A careful rethinking of the Iraq War

The term “fog of war” expresses the chaos and uncertainty of the battlefield. Often, it is only in hindsight that people can grasp what was unfolding around them.

Now, additional clarity about the Iraq War has arrived in the form of a new book by MIT political scientist Roger Petersen, which dives into the war’s battlefield operations, political dynamics, and long-term impact. The U.S. launched the Iraq War in 2003 and formally wrapped it up in 2011, but Petersen analyzes the situation in Iraq through the current day and considers what the future holds for the country.

After a decade of research, Petersen identifies four key factors for understanding Iraq’s situation. First, the U.S. invasion created chaos and a lack of clarity in terms of the hierarchy among Shia, Sunni, and Kurdish groups. Second, given these conditions, organizations that comprised a mix of militias, political groups, and religious groups came to the fore and captured elements of the new state the U.S. was attempting to set up. Third, by about 2018, the Shia groups became dominant, establishing a hierarchy, and along with that dominance, sectarian violence has fallen. Finally, the hybrid organizations established many years ago are now highly integrated into the Iraqi state.

Petersen has also come to believe two things about the Iraq War are not fully appreciated. One is how widely U.S. strategy varied over time in response to shifting circumstances.

“This was not one war,” says Petersen. “This was many different wars going on. We had at least five strategies on the U.S. side.”

And while the expressed goal of many U.S. officials was to build a functioning democracy in Iraq, the intense factionalism of Iraqi society led to further military struggles, between and among religious and ethnic groups. Thus, U.S. military strategy shifted as this multisided conflict evolved.

“What really happened in Iraq, and the thing the United States and Westerners did not understand at first, is how much this would become a struggle for dominance among Shias, Sunnis, and Kurds,” says Petersen. “The United States thought they would build a state, and the state would push down and penetrate society. But it was society that created militias and captured the state.”

Attempts to construct a well-functioning state, in Iraq or elsewhere must confront this factor, Petersen adds. Most people think in terms of groups. They think in terms of group hierarchies, and they’re motivated when they believe their own group is not in a proper space in the hierarchy. This is this emotion of resentment. I think this is just human nature.”

Petersen’s book, “Death, Dominance, and State-Building: The U.S. in Iraq and the Future of American Military Intervention,” is published today by Oxford University Press. Petersen is the Arthur and Ruth Sloan Professor of Political Science at MIT and a member of the Security Studies Program based at MIT’s Center for International Studies.

Research on the ground

Petersen spent years interviewing people who were on the ground in Iraq during the war, from U.S. military personnel to former insurgents to regular Iraqi citizens, while extensively analyzing data about the conflict.

“I didn’t really come to conclusions about Iraq until six or seven years of applying this method,” he says.

Ultimately, one core fact about the country heavily influenced the trajectory of the war. Iraq’s Sunni Muslims made up about 20 percent or less of the country’s population but had been politically dominant before the U.S. took military action. After the U.S. toppled former dictator Saddam Hussein, it created an opening for the Shia majority to grasp more power.

“The United States said, ‘We’re going to have democracy and think in individual terms,’ but this is not the way it played out,” Petersen says. “The way it played out was, over the years, the Shia organizations became the dominant force. The Sunnis and Kurds are now basically subordinate within this Shia-dominated state. The Shias also had advantages in organizing violence over the Sunnis, and they’re the majority. They were going to win.”

As Petersen details in the book, a central unit of power became the political militia, based on ethnic and religious identification. One Shia militia, the Badr Organization, had trained professionally for years in Iran. The local Iraqi leader Moqtada al-Sadr could recruit Shia fighters from among the 2 million people living in the Sadr City slum. And no political militia wanted to back a strong multiethnic government.

“They liked this weaker state,” Petersen says. “The United States wanted to build a new Iraqi state, but what we did was create a situation where multiple and large Shia militia make deals with each other.”

A captain’s war

In turn, these dynamics meant the U.S. had to shift military strategies numerous times, occasionally in high-profile ways. The five strategies Petersen identifies are clear, hold, build (CHB); decapitation; community mobilization; homogenization; and war-fighting.

“The war from the U.S. side was highly decentralized,” Petersen says. Military captains, who typically command about 140 to 150 soldiers, had fairly wide berth in terms of how they were choosing to fight.  

“It was a captain’s war in a lot of ways,” Petersen adds.

The point is emphatically driven home in one chapter, “Captain Wright goes to Baghdad,” co-authored with Col. Timothy Wright PhD ’18, who wrote his MIT political science dissertation based on his experience and company command during the surge period.

As Petersen also notes, drawing on government data, the U.S. also managed to suppress violence fairly effectively at times, particularly before 2006 and after 2008. “The professional soldiers tried to do a good job, but some of the problems they weren’t going to solve,” Petersen says.

Still, all of this raises a conundrum. If trying to start a new state in Iraq was always likely to lead to an increase in Shia power, is there really much the U.S. could have done differently?

“That’s a million-dollar question,” Petersen says.

Perhaps the best way to engage with it, Petersen notes, is to recognize the importance of studying how factional groups grasp power through use of violence, and how that emerges in society. It is a key issue running throughout Petersen’s work, and one, he notes, that has often been studied by his graduate students in MIT’s Security Studies Program.

“Death, Dominance, and State-Building” has received praise from foreign-policy scholars. Paul Staniland, a political scientist at the University of Chicago, has said the work combines “intellectual creativity with careful attention to on-the ground dynamics,” and is “a fascinating macro-level account of the politics of group competition in Iraq. This book is required reading for anyone interested in civil war, U.S. foreign policy, or the politics of violent state-building."

Petersen, for his part, allows that he was pleased when one marine who served in Iraq read the manuscript in advance and found it interesting.

“He said, ‘This is good, and it’s not the way we think about it,’” Petersen says. “That’s my biggest compliment, to have a practitioner say it make them think. If I can get that kind of reaction, I’ll be pleased.”

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Dealing with the limitations of our noisy world

Tamara Broderick first set foot on MIT’s campus when she was a high school student, as a participant in the inaugural Women’s Technology Program. The monthlong summer academic experience gives young women a hands-on introduction to engineering and computer science.

What is the probability that she would return to MIT years later, this time as a faculty member?

That’s a question Broderick could probably answer quantitatively using Bayesian inference, a statistical approach to probability that tries to quantify uncertainty by continuously updating one’s assumptions as new data are obtained.

In her lab at MIT, the newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS) uses Bayesian inference to quantify uncertainty and measure the robustness of data analysis techniques.

“I’ve always been really interested in understanding not just ‘What do we know from data analysis,’ but ‘How well do we know it?’” says Broderick, who is also a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society. “The reality is that we live in a noisy world, and we can’t always get exactly the data that we want. How do we learn from data but at the same time recognize that there are limitations and deal appropriately with them?”

Broadly, her focus is on helping people understand the confines of the statistical tools available to them and, sometimes, working with them to craft better tools for a particular situation.

For instance, her group recently collaborated with oceanographers to develop a machine-learning model that can make more accurate predictions about ocean currents. In another project, she and others worked with degenerative disease specialists on a tool that helps severely motor-impaired individuals utilize a computer’s graphical user interface by manipulating a single switch.

A common thread woven through her work is an emphasis on collaboration.

“Working in data analysis, you get to hang out in everybody’s backyard, so to speak. You really can’t get bored because you can always be learning about some other field and thinking about how we can apply machine learning there,” she says.

Hanging out in many academic “backyards” is especially appealing to Broderick, who struggled even from a young age to narrow down her interests.

A math mindset

Growing up in a suburb of Cleveland, Ohio, Broderick had an interest in math for as long as she can remember. She recalls being fascinated by the idea of what would happen if you kept adding a number to itself, starting with 1+1=2 and then 2+2=4.

“I was maybe 5 years old, so I didn’t know what ‘powers of two’ were or anything like that. I was just really into math,” she says.

Her father recognized her interest in the subject and enrolled her in a Johns Hopkins program called the Center for Talented Youth, which gave Broderick the opportunity to take three-week summer classes on a range of subjects, from astronomy to number theory to computer science.

Later, in high school, she conducted astrophysics research with a postdoc at Case Western University. In the summer of 2002, she spent four weeks at MIT as a member of the first class of the Women’s Technology Program.

