As the 2019 Wimbledon Championships drew to a close, Novak Djokovic overcame Roger Federer in one of the most memorable and exciting finals ever witnessed. Thanks to A.I. technologies, audiences could view more action from the tournament's 660 matches than ever before. The 13-day competition spans 18 courts with hundreds of players competing, making it a near-impossible task to package highlights together for every game. Even with a vastly impracticable team of hundreds of editors, the scale of the task would make it formidable. That's where IBM's A.I. technology Watson has stepped in.

These days, the latest developments in artificial intelligence (AI) research always get plenty of attention, but an AI researcher at the U.S. Naval Research Laboratory believes one AI technique might be getting a little too much. Ranjeev Mittu heads NRL's Information Management and Decision Architectures Branch and has been working in the AI field for more than two decades. "I think people have focused on an area of machine learning--deep learning (aka deep networks)--and less so on the variety of other artificial intelligence techniques," Mittu said. "The biggest limitation of deep networks is that a complete understanding of how these networks arrive at a solution is still far from reality." Deep learning is a machine learning technique that can be used to recognize patterns, such as identifying a collection of pixels as an image of a dog.

The machine learning models that can detect our face and movements are now part of our daily lives with smartphone features like face unlocking and Animoji. However, those AI models can't predict how we feel by looking at our face. That's where EmoNet comes in. Researchers from the University of Colorado and Duke University have developed the neural net that can accurately classify images in 11 emotional categories. To train the model, researchers used 2,187 videos that were clearly classified into 27 distinct emotion categories including anxiety, surprise, and sadness.

A machine learning tool called Northstar lets users play with data visually. Yang-Hui He, a mathematical physicist at the University of London, is an expert in string theory, one of the most abstruse areas of physics. But when it comes to artificial intelligence (AI) and machine learning, he was naïve. "What is this thing everyone is talking about?" he recalls thinking. Then his go-to software program, Mathematica, added machine learning tools that were ready to use, no expertise required.

Cities are an engine for human prosperity. By putting people and businesses in close proximity, cities serve as the vital hubs to exchange goods, services, and even ideas. Each year, more and more people move to cities and their surrounding metropolitan areas to take advantage of the opportunities available in these denser spaces. Technology is essential to make cities work. While putting people in close proximity has certain advantages, there are also costs associated with fitting so many people and related activities into the same place. Whether it's multistory buildings, aqueducts and water pipes, or lattice-like road networks, cities inspire people to develop new technologies that respond to the urban challenges of their day. Today, we can see the responses made possible by the advances of the second industrial revolution, namely steel and electricity. Multistory buildings and skyscrapers responded to our demand for proximity to do business in the same locations.

You could be forgiven for thinking that A.I. will soon replace human physicians based on headlines such as "The A.I. Doctor Will See You Now," "Your Future Doctor May Not Be Human," and "This A.I. Just Beat Human Doctors on a Clinical Exam." But experts say the reality is more of a collaboration than an ousting: Patients could soon find their lives partly in the hands of A.I. services working alongside human clinicians. There is no shortage of optimism about A.I. in the medical community. But many also caution the hype surrounding A.I. has yet to be realized in real clinical settings. There are also different visions for how A.I. services could make the biggest impact.

Difficult co-workers who defy authority are more likely to cheat on their partners, a new study suggests. Researchers at the University of Texas discovered the correlation after studying the records of police officers, financial advisers, white-collar criminals and senior executives who used the Ashley Madison marital infidelity website. The data suggests a strong connection between people's actions in their personal and professional lives. They found that Ashley Madison were more than twice as likely to engage in corporate misconduct. Researchers investigated four study groups totalling 11,235 individuals.

Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines with linear, quadratic, and RBF kernels, Boosted Trees, and Naïve Bayes) were applied.

Quantizers play a critical role in digital signal processing systems. Recent works have shown that the performance of quantization systems acquiring multiple analog signals using scalar analog-to-digital converters (ADCs) can be significantly improved by properly processing the analog signals prior to quantization. However, the design of such hybrid quantizers is quite complex, and their implementation requires complete knowledge of the statistical model of the analog signal, which may not be available in practice. In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine how to map an analog signal into its digital representation using deep learning tools. These representations are designed to facilitate the task of recovering underlying information from the quantized signals, which can be a set of parameters to estimate, or alternatively, a classification task. By utilizing deep learning, we circumvent the need to explicitly recover the system model and to find the proper quantization rule for it. Our main target application is multiple-input multiple-output (MIMO) communication receivers, which simultaneously acquire a set of analog signals, and are commonly subject to constraints on the number of bits. Our results indicate that, in a MIMO channel estimation setup, the proposed deep task-bask quantizer is capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model. Furthermore, for a symbol detection scenario, it is demonstrated that the proposed approach can realize reliable bit-efficient hybrid MIMO receivers capable of setting their quantization rule in light of the task, e.g., to minimize the bit error rate.

We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200,000 exams (over 1,000,000 images). Our model achieves an AUC of 0.919 in predicting malignancy in patients undergoing breast cancer screening, reducing the error rate of the baseline (Wu et al., 2019a) by 23%. In addition, the models generates bounding boxes for benign and malignant findings, providing interpretable predictions.