J-PAL North America launches ARTEMIS to train early-career researchers

The Applied Research Training in Evaluation, Methods, and Impact Studies (ARTEMIS) program, launched by J-PAL North America, equips early-career researchers with high-level technical training and practical skills to design randomized evaluations (also referred to as a randomized controlled trials, or RCTs) on issues of poverty in the U.S. context. Fully funded and highly rigorous, the program's inaugural cohort of 20 participants — including PhD students through pre-tenure faculty — is receiving training, mentorship, and proposal development support through December. 

ARTEMIS is the newest offering within J-PAL North America’s Economics Pathways program, which broadens and strengthens the pipeline of economics researchers by providing support to aspiring scholars at every stage — from undergraduate interns to research staff and, now, academic researchers preparing to launch their first randomized evaluations.

ARTEMIS programming commenced this past June with the Summer Institute — an intensive weeklong course at MIT Endicott House in Dedham, Massachusetts, that focused on delivering technical content and building participants' practical skills and confidence to design and pursue funding for rigorous evaluations to address poverty in the United States. During the Summer Institute, participants had the opportunity to learn from researchers in the J-PAL network, including Gerald Daniels, associate professor of economics at Howard University, who served as a lecturer for the program. “ARTEMIS is an amazing program that bridges the gap for researchers who haven't previously had the opportunity to work with randomized evaluations,” notes Daniels. “It takes rigorous methodologies, like randomized evaluations, and makes them approachable, helping scholars translate their research ideas into high-impact evaluation designs." 

The inaugural cohort brought together early-career researchers from institutions across the United States, each tackling a research question addressing an issue related to poverty that is well-suited for a randomized evaluation. Their interests spanned the breadth of J-PAL’s work, touching on education, climate, health and labor markets, racial equity, and social protection — reflecting the wide range of policy questions that rigorous, evidence-based research can help answer. 

“I really appreciated being a part of the ARTEMIS Institute this summer,” says Felipe Juan, an ARTEMIS inaugural cohort participant. “I initially thought the randomized evaluation methodology would be out of reach for me, but the summer training helped to break down the practical steps of conducting an RCT and identify creative ways to approach experimental designs.” 

Bolstered by the intensive Summer Institute training, ARTEMIS participants are now engaging in six months of continued study design and proposal development support, including proposal webinars with J-PAL grants staff and office hours with senior technical staff who will advise on study design and proposal challenges. ARTEMIS participants will also participate in a research design workshop, where participants present their early-stage RCT designs and receive feedback from the broader J-PAL research network. Finally, participants will receive introductions to researchers in the J-PAL network with shared research interests to forge connections that may lead to future collaboration and opportunities. Through this sustained mentorship model, ARTEMIS is designed to help participants bridge the gap between developing a promising research idea and launching a funded, policy-relevant randomized evaluation. 

For early-career researchers, this hands-on, sustained support has already proven to be transformative. “Participating in ARTEMIS Summer Institute was an incredibly valuable experience. The combination of lectures by experts in the design and implementation of randomized controlled trials, hands-on workshops, and thoughtful feedback helped me sharpen my research questions and think more carefully about research design, implementation, statistical power, and meaningful stakeholder engagement,” says Hoyoung Yoo, an ARTEMIS 2026 participant. “I also greatly appreciated the opportunity to learn from J-PAL faculty and staff and exchange ideas with an inspiring group of fellow researchers. I left the training with a clearer path toward transforming an early-stage idea into a feasible, rigorous, and policy-relevant evaluation.” 

For many, the program’s value also extends beyond technical training to the community and long-term relationships it builds. “J-PAL North America’s support has continued well beyond the Summer Institute, which has been especially valuable, and the connections I made have already started shaping how I am approaching the work,” notes Tessie Krishna, an ARTEMIS 2026 participant. “I left with a clearer proposal, a wider network, and a real sense of belonging in the J-PAL community. I am grateful to the J-PAL team for creating something so thoughtful and welcoming.” 

By building the technical capacity and confidence of the next generation of researchers, ARTEMIS is advancing J-PAL’s global mission to reduce poverty by ensuring that policy is informed by rigorous, scientific evidence. Every individual trained through ARTEMIS is joining a growing network of researchers working to answer the most critical questions of today. 

ARTEMIS is made possible by grants from Arnold Ventures, Russell Sage Foundation, and the Alfred P. Sloan Foundation. Visit the J-PAL website for more information about ARTEMIS and the Economics Pathways program.

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Q&A: Hey AI, who should I vote for this year?

