impact graduates for roles in academia and industry, while also helping mold creative, big-picture thinkers who can tackle large challenges.

Investigating big ideas

The program will encompass two master’s degrees and a PhD:

  • The Master of Science (MS) is a two-semester, thesis-based program available only to MIT undergraduates. One semester of fellowship is automatically awarded to all admitted students. The first class will enroll in fall 2025.
  • The Master of Applied Science (MAS) is a two-semester, coursework-based program available to all students. One semester of fellowship funding is automatically awarded to all admitted students. Applications for this program will open in fall 2025.
  • The PhD program is available to all students, who would apply to MIT’s School of Engineering.

Anna Huang, a new MIT assistant professor who holds a shared faculty position between the MIT Music and Theater Arts Section and the MIT Schwarzman College of Computing, is collaborating with Egozy to develop and launch the program. Huang arrived at MIT this fall after spending eight years with Magenta at Google Brain and DeepMind, spearheading efforts in generative modeling, reinforcement learning, and human-computer interaction to support human-AI partnerships in music-making.

“As a composer turned AI researcher who specializes in generative music technology, my long-term goal is to develop AI systems that can shed new light on how we understand, learn, and create music, and to learn from interactions between musicians in order to transform how we approach human-AI collaboration,” says Huang. “This new program will let us further investigate how musical applications can illuminate problems in understanding neural networks, for example.”

MIT’s new Edward and Joyce Linde Music Building, featuring enhanced music technology spaces, will also help transform music education with versatile performance venues and optimized rehearsal facilities.

A natural home for music technology

MIT’s world-class, top-ranked engineering program, combined with its focus on computation and its conservatory-level music education offerings, makes the Institute a natural home for the continued expansion of music technology education.

The collaborative nature of the new program is the latest example of interdisciplinary work happening across the Institute.

“I am thrilled that the School of Engineering is partnering with the MIT Music and Theater Arts Section on this important initiative, which represents the convergence of various engineering areas — such as AI and design — with music,” says Anantha Chandrakasan, dean of the School of Engineering, chief innovation and strategy officer, and the Vannevar Bush Professor of EECS. “I can’t wait to see the innovative projects the students will create and how they will drive this new field forward.”

“Everyone on campus knows that MIT is a great place to do music. But I want people to come to MIT because of what we do in music,” says Agustin Rayo, the Kenan Sahin Dean of SHASS. “This outstanding collaboration with the Schwarzman College of Computing and the School of Engineering will make that dream a reality, by bringing together the world’s best engineers with our extraordinary musicians to create the next generation of music technologies.”

“The new master’s program offers students an unparalleled opportunity to explore the intersection of music and technology,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of EECS. “It equips them with a deep understanding of this confluence, preparing them to advance new approaches to computational models of music and be at the forefront of an evolving area.” 

New security protocol shields data from attackers during cloud-based computation

New security protocol shields data from attackers during cloud-based computation

Deep-learning models are being used in many fields, from health care diagnostics to financial forecasting. However, these models are so computationally intensive that they require the use of powerful cloud-based servers.

This reliance on cloud computing poses significant security risks, particularly in areas like health care, where hospitals may be hesitant to use AI tools to analyze confidential patient data due to privacy concerns.

To tackle this pressing issue, MIT researchers have developed a security protocol that leverages the quantum properties of light to guarantee that data sent to and from a cloud server remain secure during deep-learning computations.

By encoding data into the laser light used in fiber optic communications systems, the protocol exploits the fundamental principles of quantum mechanics, making it impossible for attackers to copy or intercept the information without detection.

Moreover, the technique guarantees security without compromising the accuracy of the deep-learning models. In tests, the researcher demonstrated that their protocol could maintain 96 percent accuracy while ensuring robust security measures.

“Deep learning models like GPT-4 have unprecedented capabilities but require massive computational resources. Our protocol enables users to harness these powerful models without compromising the privacy of their data or the proprietary nature of the models themselves,” says Kfir Sulimany, an MIT postdoc in the Research Laboratory for Electronics (RLE) and lead author of a paper on this security protocol.

Sulimany is joined on the paper by Sri Krishna Vadlamani, an MIT postdoc; Ryan Hamerly, a former postdoc now at NTT Research, Inc.; Prahlad Iyengar, an electrical engineering and computer science (EECS) graduate student; and senior author Dirk Englund, a professor in EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE. The research was recently presented at Annual Conference on Quantum Cryptography.

A two-way street for security in deep learning

The cloud-based computation scenario the researchers focused on involves two parties — a client that has confidential data, like medical images, and a central server that controls a deep learning model.

