# Computer science and technology Published articles for Computer science and technology. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## New AI technique could make minimally invasive surgeries safer and more precise DevFeed: [New AI technique could make minimally invasive surgeries safer and more precise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-16T15:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [3D](), [navigation]() Tags: [3d](), [ai](), [artificial-intelligence](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computer-vision](), [electrical-engineering-and-computer-science-eecs](), [health-care](), [images](), [imaging](), [jameel-clinic](), [machine-learning](), [medical-devices](), [medical-imaging](), [minimally-invasive-surgery](), [mit-ibm-computing-research-lab](), [mit-schwarzman-college-of-computing](), [model](), [national-institutes-of-health-nih](), [navigation](), [paper](), [polina-golland](), [precision](), [real-time](), [research](), [school-of-engineering](), [vision](), [vivek-gopalakrishnan]() ### AI overview MIT researchers and collaborators developed xvr, an AI method that adapts to individual patients and rapidly aligns intraoperative X-rays with preoperative 3D medical scans. The technique is intended to improve surgical navigation for minimally invasive procedures. ### Source excerpt This patient-specific method, called xvr, helps doctors use X-rays for surgical navigation in fields such as orthopedics and neurosurgery. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## MIT Schwarzman College of Computing launches pilot to help educators teach AI across disciplines DevFeed: [MIT Schwarzman College of Computing launches pilot to help educators teach AI across disciplines]() Original publisher: [Read original article]() Author: Amanda Diehl | MIT Schwarzman College of Computing Published: 2026-09-09T20:40:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [MIT Schwarzman College of Computing](), [Artificial Intelligence](), [Machine learning](), [Learning](), [Electrical engineering and computer science (EECS)]() Tags: [ai](), [ai-educators-pilot](), [ai-teaching-workshop](), [artificial-intelligence](), [asu-ozdaglar](), [civil-and-environmental-engineering](), [classes-and-programs](), [computer-science-and-technology](), [computing](), [dan-huttenlocher](), [education](), [education-teaching-academics](), [electrical-engineering-and-computer-science-eecs](), [machine-learning](), [mit-class-c01-c51-modeling-with-machine-learning](), [mit-eecs](), [mit-operations-research-center](), [mit-schwarzman-college-of-computing](), [mit-sloan](), [mit-sloan-school-of-management](), [pilot](), [saurabh-amin](), [school-of-engineering](), [shen-shen](), [special-events-and-guest-speakers](), [stem-education](), [teaching](), [teaching-ai]() ### AI overview MIT's inaugural AI Educators Pilot brought higher education faculty to campus for a weeklong workshop on adapting AI and machine learning materials for teaching across disciplines. ### Source excerpt A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms. ## From MIT to IBM, expediting AI and quantum deployment DevFeed: [From MIT to IBM, expediting AI and quantum deployment]() Original publisher: [Read original article]() Author: Lauren Hinkel | MIT-IBM Computing Research Lab Published: 2026-09-02T20:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ibm](), [Reinforcement learning](), [Computing](), [LLMs]() Tags: [academic](), [ai](), [ai-agents](), [algorithms](), [alumni-ae](), [aram-harrow](), [artificial-intelligence](), [atari-games](), [careers](), [collaboration](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computing](), [data](), [electrical-engineering-and-computer-science-eecs](), [fair-ai](), [graduate-postdoctoral](), [graduate-students](), [ibm](), [industry](), [irene-ko](), [isaac-chuang](), [language-models](), [learning](), [luca-daniel](), [machine-learning](), [mit-ibm-computing-research-lab](), [mit-ibm-watson-ai-lab](), [mit-schwarzman-college-of-computing](), [pulkit-agrawal](), [quantum-computing](), [quantum-machine-learning](), [research](), [school-of-engineering](), [srinivasan-arunachalam](), [trustworthy-ai](), [vllm-hook](), [zhang-wei-hong]() ### AI overview MIT graduate students and a former postdoc who moved to IBM describe how work with the MIT-IBM Computing Research Lab helped translate rigorous research into industry applications. Their areas include quantum machine learning, reinforcement learning, AI agents, and trustworthy and fair AI. ### Source excerpt MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems. ## System helps humans predict when self-driving cars will make mistakes DevFeed: [System helps humans predict when self-driving cars will make mistakes]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-02T15:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [autonomous vehicles](), [Deep