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The SERC Scholars Program unites MIT undergraduate and graduate students to explore the social and ethical responsibilities of computing. As a SERC Scholar, students take on big questions at the intersection of technology and society while joining a community dedicated to bringing humanities, social science, responsibility, and policy perspectives into MIT’s teaching, research, and practice of computing.

A cross-cutting initiative of the MIT Schwarzman College of Computing, the mission of the Social and Ethical Responsibilities of Computing (SERC) is to facilitate the development of responsible “habits of mind and action” for those who create and deploy computing technologies and foster the creation of technologies in the public interest.

Interested in becoming a SERC Scholar?

Learn more and apply:  https://bit.ly/BecomeASERCScholar

The deadline to submit is September 16, 2025, 11:59 pm ET.

Hosted by the MIT Schwarzman College of Computing, Expanding Horizons in Computing explored four essential computing topics. Organized by MIT faculty, the weeklong series of sessions delved into deep learning, societal impacts, cryptography, security, and quantum technology, and offered a compelling look at the opportunities and challenges shaping the future of computing.

Videos from the series are now out for your viewing pleasure!

Watch at: bit.ly/ExpandingHorizonsInComputing

📸 Eric Fletcher

#DeepLearning #ArtificialIntelligence #MachineLearning #EthicalComputing #TechnologyAndSociety #Cryptography #Security #QuantumComputing #MITComputing

Situated in the heart of campus on Vassar Street, the MIT Schwarzman College of Computing building will help form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence at MIT.

“The college has a broad mandate for computing across MIT,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “The building is designed to be the computing crossroads of the campus. It’s a place to bring a mix of people together to connect, engage, and catalyze collaborations in computing, and a home to a related set of computing research groups from multiple departments and labs.”

What's inside?

Floors 1 & 2: Multiple convening areas, including a 60-seat classroom, a 250-seat lecture hall, and an assortment of spaces for studying and social interactions.

Floors 4-7: Space for 50 computing research groups. In mid-January, faculty from across MIT affiliated with the Laboratory for Information and Decision Systems and Computer Science and Artificial Intelligence Laboratory, moved into the building. The research groups form a coherent overall cluster in deep learning and generative AI, natural language processing, computer vision, robotics, reinforcement learning, game theoretic methods, and societal impact of AI.

Floor 8: An event space along with three conference rooms are located on the top floor of the building with striking views across Cambridge and Boston and of the Great Dome.

Other floors in the building are supporting a variety of college programs and activities, including the MIT Quest for Intelligence, Center for Computational Science and Engineering, and MIT-IBM Watson AI Lab. There are also dedicated areas for the dean’s office, as well as for the cross-cutting areas of the college — the Social and Ethical Responsibilities of Computing, Common Ground for Computing Education, and Special Semester Topics in Computing.

Read more about Building 45, via link in bio or at bit.ly/SCC-Building.

All photos by Dave Burk © SOM.

#MITComputing

Situated in the heart of campus on Vassar Street, the MIT Schwarzman College of Computing building will help form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence at MIT.

“The college has a broad mandate for computing across MIT,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “The building is designed to be the computing crossroads of the campus. It’s a place to bring a mix of people together to connect, engage, and catalyze collaborations in computing, and a home to a related set of computing research groups from multiple departments and labs.”

What's inside?

Floors 1 & 2: Multiple convening areas, including a 60-seat classroom, a 250-seat lecture hall, and an assortment of spaces for studying and social interactions.

Floors 4-7: Space for 50 computing research groups. In mid-January, faculty from across MIT affiliated with the Laboratory for Information and Decision Systems and Computer Science and Artificial Intelligence Laboratory, moved into the building. The research groups form a coherent overall cluster in deep learning and generative AI, natural language processing, computer vision, robotics, reinforcement learning, game theoretic methods, and societal impact of AI.

Floor 8: An event space along with three conference rooms are located on the top floor of the building with striking views across Cambridge and Boston and of the Great Dome.

Other floors in the building are supporting a variety of college programs and activities, including the MIT Quest for Intelligence, Center for Computational Science and Engineering, and MIT-IBM Watson AI Lab. There are also dedicated areas for the dean’s office, as well as for the cross-cutting areas of the college — the Social and Ethical Responsibilities of Computing, Common Ground for Computing Education, and Special Semester Topics in Computing.