People are peppering artificial intelligence chatbots everyday with questions, but when it comes to politics and the looming 2026 U.S. midterm elections, what role are AI platforms playing? Could an AI chatbot sway a voter to switch parties or re-examine their political beliefs? 

MIT researchers have built a new dashboard, called the LLM Election Observatory, to examine, in real-time, how popular AI chatbots are responding to questions about the 2026 midterm elections. The team behind the dashboard — led by Professor Adam Berinsky, Assistant Professor Chara Podimata, and Professor Charles Stewart III — has extensive experience studying voting and election administration, AI and misinformation, and how AI systems are impacting human decision making. In an effort to better understand how AI is shaping the election, they designed the dashboard to simulate how individuals across the country communicate with chatbots. 

The team tracks how different AI systems respond to the same fixed set of questions on a range of topics, including election certification, hot-button issues, result predictions, news coverage, and more. Every few days, the LLM Election Observatory team runs the questions through the chatbots again, monitoring how their responses vary as the election gets closer depending on user location, political party affiliation, identity and other factors. The goal of this work is not to merely understand how AI systems spread information, but also better comprehend how they are shaping political discourse and voter understanding of political issues across the country. 

MIT News chatted with Podimata, the Class of 1942 Career Development Professor, an assistant professor of operations research and statistics in the MIT Sloan School of Management, and a member of the Laboratory for Information and Decision Systems; and Berinsky, the Mitsui Professor and a professor in the Department of Political Science, about their work with the LLM Election Observatory, how AI could impact the midterms, and what voters should keep in mind when they interact with chatbots.

Q: What was the inspiration for this project and what are you hoping to learn through the LLM Election Observatory?

Podimata: People are increasingly using AI chatbots to find and make sense of political and election-related information. That makes it important to understand what information these systems actually provide, not just at one moment in time but throughout an election cycle. This is particularly challenging because the models themselves are continually getting updates, the political information inputs available to them are themselves changing, and their answers can depend on how a user asks a question or what information the user provides about themselves.

One basic problem is that we currently do not have a good historical record of this information environment. If an AI system gives a particular answer about an election today and gives a different answer several weeks later, it can be very difficult to reconstruct after the fact what voters would have seen at each point in time. One important goal of our project is simply to systematically document that environment as it develops and make the resulting data available to other researchers and to the public.

Ultimately, factual accuracy is only the first frontier. We absolutely need to understand whether chatbots provide accurate political information, but we also need to pay just as much attention to the subtler ways in which they may shape people’s understanding of political issues. Differences in framing, emphasis, uncertainty, and cited sources can matter enormously, even in the absence of outright misinformation.  

Q: Professor Podimata, you studied how LLMs shaped political discourse and information during the 2024 election. What were the main takeaways of that research and how did those findings inform this new project?

Podimata: The LLM Election Observatory builds on my previous work during the 2024 presidential election, which found evidence of meaningful variation in AI models’ responses over time and across prompt framings, as well as differences in how models characterized candidates and implicitly anticipated the election outcome. 

The 2026 midterms give us an opportunity to expand that work substantially. Midterm elections are particularly interesting because there are many state and local contests unfolding simultaneously, alongside broader national political narratives. Our new audit survey therefore asks both contest-specific questions about the U.S. House and Senate candidates, but also broader questions about voting information and other aspects of the election information environment.

Q: Why do you think it’s so important to be studying how chatbots are responding to political queries across different groups of users?

Podimata: We all build our own “microcosms” through our interactions with chatbots. And yet, we lack systematic ways of understanding how these individualized information environments are shaped by who we are or how they evolve over time as we and the information environment changes. 

For example, people may not realize that what their favorite chatbot tells them about a political issue may be quite different from what their friend’s favorite chatbot tells them about the exact same issue. And importantly, these differences matter even when the information being provided is factually correct. Chatbots may frame the same facts differently, emphasize different aspects of an issue, express different levels of uncertainty, or draw on entirely different sources, even when responding to identical queries between different users. As a result, two users may walk away with very different perceptions of the same underlying facts, without ever realizing that their experiences differed. 

In some ways, this brings back many of the concerns we (as researchers and society) have grappled with in the context of social media. On social media, users and researchers could, at least to some extent, observe the information circulating across different Facebook groups, Instagram accounts, or TikTok communities. With chatbots, these information environments are largely invisible to anyone outside the individual conversation. We cannot easily observe what different users are being told, let alone systematically compare those responses or track how they change over time. This is precisely why projects like the LLM Election Observatory are so important: By systematically collecting and publicly sharing chatbot responses, we can begin to make these otherwise invisible differences observable and open to scrutiny.

Q: What sorts of questions and AI responses are you tracking through the dashboard?