learning](), [Artificial Intelligence](), [Robotics](), [Simulation](), [Computer science]() Tags: [aeronautical-and-astronautical-engineering](), [ai](), [artificial-intelligence](), [autonomous-vehicles](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [concept-wrapper-network](), [cw-net](), [deep](), [electrical-engineering-and-computer-science-eecs](), [eoin-kenny](), [human-computer-interaction](), [julie-shah](), [laura-major](), [machine-learning](), [mit-schwarzman-college-of-computing](), [momchil-tomov](), [motional](), [research](), [robotics](), [safety](), [school-of-engineering](), [self-driving](), [self-driving-cars](), [simulation](), [transparency]() ### AI overview MIT and Motional researchers developed CW-Net, a method that translates an autonomous vehicle's deep-learning decisions into understandable concepts. Tests found that the explanations helped safety drivers and nonexpert users better predict vehicle behavior. ### Source excerpt A new method, called CW-Net, translates the reasoning process of an autonomous vehicle's AI system into understandable concepts that explain its behavior. ## PottsMPNN uses machine learning to improve computational protein design DevFeed: [PottsMPNN uses machine learning to improve computational protein design]() Original publisher: [Read original article]() Author: Lillian Eden | Department of Biology Published: 2026-08-27T19:20:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Machine learning](), [Artificial Intelligence]() Tags: [ai](), [ai-in-biology](), [amino-acids](), [amy-keating](), [artificial-intelligence](), [biological-engineering](), [biology](), [computational-protein-design](), [computer-science-and-technology](), [foster-birnbaum](), [machine-learning](), [mit-department-of-biology](), [native-sequence-recovery](), [novel-protein-design](), [pottsmpnn](), [proteins](), [research](), [school-of-engineering](), [school-of-science]() ### AI overview Researchers developed PottsMPNN, a machine-learning framework for computational protein design that incorporates physical principles governing protein structure and stability. The framework evaluates generated sequences by their likelihood of folding into desired structures and by its ability to predict mutation effects, rather than by how closely they match naturally occurring sequences. ### Source excerpt A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature. ## AI helps design new materials that work in the real world DevFeed: [AI helps design new materials that work in the real world]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-26T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Framework](), [Simulation and Design](), [Crystal]() Tags: [ai](), [artificial-intelligence](), [bowen-yu](), [chemical-engineering](), [chemistry](), [computer-chips](), [computer-science-and-technology](), [crystal](), [crysvcd](), [data-centers](), [department-of-energy-doe](), [diffusion-models](), [dmse](), [hao-tang](), [heather-kulik](), [ju-li](), [machine-learning](), [materials-design](), [materials-discovery](), [materials-science-and-engineering](), [mingda-li](), [mouyang-cheng](), [national-science-foundation-nsf](), [nuclear-science-and-engineering](), [paper](), [physics](), [research](), [school-of-engineering](), [school-of-science](), [semiconductors](), [weiliang-luo](), [weiwei-xie](), [yongqiang-cheng]() ### AI overview MIT researchers developed CrysVCD, a framework that applies chemistry-based valence constraints before material generation to improve the stability of generated designs. In tests, it achieved high lattice-dynamics stability in nearly 70 percent of computational material generations and supported targeting properties such as high thermal conductivity and high dielectric constant. ### Source excerpt The "CrysVCD" tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs. ## When AI art has no author: Study finds generated images often can't be traced to training data DevFeed: [When AI art has no author: Study finds generated images often can't be traced to training data]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-08-18T16:35:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [datasets](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [ablation](), [ai](), [ai-and-copyright-law](), [ai-generated-images](), [ai-governance](), [artificial-intelligence](), [arts](), [arts-technology-and-society](), [attribution-decay](), [causal-inference](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [counterfactual-analysis](), [counterfactual-radius](), [data](), [data-attribution](), [datasets](), [david-gifford](), [diffusion-ensembles](), [electrical-engineering-and-computer-science-eecs](), [ethics](), [generative-ai-images](), [generative-diffusion-models](), [image-similarity-metrics](), [images](), [law](), [machine-learning](), [machine-unlearning](), [mit-csail](), [mit-schwarzman-college-of-computing](), [model](), [model-interpretability](), [paper](), [privacy-preserving-machine-learning](), [research](), [school-of-engineering](), [science](), [technology-and-policy](), [technology-and-society](), [training-data-attribution](), [training-data-influence](), [zheng-dai]() ### AI