Read more about Building 45, via link in bio or at bit.ly/SCC-Building.

All photos by Dave Burk © SOM.

#MITComputing

Situated in the heart of campus on Vassar Street, the MIT Schwarzman College of Computing building will help form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence at MIT.

“The college has a broad mandate for computing across MIT,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “The building is designed to be the computing crossroads of the campus. It’s a place to bring a mix of people together to connect, engage, and catalyze collaborations in computing, and a home to a related set of computing research groups from multiple departments and labs.”

What's inside?

Floors 1 & 2: Multiple convening areas, including a 60-seat classroom, a 250-seat lecture hall, and an assortment of spaces for studying and social interactions.

Floors 4-7: Space for 50 computing research groups. In mid-January, faculty from across MIT affiliated with the Laboratory for Information and Decision Systems and Computer Science and Artificial Intelligence Laboratory, moved into the building. The research groups form a coherent overall cluster in deep learning and generative AI, natural language processing, computer vision, robotics, reinforcement learning, game theoretic methods, and societal impact of AI.

Floor 8: An event space along with three conference rooms are located on the top floor of the building with striking views across Cambridge and Boston and of the Great Dome.

Other floors in the building are supporting a variety of college programs and activities, including the MIT Quest for Intelligence, Center for Computational Science and Engineering, and MIT-IBM Watson AI Lab. There are also dedicated areas for the dean’s office, as well as for the cross-cutting areas of the college — the Social and Ethical Responsibilities of Computing, Common Ground for Computing Education, and Special Semester Topics in Computing.

Read more about Building 45, via link in bio or at bit.ly/SCC-Building.

All photos by Dave Burk © SOM.

#MITComputing

Situated in the heart of campus on Vassar Street, the MIT Schwarzman College of Computing building will help form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence at MIT.

“The college has a broad mandate for computing across MIT,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “The building is designed to be the computing crossroads of the campus. It’s a place to bring a mix of people together to connect, engage, and catalyze collaborations in computing, and a home to a related set of computing research groups from multiple departments and labs.”

What's inside?

Floors 1 & 2: Multiple convening areas, including a 60-seat classroom, a 250-seat lecture hall, and an assortment of spaces for studying and social interactions.

Floors 4-7: Space for 50 computing research groups. In mid-January, faculty from across MIT affiliated with the Laboratory for Information and Decision Systems and Computer Science and Artificial Intelligence Laboratory, moved into the building. The research groups form a coherent overall cluster in deep learning and generative AI, natural language processing, computer vision, robotics, reinforcement learning, game theoretic methods, and societal impact of AI.

Floor 8: An event space along with three conference rooms are located on the top floor of the building with striking views across Cambridge and Boston and of the Great Dome.

Other floors in the building are supporting a variety of college programs and activities, including the MIT Quest for Intelligence, Center for Computational Science and Engineering, and MIT-IBM Watson AI Lab. There are also dedicated areas for the dean’s office, as well as for the cross-cutting areas of the college — the Social and Ethical Responsibilities of Computing, Common Ground for Computing Education, and Special Semester Topics in Computing.

Read more about Building 45, via link in bio or at bit.ly/SCC-Building.

All photos by Dave Burk © SOM.

#MITComputing

A simple zipper idea turned into a new approach to adaptive structures and robotics.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) revived a 40-year-old idea for a three-sided “Y-zipper” that can transform soft, flexible materials into rigid structures using 3D printing.

Originally imagined in 1985 by William Freeman, MIT professor of electrical engineering and computer science, the design was impossible to manufacture at the time. Now, advances in computational design and 3D printing have brought it to life. The zipper forms a triangular tube when closed, creating strong, load-bearing shapes that can switch back to flexible when unzipped.

Potential applications include:

- Fast-deploying emergency shelters
- Shape-shifting robots

- Adjustable medical braces & casts

- Space exploration tools

The MIT CSAIL team also developed software that lets users customize the zipper into rods, arches, coils, and other forms before printing.