Podimata: There are several questions we ultimately want this dataset to help us study. For example: Do different models provide systematically different information for the same questions? Do their responses change over the course of the campaign? Does the answer a particular model gives change depending on how a user frames the question or describes themselves (e.g., does it change if the model gets informed that the user is a Democrat or a Republican)? And, finally, what are the sources that the models pull information from when answering election-related questions? 

Our public dashboard is an important part of the project. We want people both from and outside of academia to be able to see the data we are collecting as the election unfolds rather than only seeing the results months later in an academic paper. The dashboard therefore presents the underlying data and some preliminary descriptive analyses, and we plan to make the dataset available for other researchers to study as well. We expect the dashboard to evolve as the data and our methods evolve.

Through this dashboard, we wanted to build on the scientific methodology we developed in 2024 but also to make the research much more accessible and useful to the public while the election is actually unfolding. Not only that, but the addition of MIT political science experts in the team cannot be overstated; Adam and Charles have really helped expand the questions that we are asking in our audit.

Q: What do you wish voters across the country knew about AI chatbots when it comes to searching for election information?

Berinsky: People should be sure to remember that just because a chatbot sounds confident doesn’t mean it is accurate. AI systems are designed to answer the questions that people ask them in an authoritative voice even when they are wrong, incomplete, or leaning on a weak source. These answers are a reasonable place to start, but voters shouldn’t stop there.

Q: What are your findings thus far?

Podimata: We have not yet done the full, longitudinal analysis, so we are only going to talk about broad strokes here, but a few broad patterns seem to be visible in the 2026 data. Across the different user identities we present, we see differences in how the answers are presented, in which sources a model cites, and in tone — sometimes even when the underlying facts are the same. What we cannot yet say is how stable those differences are, how they move over the course of the election, or how a real person would read and react to them. Our future analyses will take this up. It also underscores why the continuous record of responses we are collecting matters more than any single snapshot that you might see in the days around the election.

Q: Professor Berinsky, your research has also shown that in some cases AI chatbots can be harnessed to help refute misinformation. Can you talk a bit about this?

Berinsky: In my undergraduate course on information and social media I have been talking for a while about how AI can cut both ways. On one hand, these tools can generate misleading claims in text, pictures, and video at a scale we have never seen. Basically, AI can help bad actors “flood the zone” with misinformation. On the other hand, some of the most promising recent work demonstrates that evidence-based dialogues with a chatbot can durably reduce belief in conspiracy theories and other beliefs that are not supported by the balance of publicly available evidence. 

AI has the potential to do such debunking at scale — something we have long struggled to do. The lesson from decades of misinformation research is that any technology (be it print, radio, or television in earlier eras) is not inherently good or bad for promoting truth. The devil is in the details in how it is built and deployed. Independent auditing like this project can help us assess the ultimate impact of such technologies.

Q: Can you talk a bit about your overall goal with this research? 

Podimata: AI is a transformative technology, and people understandably have very different expectations (and feelings) about what it will mean for society at large. All of us have people in our lives that are overly enthusiastic about AI, others that are deeply concerned, and many more that are somewhere in between trying to understand what this technology can and cannot do. Our goal here is not to begin from the assumption that AI is either good or bad for elections. Instead, we hope to shed some light onto one part of this transformation: what information major AI systems are actually giving people about an election as it unfolds.

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MIT engineers create nanoparticles for delivering personalized cancer vaccines

A nanoscale particle with a unique structure, developed at MIT, holds potential as a customizable delivery vehicle for personalized cancer vaccines.

The backbone of the vaccine is a modified, harmless bacteriophage called M13, which forms a nanofiber that can be coated with cancer antigens. By precisely controlling the density of those cancer proteins, the researchers found that they could create a vaccine that prevents the development of colon tumors in mice.

This type of delivery particle could be adapted to carry many different antigens, allowing for the creation of personalized vaccines to treat individual patients’ tumors, the researchers say.

“The great thing about this is that it’s modular and we could switch out different antigens on demand, so you could design it to be patient-specific and target a known antigen for a particular kind of cancer,” says Angela Belcher, the James Mason Crafts Professor of Biological Engineering and Materials Science and Engineering and a member of the Koch Institute.

Belcher is the senior author of the study, which appears today in the journal Advanced Healthcare Materials. Shengnan Huang, research scientist at the Koch Institute, is the paper’s lead author. Jack Rudrum, Huaiyao Peng, and Hao Nghi Luu, graduate students at the Koch Institute, are also authors of the paper.

