A team of researchers recently presented their paper on KiloGram, a new algorithm for managing large n-grams in files, to improve machine-learning detection of malware. The new algorithm is 60x faster than previous methods and can handle n-grams for n=1024 or higher. The large values of n have additional application for interpretable malware analysis and signature generation.
Source: infoq.com
We’ve seen profound advances in technology, especially with the development of artificial intelligence and deep learning which are increasingly for voice assistants. This, in turn, promises to bring about huge changes in consumer behavior — what’s being called “voice commerce”. This is a new channel, governed by a new set of rules.
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Tools that enable fast and flexible experimentation democratize and accelerate machine learning research. Take for example the development of libraries for automatic differentiation, such as Theano, Caffe, TensorFlow, and PyTorch: these libraries have been instrumental in catalyzing machine learning research, enabling gradient descent training without the tedious work of hand-computing derivatives. In these frameworks, it’s simple to experiment by adjusting the size and depth of a neural network, by changing the error function that is to be optimized, and even by inventing new architectural elements, like layers and activation functions–all without having to worry about how to derive the resulting gradient of improvement.
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Classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty. The success of deep neural networks in diverse areas as image recognition and natural language processing has been outstanding in recent years. However, classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty.
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