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Guided learning lets “untrainable” neural networks realize their potential

Algorithms, Artificial Intelligence, Center for Brains Minds and Machines, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Defense Advanced Research Projects Agency (DARPA), Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Networks, Neuroscience, Research, School of Engineering

Even networks long considered “untrainable” can learn effectively with a bit of a helping hand. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have shown that a brief period of alignment between neural networks, a method they call guidance, can dramatically improve the performance of architectures previously thought unsuitable for modern tasks.Their findings […]

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A new way to increase the capabilities of large language models

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering

Most languages use word position and sentence structure to extract meaning. For example, “The cat sat on the box,” is not the same as “The box was on the cat.” Over a long text, like a financial document or a novel, the syntax of these words likely evolves. Similarly, a person might be tracking variables in

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AI, MCP, and the Hidden Costs of Data Hoarding

AI & ML, Artificial Intelligence, Commentary, data

The Model Context Protocol (MCP) is genuinely useful. It gives people who develop AI tools a standardized way to call functions and access data from external systems. Instead of building custom integrations for each data source, you can expose databases, APIs, and internal tools through a common protocol that any AI can understand. However, I’ve

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New method improves the reliability of statistical estimations

Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Mathematics, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering

Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county.They might train a machine-learning model to estimate the magnitude of this association, since machine-learning methods are especially good at learning complex relationships.Standard machine-learning methods excel at making predictions and sometimes provide uncertainties, like

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EY and NVIDIA to help companies test and deploy physical AI

AI Business Strategy, AI Hardware & Chips, Artificial Intelligence, Business News, data, digital twins, Entrepreneurship, Infrastructure & Hardware, Manufacturing & Engineering AI, nvidia, Research, Robotics, Utilities, World of Work

AI is moving deeper into the physical world, and EY is laying out a more structured way for companies to work with robots, drones, and other smart devices. The organisation is introducing a physical AI platform built with NVIDIA tools, opening a new EY.ai Lab in Georgia, and adding new leadership to guide its work

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Researchers discover a shortcoming that makes LLMs less reliable

Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Human-computer interaction, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, Technology and society

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study.Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks.The researchers found that models can mistakenly link certain

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MIT Sea Grant students explore the intersection of technology and offshore aquaculture in Norway

Artificial Intelligence, Center for International Studies, Classes and programs, Collaboration, data, Environment, Europe, Food, Global, Industry, International initiatives, Internships, Machine Learning, Mechanical engineering, MISTI, MIT Schwarzman College of Computing, MIT Sea Grant, Oceanography and ocean engineering, Robotics, School of Engineering, School of Humanities Arts and Social Sciences, Sensors, Software, Students, UAV, Undergraduate, Undergraduate Research Opportunities Program (UROP), Water

Norway is the world’s largest producer of farmed Atlantic salmon and a top exporter of seafood, while the United States remains the largest importer of these products, according to the Food and Agriculture Organization. Two MIT students recently traveled to Trondheim, Norway to explore the cutting-edge technologies being developed and deployed in offshore aquaculture. Beckett Devoe,

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3 Questions: How AI is helping us monitor and support vulnerable ecosystems

Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), Algorithms, Animals, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, data, Ecology, Electrical engineering and computer science (EECS), Environment, Graduate, postdoctoral, Interview, Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Pollution, Research, School of Engineering, Students, Sustainability

A recent study from Oregon State University estimated that more than 3,500 animal species are at risk of extinction because of factors including habitat alterations, natural resources being overexploited, and climate change.To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay

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Charting the future of AI, from safer answers to faster thinking

Algorithms, Artificial Intelligence, Classes and programs, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Graduate, postdoctoral, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, Students

Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and

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Using generative AI to diversify virtual training grounds for robots

Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Internet, Machine Learning, MIT Schwarzman College of Computing, Research, Robotics, School of Engineering, Simulation

Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. The source of

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