Read more about the Y-zipper design, via 🔗 in bio or at bit.ly/Y-Zipper

Photos: Courtesy of the researchers; Tim Malieckal/MIT CSAIL
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#3DPrinting #HumanComputerInteraction #ComputerScienceAndTechnology #MITInnovation #MITComputing

A simple zipper idea turned into a new approach to adaptive structures and robotics.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) revived a 40-year-old idea for a three-sided “Y-zipper” that can transform soft, flexible materials into rigid structures using 3D printing.

Originally imagined in 1985 by William Freeman, MIT professor of electrical engineering and computer science, the design was impossible to manufacture at the time. Now, advances in computational design and 3D printing have brought it to life. The zipper forms a triangular tube when closed, creating strong, load-bearing shapes that can switch back to flexible when unzipped.

Potential applications include:

- Fast-deploying emergency shelters
- Shape-shifting robots

- Adjustable medical braces & casts

- Space exploration tools

The MIT CSAIL team also developed software that lets users customize the zipper into rods, arches, coils, and other forms before printing.

Read more about the Y-zipper design, via 🔗 in bio or at bit.ly/Y-Zipper

Photos: Courtesy of the researchers; Tim Malieckal/MIT CSAIL
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#3DPrinting #HumanComputerInteraction #ComputerScienceAndTechnology #MITInnovation #MITComputing

A simple zipper idea turned into a new approach to adaptive structures and robotics.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) revived a 40-year-old idea for a three-sided “Y-zipper” that can transform soft, flexible materials into rigid structures using 3D printing.

Originally imagined in 1985 by William Freeman, MIT professor of electrical engineering and computer science, the design was impossible to manufacture at the time. Now, advances in computational design and 3D printing have brought it to life. The zipper forms a triangular tube when closed, creating strong, load-bearing shapes that can switch back to flexible when unzipped.

Potential applications include:

- Fast-deploying emergency shelters
- Shape-shifting robots

- Adjustable medical braces & casts

- Space exploration tools

The MIT CSAIL team also developed software that lets users customize the zipper into rods, arches, coils, and other forms before printing.

Read more about the Y-zipper design, via 🔗 in bio or at bit.ly/Y-Zipper

Photos: Courtesy of the researchers; Tim Malieckal/MIT CSAIL
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#3DPrinting #HumanComputerInteraction #ComputerScienceAndTechnology #MITInnovation #MITComputing

A simple zipper idea turned into a new approach to adaptive structures and robotics.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) revived a 40-year-old idea for a three-sided “Y-zipper” that can transform soft, flexible materials into rigid structures using 3D printing.

Originally imagined in 1985 by William Freeman, MIT professor of electrical engineering and computer science, the design was impossible to manufacture at the time. Now, advances in computational design and 3D printing have brought it to life. The zipper forms a triangular tube when closed, creating strong, load-bearing shapes that can switch back to flexible when unzipped.

Potential applications include:

- Fast-deploying emergency shelters
- Shape-shifting robots

- Adjustable medical braces & casts

- Space exploration tools

The MIT CSAIL team also developed software that lets users customize the zipper into rods, arches, coils, and other forms before printing.

Read more about the Y-zipper design, via 🔗 in bio or at bit.ly/Y-Zipper

Photos: Courtesy of the researchers; Tim Malieckal/MIT CSAIL
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#3DPrinting #HumanComputerInteraction #ComputerScienceAndTechnology #MITInnovation #MITComputing

A simple zipper idea turned into a new approach to adaptive structures and robotics.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) revived a 40-year-old idea for a three-sided “Y-zipper” that can transform soft, flexible materials into rigid structures using 3D printing.

Originally imagined in 1985 by William Freeman, MIT professor of electrical engineering and computer science, the design was impossible to manufacture at the time. Now, advances in computational design and 3D printing have brought it to life. The zipper forms a triangular tube when closed, creating strong, load-bearing shapes that can switch back to flexible when unzipped.

Potential applications include:

- Fast-deploying emergency shelters
- Shape-shifting robots

- Adjustable medical braces & casts

- Space exploration tools

The MIT CSAIL team also developed software that lets users customize the zipper into rods, arches, coils, and other forms before printing.