The client wants to use the deep-learning model to make a prediction, such as whether a patient has cancer based on medical images, without revealing information about the patient.

In this scenario, sensitive data must be sent to generate a prediction. However, during the process the patient data must remain secure.

Also, the server does not want to reveal any parts of the proprietary model that a company like OpenAI spent years and millions of dollars building.

“Both parties have something they want to hide,” adds Vadlamani.

In digital computation, a bad actor could easily copy the data sent from the server or the client.

Quantum information, on the other hand, cannot be perfectly copied. The researchers leverage this property, known as the no-cloning principle, in their security protocol.

For the researchers’ protocol, the server encodes the weights of a deep neural network into an optical field using laser light.

A neural network is a deep-learning model that consists of layers of interconnected nodes, or neurons, that perform computation on data. The weights are the components of the model that do the mathematical operations on each input, one layer at a time. The output of one layer is fed into the next layer until the final layer generates a prediction.

The server transmits the network’s weights to the client, which implements operations to get a result based on their private data. The data remain shielded from the server.

At the same time, the security protocol allows the client to measure only one result, and it prevents the client from copying the weights because of the quantum nature of light.

Once the client feeds the first result into the next layer, the protocol is designed to cancel out the first layer so the client can’t learn anything else about the model.

“Instead of measuring all the incoming light from the server, the client only measures the light that is necessary to run the deep neural network and feed the result into the next layer. Then the client sends the residual light back to the server for security checks,” Sulimany explains.

Due to the no-cloning theorem, the client unavoidably applies tiny errors to the model while measuring its result. When the server receives the residual light from the client, the server can measure these errors to determine if any information was leaked. Importantly, this residual light is proven to not reveal the client data.

A practical protocol

Modern telecommunications equipment typically relies on optical fibers to transfer information because of the need to support massive bandwidth over long distances. Because this equipment already incorporates optical lasers, the researchers can encode data into light for their security protocol without any special hardware.

When they tested their approach, the researchers found that it could guarantee security for server and client while enabling the deep neural network to achieve 96 percent accuracy.

The tiny bit of information about the model that leaks when the client performs operations amounts to less than 10 percent of what an adversary would need to recover any hidden information. Working in the other direction, a malicious server could only obtain about 1 percent of the information it would need to steal the client’s data.

“You can be guaranteed that it is secure in both ways — from the client to the server and from the server to the client,” Sulimany says.

“A few years ago, when we developed our demonstration of distributed machine learning inference between MIT’s main campus and MIT Lincoln Laboratory, it dawned on me that we could do something entirely new to provide physical-layer security, building on years of quantum cryptography work that had also been shown on that testbed,” says Englund. “However, there were many deep theoretical challenges that had to be overcome to see if this prospect of privacy-guaranteed distributed machine learning could be realized. This didn’t become possible until Kfir joined our team, as Kfir uniquely understood the experimental as well as theory components to develop the unified framework underpinning this work.”

In the future, the researchers want to study how this protocol could be applied to a technique called federated learning, where multiple parties use their data to train a central deep-learning model. It could also be used in quantum operations, rather than the classical operations they studied for this work, which could provide advantages in both accuracy and security.

“This work combines in a clever and intriguing way techniques drawing from fields that do not usually meet, in particular, deep learning and quantum key distribution. By using methods from the latter, it adds a security layer to the former, while also allowing for what appears to be a realistic implementation. This can be interesting for preserving privacy in distributed architectures. I am looking forward to seeing how the protocol behaves under experimental imperfections and its practical realization,” says Eleni Diamanti, a CNRS research director at Sorbonne University in Paris, who was not involved with this work.

This work was supported, in part, by the Israeli Council for Higher Education and the Zuckerman STEM Leadership Program.

How social structure influences the way people share money

People around the globe often depend on informal financial arrangements, borrowing and lending money through social networks. Understanding this sheds light on local economies and helps fight poverty.

Now, a study co-authored by an MIT economist illuminates a striking case of informal finance: In East Africa, money moves in very different patterns depending on whether local societies are structured around family units or age-based groups.

That is, while much of the world uses the extended family as a basic social unit, hundreds of millions of people live in societies with stronger age-based cohorts. In these cases, people are initiated into adulthood together and maintain closer social ties with each other than with extended family. That affects their finances, too.

“We found there are major impacts in that social structure really does matter for how people form financial ties,” says Jacob Moscona, an MIT economist and co-author of a newly published paper detailing the results.

He adds: “In age-based societies when someone gets a cash transfer, the money flows in a big way to other members of their age cohort but not to other [younger or older] members of an extended family. And you see the exact opposite pattern in kin-based groups, where money is transferred within the family but not the age cohort.”

