Human-computer interaction

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Games people — and machines — play: Untangling strategic reasoning to advance AI

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, Electrical engineering and computer science (EECS), Faculty, Game theory, Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Profile, School of Engineering

Gabriele Farina grew up in a small town in a hilly winemaking region of northern Italy. Neither of his parents had college degrees, and although both were convinced they “didn’t understand math,” Farina says, they bought him the technical books he wanted and didn’t discourage him from attending the science-oriented, rather than the classical, high […]

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Q&A: MIT SHASS and the future of education in the age of AI

Administration, ai, AI (Artificial Intelligence), Artificial Intelligence, Education, teaching, academics, ethics, Faculty, history, History of MIT, Human-computer interaction, Interview, MIT Schwarzman College of Computing, Music technology, Policy, School of Engineering, School of Humanities Arts and Social Sciences, Technology and society

The MIT School of Humanities, Arts, and Social Sciences (SHASS) was founded in 1950 in response to “a new era emerging from social upheaval and the disasters of war,” as outlined in the 1949 Lewis Committee Report. The report’s findings emphasized MIT’s role and responsibility in the new nuclear age, which called for doubling down on genuine

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Human-machine teaming dives underwater

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Autonomous Underwater Vehicles (AUV), Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Lincoln Laboratory, Machine Learning, MIT Schwarzman College of Computing, Oceanography and ocean engineering, Research, Robotics, School of Engineering, Sensors

The electricity to an island goes out. To find the break in the underwater power cable, a ship pulls up the entire line or deploys remotely operated vehicles (ROVs) to traverse the line. But what if an autonomous underwater vehicle (AUV) could map the line and pinpoint the location of the fault for a diver

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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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Wristband enables wearers to control a robotic hand with their own movements

ai, AI (Artificial Intelligence), Artificial Intelligence, Assistive technology, Augmented and virtual reality, Biomechanics, Computer vision, Human-computer interaction, Machine Learning, Mechanical engineering, Research, Robotics, School of Engineering, Sensors

The next time you’re scrolling your phone, take a moment to appreciate the feat: The seemingly mundane act is possible thanks to the coordination of 34 muscles, 27 joints, and over 100 tendons and ligaments in your hand. Indeed, our hands are the most nimble parts of our bodies. Mimicking their many nuanced gestures has

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A better method for identifying overconfident large language models

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering

Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer.But this method measures self-confidence, and even the most impressive LLM might be confidently

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New MIT class uses anthropology to improve chatbots

ai, AI (Artificial Intelligence), Anthropology, Artificial Intelligence, Classes and programs, Design, Electrical engineering and computer science (EECS), Human-computer interaction, MIT Morningside Academy for Design, MIT Schwarzman College of Computing, School of Architecture and Planning, School of Engineering, School of Humanities Arts and Social Sciences, Social media, Social sciences, Students, Technology and society

Young adults growing up in the attention economy — preparing for adult life, with social media and chatbots competing for their attention — can easily fall into unhealthy relationships with digital platforms. But what if chatbots weren’t mere distractions from real life? Could they be designed humanely, as moral partners whose digital goal is to

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Improving AI models’ ability to explain their predictions

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

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output.Concept bottleneck modeling is one method that enables artificial intelligence systems to explain their decision-making process. These methods force a deep-learning model to use a

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Mixing generative AI with physics to create personal items that work in the real world

3-D printing, ai, AI (Artificial Intelligence), Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Design, Electrical engineering and computer science (EECS), Fabrication, Human-computer interaction, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering

Have you ever had an idea for something that looked cool, but wouldn’t work well in practice? When it comes to designing things like decor and personal accessories, generative artificial intelligence (genAI) models can relate. They can produce creative and elaborate 3D designs, but when you try to fabricate such blueprints into real-world objects, they

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Personalization features can make LLMs more agreeable

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer science and technology, Electrical engineering and computer science (EECS), ethics, Human-computer interaction, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Many of the latest large language models (LLMs) are designed to remember details from past conversations or store user profiles, enabling these models to personalize responses.But researchers from MIT and Penn State University found that, over long conversations, such personalization features often increase the likelihood an LLM will become overly agreeable or begin mirroring the

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