Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users. Getting started with machine learning is hard. Even to run the most basic of experiments takes a good amount of expertise. All of these new tools greatly simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.
Over the past decade, designers have developed silicon technologies that run advanced deep learning mathematics fast enough to explore and implement artificial intelligence (AI) applications such as object identification, voice and facial recognition, and more. Machine vision applications, which are now often more accurate than a human, are one of the key functions driving new system-on-chip (SoC) investments to satisfy the development of AI for everyday applications. Using convolutional neural networks (CNNs) and other deep learning algorithms in vision applications have made such an impact that AI capabilities within SoCs are becoming pervasive. It was summarized effectively by Semico's 2018 AI Report "...some level of AI function in literally every type of silicon is strong and gaining momentum." In addition to vision, deep learning is used to solve complex problems such as 5G implementation for cellular infrastructure and simplifying 5G operational tasks through the capability to configure, optimize and repair itself, known as Self Organizing Networks (SON).
Google researchers developed a way to peer inside the minds of deep-learning systems, and the results are delightfully weird. What they did: The team built a tool that combines several techniques to provide people with a clearer idea of how neural networks make decisions. Applied to image classification, it lets a person visualize how the network develops its understanding of what is, for instance, a kitten or a Labrador. The visualizations, above, are ... strange. Why it matters: Deep learning is powerful--but opaque.
UBS Card Center, which processes roughly 25 percent of all credit cards in Switzerland, has won the Security Innovation of the Year award at the Retail Banker International Awards, presented in London. UBS Card Center's fraud team used the the latest artificial intelligence and machine learning capabilities in the FICO Falcon Platform to stop 84 percent more fraudulent transactions last year than in 2015. The need to optimise costs in the face of fierce competition meant UBS Card Center had to keep fraud write-offs to the very minimum. They were facing new fraud attack volumes but needed to uphold the highest standards for customer experience and satisfaction. This required the use of machine learning to minimize consumer interruptions while investigating more potential cases of fraud, all without adding staff.