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Research

Enhancing software testing through the application of generative AI.

Generative AI, in the past few years, has gained significant popularity because of its capacity to develop realistic text and images. However, the created text and images form only a portion of the data generated today. Every interaction we have with a medical system, software application, or the effect of any environment, such as a…

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Improving software testing through the application of generative AI.

Generative AI is increasingly being utilized to generate synthetic data, enhancing organizations' abilities to deal with situations where actual data may be limited or sensitive. Over the past three years, DataCebo, an MIT spinoff initiative, has been offering a generative software system known as the Synthetic Data Vault (SDV) to enable organizations to create synthetic…

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Researchers utilize extensive language models to assist robots with navigation.

Researchers from MIT and the MIT-IBM Watson AI Lab have developed a language-based navigational strategy for AI robots. The method uses textual descriptions instead of visual information, effectively simplifying the process of robotic navigation. Visual data traditionally requires significant computational capacity and detailed hand-crafted machine-learning models to function effectively. The researchers' approach involves converting a…

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A novel approach to computer vision accelerates the screening process of electronic components.

Solar cells, transistors, LEDs, and batteries with boosted performance require better electronic materials which are often discovered from novel compositions. Scientists have turned to AI tools to identify potential materials from millions of chemical formulations, with engineers developing machines that can print hundreds of samples at a time, based on compositions identified by AI algorithms.…

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A new AI model has the potential to enhance procedures in a robot-operated warehouse.

MIT researchers have developed a deep-learning model to improve the efficiency of warehouse robots. The team used a neural network architecture to encode features including the robots' paths, tasks, and obstacles in the warehouse. This enabled the model to predict where congestion was most likely to occur and take measures to counteract it. The groundbreaking method…

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A new AI model has the potential to improve efficiency in automated warehouse processes.

MIT researchers have designed an artificial intelligence solution to help robotic warehouses operate more efficiently. Automated warehouses, which employ hundreds of robots to pick and deliver goods, are becoming more commonplace, especially in industries such as e-commerce and automotive production. However, coordinating this robot workforce to avoid collisions, while also maintaining a high operational pace,…

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