overview MIT CSAIL researchers describe attribution decay, a phenomenon in which the influence of individual training examples on a generative model's outputs diminishes as datasets grow. Their method removes training examples and retrains models to test whether generated samples change. ### Source excerpt A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves. ## GeoPT helps AI models simulate how objects respond to physical forces DevFeed: [GeoPT helps AI models simulate how objects respond to physical forces]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-08-10T19:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Models](), [Simulation](), [AI Research](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Robotics]() Tags: [3-d-imaging](), [ai](), [ai-models](), [artificial-intelligence](), [computational-fluid-dynamics-cfd](), [computer-graphics](), [computer-modeling](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [crash-simulation](), [design](), [electrical-engineering-and-computer-science-eecs](), [geometric-pre-training](), [geopt](), [haixu-wu](), [human-computer-interaction](), [kaiming-he](), [manufacturing](), [minghao-guo](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [neural-physics-simulation](), [paper](), [physics](), [physics-aware-ai](), [physics-foundation-models](), [research](), [robotics](), [school-of-engineering](), [self-supervised-learning](), [simulation](), [surrogate-modeling](), [synthetic-dynamics](), [transformer-based-simulators](), [wojciech-matusik]() ### AI overview Researchers at MIT CSAIL and Tsinghua University developed GeoPT, a pre-training approach that uses 3D simulations of mechanical interactions to help AI models learn physics more efficiently. The article reports that models using the approach reached peak performance twice as fast and trained on up to 60 percent less data than leading models. ### Source excerpt "GeoPT" helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately. ## The benefits of medical AI assistance vary based on user expertise DevFeed: [The benefits of medical AI assistance vary based on user expertise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-08-04T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [AI Development](), [AI Chat]() Tags: [ai](), [artificial-intelligence](), [bias](), [computer-science-and-technology](), [decision-making](), [dermatological-diagnosis](), [diagnosing-skin-disease](), [diagnostics](), [electrical-engineering-and-computer-science-eecs](), [explainability](), [explainable-ai](), [health-care](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [jameel-clinic](), [laboratory-for-information-and-decision-systems-lids](), [large-language-model](), [machine-learning](), [marzyeh-ghassemi](), [medicine](), [research](), [technology-and-society](), [users]() ### AI overview A study found that AI assistance improved skin-disease diagnosis for non-experts and clinicians, but explainability affected users differently. Non-experts often deferred to LLM-based explanations even when the AI was wrong, while clinicians performed best with the model's prediction alone. ### Source excerpt Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors. ## Alexander Rakhlin named director of the MIT Statistics and Data Science Center DevFeed: [Alexander Rakhlin named director of the MIT Statistics and Data Science Center]() Original publisher: [Read original article]() Author: Institute for Data, Systems, and Society Published: 2026-08-03T19:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [Machine learning]() Tags: [ai-in-statistics](), [alexander-sasha-rakhlin](), [alumni-ae](), [ankur-moitra](), [artificial-intelligence](), [brain-and-cognitive-sciences](), [computer-science-and-technology](), [data-science](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [fotini-christia](), [idss](), [laboratory-for-information-and-decision-systems-lids](), [leadership](), [lids](), [machine-learning](), [mit-bcs](), [mit-faculty-appointments](), [mit-idss](), [mit-leadership](), [mit-schwarzman-college-of-computing](), [mit-statistics-and-data-science-center-sdsc](), [philippe-rigollet](), [richard-dick-larson](), [school-of-engineering](), [school-of-science](), [statistics]() ### AI overview Alexander Rakhlin has been named the next director of MIT's Statistics and Data Science Center, succeeding Ankur Moitra. Rakhlin has been affiliated with the center since 2016 and has led its interdisciplinary doctoral program, overseeing more than 75 successful PhD defenses. ### Source excerpt An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra. ## Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83 