Read more about the Y-zipper design, via 🔗 in bio or at bit.ly/Y-Zipper

Photos: Courtesy of the researchers; Tim Malieckal/MIT CSAIL
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#3DPrinting #HumanComputerInteraction #ComputerScienceAndTechnology #MITInnovation #MITComputing

Gabriele Farina, assistant professor at @miteecs and principal investigator at the Laboratory for Information and Decision Systems, is pushing AI beyond pattern matching into true strategic reasoning where machines must make decisions with incomplete information, adapt to opponents, and navigate complex multi-agent environments.

Early on, Farina was fascinated by the idea that a machine could make predictions or decisions so much better than humans. “The fact that human-made mathematics and algorithms could create systems that, in some sense, outperform their creators, all while building on simple building blocks, has always been a major source of awe for me,” he says.

After graduating from @polimi, he pursued a PhD in computer science at @carnegiemellon. He joined the MIT faculty in 2023, where his work now blends game theory, machine learning, optimization, and statistics to tackle challenges in negotiation, competition, and cooperation.

“We have seen constant progress towards constructing algorithms that can reason strategically and make sound decisions despite large action spaces or imperfect information. I am excited about seeing these algorithms incorporated into the broader AI revolution that’s happening around us,” says Farina who is also a member of the Operations Research Center.

Read more about Farina and his research, via link in bio or at bit.ly/MIT-Farina

#ArtificialIntelligence #GameTheory #Algorithms #HumanComputerInteraction #MITComputing

Building on a long-standing MIT-IBM collaboration, the MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing.

Evolving from the MIT-IBM Watson AI Lab, which originated in 2017 on MIT’s campus, the new lab reflects a transformed technology landscape — one in which AI has entered mainstream deployment, and quantum computing is rapidly advancing toward practical impact.

The MIT-IBM Computing Research Lab will serve as a focal point for joint research between @mit and @ibm in AI, algorithms, and quantum computing, as well as the integration of these technologies into hybrid computing systems.

The lab is designed to accelerate progress toward powerful new computational approaches that take advantage of rapid advances in AI and quantum-centric supercomputing, including those that combine maturing quantum hardware with classical systems and advanced AI methods.

The lab will also continue to serve as a foundation for training the next generation of computational scientists and innovators. It will do so by engaging faculty and students across MIT departments, enabling new computational approaches to accelerate discoveries in the physical and life sciences.

Read more about the new collaboration, via link in bio or at bit.ly/MIT-IBM

#ArtificialIntelligence #Algorithms #QuantumComputing #Collaboration #MITComputing

Building on a long-standing MIT-IBM collaboration, the MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing.

Evolving from the MIT-IBM Watson AI Lab, which originated in 2017 on MIT’s campus, the new lab reflects a transformed technology landscape — one in which AI has entered mainstream deployment, and quantum computing is rapidly advancing toward practical impact.

The MIT-IBM Computing Research Lab will serve as a focal point for joint research between @mit and @ibm in AI, algorithms, and quantum computing, as well as the integration of these technologies into hybrid computing systems.

The lab is designed to accelerate progress toward powerful new computational approaches that take advantage of rapid advances in AI and quantum-centric supercomputing, including those that combine maturing quantum hardware with classical systems and advanced AI methods.

The lab will also continue to serve as a foundation for training the next generation of computational scientists and innovators. It will do so by engaging faculty and students across MIT departments, enabling new computational approaches to accelerate discoveries in the physical and life sciences.

Read more about the new collaboration, via link in bio or at bit.ly/MIT-IBM

#ArtificialIntelligence #Algorithms #QuantumComputing #Collaboration #MITComputing

Building on a long-standing MIT-IBM collaboration, the MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing.

Evolving from the MIT-IBM Watson AI Lab, which originated in 2017 on MIT’s campus, the new lab reflects a transformed technology landscape — one in which AI has entered mainstream deployment, and quantum computing is rapidly advancing toward practical impact.

The MIT-IBM Computing Research Lab will serve as a focal point for joint research between @mit and @ibm in AI, algorithms, and quantum computing, as well as the integration of these technologies into hybrid computing systems.

