To simplify the path toward enterprise AI, organizations are turning to IBM Watson Studio and Watson Machine Learning. Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business. It helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.

The complexity, heterogeneity and scale of electrical networks have grown far beyond the limits of exclusively human-based management at the Smart Grid (SG). Likewise, researchers cogitate the use of artificial intelligence and heuristics techniques to create cognitive and autonomic management tools that aim better assist and enhance SG management processes like in the grid reconfiguration. The development of self-healing management approaches towards a cognitive and autonomic distribution power network reconfiguration is a scenario in which the scalability and on-the-fly computation are issues. This paper proposes the use of Case-Based Reasoning (CBR) coupled with the HATSGA algorithm for the fast reconfiguration of large distribution power networks. The suitability and the scalability of the CBR-based reconfiguration strategy using HATSGA algorithm are evaluated. The evaluation indicates that the adopted HATSGA algorithm computes new reconfiguration topologies with a feasible computational time for large networks. The CBR strategy looks for managerial acceptable reconfiguration solutions at the CBR database and, as such, contributes to reduce the required number of reconfiguration computation using HATSGA. This suggests CBR can be applied with a fast reconfiguration algorithm resulting in more efficient, dynamic and cognitive grid recovery strategy.

Before Siri and Alexa, there was Watson. Appearing as a contestant on "Jeopardy!" made IBM's Watson a household name. But since its debut -- and win -- in 2011, the computer has morphed into something else entirely: An artificial intelligence tool for business. The company opened up Watson in the cloud wars, making the technology available on competitors' clouds last month. Behind the Watson branding are career technologists making the tool work for business customers.

Greg Zaharchuk, MD,PhD, is the co-founder of Subtle Medical and a professor of radiology and practicing neuroradiologist at Stanford University. He's an expert in advanced imaging methods, particularly applied to patients with neurological disease. Greg has received numerous awards and honors for his research and sits on several boards and advisory committees.

In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted. In particular, estimates of trustworthiness can be useful for fraud analysts who process machine learning-generated alerts of fraudulent transactions. In this work, we present a case-based reasoning (CBR) approach that provides evidence on the trustworthiness of a prediction in the form of a visualization of similar previous instances. Different from previous works, we consider similarity of local post-hoc explanations of predictions and show empirically that our visualization can be useful for processing alerts. Furthermore, our approach is perceived useful and easy to use by fraud analysts at a major Dutch bank.

Machine Learning, Data Science, and Predictive Analytics techniques are in strong demand. That's why since its launch, IBM Watson Studio has proven to be very popular with academia. Thousands of students and faculty have been drawn to Watson Studio for its powerful open source and code-free data analysis tools. Now, this all-in-one platform for data science is free to students and faculty with unlimited use with Watson Studio Desktop. Watson Studio Desktop, with unlimited compute, is now available for free to students and faculty for teaching and learning purposes via a 1 year subscription.

Each brush activates a set of neurons in a GAN that has learned to draw scenes.

How are you using Watson in your business? We wanted to improve the candidate experience by creating interactions with job seekers visiting our career site, as well as increase the number of applications we receive for hard-to-fill roles. Watson Candidate Assistant answers general questions about working at NBCUniversal, and it recommends jobs based on keyword matching between openings and the job seeker's resume. Candidates using a traditional job search may look by functional areas or job titles, but that might not match our company's vernacular. We can now drive candidates to roles they might not have found.

Machines use Google-type algorithms on biopsy images to help children get treatment faster. A study published in the open access journal JAMA Open Network today by scientists at the University of Virginia schools of Engineering and Medicine says machine learning algorithms applied to biopsy images can shorten the time for diagnosing and treating a gut disease that often causes permanent physical and cognitive damage in children from impoverished areas. In places where sanitation, potable water and food are scarce, there are high rates of children suffering from environmental enteric dysfunction, a disease that limits the gut's ability to absorb essential nutrients and can lead to stunted growth, impaired brain development and even death. The disease affects 20 percent of children under the age of 5 in low- and middle-income countries, such as Bangladesh, Zambia and Pakistan, but it also affects some children in rural Virginia. For Dr. Sana Syed, an assistant professor of pediatrics in the UVA School of Medicine, this project is an example of why she got into medicine.

Deploying AI-imbued apps and services isn't as challenging as it used to be, thanks to offerings like IBM's Watson Studio (previously Data Science Experience). Watson Studio, which debuted in 2017 after a 12-month beta period, provides an environment and tools that help to analyze, visualize, cleanse, and shape data; to ingest streaming data; and to train and optimize machine learning models in real time. And today, it's becoming even more capable with the launch of AutoAI, a set of features designed to automate tasks associated with orchestrating AI in enterprise environments. "IBM has been working closely with clients as they chart their paths to AI, and one of the first challenges many face is data prep -- a foundational step in AI," said general manager of IBM Data and AI Rob Thomas in a statement. "We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources. The automation capabilities we're putting Watson Studio are designed to smooth the process and help clients start building machine learning models and experiments faster."