Aeronautical and astronautical engineering

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System helps humans predict when self-driving cars will make mistakes

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Artificial Intelligence, Autonomous vehicles, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Machine Learning, MIT Schwarzman College of Computing, Research, Robotics, School of Engineering

Self-driving cars are often controlled by deep learning models that sometimes fail in unexpected situations. For instance, the car might inexplicably brake and block the path of an oncoming emergency vehicle. A human driver or passenger may need to react rapidly to prevent a collision.To help humans better anticipate a vehicle’s mistakes, researchers from MIT […]

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Can AI build a jet engine? JARVIS Challenge tests role of AI copilots in tough-tech engineering

3-D printing, Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Aircraft, Artificial Intelligence, Classes and programs, Contests and academic competitions, Design, Education, teaching, academics, Independent Activities Period, Lincoln Laboratory, Logistics, Machine Learning, Mechanical engineering, Robotics, School of Engineering, STEM education, Students, Undergraduate

Artificial intelligence has rapidly transformed software engineering. Generative AI and large language models (LLMs) can create huge volumes of code and documentation; machine-learning algorithms can monitor performance and detect security vulnerabilities. But when the task is to conceive, design, and make a complex physical system such as a jet engine, are those AI tools equally

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LLMs help robots understand vague instructions and focus on key details

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, automation, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, Natural Language Processing, Research, Robotics, School of Engineering

Imagine working at a warehouse or office sometime in the near future, and you’re asked to help a new trainee learn the basics of their job. The catch: It’s a robot. To teach them, you might want to play a game of “show and tell” — that is, physically showing how to do something a

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Exploring the societal impacts of AI

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Democracy, Economics, Electrical engineering and computer science (EECS), government, Labor and jobs, MIT Schwarzman College of Computing, MIT Sloan School of Management, Political science, Politics, Program in STS, Robotics, School of Engineering, School of Humanities Arts and Social Sciences, Special events and guest speakers, Technology and society

At the recent AI and Society Forum at MIT, experts from across the Institute discussed the potential benefits and dangers of technological innovation on labor, the nature of work, civil discourse, election administration, and other topics.The event featured individual research presentations and panel discussions, as well as a musical performance exploring the use of generative artificial intelligence

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New chip could help tiny robots traverse complex environments

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Augmented and virtual reality, Autonomous vehicles, Computer chips, Computer science and technology, Electrical engineering and computer science (EECS), Energy, Machine Learning, Memory, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, Research Laboratory of Electronics, Robotics, School of Engineering

A new chip developed by MIT researchers could help tiny, low-power UAVs avoid obstacles as they zip around tight corners inside an industrial HVAC system to check for gas leaks.The chip allows small autonomous robots and other battery-limited devices to construct detailed 3D maps of their environments in real-time using only about as much power

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Could AI tell you where you left your keys?

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Autonomous vehicles, Computer science and technology, Computer vision, Electrical engineering and computer science (EECS), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, Robotics, School of Engineering

An auto factory worker can remember the storage bin where she left a partly assembled component the night before, and quickly return to that spot to pick it up. But robots that may work side-by-side with her would struggle to develop and access this same type of “spatiotemporal” memory.Now, MIT researchers have developed a long-term

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The crucial human component in computing and AI

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Artificial Intelligence, Comparative Media Studies/Writing, Education, teaching, academics, Electrical engineering and computer science (EECS), ethics, Human-computer interaction, IDSS, Media Lab, MIT Schwarzman College of Computing, MIT Sloan School of Management, Music and theater arts, Philosophy, Political science, School of Architecture and Planning, School of Engineering, School of Humanities Arts and Social Sciences, School of Science, Special events and guest speakers, Technology and society

On April 30, the MIT Schwarzman College of Computing’s Social and Ethical Responsibilities of Computing (SERC) initiative hosted a full-day research symposium examining how artificial intelligence is shaping the world and its implications for society. The symposium included research talks by SERC’s latest seed grant recipients on topics such as air pollution forecasting and responsible computer vision deployment, panels on AI alignment

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Evaluating the ethics of autonomous systems

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Defense Advanced Research Projects Agency (DARPA), Electrical engineering and computer science (EECS), ethics, Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Artificial intelligence is increasingly being used to help optimize decision-making in high-stakes settings. For instance, an autonomous system can identify a power distribution strategy that minimizes costs while keeping voltages stable.But while these AI-driven outputs may be technically optimal, are they fair? What if a low-cost power distribution strategy leaves disadvantaged neighborhoods more vulnerable to

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MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Careers, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Industry, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Natural Language Processing, Research, Robotics, School of Engineering

The early years of faculty members’ careers are a formative and exciting time in which to establish a firm footing that helps determine the trajectory of researchers’ studies. This includes building a research team, which demands innovative ideas and direction, creative collaborators, and reliable resources. For a group of MIT faculty working with and on artificial

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A better method for planning complex visual tasks

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, Computer vision, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, Robotics, School of Engineering

MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques.Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations

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