Hundreds of data scientists work in the Equifax Data and Analytics Lab. They focus on connecting the companies' unique data with customers' unique needs. The data scientists do this through a relentless pursuit of innovation in machine learning, predictive analytics, and explainable AI. AI stands for Artificial Intelligence, a software technology that makes machines think like humans. It also makes them behave like humans. When we are involved in any activity, we learn as we go along.

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed.

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Abstract: Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction.

Some time ago when we talked about artificial intelligence we immediately thought of those futuristic films with technologies that seem impossible today. But just look around to realize that this area has evolved in such a way that it is already possible to integrate artificial intelligence into many of our day-to-day activities. Of course, because of the incredible possibilities that this type of technology brings, it has received attention from several industries, including marketing. By the way, did you know that artificial intelligence and marketing are already walking side by side? How does this partnership work?

WHEN SOPHIA THE ROBOT first switched on, the world couldn't get enough. It had a cheery personality, it joked with late-night hosts, it had facial expressions that echoed our own. Here it was, finally -- a robot plucked straight out of science fiction, the closest thing to true artificial intelligence that we had ever seen. There's no doubt that Sophia is an impressive piece of engineering. It didn't take much to convince people of Sophia's apparent humanity -- many of Futurism's own articles refer to the robot as "her."

"There is a battle going on for fairness, inclusion and justice in the digital world." Darren Walker, president of the Ford Foundation, was referring to burgeoning research that has uncovered systematic racial, gender, and other biases built into algorithms used for everything from Netflix "recommended" titles to surveillance systems. In one dramatic case, researchers testing facial-recognition software found that it was reliable – when using photos of white males. But for photos of darker-skinned people, the systems failed as much as 35 percent of the time to correctly identify the gender of black women (Lhor, 2018). Such facial-recognition technology is being rapidly developed for a range of applications.

When we talk about artificial intelligence, people always tend to think of the only spaces where this tech can replace humans. However, there is another vertical, which is one of those least expected, that AI has influenced in recent times -- Music composition. The music industry has also witnessed tremendous transformations done by AI over the past couple of years -- not only in terms of listening to music but also in terms of how music is made. American singer-songwriter, Taryn Southern in 2017 created a big, moody ballad song Break Free entirely produced by an AI. Not only the song but Southern also went on to create the entire album I AM AI which is the first LP to be entirely composed and produced using AI.

A group of scientists from the Massachusetts Institute of Technology (United States) has created a machine learning system that processes sounds like people. This model can understand the meaning of a word and classify a song according to its genre or style: classical, jazz, pop, rock, blues, soul, hip hop, techno, house, etc. It is the first invention of this type that mimics the way the brain works. As the experiments carried out at MIT show, it can compete in precision with humans. The research, published in the journal Neuron, is based on deep neural networks, that is, a structure inspired by brain cells that analyses information by layers.