She especially enjoyed the freedom offered by the program, and its focus on using intuition and ingenuity to achieve high-level goals. For instance, the cohort was tasked with building a device with LEGOs that they could use to biopsy a grape suspended in Jell-O.

The program showed her how much creativity is involved in engineering and computer science, and piqued her interest in pursuing an academic career.

“But when I got into college at Princeton, I could not decide — math, physics, computer science — they all seemed super-cool. I wanted to do all of it,” she says.

She settled on pursuing an undergraduate math degree but took all the physics and computer science courses she could cram into her schedule.

Digging into data analysis

After receiving a Marshall Scholarship, Broderick spent two years at Cambridge University in the United Kingdom, earning a master of advanced study in mathematics and a master of philosophy in physics.

In the UK, she took a number of statistics and data analysis classes, including her first class on Bayesian data analysis in the field of machine learning.

It was a transformative experience, she recalls.

“During my time in the U.K., I realized that I really like solving real-world problems that matter to people, and Bayesian inference was being used in some of the most important problems out there,” she says.

Back in the U.S., Broderick headed to the University of California at Berkeley, where she joined the lab of Professor Michael I. Jordan as a grad student. She earned a PhD in statistics with a focus on Bayesian data analysis. 

She decided to pursue a career in academia and was drawn to MIT by the collaborative nature of the EECS department and by how passionate and friendly her would-be colleagues were.

Her first impressions panned out, and Broderick says she has found a community at MIT that helps her be creative and explore hard, impactful problems with wide-ranging applications.

“I’ve been lucky to work with a really amazing set of students and postdocs in my lab — brilliant and hard-working people whose hearts are in the right place,” she says.

One of her team’s recent projects involves a collaboration with an economist who studies the use of microcredit, or the lending of small amounts of money at very low interest rates, in impoverished areas.

The goal of microcredit programs is to raise people out of poverty. Economists run randomized control trials of villages in a region that receive or don’t receive microcredit. They want to generalize the study results, predicting the expected outcome if one applies microcredit to other villages outside of their study.

But Broderick and her collaborators have found that results of some microcredit studies can be very brittle. Removing one or a few data points from the dataset can completely change the results. One issue is that researchers often use empirical averages, where a few very high or low data points can skew the results.

Using machine learning, she and her collaborators developed a method that can determine how many data points must be dropped to change the substantive conclusion of the study. With their tool, a scientist can see how brittle the results are.

“Sometimes dropping a very small fraction of data can change the major results of a data analysis, and then we might worry how far those conclusions generalize to new scenarios. Are there ways we can flag that for people? That is what we are getting at with this work,” she explains.

At the same time, she is continuing to collaborate with researchers in a range of fields, such as genetics, to understand the pros and cons of different machine-learning techniques and other data analysis tools.

Happy trails

Exploration is what drives Broderick as a researcher, and it also fuels one of her passions outside the lab. She and her husband enjoy collecting patches they earn by hiking all the trails in a park or trail system.

“I think my hobby really combines my interests of being outdoors and spreadsheets,” she says. “With these hiking patches, you have to explore everything and then you see areas you wouldn’t normally see. It is adventurous, in that way.”

They’ve discovered some amazing hikes they would never have known about, but also embarked on more than a few “total disaster hikes,” she says. But each hike, whether a hidden gem or an overgrown mess, offers its own rewards.

And just like in her research, curiosity, open-mindedness, and a passion for problem-solving have never led her astray.

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Power when the sun doesn’t shine

In 2016, at the huge Houston energy conference CERAWeek, MIT materials scientist Yet-Ming Chiang found himself talking to a Tesla executive about a thorny problem: how to store the output of solar panels and wind turbines for long durations.        

Chiang, the Kyocera Professor of Materials Science and Engineering, and Mateo Jaramillo, a vice president at Tesla, knew that utilities lacked a cost-effective way to store renewable energy to cover peak levels of demand and to bridge the gaps during windless and cloudy days. They also knew that the scarcity of raw materials used in conventional energy storage devices needed to be addressed if renewables were ever going to displace fossil fuels on the grid at scale.

Energy storage technologies can facilitate access to renewable energy sources, boost the stability and reliability of power grids, and ultimately accelerate grid decarbonization. The global market for these systems — essentially large batteries — is expected to grow tremendously in the coming years. A study by the nonprofit LDES (Long Duration Energy Storage) Council pegs the long-duration energy storage market at between 80 and 140 terawatt-hours by 2040. “That’s a really big number,” Chiang notes. “Every 10 people on the planet will need access to the equivalent of one EV [electric vehicle] battery to support their energy needs.”

In 2017, one year after they met in Houston, Chiang and Jaramillo joined forces to co-found Form Energy in Somerville, Massachusetts, with MIT graduates Marco Ferrara SM ’06, PhD ’08 and William Woodford PhD ’13, and energy storage veteran Ted Wiley.

“There is a burgeoning market for electrical energy storage because we want to achieve decarbonization as fast and as cost-effectively as possible,” says Ferrara, Form’s senior vice president in charge of software and analytics.

Investors agreed. Over the next six years, Form Energy would raise more than $800 million in venture capital.

Bridging gaps

The simplest battery consists of an anode, a cathode, and an electrolyte. During discharge, with the help of the electrolyte, electrons flow from the negative anode to the positive cathode. During charge, external voltage reverses the process. The anode becomes the positive terminal, the cathode becomes the negative terminal, and electrons move back to where they started. Materials used for the anode, cathode, and electrolyte determine the battery’s weight, power, and cost “entitlement,” which is the total cost at the component level.

During the 1980s and 1990s, the use of lithium revolutionized batteries, making them smaller, lighter, and able to hold a charge for longer. The storage devices Form Energy has devised are rechargeable batteries based on iron, which has several advantages over lithium. A big one is cost.

Chiang once declared to the MIT Club of Northern California, “I love lithium-ion.” Two of the four MIT spinoffs Chiang founded center on innovative lithium-ion batteries. But at hundreds of dollars a kilowatt-hour (kWh) and with a storage capacity typically measured in hours, lithium-ion was ill-suited for the use he now had in mind.

The approach Chiang envisioned had to be cost-effective enough to boost the attractiveness of renewables. Making solar and wind energy reliable enough for millions of customers meant storing it long enough to fill the gaps created by extreme weather conditions, grid outages, and when there is a lull in the wind or a few days of clouds.

To be competitive with legacy power plants, Chiang’s method had to come in at around $20 per kilowatt-hour of stored energy — one-tenth the cost of lithium-ion battery storage.

But how to transition from expensive batteries that store and discharge over a couple of hours to some as-yet-undefined, cheap, longer-duration technology?

“One big ball of iron”

That’s where Ferrara comes in. Ferrara has a PhD in nuclear engineering from MIT and a PhD in electrical engineering and computer science from the University of L’Aquila in his native Italy. In 2017, as a research affiliate at the MIT Department of Materials Science and Engineering, he worked with Chiang to model the grid’s need to manage renewables’ intermittency.

How intermittent depends on where you are. In the United States, for instance, there’s the windy Great Plains; the sun-drenched, relatively low-wind deserts of Arizona, New Mexico, and Nevada; and the often-cloudy Pacific Northwest.

Ferrara, in collaboration with Professor Jessika Trancik of MIT’s Institute for Data, Systems, and Society and her MIT team, modeled four representative locations in the United States and concluded that energy storage with capacity costs below roughly $20/kWh and discharge durations of multiple days would allow a wind-solar mix to provide cost-competitive, firm electricity in resource-abundant locations.

Now that they had a time frame, they turned their attention to materials. At the price point Form Energy was aiming for, lithium was out of the question. Chiang looked at plentiful and cheap sulfur. But a sulfur, sodium, water, and air battery had technical challenges.

Thomas Edison once used iron as an electrode, and iron-air batteries were first studied in the 1960s. They were too heavy to make good transportation batteries. But this time, Chiang and team were looking at a battery that sat on the ground, so weight didn’t matter. Their priorities were cost and availability.

“Iron is produced, mined, and processed on every continent,” Chiang says. “The Earth is one big ball of iron. We wouldn’t ever have to worry about even the most ambitious projections of how much storage that the world might use by mid-century.” If Form ever moves into the residential market, “it’ll be the safest battery you’ve ever parked at your house,” Chiang laughs. “Just iron, air, and water.”

