Top Artificial Intelligence Influencers To Follow in 2019 MarkTechPost

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Yoshua Bengio: Yoshua BengioOCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadiancognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.


Scientists teach neural network to identify a writer's gender

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A team of researchers from the National Research Nuclear University MEPhI, the National Research Center Kurchatov Institute and the Voronezh State University has developed a new learning algorithm that allows a neural network to identify a writer's gender by the written text on a computer with up to 80 percent accuracy. This is a new development in the field of computational linguistics. The research was funded by a Russian Science Foundation grant. The findings were published in the Procedia Computer Science journal. Many scientific studies show that writing style can reflect certain characteristics of a writer – gender, physiological personality traits, and level of education.


Scientists Created Artificial Intelligence From DNA

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Researchers at Caltech have figured out a novel way to design an artificial neural network from synthetic DNA, according to a press release from the university. Because DNA is composed of four foundational nucleotides that bond in specific pairings--adenine with thymine, cytosine with guanine--DNA is particularly well poised to be predicted using an algorithm using artificial intelligence and computers to create chemical reactions. That means that machines could create new ways to help ease suffering from diseases or increase human capacity in previously unforeseen ways. "Similar to how electronic computers and smart phones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come," Lulu Qian, an assistant professor of bioengineering whose lab was responsible for the finding, said.


What is the difference between artificial intelligence and neural networks?

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Artificial intelligence (AI) and artificial neural networks (ANN) are two exciting and intertwined fields in computer science. There are, however, several differences between the two that are worth knowing about. The key difference is that neural networks are a stepping stone in the search for artificial intelligence. Artificial intelligence is a vast field that has the goal of creating intelligent machines, something that has been achieved many times depending on how you define intelligence. Despite the fact that we have computers that can win at "Jeopardy" and beat chess champions, the goal of AI is generally seen as a quest for general intelligence, or intelligence that can be applied to diverse and unrelated situational problems.


Russian Scientists Improve Deep Learning Method for Neural Networks

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Today, deep neural networks with different architectures, such as convolutional, recurrent and autoencoder networks, are becoming an increasingly popular area of research. A number of high-tech companies, including Microsoft and Google, are using deep neural networks to design various intelligent systems. Together with deep neural networks, the term "deep" learning has gained currency. In deep learning systems, the processes of feature selection and configuration are automated, which means that the networks can choose between the most effective algorithms for hierarchal feature extraction on their own. Deep learning is characterized by learning with the help of large samples using a single optimization algorithm.