Viral vaccine assembly

For more than 20 years, Belcher’s lab has been deploying the M13 bacteriophage in a variety of applications. This virus, which is harmless to humans, consists of a single strand of DNA coated in a protein shell, or capsid. 

Belcher has previously developed techniques for generating materials by engineering these viruses to self-assemble into nanoscale scaffolds. By coating these scaffolds with inorganic materials, they can create functional devices such as batteries, semiconductors, solar cells, and catalysts, with precise control over the electronic, optical, and magnetic properties of the materials.

More recently, Belcher’s lab has been developing methods to control features of the phage that could allow it to be repurposed for medical applications, including vaccine delivery. 

“We’re taking this toolkit that we’ve worked on and developed over decades and formatting it to be specific for vaccine therapeutics,” Belcher says. “Some of the lessons learned from making batteries and all these other things are now being fine-tuned to make personalized vaccines.”

The capsid that surrounds the M13 phage forms a long filament made from viral proteins. To create vaccines, the researchers modify one of these proteins, known as pVIII, by attaching an antigen to the end of the protein. This can be done by adding the gene for the antigen to the virus’s DNA.

Some of the lab’s previous work had suggested that changing the density of cancer antigens attached to the capsid could affect the strength of the resulting immune response. In the new study, Huang wanted to find a way to control that density. 

“The antigen is the specific target we want to initiate the immune response against, which means we have to ensure the right dose of antigen to make sure that we can have a strong immune response,” she says.

Huang discovered that she could increase the density of the modified pVIII proteins by adding one or two negatively charged amino acids to the protein sequence. The density could be further fine-tuned by adding a genetic mutation that slows down production of the antigen protein.

In this study, the researchers also figured out how to boost the phage’s own ability to act as a vaccine adjuvant, by changing the genetic sequence of the virus’s DNA. Most vaccines need to be delivered along with an adjuvant such as aluminum hydroxide, which helps to stimulate a strong immune response. However, the M13 phage can act as an adjuvant itself because single-stranded DNA activates a pathway called TLR9, which is part of the body’s innate immune system.

“We can control the exact length. We can control the exact antigen expression, density, pattern, the exact adjuvant amount. A lot of times with a vaccine, you’ll have to put a different adjuvant with it, but now it’s all genetically encoded,” Belcher says. 

Targeting tumors

Using these techniques, the researchers showed that they could design phages that express an antigen called SIINFEKL at densities ranging from 6 percent to 70 percent. SIINFEKL, a small protein that can be engineered into tumors, is often used as a model to test vaccine effectiveness.

The researchers tested two of these vaccines in mice, one with 6 percent density and one with 35 percent, and found that the one with 35 percent was the most protective. 

After vaccination with the higher-density particles, mice were much less likely to develop tumors when later exposed to melanoma cancer cells expressing the SIINFEKL antigen. These mice also generated more T cells targeting SIINFEKL. This is, in part, because dendritic cells, which help to activate T cells, were more likely to take up vaccines with higher antigen density, the researchers found.

In another set of experiments, the researchers created and tested a vaccine carrying a neoantigen, which is only present in the tumor cells. This protein, known as ADPGK, is specifically expressed by colon tumors in a mouse model of cancer. When given the higher density vaccine, these mice were much less likely to develop colon tumors.

In this study, the researchers tested the vaccine as a way to prevent cancer from developing, and in their previous work, they adapted it for use to treat mouse colon tumors that have already formed. In the future, they also hope to use it to combat other types of cancer.

“This interesting work adds another cog to improve the translational potential of the M13 phage platform for cancer immunotherapeutics,” says Alberto Danielli, an associate professor of molecular biology at the University of Bologna, who was not involved in the study. “I look forward to seeing whether this platform is also effective against established tumors and whether additional tumor antigens can be displayed with comparable molecular and immunological efficacy.”

This platform could be especially well-suited for personalized cancer vaccines, because many different antigens could be swapped into the vaccine.

“Because this is a modularly engineered platform, we can change the antigen we are interested in. For colon cancer, we use this specific antigen. For other types of cancer, we can just plug in other antigens and they can be assembled in this platform,” Huang says.

The research was funded, in part, by the Koch Institute Frontier Research Program, the Marble Center for Cancer Nanomedicine, and the U.S. Defense Advanced Research Projects Agency.

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Next Haunt: A scary “problem set” in the basement of Next House

You know it’s autumn when the scent of pumpkin spice latte begins to waft through the air at the Dunkin’ in the Stratton Student Center, and students in MIT’s Next House residence hall diligently begin to piece together the many parts needed to showcase Next Haunt — a combination of a Halloween haunted house and escape room, built in the basement of the hall.

