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" ... Probably, the big surprise is that AI innovation is even faster than I thought. By that I mean the speed at which AI innovation goes from research to products. Everyone knew that was going faster, but it is even faster now. As an example, computer vision algorithms and convolutional neural networks took 30 years from the first research paper to eventually becoming a pervasively used technology. Contrast that with the more recent natural language breakthroughs. Google published an academic paper on the subject, and in just two years just about every commercial company has it in their products. This makes us even more confident that the quick adoption of AI into all kinds of domains and countries will reach commercial applications much faster than just about any other technology we can imagine. ... "
" ... The moderation team hand-picks the content based on guidelines and trends, which provides the company with a certain degree of confidence that the content chosen meets the quality standards and matches the product needs. We define this task as a binary classification problem where featured content constitutes the positive class, and the rest is rendered negative. We have built a custom convolutional neural network that is based on some pretrained architectures. ... "
" ... • Convolutional Layer: The convolutional layer consists of a set of cube-shaped filters that are convolved with the input data by computing the dot product between both, resulting in a so-called convolved feature map. The objective of a convolutional layer is to extract features from the input data, and usually, multiple convolutional layers are used within the same network to allow it to learn increasingly more complex features as the data is propagated. ... "