Google recently released research about creating a tool for searching Similar Medical Images Like Yours (SMILY). The research uses embeddings for image-based search and allows users to influence the search through the interactive refinement of concepts.
Source: infoq.com
A Convolutional Neural Network (CNN) is a class of deep, feed-forward artificial neural networks most commonly applied to analyzing visual imagery. The architecture of these networks was loosely inspired by biological neurons that communicate with each other and generate outputs dependent on the inputs. Though work on CNNs started in the early 1980s, they only became popular with recent technology advancements and computational capabilities that allow the processing of large amounts of data and the training of sophisticated algorithms in a reasonable amount of time.
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To unlock the full potential of virtual reality (VR) and augmented reality (AR) experiences, the technology needs to work anywhere, adapting to the spaces where people live and how they move within those real-world environments. When we developed Oculus Quest, the first all-in-one, completely wire-free VR gaming system, we knew we needed positional tracking that was precise, accurate, and available in real time — within the confines of a standalone headset, meaning it had to be compact and energy efficient. At last year’s Oculus Connect event we shared some details about Oculus Insight, the cutting-edge technology that powers both Quest and Rift S. Now that both of those products are available, we’re providing a deeper look at the AI systems and techniques that power this VR technology.
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