Every fall since 2014, students — also known as hauntmasters — spend several weekends building the room and preparing puzzles, physical tasks, or word challenges to obtain a code to open locks that let participants escape the house. This year’s theme, titled “Where the Light Ends,” follows “Terror Under Destruction,” “Next Stop: MBTA,” and last year’s theme: “Get in the HOLE.”

Next Haunt is free and open to all MIT students, also known as “test subjects.” Participants typically spend around 20 minutes in small, dark, enclosed rooms, working with four to six teammates to find the clues they need to escape.

Along the way, they have to navigate zombies — played by student actors — who may shout at them or splash them with blood (fake) while they solve the puzzles. If a zombie touches a participant, they become “paralyzed” and can no longer move, but they can still communicate and relay information to their teammates.

Teams must work closely, strategize, delegate tasks, and communicate effectively as the clock counts down. The group with the quickest escape time gets bragging rights, and every group signs their names on a wooden board to show that they participated.

MIT junior Kiyoko Hayano, Next Haunt co-producer, says, “I became involved in Next Haunt during my first semester at MIT because I had experience with larger-scale painting. I split the build director’s role with two other freshmen, including Michelle Liang, our current assistant producer. I thought this would be a great opportunity to take a break from psets and work with my hands, and fell in love with the club.”

Safety first

Before the room is built and the puzzles and costumes are created, careful planning takes place to ensure the escape room is safe, fire-resistant, and passes an on-site inspection. MIT’s Campus Construction and Environment, Health, and Safety offices are involved from the start, and all students who partake in the build are required to attend safety orientation and tool training.

“We currently have 18 people on our executive team, and we have 12 outside volunteers come help over the first few weekends of Next Haunt build,” says Hayano. “We then run Next Haunt for six-and-a-half hours every night for two weekends at the end of October.”

Once the build is complete, several alumni volunteers conduct a “test run” to make sure the puzzles and clues are hard, but not impossible, and anyone who wants to leave the room before the allotted time can easily find the door out built with Velcro closures. 

Escaping by solving the puzzles is not easy, and not all participants are successful. Last year, only 16 percent of the 73 teams successfully got out of the room. The winning team escaped in just under 15 minutes.

Alumnus and previous Next Haunt producer Allen Lin ’26 notes, “I became involved with Next Haunt in my freshman year after being a test subject myself, and I loved the creative and building process so much I wanted to join. My favorite part was seeing all the teams go through the house, especially on the last day when we hosted past Haunt alumni. It was just as rewarding to see how much our test subjects enjoy the house as it was to share the many laughs and moments of camaraderie among my team. I’m excited to come back as an alum and see what the Next Haunt team built this year!”

Environmentally mindful

At the end of Next Haunt, the escape room is dismantled, and anything that can be repurposed safely is stored for the following year’s build. The executive crew then goes to a Boston-area escape room to celebrate their hard work.

“It’s a joy working collaboratively with students and connecting them to campus partners who lend their expertise as the build develops," says Milo Alto, manager of special projects, housing and residential services. “Students get very creative every year with the theme and costumes, but they also learn valuable leadership skills along the way — time management, delegation, working with peers, and implementation of a detailed plan. It’s also a great way to build community.”

Interested MIT students can sign up to help plan and work on the 2027 build.

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Magnetic sensors track muscle movement, for better prosthetic limb control

MIT researchers have shown that they can accurately track muscle movement using tiny magnetic beads implanted in the muscle. These measurements could make it easier for people with certain types of amputations to control their prosthetic limbs.

For decades, researchers have been working to develop neuroprosthetic limbs, which use neural information from muscles remaining after an amputation to guide the prosthetic limb. Measurements of a muscle’s length, velocity and activation can be fed into the control algorithms that guide the limbs, allowing individuals with leg amputation to walk with a more natural gait, or those with arm amputation to manipulate objects more effectively.

Until now, these devices have relied on muscle activity measurements from electrodes placed on the surface of the skin. However, those measurements have not proven reliable enough to ensure safety while walking with a neuroprosthetic leg. 

The MIT team hopes that their new magnetic tracking system, which offers more accurate measurements, will enable commercial development of such limbs.

“For four decades, the standard methodology for neurally controlling prostheses has been to use skin electrodes to measure muscle activation. However, to control a prosthetic limb, joint position, torque, or impedance has to be controlled, and one cannot directly determine such mechanical signals using electrical activation alone,” says Hugh Herr, a professor of media arts and sciences, co-director of the K. Lisa Yang Center for Bionics at MIT, an associate member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

In the new study, which appears today in Nature Sensors, the researchers demonstrated the accuracy of the magnetic measurements in three participants with below-knee amputations.