DevFeed: [Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83]() Original publisher: [Read original article]() Author: Jane Halpern | Department of Electrical Engineering and Computer Science Published: 2026-07-22T17:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Optimization](), [Computer science](), [Electrical engineering and computer science (EECS)](), [AI Research](), [Artificial Intelligence](), [Reinforcement learning]() Tags: [alumni-ae](), [artificial-intelligence](), [asu-ozdaglar](), [athena-scientific](), [bayforest-technologies](), [books-and-authors](), [computer-science](), [computer-science-and-technology](), [convex-analysis](), [dimitri-bertsekas](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [laboratory-for-information-and-decision-systems-lids](), [large-scale-computation](), [mit-books-and-authors](), [mit-eecs-faculty](), [mit-faculty-obituary](), [mit-lids](), [mit-photographers](), [mit-sandbox-innovation-fund-program](), [mit-schwarzman-college-of-computing](), [munther-dahleh](), [network-optimization](), [networks](), [neurodynamic-programming](), [obituaries](), [optimization](), [photography](), [programming](), [reinforcement-learning](), [research](), [robert-gallager](), [school-of-engineering]() ### AI overview An obituary remembers Dimitri Bertsekas, an MIT professor emeritus and influential computer scientist who died at 83. His work shaped optimization, control, large-scale computation, reinforcement learning, and artificial intelligence, while his books and mentorship influenced generations of students and researchers. ### Source excerpt Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence. ## Bailey Flanigan uses computational and mathematical tools to study democratic participation DevFeed: [Bailey Flanigan uses computational and mathematical tools to study democratic participation]() Original publisher: [Read original article]() Author: Michaela Jarvis | MIT Laboratory for Information and Decision Systems Published: 2026-07-17T17:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Computer science](), [Electrical engineering and computer science (EECS)](), [math](), [MIT Schwarzman College of Computing]() Tags: [ai-and-democracy](), [ai-in-politics](), [algorithms](), [artificial-intelligence](), [bailey-flanigan](), [computer-science](), [computer-science-and-technology](), [defending-democracy](), [democracy](), [democratic-decision-making](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [government](), [labor-and-jobs](), [laboratory-for-information-and-decision-systems-lids](), [math](), [mit-eecs-faculty](), [mit-faculty-profile](), [mit-lids](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [political-legitimacy](), [political-science](), [politics](), [profile](), [public-health](), [research](), [school-of-engineering](), [school-of-humanities-arts-and-social-sciences](), [science](), [technology-and-society](), [wisconsin-innocence-project]() ### AI overview An MIT faculty member, Bailey Flanigan studies how computational and mathematical tools can create new avenues for meaningful democratic participation. The article describes her interdisciplinary path across computer science, political science, medicine, public health, economics, and related fields. ### Source excerpt Assistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive. ## A better way to turn 2D designs into 3D models for rapid prototyping DevFeed: [A better way to turn 2D designs into 3D models for rapid prototyping]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-07-16T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [3D](), [vlm](), [Artificial Intelligence](), [AI Development](), [Computing](), [coding]() Tags: [3d](), [ai](), [ai-models](), [algorithms](), [artificial-intelligence](), [automated](), [computer-aided-design-cad](), [computer-modeling](), [computer-science-and-technology](), [computing](), [design](), [efficiently](), [faez-ahmed](), [generation](), [generative-ai](), [geometric-inference-feedback-tuning-gift](), [giorgio-giannone](), [machine-learning](), [mechanical-engineering](), [mit-ibm-computing-research-lab](), [mit-schwarzman-college-of-computing](), [performance](), [rapid-prototyping](), [research](), [school-of-engineering](), [systems-design](), [vision-language-models-vlms]() ### AI overview Researchers developed an automated framework that teaches vision-language models to convert 2D designs into more accurate and functional CAD programs while using less computation. The system turns model failures into training data to improve CAD generation and support rapid prototyping. ### Source excerpt Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently. ## 3 Questions: Neural transparency and the future of AI design DevFeed: [3 Questions: Neural transparency and the future of AI design]() Original publisher: [Read original article]() Author: Media Lab Published: 2026-07-15T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Neural Network](), [User Interfaces](), [interface](), [Language models]() Tags: [3-questions](), [ai](), [ai-companion](), [ai-empathy](), [ai-hallucinations](), [ai-personalization](), [ai-safety](), [ai-sycophancy](), [ai-toxicity](), [algorithms](), [anthony-baez](), [apps](), [artificial-intelligence](), [behavioral-prediction](), [character-ai](), [chatbot-behavior](), [computer-science-and-technology](), [data](), [ethics](), [faculty](), [human-ai-interaction](), [human-computer-interaction](), [interface](), [interview](), [language-models](), [large-language-models](), [machine-learning](), [mechanistic-interpretability](), [media-lab](), [mental-health](), [mit-faculty-interview](), [mit-media-lab](), [neural](), [neural-transparency](), [pat-pataranutaporn](), [persona-scores](), [persona-vectors](), [personalized-ai](), [research](), [safety](), [school-of-architecture-and-planning](), [sheer-karny](), [sunburst-visualization](), [technology-and-society](), [transparency](), [users]() ### AI overview An MIT Media Lab team introduces "neural transparency," an interface that visualizes internal patterns in AI models to help users anticipate how personalized chatbots may behave. The approach compares model activations associated with contrasting traits such as empathy, honesty, toxicity, hallucination, and sycophancy, then maps custom system prompts onto those behavior directions. ### Source excerpt Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word. ## Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making DevFeed: [Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making]() Original publisher: [Read original article]() Author: David Chandler | Laboratory for Information and Decision Systems Published: 2026-07-14T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [decision-making](), [Structured-data](), [AI Models](), [data](), [Time Series](), [Computer science](), [Electrical engineering and computer science (EECS)](), [Temporal data](), [systems]() Tags: [ai](), [ai-and-decision-making](), [ai-in-business-planning](), [ai-models](), [algorithms](), [artificial-intelligence](), [business-and-management](), [business-modeling](), [celonis-context-model](), [computer-science-and-technology](), [data](), [data-systems](), [decision-making](), [devavrat-shah](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [forecasting](), [idss](), [ikigailabs](), [laboratory-for-information-and-decision-systems-lids](), [mit-eecs](), [mit-idss](), [mit-intellectual-property](), [mit-lids](), [mit-schwarzman-college-of-computing](), [predictions](), [profile](), [real-time](), [research](), [school-of-engineering](), [startups](), [structured](), [tabular-data](), [time-series-data]() ### AI overview MIT Professor Devavrat Shah's research led to a foundation model for tabular and time-series enterprise data. Developed with Ikigai Labs, the system continuously tests predictions against real outcomes to support large-scale forecasting and decision-making. ### Source excerpt Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. ## How MIT students are helping to prevent cyberattacks DevFeed: [How MIT students are helping to prevent cyberattacks]() Original publisher: [Read original article]() Author: Nicole Estvanik Taylor | Department of Urban Studies and Planning Published: 2026-07-13T19:10:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Cybersecurity](), [Security & Privacy](), [ransomware](), [Business Security]() Tags: [ai-and-security](), [ai-in-cyber-crime](), [artificial-intelligence](), [attacks](), [business-and-management](), [classes-and-programs](), [collaboration](), [computer-science-and-technology](), [cyber-attacks](), [cybersecurity](), [government](), [health-care](), [jungwoo-chun](), [lawrence-susskind](), [local](), [massive-open-online-courses-moocs](), [mit-class-11-074-11-274-cybersecurity-clinic](), [mit-cybersecurity-clinic](), [mit-dusp](), [mitx](), [organizations](), [prevent](), [public-sector](), [ransomware-attacks](), [school-of-architecture-and-planning](), [software](), [technology-and-society](), [training](), [urban-studies-and-planning](), [vulnerabilities]() ### AI overview MIT's Cybersecurity Clinic trains students to assess cyberattack vulnerabilities and provide free, confidential security assessments to municipalities, health-care organizations, and other at-risk communities. ### Source excerpt Students from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats. ## SceneSmith uses collaborative AI agents to create 3D environments for robot training DevFeed: [SceneSmith uses collaborative AI agents to create 3D environments for robot training]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-07-13T18:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI research agents](), [robot grasping