The lab is designed to accelerate progress toward powerful new computational approaches that take advantage of rapid advances in AI and quantum-centric supercomputing, including those that combine maturing quantum hardware with classical systems and advanced AI methods.

The lab will also continue to serve as a foundation for training the next generation of computational scientists and innovators. It will do so by engaging faculty and students across MIT departments, enabling new computational approaches to accelerate discoveries in the physical and life sciences.

Read more about the new collaboration, via link in bio or at bit.ly/MIT-IBM

#ArtificialIntelligence #Algorithms #QuantumComputing #Collaboration #MITComputing

Some before and after photos of Building 45, the headquarters of the MIT Schwarzman College of Computing.

Located along Vassar Street in the heart of MIT’s campus in Cambridge, MA, Building 45 quickly established itself as a central hub for computing since opening its doors just over two years ago.

Envisioned as a “computing crossroads,” the building brings together a vibrant mix of researchers, students, and collaborators, fostering connection, engagement, and interdisciplinary innovation.

More than just a shared space, Building 45 reflects the mission of the MIT Schwarzman College of Computing: to strengthen core computer science and artificial intelligence; infuse the forefront of computing with disciplines across MIT; and advance social, ethical, and policy dimensions of computing.

Where is Building 45?
👉 whereis.mit.edu/?go=45

#MITComputing

Some before and after photos of Building 45, the headquarters of the MIT Schwarzman College of Computing.

Located along Vassar Street in the heart of MIT’s campus in Cambridge, MA, Building 45 quickly established itself as a central hub for computing since opening its doors just over two years ago.

Envisioned as a “computing crossroads,” the building brings together a vibrant mix of researchers, students, and collaborators, fostering connection, engagement, and interdisciplinary innovation.

More than just a shared space, Building 45 reflects the mission of the MIT Schwarzman College of Computing: to strengthen core computer science and artificial intelligence; infuse the forefront of computing with disciplines across MIT; and advance social, ethical, and policy dimensions of computing.

Where is Building 45?
👉 whereis.mit.edu/?go=45

#MITComputing

Some before and after photos of Building 45, the headquarters of the MIT Schwarzman College of Computing.

Located along Vassar Street in the heart of MIT’s campus in Cambridge, MA, Building 45 quickly established itself as a central hub for computing since opening its doors just over two years ago.

Envisioned as a “computing crossroads,” the building brings together a vibrant mix of researchers, students, and collaborators, fostering connection, engagement, and interdisciplinary innovation.

More than just a shared space, Building 45 reflects the mission of the MIT Schwarzman College of Computing: to strengthen core computer science and artificial intelligence; infuse the forefront of computing with disciplines across MIT; and advance social, ethical, and policy dimensions of computing.

Where is Building 45?
👉 whereis.mit.edu/?go=45

#MITComputing

Some before and after photos of Building 45, the headquarters of the MIT Schwarzman College of Computing.

Located along Vassar Street in the heart of MIT’s campus in Cambridge, MA, Building 45 quickly established itself as a central hub for computing since opening its doors just over two years ago.

Envisioned as a “computing crossroads,” the building brings together a vibrant mix of researchers, students, and collaborators, fostering connection, engagement, and interdisciplinary innovation.

More than just a shared space, Building 45 reflects the mission of the MIT Schwarzman College of Computing: to strengthen core computer science and artificial intelligence; infuse the forefront of computing with disciplines across MIT; and advance social, ethical, and policy dimensions of computing.

Where is Building 45?
👉 whereis.mit.edu/?go=45

#MITComputing

Some before and after photos of Building 45, the headquarters of the MIT Schwarzman College of Computing.

Located along Vassar Street in the heart of MIT’s campus in Cambridge, MA, Building 45 quickly established itself as a central hub for computing since opening its doors just over two years ago.

Envisioned as a “computing crossroads,” the building brings together a vibrant mix of researchers, students, and collaborators, fostering connection, engagement, and interdisciplinary innovation.

More than just a shared space, Building 45 reflects the mission of the MIT Schwarzman College of Computing: to strengthen core computer science and artificial intelligence; infuse the forefront of computing with disciplines across MIT; and advance social, ethical, and policy dimensions of computing.