Scientists call it reversible rusting. While discharging, the battery takes in oxygen and converts iron to rust. Applying an electrical current converts the rusty pellets back to iron, and the battery “breathes out” oxygen as it charges. “In chemical terms, you have iron, and it becomes iron hydroxide,” Chiang says. “That means electrons were extracted. You get those electrons to go through the external circuit, and now you have a battery.”

Form Energy’s battery modules are approximately the size of a washer-and-dryer unit. They are stacked in 40-foot containers, and several containers are electrically connected with power conversion systems to build storage plants that can cover several acres.

The right place at the right time

The modules don’t look or act like anything utilities have contracted for before.

That’s one of Form’s key challenges. “There is not widespread knowledge of needing these new tools for decarbonized grids,” Ferrara says. “That’s not the way utilities have typically planned. They’re looking at all the tools in the toolkit that exist today, which may not contemplate a multi-day energy storage asset.”

Form Energy’s customers are largely traditional power companies seeking to expand their portfolios of renewable electricity. Some are in the process of decommissioning coal plants and shifting to renewables.

Ferrara’s research pinpointing the need for very low-cost multi-day storage provides key data for power suppliers seeking to determine the most cost-effective way to integrate more renewable energy.

Using the same modeling techniques, Ferrara and team show potential customers how the technology fits in with their existing system, how it competes with other technologies, and how, in some cases, it can operate synergistically with other storage technologies.

“They may need a portfolio of storage technologies to fully balance renewables on different timescales of intermittency,” he says. But other than the technology developed at Form, “there isn’t much out there, certainly not within the cost entitlement of what we’re bringing to market.”  Thanks to Chiang and Jaramillo’s chance encounter in Houston, Form has a several-year lead on other companies working to address this challenge. 

In June 2023, Form Energy closed its biggest deal to date for a single project: Georgia Power’s order for a 15-megawatt/1,500-megawatt-hour system. That order brings Form’s total amount of energy storage under contracts with utility customers to 40 megawatts/4 gigawatt-hours. To meet the demand, Form is building a new commercial-scale battery manufacturing facility in West Virginia.

The fact that Form Energy is creating jobs in an area that lost more than 10,000 steel jobs over the past decade is not lost on Chiang. “And these new jobs are in clean tech. It’s super exciting to me personally to be doing something that benefits communities outside of our traditional technology centers.

“This is the right time for so many reasons,” Chiang says. He says he and his Form Energy co-founders feel “tremendous urgency to get these batteries out into the world.”

This article appears in the Winter 2024 issue of Energy Futures, the magazine of the MIT Energy Initiative.

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Brain surgery training from an avatar

Benjamin Warf, a renowned neurosurgeon at Boston Children’s Hospital, stands in the MIT.nano Immersion Lab. More than 3,000 miles away, his virtual avatar stands next to Matheus Vasconcelos in Brazil as the resident practices delicate surgery on a doll-like model of a baby’s brain.

With a pair of virtual-reality goggles, Vasconcelos is able to watch Warf’s avatar demonstrate a brain surgery procedure before replicating the technique himself and while asking questions of Warf’s digital twin.

“It’s an almost out-of-body experience,” Warf says of watching his avatar interact with the residents. “Maybe it’s how it feels to have an identical twin?”

And that’s the goal: Warf’s digital twin bridged the distance, allowing him to be functionally in two places at once. “It was my first training using this model, and it had excellent performance,” says Vasconcelos, a neurosurgery resident at Santa Casa de São Paulo School of Medical Sciences in São Paulo, Brazil. “As a resident, I now feel more confident and comfortable applying the technique in a real patient under the guidance of a professor.”

Warf’s avatar arrived via a new project launched by medical simulator and augmented reality (AR) company EDUCSIM. The company is part of the 2023 cohort of START.nano, MIT.nano’s deep-tech accelerator that offers early-stage startups discounted access to MIT.nano’s laboratories.

In March 2023, Giselle Coelho, EDUCSIM’s scientific director and a pediatric neurosurgeon at Santa Casa de São Paulo and Sabará Children’s Hospital, began working with technical staff in the MIT.nano Immersion Lab to create Warf’s avatar. By November, the avatar was training future surgeons like Vasconcelos.

“I had this idea to create the avatar of Dr. Warf as a proof of concept, and asked, ‘What would be the place in the world where they are working on technologies like that?’” Coelho says. “Then I found MIT.nano.”

Capturing a Surgeon

As a neurosurgery resident, Coelho was so frustrated by the lack of practical training options for complex surgeries that she built her own model of a baby brain. The physical model contains all the structures of the brain and can even bleed, “simulating all the steps of a surgery, from incision to skin closure,” she says.

She soon found that simulators and virtual reality (VR) demonstrations reduced the learning curve for her own residents. Coelho launched EDUCSIM in 2017 to expand the variety and reach of the training for residents and experts looking to learn new techniques.

Those techniques include a procedure to treat infant hydrocephalus that was pioneered by Warf, the director of neonatal and congenital neurosurgery at Boston Children’s Hospital. Coelho had learned the technique directly from Warf and thought his avatar might be the way for surgeons who couldn’t travel to Boston to benefit from his expertise.

To create the avatar, Coelho worked with Talis Reks, the AR/VR/gaming/big data IT technologist in the Immersion Lab.

“A lot of technology and hardware can be very expensive for startups to access as they start their company journey,” Reks explains. “START.nano is one way of enabling them to utilize and afford the tools and technologies we have at MIT.nano’s Immersion Lab.”

Coelho and her colleagues needed high-fidelity and high-resolution motion-capture technology, volumetric video capture, and a range of other VR/AR technologies to capture Warf’s dexterous finger motions and facial expressions. Warf visited MIT.nano on several occasions to be digitally “captured,” including performing an operation on the physical baby model while wearing special gloves and clothing embedded with sensors.

“These technologies have mostly been used for entertainment or VFX [visual effects] or CGI [computer-generated imagery],” says Reks, “But this is a unique project, because we’re applying it now for real medical practice and real learning.”

One of the biggest challenges, Reks says, was helping to develop what Coelho calls “holoportation”— transmitting the 3D, volumetric video capture of Warf in real-time over the internet so that his avatar can appear in transcontinental medical training.

The Warf avatar has synchronous and asynchronous modes. The training that Vasconcelos received was in the asynchronous mode, where residents can observe the avatar’s demonstrations and ask it questions. The answers, delivered in a variety of languages, come from AI algorithms that draw from previous research and an extensive bank of questions and answers provided by Warf.

In the synchronous mode, Warf operates his avatar from a distance in real time, Coelho says. “He could walk around the room, he could talk to me, he could orient me. It’s amazing.”

Coelho, Warf, Reks, and other team members demonstrated a combination of the modes in a second session in late December. This demo consisted of volumetric live video capture between the Immersion Lab and Brazil, spatialized and visible in real-time through AR headsets. It significantly expanded upon the previous demo, which had only streamed volumetric data in one direction through a two-dimensional display.

Powerful impacts

Warf has a long history of training desperately needed pediatric neurosurgeons around the world, most recently through his nonprofit Neurokids. Remote and simulated training has been an increasingly large part of training since the pandemic, he says, although he doesn’t feel it will ever completely replace personal hands-on instruction and collaboration.

“But if in fact one day we could have avatars, like this one from Giselle, in remote places showing people how to do things and answering questions for them, without the cost of travel, without the time cost and so forth, I think it could be really powerful,” Warf says.

The avatar project is especially important for surgeons serving remote and underserved areas like the Amazon region of Brazil, Coelho says. “This is a way to give them the same level of education that they would get in other places, and the same opportunity to be in touch with Dr. Warf.”

One baby treated for hydrocephalus at a recent Amazon clinic had traveled by boat 30 hours for the surgery, according to Coelho.

Training surgeons with the avatar, she says, “can change reality for this baby and can change the future.”

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Professor Edward Roberts, management scholar, champion of entrepreneurship, and “MIT icon,” dies at 88

Edward B. Roberts ’58, SM ’58, SM ’60, PhD ’62, a visionary management professor who studied entrepreneurship while building a flourishing innovation ecosystem at MIT, died on Tuesday. He was 88 years old.

Over a remarkable seven-decade career at the Institute, Roberts was a prolific scholar and mentor who founded what is now the Martin Trust Center for MIT Entrepreneurship, a unique resource that has guided thousands of innovators as they have brought inventions and ideas to the market.

