Back in November, we open-sourced our implementation of Mask R-CNN, and since then it’s been forked 1400 times, used in a lot of projects, and improved upon by many generous contributors. We received a lot of questions as well, so in this post I’ll explain how the model works and show how to use it in a real application.
Source: matterport.com
The brain has evolved over a long time, from very simple worm brains 500 million years ago to a diversity of modern structures today. The human brain, for example, can accomplish a wide variety of activities, many of them effortlessly — telling whether a visual scene contains animals or buildings feels trivial to us, for example. To perform activities like these, artificial neural networks require careful design by experts over years of difficult research, and typically address one specific task, such as to find what’s in a photograph, to call a genetic variant, or to help diagnose a disease.
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Researchers have used artificial intelligence (AI) to discover nearly 6,000 previously unknown species of virus. The work, presented on 15 March at a meeting organized by the US Department of Energy (DOE), illustrates an emerging tool for exploring the enormous, largely unknown diversity of viruses on Earth.
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This report examines the intersection of two subjects, China and artificial intelligence, both of which are already difficult enough to comprehend on their own. It provides context for China’s AI strategy with respect to past science and technology plans, and it also connects the consistent and new features of China’s AI approach to the drivers of AI development (e.g. hardware, data, and talented scientists). In addition, it benchmarks China’s current AI capabilities by developing a novel index to measure any country’s AI potential and highlights the potential implications of China’s AI dream for issues of AI safety, national security, economic development, and social governance.
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