“This interface is minimally invasive, so it could be accessible to more people. You place two beads in the muscle and you have access to all of this rich information about the muscle dynamics,” says Christopher Shallal, an MIT graduate student in the Harvard-MIT Program in Health Sciences and Technology and one of the lead authors of the paper.

The paper’s other lead author is Cameron Taylor PhD ’20, an assistant professor of biomedical engineering at the University of North Carolina at Chapel Hill.

Tracking muscles

For many years, Herr’s lab has been developing human-machine interfaces that can be used to control an artificial limb in a way that makes it more like using a natural limb. Obtaining accurate measurements of how the user is moving the muscles at the amputation site is critical to the success of this type of prosthesis.

Electrodes on the surface of the skin can reveal the electrical activation of the muscle below, and those measurements can be converted to estimates of how much a muscle is contracting or flexing. Based on that input, a robotic controller generates commands that guide the prosthetic limb in the user’s intended direction. 

However, that measurement approach, known as electromyography, does not always provide accurate enough information. The electrical signal is not a perfect proxy for muscle dynamics; for example, when a muscle is held at a fixed length for several seconds, the signal fades. Additionally, the electrodes may pick up signals from muscles other than the intended target, and sweat can interfere with detection of the signal.

Last year, Herr and his colleagues reported a “bionic knee” prosthesis that uses electrodes implanted in the muscles to provide accurate measurements and feed them into a robotic controller. This approach proved successful in a small clinical study, but alongside that work, Herr’s team has also been working on a less invasive approach that relies on magnets instead of electrodes.

With this approach, known as magnetomicrometry, pairs of magnets are implanted into muscles. When a muscle contracts or stretches, external sensors placed on the skin can measure the change in distance between the magnets, allowing muscle length and velocity to be calculated.

“Magnets can be tracked wirelessly through the body, so you can get very specific muscle signals and measure the mechanical output of the muscle without needing to be extremely invasive,” Shallal says.

Better control

In a 2021 study led by Taylor, Herr’s lab first demonstrated the magnet-based technique in animal models. The new Nature Sensors study extends the approach to humans with long-term magnetic implants. For the study, the researchers recruited three participants who have below-knee amputations.

All of these participants had a type of amputation that creates what’s called an agonist-antagonist myoneural interface (AMI). With this surgical procedure, muscle pairs are reconnected during surgery so that they still dynamically communicate with each other within the residual limb. This sensory feedback, called proprioception, helps the wearer of the prosthesis to decide how to position and move their prosthetic limb. 

Participants had magnets implanted in up to four muscles of the ankle joint. The surgeries were performed at Brigham and Women’s Hospital in collaboration with plastic surgeon Matthew Carty, a co-author of the study. The beads are 3 millimeters in diameter, about the size of a sesame seed. The magnets are made from an alloy of neodymium, iron, and boron, and are coated with gold and a polymer called parylene C to make them biocompatible. The magnetic signals can be read from a depth of up to 6 centimeters from the skin’s surface, using a customized external sensor that is worn over the skin, positioned over the muscles that are being tracked. 

Each of the participants in this study also had electrodes implanted into the same muscles that received magnetic beads. This allowed the researchers to compare the accuracy of three different types of measurements: magnetomicrometry, electromyography with implanted electrodes, and electromyography with electrodes on the surface of the skin.

During the experiments, the participants were not wearing a prosthetic limb but were asked to perform tasks such as moving a cursor on a screen to a particular target using their muscle movements. Using each of the three measurement techniques, the signals generated by the muscle movements were translated into control signals that, in this case, moved the cursor but in the future could be fed into the controller of a robotic ankle-foot prosthesis.  

“The cursor on the screen represented the degrees of freedom that they would control on a robotic ankle-foot prosthesis. This virtual neuroprosthetic task allowed us to compare the performance across different neural interfaces, and our results suggest that the magnet-based approach can offer more accurate and stable control,” Shallal says.

The researchers are now performing a larger study in which they are evaluating how well people can control prosthetic limbs using measurements from the magnetic beads. 

The research was funded, in part, by the Yang Tan Collective at MIT as well as a National Science Foundation Graduate Research Fellowship.

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New system gives users more control over their online experience

A user who is frustrated with changes a social media company made to their favorite messaging platform, like the introduction of an annoying AI chatbot, can’t do much to address the situation, short of leaving the platform.

Now, MIT researchers are offering an alternative path.

They’ve created Social.Wiki, a system that allows users to easily edit any social application made within Social.Wiki, and choose which of the edits made by others they want to see. 

