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Engineers from MIT have devised a method to decipher the behavior of material surfaces.

Artificial Intelligence, Chemistry, Department of Defense (DoD), DMSE, Machine learning, Materials science and engineering, National Science Foundation (NSF), Research, School of Engineering, UncategorizedMarch 7, 202439Views0Likes0Comments
MIT researchers have developed a machine learning-based method for designing new compounds or alloys for use as catalysts in chemical reactions. Traditional methods of designing such materials rely on static observations of a single configuration, out of millions of possibilities, and the intuition of experienced chemists. However, the new method employs machine learning algorithms to…

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The automated system instructs users on appropriate timings for collaboration with an AI assistant.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Data, Electrical Engineering & Computer Science (eecs), Health care, Human-computer interaction, IDSS, Institute for Medical Engineering and Science (IMES), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, UncategorizedMarch 7, 202434Views0Likes0Comments
Researchers at MIT and the MIT-IBM Watson AI Lab have developed an onboarding process that efficiently combines human and AI efforts. The system educates a user when to collaborate with an AI assistant and when not. This method can find situations when a user trusts the AI model's advice, but the model is incorrect. The…

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Bridging the gap between design and production of optical gadgets.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedMarch 7, 202441Views0Likes0Comments
Researchers from MIT and the Chinese University of Hong Kong have developed a digital simulator that seeks to improve photolithography's precision, often used in computer chips and optical devices manufacture. The process uses light to etch intricate designs onto surfaces, but minor discrepancies often cause devices' final performance to deviate from designers' initial intentions. The…

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Deep neural networks exhibit potential in modelling human auditory systems.

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedMarch 7, 202440Views0Likes0Comments
MIT researchers have been utilizing computational models derived from deep neural networks which mimic the structure and function of the human auditory system, a development that could help in the design of better hearing aids, cochlear implants, and brain-machine interfaces. This represents a significant step in understanding how the human brain processes sound and how…

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Computational model accurately represents the transient transition states in chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedMarch 7, 202434Views0Likes0Comments
Chemists often struggle to predict the outcome of a chemical reaction as it depends on the so-called "transition state," a fleeting moment into which molecules enter and from which they can never return unchanged. The challenge lies in the fact that the transition state is extremely ephemeral and difficult to capture in real-world experiments. This…

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The unseen hurdle of today’s AI: Accuracy in image identification.

Artificial Intelligence, Center for Brains Minds and Machines, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Data, Electrical Engineering & Computer Science (eecs), Image Processing, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, Uncategorized, VisionMarch 7, 202444Views0Likes0Comments
MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers, in collaboration with the MIT-IBM Watson AI Lab, have developed a new metric, the "minimum viewing time" (MVT), to measure the difficulty of recognizing an image. The researchers aimed to close the gap between the performance of deep learning-based AI models and humans in recognizing and…

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A versatile approach to assist artists in enhancing animation.

Algorithms, Artificial Intelligence, Arts, Augmented and virtual reality, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Mathematics, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Research, UncategorizedMarch 7, 202437Views0Likes0Comments
MIT researchers have developed a new tool that provides better control to animators in shaping their characters. The new technique works by generating mathematical functions, known as barycentric coordinates, that describe how 2D and 3D shapes in animations can move, stretch, and deform in space. By using these functions, an animator can tailor the movement…

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MIT researchers harness artificial intelligence to discover potential new antibiotic classes.

Antibiotics, Artificial Intelligence, Biological engineering, Broad Institute, Drug discovery, Drug resistance, Institute for Medical Engineering and Science (IMES), Jameel Clinic, Machine learning, Research, School of Engineering, UncategorizedMarch 7, 202437Views0Likes0Comments
Using deep learning, a form of artificial intelligence, researchers at MIT have discovered a group of compounds that can eliminate methicillin-resistant Staphylococcus aureus (MRSA), a drug-resistant bacterium that causes over 10,000 deaths in the US annually. The compounds, which display low toxicity against human cells, are considered good potential drug candidates. In a paper published in…

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Reviewing MIT’s Media Presence in 2023

academics, Administration, Alumni/ae, Artificial Intelligence, astronomy and planetary science, Boston and region, Cambridge, Climate change, Community, Computer science and technology, Diversity and inclusion, Education, Faculty, Global, Government, Health sciences and technology, Innovation and Entrepreneurship (I&E), MIT Schwarzman College of Computing, MIT Sloan School of Management, Policy, President Sally Kornbluth, Research, School of Architecture and Planning, School of Engineering, School of Humanities Arts and Social Sciences, School of Science, Social justice, Space, Staff, Students, teaching, UncategorizedMarch 7, 202440Views0Likes0Comments
In 2023, Massachusetts Institute of Technology (MIT) has had a busy and eventful year. Under the leadership of newly inaugurated President Sally Kornbluth, the institute saw significant advances in a range of fields, such as artificial intelligence (AI), clean energy solutions, disease detection, and even the science of kindness. Professor Moungi Bawendi was a notable…

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Utilizing language to comprehend machines.

Artificial Intelligence, Classes and programs, Collaboration, Data, Electrical Engineering & Computer Science (eecs), Graduate, Human-computer interaction, Industry, Linguistics, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Natural language processing, postdoctoral, Research, School of Engineering, School of Humanities Arts and Social Sciences, Students, UncategorizedMarch 7, 202441Views0Likes0Comments
MIT engineering students Irene Terpstra ’23 and Rujul Gandhi ’22 are collaborating with the MIT-IBM Watson AI Lab to advance Artificial Intelligence (AI) systems using Natural Language Processing (NLP), taking advantage of the vast amount of natural language data available. Terpstra is focusing on the application of AI algorithms for computer chip design, leveraging the…

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Intricate and unknown phrases influence the brain’s linguistic network to exert more effort.

Artificial Intelligence, Brain and cognitive sciences, Computer science and technology, Language, Machine learning, McGovern Institute, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Neuroscience, Research, School of Engineering, School of Science, UncategorizedMarch 7, 202435Views0Likes0Comments
A study by MIT neuroscientists, utilising an artificial language network, discovered the type of sentences most likely to stimulate the brain’s key language processing centers. The study concluded that complex sentences, with unusual grammar or unexpected meaning, generate stronger responses. In contrast, simplistic sentences marginally engaged these regions, while nonsensical sequences of words had little…

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