Roberts, the David Sarnoff Professor of Management of Technology at the MIT Sloan School of Management, was an energetic and encouraging presence who espoused the value of founding companies organized around a clear core idea, and of having significant new technology to apply to that idea. Generations of MIT students as well as faculty found a path forward for their startups as a result, benefitting from the structure of the Martin Trust Center and influenced by Roberts’ work.

“It is not too much to say that MIT’s flourishing entrepreneurial culture and global reputation as a source of influential start-ups grew from seeds Ed planted here 50 years ago,” MIT President Sally Kornbluth wrote in a letter to the MIT community yesterday.

Kornbluth called Roberts an “MIT icon” who was “always doing things no one had done before,” including “pioneering the very idea that entrepreneurship is a craft that can be systematically studied and successfully taught.”

In 2015 Roberts co-authored a report estimating that, as of 2014, MIT alumni had launched 30,200 active companies employing roughly 4.6 million people and generating roughly $1.9 trillion in annual revenues, a figure that would have ranked among the top 10 countries in the world in GDP.

“I have helped MIT to become a much more entrepreneurial place,” Roberts said — in something of an understatement — during a 2011 interview for an MIT Sloan oral history series.

Wide-ranging intellect, entrepreneurial spirit

Born in 1935, Roberts grew up in nearby Chelsea, Massachusetts, commuting to MIT as an undergraduate. Through his intellectual life as a student, as well as his later career as a scholar, Roberts personified the interdisciplinary possibilities of MIT.

Even while earning his undergraduate degree and a master’s degree in electrical engineering, Roberts was often taking two additional courses in economics and at MIT Sloan — despite, as he once recalled, the vocal concerns of his faculty advisor.

As a graduate student, by the late 1950s, Roberts had begun working with MIT faculty member Jay Forrester, a computing pioneer who had started developing many core ideas now integral to the study of system dynamics. Roberts became increasingly interested in the application of those ideas to management, also helping to create a framework for the field then known as industrial dynamics.

Assisted by the extra courses he had already been taking, Roberts earned his master’s in management from MIT Sloan, and then his PhD in economics, with his doctoral work focused on applying system dynamics to the management of research and development. It was MIT’s first doctoral dissertation in system dynamics.

Having joined MIT as a student, Roberts never left. He took a position as a faculty member at MIT Sloan and began working on wide-ranging and important studies of organizational practices in areas that included health care management, among other things.

Along the way, Roberts practiced what he advocated: In the 1960s, while still a junior faculty member, he co-founded his own firm, Pugh-Roberts Associates, which took the ideas of system dynamics to partners in the private sector and government. The firm still exists today, as the Sage Analysis Group.

The books Roberts co-authored early in his career include “The Persistent Poppy” (1975), examining the social and economic impact of heroin use, and “The Dynamics of Human Service Delivery” (1976), applying system dynamics analysis to the service sector.

Over time, Roberts’ work became increasingly focused on the components of successful entrepreneurship. His high-profile 1991 book, “Entrepreneurs in High-Technology: Lessons from MIT and Beyond,” was based on a thorough examination of 113 companies founded by entrepreneurs, moving the field forward through its extensive empirical work.

That overlapped with Roberts’ work building a framework for encouraging entrepreneurship at MIT. The MIT Center for Entrepreneurship opened in 1990, providing an essential resource for potential firm founders at the Institute. As the center grew, Roberts himself became a vital figure to many budding entrepreneurs, a vigorous presence offering input based on expert analysis.

“Ed will always be remembered at MIT Sloan as a campus pillar,” wrote Georgia Perakis, interim John C. Head III Dean of MIT Sloan, along with Deputy Dean Michael Cusumano, in a letter to the MIT Sloan community on Tuesday. “He could be found walking the halls, visiting faculty, staff, students, and alumni at the school, and sharing with them parts of the history of MIT Sloan. He remained connected to generations of MIT entrepreneurs, offering advice and guidance as companies were launched. Those of us who knew Ed count ourselves lucky to have had his counsel and will miss him dearly.”

“Virtually everything today in the MIT entrepreneurial ecosystem, from classes to extracurricular activities, has some level of Ed’s DNA at it core,” says Bill Aulet, professor of the practice at MIT Sloan and the managing director of the Martin Trust Center for MIT Entrepreneurship. “But his impact also went well beyond MIT, where Ed Roberts was a generational figure in entrepreneurship as a field of research and instruction.”

MIT faculty who studied with Roberts also recall the impact his teaching had on their own careers.

“I, and many others in the system dynamics group here, took Ed’s course as a doctoral student and learned a great deal about how to work with policymakers and other leaders to increase the chances that the results of modeling would be implemented and have sustained beneficial impact in organizations,” recalls John Sterman, the Jay W. Forrester Professor of Management at MIT Sloan and a professor in the Institute for Data, Systems, and Society.

A celebration of MIT pioneers

In all, Roberts published 12 books and over 160 articles on entrepreneurship and management, with an audience both inside academia and in technology-driven growth industries.

Among his recent works, Roberts’ 2020 book, “Celebrating Entrepreneurs: How MIT Nurtured Pioneering Entrepreneurs Who Built Great Companies,” examined how the Institute developed its formal framework and culture of entrepreneurship across a variety of industries.

In addition to founding the Martin Trust Center for MIT Entrepreneurship, Roberts at one point chaired the MIT Management of Technology (MOT) program. He also co-created the MIT Sloan Entrepreneurship and Innovation Certificate program.

Roberts was also an active presence as a co-founder, board member, and investor in startups, including the health care information firm Medical Information Technology, Inc. In addition, Roberts co-founded a group of Zero Stage Capital equity funds, which provided early-stage capital for promising tech startups. All told, Roberts was a board member for more than 40 firms and a co-founder of 14 companies.

Roberts is survived by his wife, Nancy; his children, Valerie and her husband, Mark Friedman, Mitchell and his wife, Jill, and Andrea and her husband, Marc Foster; and nine grandchildren. Donations can be made to the Combined Jewish Philanthropies of Boston in his memory.

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How cognition changes before dementia hits

Individuals with mild cognitive impairment, especially of the “amnestic subtype” (aMCI), are at increased risk for dementia due to Alzheimer’s disease relative to cognitively healthy older adults. Now, a study co-authored by researchers from MIT, Cornell University, and Massachusetts General Hospital has identified a key deficit in people with aMCI, which relates to producing complex language.

This deficit is independent of the memory deficit that characterizes this group and may provide an additional “cognitive biomarker” to aid in early detection — the time when treatments, as they continue to be developed, are likely to be most effective.

The researchers found that while individuals with aMCI could appreciate the basic structure of sentences (syntax) and their meaning (semantics), they struggled with processing certain ambiguous sentences in which pronouns alluded to people not referenced in the sentences themselves.

“These results are among the first to deal with complex syntax and really get at the abstract computation that’s involved in processing these linguistic structures,” says MIT linguistics scholar Suzanne Flynn, co-author of a paper detailing the results.

The focus on subtleties in language processing, in relation to aMCI and its potential transition to dementia such as Alzheimer’s disease is novel, the researchers say.

“Previous research has looked most often at single words and vocabulary,” says co-author Barbara Lust, a professor emerita at Cornell University. “We looked at a more complex level of language knowledge. When we process a sentence, we have to both grasp its syntax and construct a meaning. We found a breakdown at that higher level where you’re integrating form and meaning.”

The paper, “Disintegration at the syntax-semantics interface in prodromal Alzheimer’s disease: New evidence from complex sentence anaphora in amnestic Mild Cognitive Impairment (aMCI),” appears in the Journal of Neurolinguistics.

The paper’s authors are Flynn, a professor in MIT’s Department of Linguistics and Philosophy; Lust, a professor emerita in the Department of Psychology at Cornell and a visiting scholar and research affiliate in the MIT Department of Linguistics and Philosophy; Janet Cohen Sherman, an associate professor of psychology in the Department of Psychiatry at Massachusetts General Hospital and director of the MGH Psychology Assessment Center; and, posthumously, the scholars James Gair and Charles Henderson of Cornell University.

Anaphora and ambiguity

To conduct the study, the scholars ran experiments comparing the cognitive performance of aMCI patients to cognitively healthy individuals in separate younger and older control groups. The research involved 61 aMCI patients of Massachusetts General Hospital, with control group research conducted at Cornell and MIT.

