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Comprehending the visual comprehension of language models.

Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJune 18, 202434Views0Likes0Comments
Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory (CSAIL) has revealed that language models without image experience still understand the visual world. The team found that even without seeing images, language models could write image-rendering code that could generate detailed and complicated scenes. The knowledge that enabled this process came from the vast…

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Scientists improve the peripheral vision capabilities in AI models.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 17, 202443Views0Likes0Comments
Researchers at Massachusetts Institute of Technology (MIT) have developed an image dataset to simulate peripheral vision in artificial intelligence (AI) models. This step is aimed at helping such models detect approaching dangers more effectively, or predict whether a human driver would take note of an incoming object. Peripheral vision in humans allows us to see…

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Three inquiries: Understanding the essentials about audio deepfakes.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Ethics, Interview, Machine learning, MIT Schwarzman College of Computing, Natural language processing, School of Engineering, Technology and society, UncategorizedJune 17, 202439Views0Likes0Comments
The recent misuse of audio deepfakes, including a robocall purporting to be Joe Biden in New Hampshire and spear-phishing campaigns, has prompted questions about the ethical considerations and potential benefits of this emerging technology. Nauman Dawalatabad, a postdoctoral researcher, discussed these concerns in a Q&A prepared for MIT News. According to Dawalatabad, the attempt to obscure…

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Improving software testing through the application of generative artificial intelligence.

Alumni/ae, Artificial Intelligence, Data, Electrical Engineering & Computer Science (eecs), Faculty, Innovation and Entrepreneurship (I&E), Laboratory for Information and Decision Systems (LIDS), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, Startups, UncategorizedJune 17, 202435Views0Likes0Comments
Generative AI, renowned for its capability to autonomously produce text and images, plays a crucial role in creating realistic synthetic data from diverse scenarios, helping organizations optimize operations. A notable initiative in the field is the Synthetic Data Vault (SDV), developed by DataCebo, an MIT spinoff. This generative system aids organizations in creating synthetic data…

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Scientists improve side vision capabilities in AI modules.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 17, 202440Views0Likes0Comments
Researchers from MIT have developed an image dataset that simulates peripheral vision in machine learning models, improving their object detection capabilities. However, even with this modification, the AI models still fell short of human performance. The researchers discovered that size and visual clutter, factors that impact human performance, largely did not affect the AI's ability.…

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Three Inquiries: Essential Information on Audio Deepfakes You Should Understand

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Ethics, Interview, Machine learning, MIT Schwarzman College of Computing, Natural language processing, School of Engineering, Technology and society, UncategorizedJune 17, 202436Views0Likes0Comments
Audio deepfakes have recently been in the news, particularly in regards to their negative impacts, such as fraudulent robocalls pretending to be Joe Biden, encouraging people not to vote. These malicious uses could negatively affect political campaigns, financial markets, and lead to identity theft. However, Nauman Dawalatabad, a postdoc student at MIT, argues that deepfakes…

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Improving software testing through the utilization of generative AI

Alumni/ae, Artificial Intelligence, Data, Electrical Engineering & Computer Science (eecs), Faculty, Innovation and Entrepreneurship (I&E), Laboratory for Information and Decision Systems (LIDS), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, Startups, UncategorizedJune 16, 202434Views0Likes0Comments
Generative AI has vast potential in creating synthetic data that can mimic real-world scenarios, which in turn can aid organizations in improving their operations. In line with this, DataCebo, a spinout from MIT, has developed a generative software system referred to as the Synthetic Data Vault (SDV), which has been employed by thousands of data…

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Scientists improve the side vision capabilities in artificial intelligence models.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 16, 202434Views0Likes0Comments
Peripheral vision, most humans' mechanism to see objects not directly in their line of sight, although with less detail, does not exist in AI. However, researchers at MIT have made significant progress towards this by developing an image dataset to simulate peripheral vision in machine learning models. The research indicated that models trained with this…

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Three Queries: Essential Information Regarding Deepfakes in the Audio Realm

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Ethics, Interview, Machine learning, MIT Schwarzman College of Computing, Natural language processing, School of Engineering, Technology and society, UncategorizedJune 16, 202433Views0Likes0Comments
Nauman Dawalatabad, a postdoctoral researcher discusses the concerns and potential benefits of audio deepfake technology in a Q&A with MIT News. He addresses ethical considerations regarding the concealment of a source speaker’s identity in audio deepfakes, noting that speech contains a wealth of sensitive personal information beyond identity and content, such as age, gender and…

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Improving software testing through the application of generative AI.

Alumni/ae, Artificial Intelligence, Data, Electrical Engineering & Computer Science (eecs), Faculty, Innovation and Entrepreneurship (I&E), Laboratory for Information and Decision Systems (LIDS), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, Startups, UncategorizedJune 16, 202437Views0Likes0Comments
Generative AI, which can create text and images, is becoming an essential tool in today's data-driven society. It's now being utilized to produce realistic synthetic data, which can effectively solve problems where real data is limited or sensitive. For the past three years, DataCebo, an MIT spinoff, has been offering a Synthetic Data Vault (SDV)…

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Scientists improve sideline sight in AI prototypes.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 16, 202438Views0Likes0Comments
MIT researchers are replicating peripheral vision—a human's ability to detect objects outside their direct line of sight—in AI systems, which could enable these machines to more effectively identify imminent dangers or predict human behavior. By equipping machine learning models with an extensive image dataset to imitate peripheral vision, the team found these models were better…

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