simulation](), [Simulation](), [vlm](), [Artificial Intelligence](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [3-d](), [3d](), [adversarial-machine-learning](), [agents](), [ai-agents](), [artificial-intelligence](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [electrical-engineering-and-computer-science-eecs](), [foundation-models](), [general-purpose-robotics](), [gpt-5-2](), [machine-learning](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [national-science-foundation-nsf](), [nicholas-pfaff](), [research](), [robot-simulations](), [robotics](), [robots](), [russ-tedrake](), [scene-generation](), [scenesmith](), [school-of-engineering](), [simulation](), [simulation-ready-indoor-scenes](), [virtual-playgrounds](), [vision-language-models-vlms](), [zero-shot-policy]() ### AI overview MIT CSAIL and Toyota Research Institute researchers developed SceneSmith, a system that uses three collaborative AI agents to create realistic 3D environments for robot training. The scenes can be loaded into physics simulation software, allowing robots to practice tasks before real-world testing. ### Source excerpt "SceneSmith" system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores. ## New method aims to keep kids safe from illegal AI-generated content DevFeed: [New method aims to keep kids safe from illegal AI-generated content]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-07-13T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ai safety](), [AI Research](), [Open Source]() Tags: [ai](), [ai-deepfakes](), [ai-safety](), [algorithms](), [artificial-intelligence](), [ashia-wilson](), [computer-science-and-technology](), [csam](), [ethics](), [generative](), [generative-ai](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [marzyeh-ghassemi](), [mit-schwarzman-college-of-computing](), [models](), [online-child-safety](), [open-source](), [privacy](), [public-health](), [research](), [safety](), [school-of-engineering](), [vinith-suriyakumar]() ### AI overview MIT researchers and Thorn developed an auditing technique that assesses whether a generative AI model has been specialized to produce child sexual abuse material without generating illegal outputs. In testing, the procedure identified specialized model variants with 100 percent accuracy. ### Source excerpt Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. ## Tiny robot boats build floating structures DevFeed: [Tiny robot boats build floating structures]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-07-09T15:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Robotics](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Computer science]() Tags: [alejandro-gonzalez-garcia](), [aquatic-robots](), [architecture](), [artificial-intelligence](), [autonomous-vehicles](), [bioinspiration](), [cities](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [daniela-rus](), [electrical-engineering-and-computer-science-eecs](), [fire-ants](), [floatform](), [floating-infrastructure](), [hybrid-coordination](), [infrastructure](), [machine-learning](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [mit-sea-grant](), [mit-sea-grant-program](), [mit-senseable-city-lab](), [modular-robotic-boats](), [modular-self-reconfigurable-robots](), [niklas-hagemann](), [nonlinear-hydrodynamics](), [raft](), [research](), [robot-boats](), [robot-self-assembly](), [robotics](), [robots](), [school-of-architecture-and-planning](), [school-of-engineering](), [science](), [self-reconfiguration](), [senseable-city-lab](), [snap](), [swarm](), [swarm-robots](), [tiny](), [transportation](), [ultrasonic-beacon-system](), [urban-studies-and-planning](), [water](), [wei-wang]() ### AI overview MIT researchers developed FloatForm, a swarm of small robotic boats that can assemble into reconfigurable structures on the water and later break apart and reassemble into new configurations with minimal human direction. ### Source excerpt MIT researchers developed FloatForm, a swarm of small aquatic robots that snap together like ants forming a raft, assembling into reconfigurable structures on the water. ## How novice coders can develop AI programs for military applications DevFeed: [How novice coders can develop AI programs for military applications]() Original publisher: [Read original article]() Author: Haley Wahl | MIT Lincoln Laboratory Published: 2026-07-07T17:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Vibe coding](), [AI Development](), [AI Chat](), [Language models](), [Software Engineering](), [Development]() Tags: [ai](), [ai-and-military](), [ai-assisted-target-recognition](), [ai-chatbots](), [ai-programming](), [ai-writing-software](), [artificial-intelligence](), [autonomous-striking](), [battlefield-communications](), [chatbots](), [chatgpt](), [claude](), [coding](), [computer-science-and-technology](), [daf-mit-ai-accelerator-phantom-program](), [gemini-llm](), [google-ai-studio-app](), [language-models](), [laura-niss](), [lincoln-laboratory](), [military-ai](), [mit-lincoln-laboratory](), [novice-coders](), [programming](), [remote-operating-modular-augmentation-device-romad-ai](), [research](), [security-and-military-studies](), [software](), [software-development](), [u-s-armed-forces](), [vibe-coding](), [vision-language-models-vlms]() ### AI