The study pinpointed how well people process and reproduce sentences involving “anaphora.” In linguistics terms, this generally refers to the relation between a word and another form in the sentence, such the use of “his” in the sentence, “The electrician repaired his equipment.” (The term “anaphora” has another related use in the field of rhetoric, involving the repetition of terms.)

In the study, the researchers ran a variety of sentence constructions past aMCI patients and the control groups. For instance, in the sentence, “The electrician fixed the light switch when he visited the tenant,” it is not actually clear if “he” refers to the electrician, or somebody else entirely. The “he” could be a family member, friend, or landlord, among other possibilities.

On the other hand, in the sentence, “He visited the tenant when the electrician repaired the light switch,” “he” and the electrician cannot be the same person. Alternately, in the sentence, “The babysitter emptied the bottle and prepared the formula,” there is no reference at all to a person beyond the sentence.

Ultimately, aMCI patients performed significantly worse than the control groups when producing sentences with “anaphoric coreference,” the ones with ambiguity about the identity of the person referred to via a pronoun.

“It’s not that aMCI patients have lost the ability to process syntax or put complex sentences together, or lost words; it’s that they’re showing a deficit when the mind has to figure out whether to stay in the sentence or go outside it, to figure out who we’re talking about,” Lust explains. “When they didn’t have to go outside the sentence for context, sentence production was preserved in the individuals with aMCI whom we studied.”

Flynn notes: “This adds to our understanding of the deterioration that occurs in early stages of the dementia process. Deficits extend beyond memory loss. While the participants we studied have memory deficits, their memory difficulties do not explain our language findings, as evidenced by a lack of correlation in their performance on the language task and their performances on measures of memory. This suggests that in addition to the memory difficulties that individuals with aMCI experience, they are also struggling with this central aspect of language.”

Looking for a path to treatment

The current paper is part of an ongoing series of studies that Flynn, Lust, Sherman, and their colleagues have performed. The findings have implications for potentially steering neuroscience studies toward regions of the brain that process language, when investigating MCI and other forms  of dementia, such as primary progressive aphasia. The study may also help inform linguistics theory concerning various forms of anaphora.

Looking ahead, the scholars say they would like to increase the size of the studies as part of an effort to continue to define how it is that diseases progress and how language may be a predictor of that.

“Our data is a small population but very richly theoretically guided,” Lust says. “You need hypotheses that are linguistically informed to make advances in neurolinguistics. There’s so much interest in the years before Alzheimer’s disease is diagnosed, to see if it can be caught and its progression stopped.”

As Flynn adds, “The more precise we can become about the neuronal locus of deterioration, that’s going to make a big difference in terms of developing treatment.”

Support for the research was provided by the Cornell University Podell Award, Shamitha Somashekar and Apple Corporation, Federal Formula Funds, Brad Hyman at Massachusetts General Hospital, the Cornell Bronfenbrenner Center for Life Course Development, the Cornell Institute for Translational Research on Aging, the Cornell Institute for Social Science Research, and the Cornell Cognitive Science Program.

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Investigating and preserving Quechua

Soledad Chango, a native of Ecuador and a graduate student in MIT’s Indigenous Language Initiative, began preparations for her Quechua course with a clear idea about its purpose.

“Our language matters,” she says. “It’s worth studying and spreading.”

Quechua at MIT, a new two-week introductory class hosted by MIT Global Languages during the Institute’s Independent Activities Period in January, introduced students to the basics of Kichwa, a Quechua variant that is the most widely spoken language in the Americas. The class, which featured an interactive approach, focused on oral and written skills, emphasizing tasks based on familiar contexts. “I prepared conversations that reflect cultural values,” Chango emphasizes. 

Chango, a scholar of language acquisition, credited her advisor, MIT Linguistics professor Norvin Richards, and postdoc Cora Lesure with helping shape the course. Global Languages section head Per Urlaub helped ready the course for the classroom. “They helped me refine my ideas about what to teach and how to teach it,” she says.

Cultural immersion, value, and language acquisition

Because language can often be better understood when connected with its cultural context, Chango introduced students to the history, culture, and geography of the Andes mountains where the language’s speakers live, work, and play. Cultural discussions and interactions with artifacts were designed to help students understand the value of the endangered language.

“Every day, we dedicated time to individually review our writing and grammar skills,” says Isabel Naty Sanchez Taipe, a computer science and education student at Wellesley College and a cross-registered student and student researcher at MIT. “We practiced the pronunciation of new vocabulary and sentences out loud, and engaged in diverse group activities and games where we spoke Quechua as much as possible.” 

Chango sought to emphasize the importance of keeping Kichwa and Quechua alive. When endangered languages disappear, so do the communities and culture from which they rose. 

“In 2014, I was investigating Indigenous language advancement, tracking changes and usage,” she says. “Research shows the youngest Indigenous people retain and value their native languages the least.” 

Multilingualism as a tool for improvement

Multilingualism can prove valuable both academically and professionally.

“I would definitely recommend that people explore languages taught in this manner,” says Prahlad Balaji Iyengar, a PhD student in electrical engineering and computer science who took the course. “I think this was a great opportunity for me to learn a new mode of thought.”

As Chango continues to review and refine the course, she looks to technology to both help retain Quechua’s distinctive traits and reverse its trajectory toward extinction. She wants to ensure languages like Kichwa find interested audiences outside of their native cultures.

“Technology can help spread the word and increase interest in Indigenous languages like Quechua,” she says. “I want to expand its reach from oral tradition and transmission and develop it so it supports quantifiable and replicable language instruction.”

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Study unlocks nanoscale secrets for designing next-generation solar cells

Perovskites, a broad class of compounds with a particular kind of crystal structure, have long been seen as a promising alternative or supplement to today’s silicon or cadmium telluride solar panels. They could be far more lightweight and inexpensive, and could be coated onto virtually any substrate, including paper or flexible plastic that could be rolled up for easy transport.

In their efficiency at converting sunlight to electricity, perovskites are becoming comparable to silicon, whose manufacture still requires long, complex, and energy-intensive processes. One big remaining drawback is longevity: They tend to break down in a matter of months to years, while silicon solar panels can last more than two decades. And their efficiency over large module areas still lags behind silicon. Now, a team of researchers at MIT and several other institutions has revealed ways to optimize efficiency and better control degradation, by engineering the nanoscale structure of perovskite devices.

The study reveals new insights on how to make high-efficiency perovskite solar cells, and also provides new directions for engineers working to bring these solar cells to the commercial marketplace. The work is described today in the journal Nature Energy, in a paper by Dane deQuilettes, a recent MIT postdoc who is now co-founder and chief science officer of the MIT spinout Optigon, along with MIT professors Vladimir Bulovic and Moungi Bawendi, and 10 others at MIT and in Washington state, the U.K., and Korea.

“Ten years ago, if you had asked us what would be the ultimate solution to the rapid development of solar technologies, the answer would have been something that works as well as silicon but whose manufacturing is much simpler,” Bulovic says. “And before we knew it, the field of perovskite photovoltaics appeared. They were as efficient as silicon, and they were as easy to paint on as it is to paint on a piece of paper. The result was tremendous excitement in the field.”

Nonetheless, “there are some significant technical challenges of handling and managing this material in ways we’ve never done before,” he says. But the promise is so great that many hundreds of researchers around the world have been working on this technology. The new study looks at a very small but key detail: how to “passivate” the material’s surface, changing its properties in such a way that the perovskite no longer degrades so rapidly or loses efficiency.

“The key is identifying the chemistry of the interfaces, the place where the perovskite meets other materials,” Bulovic says, referring to the places where different materials are stacked next to perovskite in order to facilitate the flow of current through the device.

Engineers have developed methods for passivation, for example by using a solution that creates a thin passivating coating. But they’ve lacked a detailed understanding of how this process works — which is essential to make further progress in finding better coatings. The new study “addressed the ability to passivate those interfaces and elucidate the physics and science behind why this passivation works as well as it does,” Bulovic says.

The team used some of the most powerful instruments available at laboratories around the world to observe the interfaces between the perovskite layer and other materials, and how they develop, in unprecedented detail. This close examination of the passivation coating process and its effects resulted in “the clearest roadmap as of yet of what we can do to fine-tune the energy alignment at the interfaces of perovskites and neighboring materials,” and thus improve their overall performance, Bulovic says.

