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A novel approach incorporates feedback from the public to assist in teaching robots.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, Robots, UncategorizedMarch 20, 202464Views0Likes0Comments
Researchers from MIT, Harvard, and the University of Washington have developed a new method for training AI agents using reinforcement learning. Their approach replaces a process often involving a time-consuming design of a reward function by a human expert with feedback crowdsourced from non-expert users. Traditionally, AI reinforcement learning has used a reward function, designed by…

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This fresh approach leverages input from the masses to assist in educating robots.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, Robots, UncategorizedMarch 20, 202470Views0Likes0Comments
Teaching AI agents new tasks can be a challenging and time-consuming process, often involving iteratively updating a reward function designed by a human expert to motivate the AI’s exploration of possible actions. However, researchers from the Massachusetts Institute of Technology, Harvard University, and the University of Washington have developed a new reinforcement learning approach that…

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A novel approach uses collective user feedback to assist in the training of robots.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, Robots, UncategorizedMarch 19, 202472Views0Likes0Comments
Researchers at MIT, Harvard, and the University of Washington have shunned traditional reinforcement learning approaches, using crowdsourced feedback to teach artificial intelligence (AI) new skills instead. Traditional methods to teach AI tasks often required a reward function, which was updated and managed by a human expert. This limited scalability and was often time-consuming, particularly if…

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Method allows AI in peripheral devices to continuously update its knowledge over time.

Algorithms, Artificial Intelligence, Computer science and technology, Data, Electrical Engineering & Computer Science (eecs), Internet of things, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Mobile devices, National Science Foundation (NSF), Privacy, Research, School of Engineering, UncategorizedMarch 7, 202476Views0Likes0Comments
Machine learning models are widely used today in smart devices like smartphones, with diverse applications like autocorrecting keyboards or improved chatbot responses. However, fine-tuning these models requires considerable computational resources and transfers of data to and from cloud servers – which can pose both energy and security issues. The team of researchers from MIT and…

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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, 202480Views0Likes0Comments
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, 202475Views0Likes0Comments
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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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, 202477Views0Likes0Comments
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, 202477Views0Likes0Comments
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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AI representatives assist in elucidating other AI frameworks.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Language, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedMarch 7, 202472Views0Likes0Comments
Interpreting the functions and behaviors of large-scale neural networks remains a complex task and a significant challenge in the field of Artificial Intelligence. To tackle this problem, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a strategy that uses AI models to investigate the computations inside other AI systems.  Central to this…

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This method may effectively resolve partial differential equations for a multitude of uses.

Algorithms, Artificial Intelligence, Computer science and technology, Data, Machine learning, Mathematics, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Physics, Research, School of Science, UncategorizedMarch 7, 202481Views0Likes0Comments
Partial differential equations (PDEs) are used in fields like physics and engineering to model complex physical processes, offering insight into some of the world's most intricate systems. To solve these equations, researchers use high-fidelity numerical solvers, which are time-consuming and computationally expensive. A simplified alternative, data-driven surrogate models, compute the goal property of a solution…

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Numerous AI systems assist robots in carrying out intricate strategies with greater clarity.

Algorithms, Artificial Intelligence, Computer science and technology, Computer vision, Data, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Research, Robotics, School of Engineering, UncategorizedMarch 7, 202470Views0Likes0Comments
MIT's Improbable AI Lab has developed a novel multimodal framework for artificial intelligence (AI) called the Compositional Foundation Models for Hierarchical Planning (HiP). The aim of this system is to help robots conduct complex tasks that involve numerous smaller steps, from household chores to more elaborate industrial processes. Traditionally, AI systems have required paired visual,…

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