overview A U.S. Air Force cadet with no coding experience used generative AI chatbots and a process called vibe-coding to develop a military application. The project examined whether nontechnical service members could create useful software for military problems. ### Source excerpt A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems. ## Q&A: What is agentic AI today, and what do we want it to be? DevFeed: [Q&A: What is agentic AI today, and what do we want it to be?]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-06-30T15:30:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [what is agentic AI](), [Artificial Intelligence](), [AI Models]() Tags: [agentic-ai](), [ai](), [ai-agents](), [ai-models](), [algorithms](), [artificial-intelligence](), [chatgpt](), [claude](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [data](), [electrical-engineering-and-computer-science-eecs](), [generative-ai](), [human-computer-interaction](), [interview](), [large-language-models-llms](), [machine-learning](), [mit-schwarzman-college-of-computing](), [phillip-isola](), [school-of-engineering]() ### AI overview This Q&A explains agentic AI as systems that take actions in the physical or digital world, distinguishing them from generative AI systems that primarily produce content. It describes how agents combine foundation models such as Claude with application-specific tools and memory, and identifies limited training data for real-world tasks as a major development challenge. ### Source excerpt Computer scientist Phillip Isola cuts through the hype to explain how AI agents work and what the future might hold for this rapidly advancing technology. ## 3 Questions: Beyond data-driven aesthetics DevFeed: [3 Questions: Beyond data-driven aesthetics]() Original publisher: [Read original article]() Author: School of Architecture and Planning Published: 2026-06-29T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Data visualization](), [Algorithms](), [Mathematics](), [Computer science](), [Machine learning]() Tags: [3-questions](), [aesthetic-judgment](), [ai](), [ai-and-aesthetics](), [alexandros-haridis](), [algorithms](), [alumni-ae](), [applied-arts](), [architecture](), [artificial-intelligence](), [beyond-data-driven-aesthetics](), [computational-aesthetics](), [computer-science-and-technology](), [data](), [data-visualization](), [design](), [design-computation](), [exhibits](), [interactive-installations](), [interview](), [machine-learning](), [mit-architecture](), [mit-exhibits](), [mit-keller-gallery](), [mit-sa-plus-p](), [philosophy](), [research](), [school-of-architecture-and-planning](), [shape-grammars](), [special-events-and-guest-speakers]() ### AI overview An MIT Keller Gallery exhibition by Alexandros Haridis examines the history of aesthetic judgment and creative production in computing, connecting architecture, design computation, algorithms, and machine-learning systems. ### Source excerpt In a new Keller Gallery exhibition, Alexandros Haridis SM '17, PhD '22 traces centuries of ideas about aesthetic judgment and explores how design can make complex computational systems visible. ## Improving the speed and energy-efficiency of AI agents DevFeed: [Improving the speed and energy-efficiency of AI agents]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-06-25T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [agentic workflows](), [Loop Engineering](), [Green Software](), [Cloud](), [Microsoft](), [microsoft-azure]() Tags: [adam-belay](), [agentic-workflows](), [agents](), [ai](), [ai-agents](), [algorithms](), [artificial-intelligence](), [cloud](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [data](), [data-centers](), [defense-advanced-research-projects-agency-darpa](), [deployment](), [efficiency](), [electrical-engineering-and-computer-science-eecs](), [electronics](), [energy](), [energy-efficiency](), [gohar-chaudhry](), [machine-learning](), [microsoft](), [microsoft-azure](), [mit-schwarzman-college-of-computing](), [murakkab](), [research](), [school-of-engineering](), [software](), [sustainability](), [sustainable-computing](), [workflows]() ### AI overview MIT and Microsoft researchers developed Murakkab, a system that automatically designs and deploys agentic workflows by selecting models, tools, hardware configurations, and computational resources according to user priorities. Tests found that it reduced computational requirements, energy use, and costs without reducing performance. ### Source excerpt A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications. [Next page]()