While the bulk of a perovskite material is in the form of a perfectly ordered crystalline lattice of atoms, this order breaks down at the surface. There may be extra atoms sticking out or vacancies where atoms are missing, and these defects cause losses in the material’s efficiency. That’s where the need for passivation comes in.

“This paper is essentially revealing a guidebook for how to tune surfaces, where a lot of these defects are, to make sure that energy is not lost at surfaces,” deQuilettes says. “It’s a really big discovery for the field,” he says. “This is the first paper that demonstrates how to systematically control and engineer surface fields in perovskites.”

The common passivation method is to bathe the surface in a solution of a salt called hexylammonium bromide, a technique developed at MIT several years ago by Jason Jungwan Yoo PhD ’20, who is a co-author of this paper, that led to multiple new world-record efficiencies. By doing that “you form a very thin layer on top of your defective surface, and that thin layer actually passivates a lot of the defects really well,” deQuilettes says. “And then the bromine, which is part of the salt, actually penetrates into the three-dimensional layer in a controllable way.” That penetration helps to prevent electrons from losing energy to defects at the surface.

These two effects, produced by a single processing step, produces the two beneficial changes simultaneously. “It’s really beautiful because usually you need to do that in two steps,” deQuilettes says.

The passivation reduces the energy loss of electrons at the surface after they have been knocked loose by sunlight. These losses reduce the overall efficiency of the conversion of sunlight to electricity, so reducing the losses boosts the net efficiency of the cells.

That could rapidly lead to improvements in the materials’ efficiency in converting sunlight to electricity, he says. The recent efficiency records for a single perovskite layer, several of them set at MIT, have ranged from about 24 to 26 percent, while the maximum theoretical efficiency that could be reached is about 30 percent, according to deQuilettes.

An increase of a few percent may not sound like much, but in the solar photovoltaic industry such improvements are highly sought after. “In the silicon photovoltaic industry, if you’re gaining half of a percent in efficiency, that’s worth hundreds of millions of dollars on the global market,” he says. A recent shift in silicon cell design, essentially adding a thin passivating layer and changing the doping profile, provides an efficiency gain of about half of a percent. As a result, “the whole industry is shifting and rapidly trying to push to get there.” The overall efficiency of silicon solar cells has only seen very small incremental improvements for the last 30 years, he says.

The record efficiencies for perovskites have mostly been set in controlled laboratory settings with small postage-stamp-size samples of the material. “Translating a record efficiency to commercial scale takes a long time,” deQuilettes says. “Another big hope is that with this understanding, people will be able to better engineer large areas to have these passivating effects.”

There are hundreds of different kinds of passivating salts and many different kinds of perovskites, so the basic understanding of the passivation process provided by this new work could help guide researchers to find even better combinations of materials, the researchers suggest. “There are so many different ways you could engineer the materials,” he says.

“I think we are on the doorstep of the first practical demonstrations of perovskites in the commercial applications,” Bulovic says. “And those first applications will be a far cry from what we’ll be able to do a few years from now.” He adds that perovskites “should not be seen as a displacement of silicon photovoltaics. It should be seen as an augmentation — yet another way to bring about more rapid deployment of solar electricity.”

“A lot of progress has been made in the last two years on finding surface treatments that improve perovskite solar cells,” says Michael McGehee, a professor of chemical engineering at the University of Colorado who was not associated with this research. “A lot of the research has been empirical with the mechanisms behind the improvements not being fully understood. This detailed study shows that treatments can not only passivate defects, but can also create a surface field that repels carriers that should be collected at the other side of the device. This understanding might help further improve the interfaces.”

The team included researchers at the Korea Research Institute of Chemical Technology, Cambridge University, the University of Washington in Seattle, and Sungkyunkwan University in Korea. The work was supported by the Tata Trust, the MIT Institute for Soldier Nanotechnologies, the U.S. Department of Energy, and the U.S. National Science Foundation.

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Moving past the Iron Age

MIT graduate student Sydney Rose Johnson has never seen the steel mills in central India. She’s never toured the American Midwest’s hulking steel plants or the mini mills dotting the Mississippi River. But in the past year, she’s become more familiar with steel production than she ever imagined.

A fourth-year dual degree MBA and PhD candidate in chemical engineering and a graduate research assistant with the MIT Energy Initiative (MITEI) as well as a 2022-23 Shell Energy Fellow, Johnson looks at ways to reduce carbon dioxide (CO₂) emissions generated by industrial processes in hard-to-abate industries. Those include steel.

Almost every aspect of infrastructure and transportation — buildings, bridges, cars, trains, mass transit — contains steel. The manufacture of steel hasn’t changed much since the Iron Age, with some steel plants in the United States and India operating almost continually for more than a century, their massive blast furnaces re-lined periodically with carbon and graphite to keep them going.

According to the World Economic Forum, steel demand is projected to increase 30 percent by 2050, spurred in part by population growth and economic development in China, India, Africa, and Southeast Asia.

The steel industry is among the three biggest producers of CO₂ worldwide. Every ton of steel produced in 2020 emitted, on average, 1.89 tons of CO₂ into the atmosphere — around 8 percent of global CO₂ emissions, according to the World Steel Association.

A combination of technical strategies and financial investments, Johnson notes, will be needed to wrestle that 8 percent figure down to something more planet-friendly.

Johnson’s thesis focuses on modeling and analyzing ways to decarbonize steel. Using data mined from academic and industry sources, she builds models to calculate emissions, costs, and energy consumption for plant-level production.

“I optimize steel production pathways using emission goals, industry commitments, and cost,” she says. Based on the projected growth of India’s steel industry, she applies this approach to case studies that predict outcomes for some of the country’s thousand-plus factories, which together have a production capacity of 154 million metric tons of steel. For the United States, she looks at the effect of Inflation Reduction Act (IRA) credits. The 2022 IRA provides incentives that could accelerate the steel industry’s efforts to minimize its carbon emissions.

Johnson compares emissions and costs across different production pathways, asking questions such as: “If we start today, what would a cost-optimal production scenario look like years from now? How would it change if we added in credits? What would have to happen to cut 2005 levels of emissions in half by 2030?”

“My goal is to gain an understanding of how current and emerging decarbonization strategies will be integrated into the industry,” Johnson says.

Grappling with industrial problems

Growing up in Marietta, Georgia, outside Atlanta, the closest she ever came to a plant of any kind was through her father, a chemical engineer working in logistics and procuring steel for an aerospace company, and during high school, when she spent a semester working alongside chemical engineers tweaking the pH of an anti-foaming agent.

At Kennesaw Mountain High School, a STEM magnet program in Cobb County, students devote an entire semester of their senior year to an internship and research project.

Johnson chose to work at Kemira Chemicals, which develops chemical solutions for water-intensive industries with a focus on pulp and paper, water treatment, and energy systems.

“My goal was to understand why a polymer product was falling out of suspension — essentially, why it was less stable,” she recalls. She learned how to formulate a lab-scale version of the product and conduct tests to measure its viscosity and acidity. Comparing the lab-scale and regular product results revealed that acidity was an important factor. “Through conversations with my mentor, I learned this was connected with the holding conditions, which led to the product being oxidized,” she says. With the anti-foaming agent’s problem identified, steps could be taken to fix it.

“I learned how to apply problem-solving. I got to learn more about working in an industrial environment by connecting with the team in quality control as well as with R&D and chemical engineers at the plant site,” Johnson says. “This experience confirmed I wanted to pursue engineering in college.”

As an undergraduate at Stanford University, she learned about the different fields — biotechnology, environmental science, electrochemistry, and energy, among others — open to chemical engineers. “It seemed like a very diverse field and application range,” she says. “I was just so intrigued by the different things I saw people doing and all these different sets of issues.”

Turning up the heat

At MIT, she turned her attention to how certain industries can offset their detrimental effects on climate.

“I’m interested in the impact of technology on global communities, the environment, and policy. Energy applications affect every field. My goal as a chemical engineer is to have a broad perspective on problem-solving and to find solutions that benefit as many people, especially those under-resourced, as possible,” says Johnson, who has served on the MIT Chemical Engineering Graduate Student Advisory Board, the MIT Energy and Climate Club, and is involved with diversity and inclusion initiatives.

The steel industry, Johnson acknowledges, is not what she first imagined when she saw herself working toward mitigating climate change.

“But now, understanding the role the material has in infrastructure development, combined with its heavy use of coal, has illuminated how the sector, along with other hard-to-abate industries, is important in the climate change conversation,” Johnson says.