Where is Building 45?
👉 whereis.mit.edu/?go=45

#MITComputing

Some before and after photos of Building 45, the headquarters of the MIT Schwarzman College of Computing.

Located along Vassar Street in the heart of MIT’s campus in Cambridge, MA, Building 45 quickly established itself as a central hub for computing since opening its doors just over two years ago.

Envisioned as a “computing crossroads,” the building brings together a vibrant mix of researchers, students, and collaborators, fostering connection, engagement, and interdisciplinary innovation.

More than just a shared space, Building 45 reflects the mission of the MIT Schwarzman College of Computing: to strengthen core computer science and artificial intelligence; infuse the forefront of computing with disciplines across MIT; and advance social, ethical, and policy dimensions of computing.

Where is Building 45?
👉 whereis.mit.edu/?go=45

#MITComputing

Some before and after photos of Building 45, the headquarters of the MIT Schwarzman College of Computing.

Located along Vassar Street in the heart of MIT’s campus in Cambridge, MA, Building 45 quickly established itself as a central hub for computing since opening its doors just over two years ago.

Envisioned as a “computing crossroads,” the building brings together a vibrant mix of researchers, students, and collaborators, fostering connection, engagement, and interdisciplinary innovation.

More than just a shared space, Building 45 reflects the mission of the MIT Schwarzman College of Computing: to strengthen core computer science and artificial intelligence; infuse the forefront of computing with disciplines across MIT; and advance social, ethical, and policy dimensions of computing.

Where is Building 45?
👉 whereis.mit.edu/?go=45

#MITComputing

Some before and after photos of Building 45, the headquarters of the MIT Schwarzman College of Computing.

Located along Vassar Street in the heart of MIT’s campus in Cambridge, MA, Building 45 quickly established itself as a central hub for computing since opening its doors just over two years ago.

Envisioned as a “computing crossroads,” the building brings together a vibrant mix of researchers, students, and collaborators, fostering connection, engagement, and interdisciplinary innovation.

More than just a shared space, Building 45 reflects the mission of the MIT Schwarzman College of Computing: to strengthen core computer science and artificial intelligence; infuse the forefront of computing with disciplines across MIT; and advance social, ethical, and policy dimensions of computing.

Where is Building 45?
👉 whereis.mit.edu/?go=45

#MITComputing

New research from MIT’s Computer Science and Artificial Intelligence Laboratory tackles one of AI’s biggest flaws: overconfidence.

Today’s AI models aren’t just wrong sometimes. They’re often confidently wrong. Standard training gives them no incentive to admit uncertainty, so they learn to guess and sound sure doing it.

A technique developed by the @mit_csail team, called RLCR (Reinforcement Learning with Calibration Rewards), trains language models to answer and estimate how confident they are in those answers. In experiments, RLCR reduced calibration error by up to 90% while maintaining or improving accuracy, including on entirely new tasks.

Why does this matter? When models are deployed in medicine, law, finance, or any setting where users make decisions based on AI outputs, false confidence is more dangerous than a wrong answer. If a model says “I’m 95% sure” but is right only half the time, users have no signal to question it or seek a second opinion.

"The standard training approach is simple and powerful, but it gives the model no incentive to express uncertainty or say I don’t know," says Mehul Damani, an MIT PhD student in electrical engineering and computer science and co-lead author on the paper about the new method. "So the model naturally learns to guess when it is unsure." 

By teaching AI to say “I’m not sure,” this approach could be a major step toward making it more trustworthy.

Read more about the technique, via 🔗 in bio or at bit.ly/RLCR
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#MITComputing

New research from MIT’s Computer Science and Artificial Intelligence Laboratory tackles one of AI’s biggest flaws: overconfidence.

Today’s AI models aren’t just wrong sometimes. They’re often confidently wrong. Standard training gives them no incentive to admit uncertainty, so they learn to guess and sound sure doing it.

A technique developed by the @mit_csail team, called RLCR (Reinforcement Learning with Calibration Rewards), trains language models to answer and estimate how confident they are in those answers. In experiments, RLCR reduced calibration error by up to 90% while maintaining or improving accuracy, including on entirely new tasks.

