Once you've mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images.

GPUs are great for tasks that can be broken up into multiple parts and processed in parallel. If you think of the central processing (CPU) of your laptop as its'brain', the GPU is like a swarm of tiny, specialized'brains'. Chipmakers are cranking up their GPUs to keep up with the exploding demand for AI in everything from chatbots to the computer vision of guided missiles. Industry leader Nvidia reported $5 billion revenue in the last quarter. Amid the heady commercial success of GPU makers, it is hard to make a business case for a new approach.

At multiple restaurants operated by one of its franchisees, Outback Steakhouse is testing machine learning (ML) technology from Presto. The test is focused on the restaurant lobby, but could grow to the kitchen, curbside pickup and dining room areas, CNBC reported. Presto Founder and CEO Rajat Suri said the technology assists restaurants in fixing blind spots, noting that "managers can't be everywhere at all times." Cameras capture and analyze the actions of waiters, hosts and customers to bolster diner satisfaction. The technology can track cleanliness (or lack thereof) of lobbies, extended wait times and the number of diners who leave without being acknowledged or shown to a table.

The next Outback Steakhouse you walk into could be more similar to an Amazon Go store than you would think. The casual-dining chain, which is owned by Bloomin' Brands, is piloting machine learning technology from Presto at several restaurants operated by one of its franchisees, Evergreen Restaurant Group. Cameras, discreetly placed in the lobby, capture hosts, waiters and customers and analyze their interactions to improve customer satisfaction. The technology can track long wait times, the cleanliness of lobbies and the number of customers who leave without being seated or greeted. Managers and staff members can receive notifications in real time, before customers leave angry Yelp reviews.

At multiple restaurants operated by one of its franchisees, Outback Steakhouse is testing machine learning (ML) technology from Presto. The test is focused on the restaurant lobby, but could grow to the kitchen, curbside pickup and dining room areas, CNBC reported. Presto Founder and CEO Rajat Suri said the technology assists restaurants in fixing blind spots, noting that "managers can't be everywhere at all times." Cameras capture and analyze the actions of waiters, hosts and customers to bolster diner satisfaction. The technology can track cleanliness (or lack thereof) of lobbies, extended wait times and the number of diners who leave without being acknowledged or shown to a table.

As casual dining chains have declined in popularity, many have experimented with surveillance technology designed to maximize employee efficiency and performance. Earlier this week, one Outback Steakhouse franchise announced it would begin testing such a tool, a computer vision program called Presto Vision, at a single outpost in the Portland, Oregon area. Your Bloomin' Onion now comes with a side of Big Brother. According to Presto CEO Rajat Suri, Presto Vision takes advantage of preexisting surveillance cameras that many restaurants already have installed. The system uses machine learning to analyze footage of restaurant staff at work and interacting with guests.

Feature selection, which searches for the most representative features in observed data, is critical for health data analysis. Unlike feature extraction, such as PCA and autoencoder based methods, feature selection preserves interpretability, meaning that the selected features provide direct information about certain health conditions (i.e., the label). Thus, feature selection allows domain experts, such as clinicians, to understand the predictions made by machine learning based systems, as well as improve their own diagnostic skills. Mutual information is often used as a basis for feature selection since it measures dependencies between features and labels. In this paper, we introduce a novel mutual information based feature selection (MIBFS) method called SURI, which boosts features with high unique relevant information. We compare SURI to existing MIBFS methods using 3 different classifiers on 6 publicly available healthcare data sets. The results indicate that, in addition to preserving interpretability, SURI selects more relevant feature subsets which lead to higher classification performance. More importantly, we explore the dynamics of mutual information on a public low-dimensional health data set via exhaustive search. The results suggest the important role of unique relevant information in feature selection and verify the principles behind SURI.

"The human brain has 100 billion neurons, each neuron connected to 10 thousand other neurons. Sitting on your shoulders is the most complicated object in the known universe," Michio Kaku, Physicist and Futurist The human brain, which not just stores but also computes, is by far the most powerful and complex computers in the world that occupies just 1.3 litres of space and consumes about 20 watts of power. In comparison, the finest supercomputers in the world require gigawatts of power, massive real estate, infrastructure, and dedicated cooling systems while attempting to perform brain-like tasks. Understanding how the human brain functions and replicating it has been a lifelong quest for the scientific and research community. Enter neuromorphic computing, a concept developed by American scientist and researcher Carver Andress Mead in the late 1980s – which tries to emulate certain functions of the human brain in silicon.

In another huge setback to Infosys CEO Vishal Sikka's strategy to strengthen the company's new digital and innovation capabilities, Yusuf Bashir, MD of the $500-million Infosys Innovation Fund, has resigned. The MIT-alumnus had worked closely with Sikka at SAP as VP of new products before joining Infosys in March 2015. Together with another former SAP executive, Ritika Suri, Bashir was to help Infosys take a leap into the new digital world that has become important to global clients. Bashir, based in Palo Alto, had the mandate to identify and invest in early-stage companies doing cutting-edge work in areas including AI, machine learning, big data, cloud and analytics. Acquisitions has been a key strategy used by Accenture, for instance, to build the new digital capabilities.