This leads to measurable health effects. In kin-based societies, grandparents often share their pension payments with grandchildren. In Uganda, the study reveals, an additional year of pension payments to a senior citizen in a kin-based society reduces the likelihood of child malnourishment by 5.5 percent, compared to an age-based society where payments are less likely to move across generations.

The paper, “Age Set versus Kin: Culture and Financial Ties in East Africa,” is published in the September issue of the American Economic Review. The authors are Moscona, the 3M Career Development Assistant Professor of Economics in MIT’s Department of Economics; and Awa Ambra Seck, an assistant professor at Harvard Business School.

Studying informal financial arrangements has long been an important research domain for economists. MIT Professor Robert Townsend, for one, helped advance this area of scholarship with innovative studies of finances in rural Thailand.

At the same time, the specific matter of analyzing how age-based social groups function, in comparison to the more common kin-based groups, has tended to be addressed more by anthropologists than economists. Among the Maasai people in Northern Kenya, for example, anthropologists have observed that age-group friends have closer ties to each other than anyone apart from a spouse and children. Maasai age-group cohorts frequently share food and lodging, and more extensively than they do even with siblings. The current study adds economic data points to this body of knowledge.

To conduct the research, the scholars first analyzed the Kenyan government’s Hunger Safety Net Program (HSNP), a cash transfer project initiated in 2009 covering 48 locations in Northern Kenya. The program included both age-based and kin-based social groups, allowing for a comparison of its effects.

In age-based societies, the study shows, there was a spillover in spending by HSNP recipients on others in the age cohort, with zero additional cash flows to those in other generations; in kin-based societies, they also found a spillover across generations, but without informal cash flows otherwise.

In Uganda, where both kin-based and age-based societies exist, the researchers studied the national roll-out of the Senior Citizen Grant (SCG) program, initiated in 2011, which consists of a monthly cash transfer to seniors of about $7.50, equivalent to roughly 20 percent of per-capita spending. Similar programs exist or are being rolled out across sub-Saharan Africa, including in regions where age-based organization is common.

Here again, the researchers found financial flows aligned to kin-based and age-based social ties. In particular, they show that the pension program had large positive effects on child nutrition in kin-based households, where ties across generations are strong; the team found zero evidence of these effects in age-based societies.

“These policies had vastly different effects on these two groups, on account of the very different structure of financial ties,” Moscona says.

To Moscona, there are at least two large reasons to evaluate the variation between these financial flows: understanding society more thoroughly and rethinking how to design social programs in these circumstances.

“It’s telling us something about how the world works, that social structure is really important for shaping these [financial] relationships,” Moscona says. “But it also has a big potential impact on policy.”

After all, if a social policy is designed to help limit childhood poverty, or senior poverty, experts will want to know how the informal flow of cash in a society interacts with it. The current study shows that understanding social structure should be a high-order concern for making policies more effective.

“In these two ways of organizing society, different people are on average more vulnerable,” Moscona says. “In the kin-based groups, because the young and the old share with each other, you don’t see as much inequality across generations. But in age-based groups, the young and the old are left systematically more vulnerable. And in kin-based groups, some entire families are doing much worse than others, while in age-based societies the age sets often cut across lineages or extended families, making them more equal. That’s worth considering if you’re thinking about poverty reduction.”

Mars’ missing atmosphere could be hiding in plain sight

Mars wasn’t always the cold desert we see today. There’s increasing evidence that water once flowed on the Red Planet’s surface, billions of years ago. And if there was water, there must also have been a thick atmosphere to keep that water from freezing. But sometime around 3.5 billion years ago, the water dried up, and the air, once heavy with carbon dioxide, dramatically thinned, leaving only the wisp of an atmosphere that clings to the planet today.

Where exactly did Mars’ atmosphere go? This question has been a central mystery of Mars’ 4.6-billion-year history.

For two MIT geologists, the answer may lie in the planet’s clay. In a paper appearing today in Science Advances, they propose that much of Mars’ missing atmosphere could be locked up in the planet’s clay-covered crust.

The team makes the case that, while water was present on Mars, the liquid could have trickled through certain rock types and set off a slow chain of reactions that progressively drew carbon dioxide out of the atmosphere and converted it into methane — a form of carbon that could be stored for eons in the planet’s clay surface.

Similar processes occur in some regions on Earth. The researchers used their knowledge of interactions between rocks and gases on Earth and applied that to how similar processes could play out on Mars. They found that, given how much clay is estimated to cover Mars’ surface, the planet’s clay could hold up to 1.7 bar of carbon dioxide, which would be equivalent to around 80 percent of the planet’s initial, early atmosphere.