For example, a user could change a messaging site on Social.Wiki to remove an AI chatbot and instead add a restaurant voting feature so their group chat can finally decide where they are going out for dinner.

“The infrastructure that we are all dependent on online is very rigid. It is based on these one-size-fits-all applications that we don’t have a lot of power to change. This project is about trying to imagine what a different web would look like where anyone can participate,” says Theia Henderson, an electrical engineering and computer science (EECS) graduate student, and lead author of a new paper on Social.Wiki.

The researchers envision a world in which, if a user is unhappy with their social app, they can edit it to change it however they like — without getting cut off from their social connections.

The platform enables users with no coding expertise to create a range of websites, including social sites like photo sharing networks, discussion forums, and dating apps. It handles all back-end work, eliminating the need for creators to set up servers or arrange databases.

Social.Wiki employs a decentralized infrastructure that allows users to retain ownership of their data as they create, edit, and navigate social websites within the platform. It also includes a “data guard” that protects user data from malicious or buggy applications. 

Through case studies and user tests, the researchers demonstrated the breadth of applications people can create with Social.Wiki, including a rideshare app where riders propose the prices they are willing to pay and a collaborative canvas for creating art together.

While Social.Wiki is still in its early days, the researchers plan to continue refining the platform as they deploy it to a wider audience. 

“We want to empower the creation of far more social applications that meet the needs of unusual small groups. Each group is unusual, but everyone is in unusual groups. Everybody has their own needs for social applications that we want to be able to address,” says David Karger, professor of EECS, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of the paper.

Henderson and Karger are joined on the paper by EECS graduate students Carmel Schare and Ana Dodik; Clemens Klokmose, a professor of computer science at Aarhus University in Denmark; Ziv Epstein, a postdoc in the MIT Media Lab; and David D. Clark, an MIT research scientist. The paper will be presented at the ACM Symposium on User Interface Software and Technology in November.

Letting users choose

Social.Wiki is a Wikipedia-like system in which users can navigate to different web applications or create their own web application within the Social.Wiki ecosystem. Each website has an “edit” button that allows any user to change the content of the site, from the text color on a message board to the pricing algorithm in a ride-sharing app.

Clicking the “edit” button in Social.Wiki opens a page that lets users view and edit the source code manually or prompt an AI assistant to make changes to the site. 

This platform builds on the researchers’ prior work on Graffiti, a web framework with a decentralized infrastructure that makes building personalized applications easier. 

With Social.Wiki, the researchers sought a collaboratively editable system that would allow anyone to revise web applications to be ideally suited for their needs and the needs of groups they belong to. 

To enable this structure, Social.Wiki relies on a strategy the researchers call “plural governance.” Instead of a central moderator deciding which content users will see, each Social.Wiki user can choose the rules that determine which edits they will see on Social.Wiki webpages.

Users can also identify “trusted editors,” so they will see edits from individuals or organizations they consider reliable.

For instance, a user could customize their rules, or “governance policy,” so they only see edits to a site that have been endorsed by at least two of their friends. When the Social.Wiki browser loads a site for a user, it applies the governance policy that user selected to decide which version of the site to present.

For example, based on each user’s choices and edits, the system might show one user a ride-sharing app with a uniform pricing algorithm while another user might see the same app with a dynamic pricing algorithm.

“No website is tied to a particular machine. This means we have no centralized ownership over the websites or the underlying infrastructure,” Henderson explains.

Security and creativity

In this decentralized infrastructure, user data are stored separately from the websites that access it, which helps keep data secure.

Social.Wiki also employs a data guard, which prompts a user to review each request from a website that is attempting to access their data.

“In Social.Wiki, these websites are held in common and anybody can edit them, but the data guard makes sure that each user stays in control of what happens to their data,” Karger adds.

The researchers evaluated Social.Wiki through several case studies. For one, they developed a messaging platform that allows participants to modify the chat’s styling and interface without affecting the experience of others. In another, they built a collaborative ride-sharing app that enables riders to adjust the fare they want to pay using a transparent “living wage” calculator.

They also sponsored a hackathon in which users built Social.Wiki applications.

Participants developed a range of creative sites, including a housing co-op communication tool that organizes chats on a map of the co-op, as well as a garden-themed microblogging site where posts are “planted” as flowers.

“Even the little bit that has been done so far with Social.Wiki demonstrates that there is so much more that could be done with social apps if users could specialize them to meet a particular group’s needs,” Karger says.

Moving forward, the researchers are working to deploy Social.Wiki to a broader audience and gather feedback from more diverse user groups. They also want to make technical upgrades to enable end-to-end encryption on private message data and provide better developer tools.