Why does this matter? When models are deployed in medicine, law, finance, or any setting where users make decisions based on AI outputs, false confidence is more dangerous than a wrong answer. If a model says “I’m 95% sure” but is right only half the time, users have no signal to question it or seek a second opinion.

"The standard training approach is simple and powerful, but it gives the model no incentive to express uncertainty or say I don’t know," says Mehul Damani, an MIT PhD student in electrical engineering and computer science and co-lead author on the paper about the new method. "So the model naturally learns to guess when it is unsure." 

By teaching AI to say “I’m not sure,” this approach could be a major step toward making it more trustworthy.

Read more about the technique, via 🔗 in bio or at bit.ly/RLCR
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#MITComputing

New research from MIT’s Computer Science and Artificial Intelligence Laboratory tackles one of AI’s biggest flaws: overconfidence.

Today’s AI models aren’t just wrong sometimes. They’re often confidently wrong. Standard training gives them no incentive to admit uncertainty, so they learn to guess and sound sure doing it.

A technique developed by the @mit_csail team, called RLCR (Reinforcement Learning with Calibration Rewards), trains language models to answer and estimate how confident they are in those answers. In experiments, RLCR reduced calibration error by up to 90% while maintaining or improving accuracy, including on entirely new tasks.

Why does this matter? When models are deployed in medicine, law, finance, or any setting where users make decisions based on AI outputs, false confidence is more dangerous than a wrong answer. If a model says “I’m 95% sure” but is right only half the time, users have no signal to question it or seek a second opinion.

"The standard training approach is simple and powerful, but it gives the model no incentive to express uncertainty or say I don’t know," says Mehul Damani, an MIT PhD student in electrical engineering and computer science and co-lead author on the paper about the new method. "So the model naturally learns to guess when it is unsure." 

By teaching AI to say “I’m not sure,” this approach could be a major step toward making it more trustworthy.

Read more about the technique, via 🔗 in bio or at bit.ly/RLCR
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#MITComputing

Jacob Andreas, an associate professor in MIT's Department of Electrical Engineering and Computer Science (EECS), has been selected as a winner of the 2026 Harold E. Edgerton Faculty Achievement Award.

Established in 1982 as a permanent tribute to MIT Institute Professor Emeritus Harold E. Edgerton’s great and enduring support for younger faculty members, this award is given annually in recognition of exceptional distinction in teaching, research, and service.

Andreas joined the MIT faculty in July 2019, and is affiliated with @mit_csail. His work is in natural language processing, and more broadly in AI. He aims to understand the computational foundations of language learning, and to build intelligent systems that can learn from human guidance.

Within EECS, Andreas has developed multiple advanced courses in natural language processing, as well as new exercises designed to get students to grapple with important social and ethical considerations in machine learning deployment. 

“Jacob has taken a leading role in completely modernizing and extending our course offerings in natural language processing,” says award nominator Leslie Kaelbling, Panasonic Professor in the Department of EECS. “He has led the development of a modern two-course sequence, which is a cornerstone of the new AI+D [artificial intelligence and decision-making] major, routinely enrolling several hundred students each semester. His command of the area is broad and deep, and his classes integrate classical structural understanding of language with the most modern learning-based approaches. He has put MIT EECS on the worldwide map as a place to study natural language at every level.”

Andreas joins Brett McGuire, an associate professor in MIT’s Department of Chemistry, in receiving this year’s award.

Read more about the 2026 Edgerton Award winners: bit.ly/2026EdgertonAward

#MITComputing

A new course, 6.S044/24.S00 (AI and Rationality) co-taught by Brian Hedden - a professor with a shared appointment in @mit_philosophy and @miteecs through @mitcomputing - and computer science and engineering professor Leslie Kaelbling, challenges students to explore rationality and other philosophical problems through the lens of AI research.

In this video, Hedden and Kaelbling discuss the course and the importance of giving students tools to consider AI and its design and implementation carefully and critically.

At MIT, an unlikely friendship between a computer scientist and an anthropologist sparked a new class that’s rethinking how chatbots are built.

Combining two seemingly disparate disciplines, the class, 6.S061/21A.S02 (Humane User Experience Design, a.k.a. Humane UXD), teaches students how to design AI that actually understands human interaction, not just output answers. Using methods from linguistic anthropology, students learn to build chatbots that supports real conversations and help users grow.