Despite the advanced age of many steel mills, some are quite energy-efficient, she notes. Yet these operations, which produce heat upwards of 3,000 degrees Fahrenheit, are still emission-intensive.

Steel is made from iron ore, a mixture of iron, oxygen, and other minerals found on virtually every continent, with Brazil and Australia alone exporting millions of metric tons per year. Commonly based on a process dating back to the 19th century, iron is extracted from the ore through smelting — heating the ore with blast furnaces until the metal becomes spongy and its chemical components begin to break down.

A reducing agent is needed to release the oxygen trapped in the ore, transforming it from its raw form to pure iron. That’s where most emissions come from, Johnson notes.

“We want to reduce emissions, and we want to make a cleaner and safer environment for everyone,” she says. “It’s not just the CO₂ emissions. It’s also sometimes NO_x and SO_x [nitrogen oxides and sulfur oxides] and air pollution particulate matter at some of these production facilities that can affect people as well.”

In 2020, the International Energy Agency released a roadmap exploring potential technologies and strategies that would make the iron and steel sector more compatible with the agency’s vision of increased sustainability. Emission reductions can be accomplished with more modern technology, the agency suggests, or by substituting the fuels producing the immense heat needed to process ore. Traditionally, the fuels used for iron reduction have been coal and natural gas. Alternative fuels include clean hydrogen, electricity, and biomass.

Using the MITEI Sustainable Energy System Analysis Modeling Environment (SESAME), Johnson analyzes various decarbonization strategies. She considers options such as switching fuel for furnaces to hydrogen with a little bit of natural gas or adding carbon-capture devices. The models demonstrate how effective these tactics are likely to be. The answers aren’t always encouraging.

“Upstream emissions can determine how effective the strategies are,” Johnson says. Charcoal derived from forestry biomass seemed to be a promising alternative fuel, but her models showed that processing the charcoal for use in the blast furnace limited its effectiveness in negating emissions.

Despite the challenges, “there are definitely ways of moving forward,” Johnson says. “It’s been an intriguing journey in terms of understanding where the industry is at. There’s still a long way to go, but it’s doable.”

Johnson is heartened by the steel industry’s efforts to recycle scrap into new steel products and incorporate more emission-friendly technologies and practices, some of which result in significantly lower CO₂ emissions than conventional production.

A major issue is that low-carbon steel can be more than 50 percent more costly than conventionally produced steel. “There are costs associated with making the transition, but in the context of the environmental implications, I think it’s well worth it to adopt these technologies,” she says.

After graduation, Johnson plans to continue to work in the energy field. “I definitely want to use a combination of engineering knowledge and business knowledge to work toward mitigating climate change, potentially in the startup space with clean technology or even in a policy context,” she says. “I’m interested in connecting the private and public sectors to implement measures for improving our environment and benefiting as many people as possible.”

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Sadhana Lolla named 2024 Gates Cambridge Scholar

MIT senior Sadhana Lolla has won the prestigious Gates Cambridge Scholarship, which offers students an opportunity to pursue graduate study in the field of their choice at Cambridge University in the U.K.

Established in 2000, the Gates Cambridge Scholarship offers full-cost post-graduate scholarships to outstanding applicants from countries outside of the U.K. The mission of the scholarship is to build a global network of future leaders committed to improving the lives of others.

Lolla, a senior from Clarksburg, Maryland, is majoring in computer science and minoring in mathematics and literature. At Cambridge, she will pursue an MPhil in technology policy.

In the future, Lolla aims to lead conversations on deploying and developing technology for marginalized communities, such as the rural Indian village that her family calls home, while also conducting research in embodied intelligence.

At MIT, Lolla conducts research on safe and trustworthy robotics and deep learning at the Distributed Robotics Laboratory with Professor Daniela Rus. Her research has spanned debiasing strategies for autonomous vehicles and accelerating robotic design processes. At Microsoft Research and Themis AI, she works on creating uncertainty-aware frameworks for deep learning, which has impacts across computational biology, language modeling, and robotics. She has presented her work at the Neural Information Processing Systems (NeurIPS) conference and the International Conference on Machine Learning (ICML). 

Outside of research, Lolla leads initiatives to make computer science education more accessible globally. She is an instructor for class 6.s191 (MIT Introduction to Deep Learning), one of the largest AI courses in the world, which reaches millions of students annually. She serves as the curriculum lead for Momentum AI, the only U.S. program that teaches AI to underserved students for free, and she has taught hundreds of students in Northern Scotland as part of the MIT Global Teaching Labs program.

Lolla was also the director for xFair, MIT’s largest student-run career fair, and is an executive board member for Next Sing, where she works to make a cappella more accessible for students across musical backgrounds. In her free time, she enjoys singing, solving crossword puzzles, and baking. 

“Between Sadhana's impressive research in the Distributed Robotics Group, her volunteer teaching with Momentum AI, and her internship and extracurricular experiences, she has developed the skills to be a leader,” says Kim Benard, associate dean of distinguished fellowships in Career Advising and Professional Development. “Her work at Cambridge will allow her the time to think about reducing bias in systems and the ethical implications of her work. I am proud that she will be representing MIT in the Gates Cambridge community.”

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New AI model could streamline operations in a robotic warehouse

Hundreds of robots zip back and forth across the floor of a colossal robotic warehouse, grabbing items and delivering them to human workers for packing and shipping. Such warehouses are increasingly becoming part of the supply chain in many industries, from e-commerce to automotive production.

However, getting 800 robots to and from their destinations efficiently while keeping them from crashing into each other is no easy task. It is such a complex problem that even the best path-finding algorithms struggle to keep up with the breakneck pace of e-commerce or manufacturing. 

In a sense, these robots are like cars trying to navigate a crowded city center. So, a group of MIT researchers who use AI to mitigate traffic congestion applied ideas from that domain to tackle this problem.

They built a deep-learning model that encodes important information about the warehouse, including the robots, planned paths, tasks, and obstacles, and uses it to predict the best areas of the warehouse to decongest to improve overall efficiency.

Their technique divides the warehouse robots into groups, so these smaller groups of robots can be decongested faster with traditional algorithms used to coordinate robots. In the end, their method decongests the robots nearly four times faster than a strong random search method.

In addition to streamlining warehouse operations, this deep learning approach could be used in other complex planning tasks, like computer chip design or pipe routing in large buildings.

“We devised a new neural network architecture that is actually suitable for real-time operations at the scale and complexity of these warehouses. It can encode hundreds of robots in terms of their trajectories, origins, destinations, and relationships with other robots, and it can do this in an efficient manner that reuses computation across groups of robots,” says Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu, senior author of a paper on this technique, is joined by lead author Zhongxia Yan, a graduate student in electrical engineering and computer science. The work will be presented at the International Conference on Learning Representations.

Robotic Tetris

From a bird’s eye view, the floor of a robotic e-commerce warehouse looks a bit like a fast-paced game of “Tetris.”

When a customer order comes in, a robot travels to an area of the warehouse, grabs the shelf that holds the requested item, and delivers it to a human operator who picks and packs the item. Hundreds of robots do this simultaneously, and if two robots’ paths conflict as they cross the massive warehouse, they might crash.

Traditional search-based algorithms avoid potential crashes by keeping one robot on its course and replanning a trajectory for the other. But with so many robots and potential collisions, the problem quickly grows exponentially.

“Because the warehouse is operating online, the robots are replanned about every 100 milliseconds. That means that every second, a robot is replanned 10 times. So, these operations need to be very fast,” Wu says.

Because time is so critical during replanning, the MIT researchers use machine learning to focus the replanning on the most actionable areas of congestion — where there exists the most potential to reduce the total travel time of robots.

Wu and Yan built a neural network architecture that considers smaller groups of robots at the same time. For instance, in a warehouse with 800 robots, the network might cut the warehouse floor into smaller groups that contain 40 robots each.

Then, it predicts which group has the most potential to improve the overall solution if a search-based solver were used to coordinate trajectories of robots in that group.

An iterative process, the overall algorithm picks the most promising robot group with the neural network, decongests the group with the search-based solver, then picks the next most promising group with the neural network, and so on.

Considering relationships

The neural network can reason about groups of robots efficiently because it captures complicated relationships that exist between individual robots. For example, even though one robot may be far away from another initially, their paths could still cross during their trips.