It’s possible that this sequestered Martian carbon could one day be recovered and converted into propellant to fuel future missions between Mars and Earth, the researchers propose.

“Based on our findings on Earth, we show that similar processes likely operated on Mars, and that copious amounts of atmospheric CO2 could have transformed to methane and been sequestered in clays,” says study author Oliver Jagoutz, professor of geology in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “This methane could still be present and maybe even used as an energy source on Mars in the future.”

The study’s lead author is recent EAPS graduate Joshua Murray PhD ’24.

In the folds

Jagoutz’ group at MIT seeks to identify the geologic processes and interactions that drive the evolution of Earth’s lithosphere — the hard and brittle outer layer that includes the crust and upper mantle, where tectonic plates lie.

In 2023, he and Murray focused on a type of surface clay mineral called smectite, which is known to be a highly effective trap for carbon. Within a single grain of smectite are a multitude of folds, within which carbon can sit undisturbed for billions of years. They showed that smectite on Earth was likely a product of tectonic activity, and that, once exposed at the surface, the clay minerals acted to draw down and store enough carbon dioxide from the atmosphere to cool the planet over millions of years.

Soon after the team reported their results, Jagoutz happened to look at a map of the surface of Mars and realized that much of that planet’s surface was covered in the same smectite clays. Could the clays have had a similar carbon-trapping effect on Mars, and if so, how much carbon could the clays hold?

“We know this process happens, and it is well-documented on Earth. And these rocks and clays exist on Mars,” Jagoutz says. “So, we wanted to try and connect the dots.”

“Every nook and cranny”

Unlike on Earth, where smectite is a consequence of continental plates shifting and uplifting to bring rocks from the mantle to the surface, there is no such tectonic activity on Mars. The team looked for ways in which the clays could have formed on Mars, based on what scientists know of the planet’s history and composition.

For instance, some remote measurements of Mars’ surface suggest that at least part of the planet’s crust contains ultramafic igneous rocks, similar to those that produce smectites through weathering on Earth. Other observations reveal geologic patterns similar to terrestrial rivers and tributaries, where water could have flowed and reacted with the underlying rock.

Jagoutz and Murray wondered whether water could have reacted with Mars’ deep ultramafic rocks in a way that would produce the clays that cover the surface today. They developed a simple model of rock chemistry, based on what is known of how igneous rocks interact with their environment on Earth.

They applied this model to Mars, where scientists believe the crust is mostly made up of igneous rock that is rich in the mineral olivine. The team used the model to estimate the changes that olivine-rich rock might undergo, assuming that water existed on the surface for at least a billion years, and the atmosphere was thick with carbon dioxide.

“At this time in Mars’ history, we think CO2 is everywhere, in every nook and cranny, and water percolating through the rocks is full of CO2 too,” Murray says.

Over about a billion years, water trickling through the crust would have slowly reacted with olivine — a mineral that is rich in a reduced form of iron. Oxygen molecules in water would have bound to the iron, releasing hydrogen as a result and forming the red oxidized iron which gives the planet its iconic color. This free hydrogen would then have combined with carbon dioxide in the water, to form methane. As this reaction progressed over time, olivine would have slowly transformed into another type of iron-rich rock known as serpentine, which then continued to react with water to form smectite.

“These smectite clays have so much capacity to store carbon,” Murray says. “So then we used existing knowledge of how these minerals are stored in clays on Earth, and extrapolate to say, if the Martian surface has this much clay in it, how much methane can you store in those clays?”

He and Jagoutz found that if Mars is covered in a layer of smectite that is 1,100 meters deep, this amount of clay could store a huge amount of methane, equivalent to most of the carbon dioxide in the atmosphere that is thought to have disappeared since the planet dried up.

“We find that estimates of global clay volumes on Mars are consistent with a significant fraction of Mars’ initial CO2 being sequestered as organic compounds within the clay-rich crust,” Murray says. “In some ways, Mars’ missing atmosphere could be hiding in plain sight.”

“Where the CO2 went from an early, thicker atmosphere is a fundamental question in the history of the Mars atmosphere, its climate, and the habitability by microbes,” says Bruce Jakosky, professor emeritus of geology at the University of Colorado and principal investigator on the Mars Atmosphere and Volatile Evolution (MAVEN) mission, which has been orbiting and studying Mars’ upper atmosphere since 2014. Jakosky was not involved with the current study. “Murray and Jagoutz examine the chemical interaction of rocks with the atmosphere as a means of removing CO2. At the high end of our estimates of how much weathering has occurred, this could be a major process in removing CO2 from Mars’ early atmosphere.”

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