A collaboration between Prof. Arvind Satyanarayan, a computer scientist whose research develops tools for interactive data visualization and user interfaces, and Prof. Graham Jones, an anthropologist whose research focuses on communication, Humane UXD was developed under the auspices of the Common Ground for Computing Education, an initiative of the MIT Schwarzman College of Computing that brings together departments to create courses integrating computing with other disciplines.

As part of the class, students team up on projects using tools like Google’s Gemini, showing what’s possible when “technology and the humanities are deeply intertwined,” as Jones puts it.

Read more about the class and student projects, via link in bio or at bit.ly/HumaneUXD.

#TechnologyAndSociety #Design #SocialSciences #ComputerScience #ArtificialIntelligence #Anthropology #HumanComputerInteraction #TechForGood #MITComputing

At MIT, an unlikely friendship between a computer scientist and an anthropologist sparked a new class that’s rethinking how chatbots are built.

Combining two seemingly disparate disciplines, the class, 6.S061/21A.S02 (Humane User Experience Design, a.k.a. Humane UXD), teaches students how to design AI that actually understands human interaction, not just output answers. Using methods from linguistic anthropology, students learn to build chatbots that supports real conversations and help users grow.

A collaboration between Prof. Arvind Satyanarayan, a computer scientist whose research develops tools for interactive data visualization and user interfaces, and Prof. Graham Jones, an anthropologist whose research focuses on communication, Humane UXD was developed under the auspices of the Common Ground for Computing Education, an initiative of the MIT Schwarzman College of Computing that brings together departments to create courses integrating computing with other disciplines.

As part of the class, students team up on projects using tools like Google’s Gemini, showing what’s possible when “technology and the humanities are deeply intertwined,” as Jones puts it.

Read more about the class and student projects, via link in bio or at bit.ly/HumaneUXD.

#TechnologyAndSociety #Design #SocialSciences #ComputerScience #ArtificialIntelligence #Anthropology #HumanComputerInteraction #TechForGood #MITComputing

At MIT, an unlikely friendship between a computer scientist and an anthropologist sparked a new class that’s rethinking how chatbots are built.

Combining two seemingly disparate disciplines, the class, 6.S061/21A.S02 (Humane User Experience Design, a.k.a. Humane UXD), teaches students how to design AI that actually understands human interaction, not just output answers. Using methods from linguistic anthropology, students learn to build chatbots that supports real conversations and help users grow.

A collaboration between Prof. Arvind Satyanarayan, a computer scientist whose research develops tools for interactive data visualization and user interfaces, and Prof. Graham Jones, an anthropologist whose research focuses on communication, Humane UXD was developed under the auspices of the Common Ground for Computing Education, an initiative of the MIT Schwarzman College of Computing that brings together departments to create courses integrating computing with other disciplines.

As part of the class, students team up on projects using tools like Google’s Gemini, showing what’s possible when “technology and the humanities are deeply intertwined,” as Jones puts it.

Read more about the class and student projects, via link in bio or at bit.ly/HumaneUXD.

#TechnologyAndSociety #Design #SocialSciences #ComputerScience #ArtificialIntelligence #Anthropology #HumanComputerInteraction #TechForGood #MITComputing

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a smarter way to run data centers that boosts performance without adding more hardware.

Their new system dynamically balances workloads across storage devices in real time, tackling multiple sources of inefficiency at once. The result? Nearly 2x performance gains in tests like AI training and image processing.

Instead of scaling by adding more machines (and energy use), this approach extracts more value out of existing infrastructure, making data centers faster, cheaper, and more sustainable.

“With our adaptive software solution, you can still squeeze a lot of performance out of your existing devices before you need to throw them away and buy new ones,” says Gohar Chaudhry, an electrical engineering and computer science graduate student working in Prof. Adam Belay’s lab at @mit_csail and lead author of a paper on this technique.

Read more about the research, via link in bio or at bit.ly/DataCentersLessHardware.

#ArtificialIntelligence #MachineLearning #Algorithms #Data #Electronics #Sustainability