The technique also streamlines computation by encoding constraints only once, rather than repeating the process for each subproblem. For instance, in a warehouse with 800 robots, decongesting a group of 40 robots requires holding the other 760 robots as constraints. Other approaches require reasoning about all 800 robots once per group in each iteration.

Instead, the researchers’ approach only requires reasoning about the 800 robots once across all groups in each iteration.

“The warehouse is one big setting, so a lot of these robot groups will have some shared aspects of the larger problem. We designed our architecture to make use of this common information,” she adds.

They tested their technique in several simulated environments, including some set up like warehouses, some with random obstacles, and even maze-like settings that emulate building interiors.

By identifying more effective groups to decongest, their learning-based approach decongests the warehouse up to four times faster than strong, non-learning-based approaches. Even when they factored in the additional computational overhead of running the neural network, their approach still solved the problem 3.5 times faster.

In the future, the researchers want to derive simple, rule-based insights from their neural model, since the decisions of the neural network can be opaque and difficult to interpret. Simpler, rule-based methods could also be easier to implement and maintain in actual robotic warehouse settings.

“This approach is based on a novel architecture where convolution and attention mechanisms interact effectively and efficiently. Impressively, this leads to being able to take into account the spatiotemporal component of the constructed paths without the need of problem-specific feature engineering. The results are outstanding: Not only is it possible to improve on state-of-the-art large neighborhood search methods in terms of quality of the solution and speed, but the model generalizes to unseen cases wonderfully,” says Andrea Lodi, the Andrew H. and Ann R. Tisch Professor at Cornell Tech, and who was not involved with this research.

This work was supported by Amazon and the MIT Amazon Science Hub.

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Cybersecurity software wins a 2024 Federal Laboratory Consortium Excellence in Technology Transfer Award

The Federal Laboratory Consortium (FLC) has selected MIT Lincoln Laboratory’s Timely Address Space Randomization (TASR) as one of the recipients of their 2024 Excellence in Technology Transfer Award. This cybersecurity technology was transferred in 2019 and 2021 to two companies that develop cloud-based services.

TASR has the potential to help harden many cloud-based servers and user applications against rampant information-leakage attacks. These attacks have been involved in several recent high-profile breaches in which cyber criminals used sensitive information to commit fraud or identity theft, steal financial assets, or gain unauthorized access to other restricted or mission-critical systems. TASR is the first technology that mitigates the impact of such attacks regardless of the attack mechanism or underlying system vulnerability.

A nationwide network of more than 300 government laboratories, agencies, and research centers, FLC helps facilitate the transfer of technologies out of research labs and into the marketplace to benefit the U.S. economy, society, and national security. On an annual basis, FLC confers awards to commend outstanding technology transfer achievements of employees of FLC member labs and their partners from industry, academia, nonprofits, and state and local governments. The Excellence in Technology Transfer Award recognizes exemplary transfer of federally developed technology.

“We are honored to receive this FLC award recognizing our excellence in such technology transfer — in this case, of a cutting-edge cybersecurity technology for protecting everyday users of cloud infrastructure,” says Lincoln Laboratory Chief Technology Ventures Officer Asha Rajagopal.

The Lincoln Laboratory team behind TASR initially developed the technology under sponsorship by the National Security Agency (NSA), following a survey of existing cyber defenses and their vulnerabilities. The three-year development of TASR led to a research prototype in 2015 and a U.S. patent in 2019. In 2020, the U.S. Department of Homeland Security (DHS) selected TASR for its Commercialization Accelerator Program, through which the team matured the technology and connected with commercial companies. Given the growing need for hardening cloud-based services, TASR offers an attractive solution, as it protects Linux-based applications and servers from cyberattacks. Originally developed for personal computers based on Intel’s x86 architecture, the Linux operating system now runs more than 80 percent of all internet servers, 90 percent of public cloud workloads, all 500 of the world’s fastest supercomputers, and the majority of smartphones using Android.

TASR works by automatically and transparently shuffling (rerandomizing) the location of code in memory every time an application processes an input-and-output pair. Information may leak to an attacker whenever the application sends an output, such as a file write or data packet transmitted over a network. But with TASR, the information that may be leaked during system output will have changed at the next point the attacker is able to act on such information (i.e., at system input). Through this moving-target approach, TASR addresses a significant problem contributing to information-leakage attacks: target homogeneity. Once attackers devise an attack against an application, they can easily compromise millions of computers at once because all installations of that application look alike internally. By continuously rerandomizing memory throughout the application’s execution, TASR prevents such action.

“From the first day we started working on TASR, our focus was on making the technology as practical as possible to facilitate its transition to real users. We are honored to be recognized by the FLC for the decade-long journey leading to the transfer of TASR,” says principal investigator Hamed Okhravi, senior staff in the laboratory’s Secure Resilient Systems and Technology Group. Okhravi led the nearly decade-long process of conception, NSA and DHS sponsorship, development, maturation, and transfer phases for TASR, with support from the laboratory’s Technology Ventures Office and MIT’s Technology Licensing Office. The other team members are David Bigelow, Jason Martin, and William Streilein, and former staff members Thomas Hobson and Robert Rudd. TASR was previously recognized with a 2022 R&D 100 Award, acknowledged as one of the year’s 100 most innovative technologies available for sale or license.

The TASR team and awardees in the other categories will be honored at an award ceremony on April 10 during the 2024 FLC National Meeting in Dallas, Texas.

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“We offer another place for knowledge”

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

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

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

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

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

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

Doing something different in the community

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

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

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

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

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

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

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

Bridging the AI gap to unmet communities

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

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

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

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

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

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

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

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

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

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Generative AI for smart grid modeling

MIT’s Laboratory for Information and Decision Systems (LIDS) has been awarded $1,365,000 in funding from the Appalachian Regional Commission (ARC) to support its involvement with an innovative project, “Forming the Smart Grid Deployment Consortium (SGDC) and Expanding the HILLTOP+ Platform.”

The grant was made available through ARC's Appalachian Regional Initiative for Stronger Economies, which fosters regional economic transformation through multi-state collaboration.

Led by Kalyan Veeramachaneni, research scientist and principal investigator at LIDS' Data to AI Group, the project will focus on creating AI-driven generative models for customer load data. Veeramachaneni and colleagues will work alongside a team of universities and organizations led by Tennessee Tech University, including collaborators across Ohio, Pennsylvania, West Virginia, and Tennessee, to develop and deploy smart grid modeling services through the SGDC project.

These generative models have far-reaching applications, including grid modeling and training algorithms for energy tech startups. When the models are trained on existing data, they create additional, realistic data that can augment limited datasets or stand in for sensitive ones. Stakeholders can then use these models to understand and plan for specific what-if scenarios far beyond what could be achieved with existing data alone. For example, generated data can predict the potential load on the grid if an additional 1,000 households were to adopt solar technologies, how that load might change throughout the day, and similar contingencies vital to future planning.

The generative AI models developed by Veeramachaneni and his team will provide inputs to modeling services based on the HILLTOP+ microgrid simulation platform, originally prototyped by MIT Lincoln Laboratory. HILLTOP+ will be used to model and test new smart grid technologies in a virtual “safe space,” providing rural electric utilities with increased confidence in deploying smart grid technologies, including utility-scale battery storage. Energy tech startups will also benefit from HILLTOP+ grid modeling services, enabling them to develop and virtually test their smart grid hardware and software products for scalability and interoperability.

The project aims to assist rural electric utilities and energy tech startups in mitigating the risks associated with deploying these new technologies. “This project is a powerful example of how generative AI can transform a sector — in this case, the energy sector,” says Veeramachaneni. “In order to be useful, generative AI technologies and their development have to be closely integrated with domain expertise. I am thrilled to be collaborating with experts in grid modeling, and working alongside them to integrate the latest and greatest from my research group and push the boundaries of these technologies.”

“This project is testament to the power of collaboration and innovation, and we look forward to working with our collaborators to drive positive change in the energy sector,” says Satish Mahajan, principal investigator for the project at Tennessee Tech and a professor of electrical and computer engineering. Tennessee Tech’s Center for Rural Innovation director, Michael Aikens, adds, “Together, we are taking significant steps towards a more sustainable and resilient future for the Appalachian region.”

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Putting AI into the hands of people with problems to solve

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

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

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

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

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

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

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

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

A project with purpose

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

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

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

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

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

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

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

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

Building a better AI future

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

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

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

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

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

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

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