This work was funded, in part, by Schmidt Sciences and the National Science Foundation (NSF). 

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A soft robot could help doctors diagnose and treat disorders of the small intestine

Disorders of the small intestine are often difficult to diagnose and treat. More than 20 feet long, an inch in diameter, and located deep within the body, this organ is harder to access than other parts of the digestive tract, such as the colon or stomach.

To overcome those challenges, MIT engineers have designed a new robotic enteroscope that can move easily through the narrow passage of the small intestine, allowing it to perform imaging, deliver drugs, and remove small polyps or tissue samples for biopsy.

In designing the new robot, the researchers were inspired by the locomotion of snakes, as well as the scaly skin that helps them gain traction on slippery surfaces. A special textured coating helps the robot remain attached to the mucosal lining of the intestine as it inches its way through the narrow tube. 

Similar to an endoscope, the robot contains a variety of tools to help detect and treat any abnormalities that it might encounter.

“When we evaluate the small intestine, we do it for either diagnosis or treatment. If we find a little polyp that needs to be removed, this robot allows us to take a biopsy or remove it,” says Giovanni Traverso, professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, and an associate member of the Broad Institute of MIT and Harvard.

Traverso is the senior author of the study, which appears today in Science Robotics. The paper’s lead authors are former MIT postdoc Binbin Ying, who is now an assistant professor at University of Texas Southwestern Medical Center; MIT graduate student Ahmad Mujtaba Jebran; and former MIT research scientist Sahab Babaee.

Snaking through the intestine

The most common problems that doctors need to evaluate within the small intestine include bleeding, lesions, and blockages.

Currently, the only way to diagnose those intestinal complications is with a procedure known as double balloon enteroscopy. During the procedure, an enteroscope advances through the small intestine as a health care worker alternately inflates and deflates two balloons that help propel it through the tract. 

This process is slow and laborious, so the MIT team wanted to design an alternative enteroscope that would be easier to control and could move faster through the intestine.

“The fundamental challenge that we’re trying to overcome is how to evaluate that 20-to-25-foot segment of the small intestine in a way that can be more automated,” Traverso says.

The new tube-like robot moves through the narrow small intestine using a locomotion strategy similar to that of the snake. When slithering along the ground, a snake can steer itself by first contracting muscles on one side of the body, then the other. 

To mimic this, the researchers designed multifunctional actuators that control the robot’s movement. Each actuator contains four chambers, which can be inflated in different combinations, prompting the tube to extend or bend in any direction. 

“By tuning the amount of air that goes into these chambers, you can tune the direction that the robot is moving in,” Traverso says.

The head and tail segments of the robot contain additional expandable anchoring balloons that take turns inflating and deflating, helping the robot move forward. These head and tail segments are coated with special patterned sleeves inspired by kirigami — a variation of origami that involves cutting paper as well as folding it.

Using this technique, the researchers created coatings that lie flat until they are stretched by the expansion of the balloon, which causes three-dimensional kirigami needles to pop out. Similar to the scales of a snake, these needles give the robot more traction within the slippery lining of the intestine. 

In previous work, Traverso’s lab has used this approach to create coatings for shoe soles that give them more traction, as well as stents that can temporarily lodge in the esophagus and release drugs. 

“By using kirigami sleeves to enhance surface friction, the robot can generate localized tissue engagement traction for locomotion without relying on excessive stretching of the delicate small intestinal tissue,” Jebran says.

Diagnosis and treatment

The system also includes electronics that allow for navigation, with a controller that the person operating the device can use to guide the robot through narrow passages and around tight corners. The soft robot is tethered to the external controllers by a cable and is designed so that it could enter the body through either the mouth or the rectum. 

The robot contains tools including a camera for imaging, a sprayer for drug delivery, and clips that can take tissue biopsies or remove polyps. 

In animal studies, the researchers showed that the robot could travel about six and a half feet through the small intestine. They also successfully demonstrated the drug sprayer, tissue clips, and camera. The team is now working on improving the navigation systems and testing this approach for additional applications in the small intestine.

“We hope we can continue to move it forward to eventually develop systems that can help achieve more automated evaluation of the small intestine,” Traverso says.

The research was funded, in part, by the Karl van Tassel Career Development Professorship, the MIT Department of Mechanical Engineering, a Banting Postdoctoral Fellowship, an NSERC Postdoctoral Fellowship, the UT Southwestern Medical Center Startup Fund, Samsung Semiconductor and Eurnekian Nanotechnology Graduate Student Fellowships, the Cooke Undergraduate Transfer Scholarship, and the MIT Undergraduate Research Opportunities Program.

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