Automobile insurers are increasingly turning to machine learning models to calculate the cost of car repairs. The pandemic has made it difficult for human assessors to visit vehicles damaged in crashes, so the insurance industry is embracing automation.
Automobile insurers are increasingly turning to machine learning models to calculate the cost of car repairs. The pandemic has made it difficult for human assessors to visit vehicles damaged in crashes, so the insurance industry is embracing automation.
Neural networks for image generation don’t just create new art — they can help recreate works that have been lost for ages. Oxia Palus, a UK startup dedicated to resurrecting lost art through AI.
Researchers at the University of Bristol led by Michael Wray propose a new benchmark, Semantic Similarity Video Retrieval (SVR), that evaluates video retrieval systems by their ability to rank many similar videos. They also built a system that performed well on it.
Researchers at the University of Bristol led by Michael Wray propose a new benchmark, Semantic Similarity Video Retrieval (SVR), that evaluates video retrieval systems by their ability to rank many similar videos. They also built a system that performed well on it.
Neural networks for image generation don’t just create new art — they can help recreate works that have been lost for ages. Oxia Palus, a UK startup dedicated to resurrecting lost art through AI.
A robot inspector is looking over the shoulders of robot welders. Farm equipment maker John Deere described a computer vision system that spots defective joints, helping to ensure that its heavy machinery leaves the production line ready to roll.
A robot inspector is looking over the shoulders of robot welders. Farm equipment maker John Deere described a computer vision system that spots defective joints, helping to ensure that its heavy machinery leaves the production line ready to roll.
Key machine learning datasets are riddled with mistakes. Several benchmark datasets are shot through with incorrect labels. On average, 3.4 percent of examples in 10 commonly used datasets are mislabeled and the detrimental impact of such errors rises with model size.
Key machine learning datasets are riddled with mistakes. Several benchmark datasets are shot through with incorrect labels. On average, 3.4 percent of examples in 10 commonly used datasets are mislabeled and the detrimental impact of such errors rises with model size.
It’s well established that pretraining a model on a large dataset improves performance on fine-tuned tasks. In sufficient quantity and paired with a big model, even data scraped from the internet at random can contribute to the performance boost.
Some doctors don’t trust a second opinion when it comes from an AI system. A team at MIT and Regensburg University investigated how physicians responded to diagnostic advice they received from a machine learning model versus a human expert.
It’s well established that pretraining a model on a large dataset improves performance on fine-tuned tasks. In sufficient quantity and paired with a big model, even data scraped from the internet at random can contribute to the performance boost.
Some doctors don’t trust a second opinion when it comes from an AI system. A team at MIT and Regensburg University investigated how physicians responded to diagnostic advice they received from a machine learning model versus a human expert.
ImageNet now comes with privacy protection.What’s new: The team that manages the machine learning community’s go-to image dataset blurred all the human faces pictured in it and tested how models trained on the modified images on a variety of image recognition tasks.
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