This week, a new AR-centric social networking app launched, which will let users pull up their friends' profiles by pointing their smartphone camera at them. Called Octi, the app will let people make a personal profile by choosing photos, favorite songs from Spotify, fun links from YouTube, and sticker-like custom messages. Instead, they'll hover around you as a halo of thumbnail-sized icons whenever someone with the app on their phone points their camera at you. Octi uses facial recognition software to identify whether someone you point your camera at has a profile, and will automatically pull it up as an AR overlay if you're already connected as friends. The app will also let you add new friends by pointing your camera at someone and then send them a friend request, so long as they have a profile with Octi.

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It is a 32 hrs instructor led machine learning training provided by Intellipaat which is completely aligned with industry standards and certification bodies. If you've enjoyed this machine learning training, Like us and Subscribe to our channel for more similar machine learning videos and free tutorials. Ask us in the comment section below. Machine learning is one of the fastest growing arms of the domain of artificial intelligence. It has far reaching consequences and in the next couple of years we will be seeing every industry deploying the principles of artificial intelligence, machine learning and deep learning technologies at scale.

Nowadays, we are immersed in tens of newly-proposed evolutionary and swam-intelligence metaheuristics, which makes it very difficult to choose a proper one to be applied on a specific optimization problem at hand. On the other hand, most of these metaheuristics are nothing but slightly modified variants of the basic metaheuristics. For example, Differential Evolution (DE) or Shuffled Frog Leaping (SFL) are just Genetic Algorithms (GA) with a specialized operator or an extra local search, respectively. Therefore, what comes to the mind is whether the behavior of such newly-proposed metaheuristics can be investigated on the basis of studying the specifications and characteristics of their ancestors. In this paper, a comprehensive evaluation study on some basic metaheuristics i.e. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Teaching-Learning-Based Optimization (TLBO), and Cuckoo Optimization algorithm (COA) is conducted, which give us a deeper insight into the performance of them so that we will be able to better estimate the performance and applicability of all other variations originated from them. A large number of experiments have been conducted on 20 different combinatorial optimization benchmark functions with different characteristics, and the results reveal to us some fundamental conclusions besides the following ranking order among these metaheuristics, {ABC, PSO, TLBO, GA, COA} i.e. ABC and COA are the best and the worst methods from the performance point of view, respectively. In addition, from the convergence perspective, PSO and ABC have significant better convergence for unimodal and multimodal functions, respectively, while GA and COA have premature convergence to local optima in many cases needing alternative mutation mechanisms to enhance diversification and global search.

The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.

A group of high school students was one of the top teams to emerge from the recent AI Tech Sprint by the Department of Veterans Affairs, delivering a web application that could help match cancer patients to clinical trials. The three students from Northern Virginia entered their work in a competition that included software companies like Oracle Healthcare and MyCancerDB. Digital consulting company Composite App took the $20,000 first place prize for its solution -- a tool for helping patients stay on track with their care plan -- but the clinical trials team got an honorable mention. The tech sprint was organized by the VA's new AI institute, and it focused on partnering with outside organizations and companies interested in applying artificial intelligence tools and techniques to VA data. The high school team's members -- Shreeja Kikkisetti, Ethan Ocasio and Neeyanth Kopparapu -- met as part of the Northern Virginia-based nonprofit Girls Computing League. They were unique in a competition otherwise dominated by adult professionals from software and health care companies.

This article will discuss about basic programming languages that you need for doing data science. For essential math skills needed, please see the following: Essential Math Skills for Machine Learning.

This course was designed to bring anyone up to speed on Machine Learning & Deep Learning in the shortest time. This particular field in computer engineering has gained an exponential growth in interest worldwide following major progress in this field. The course starts with building on foundation concepts relating to Neural Networks. Then the course goes over Tensorflow libraries and Python language to get the students ready to build practical projects. You will build a practical Tensorflow project for each of the above Neural Networks.

Neural architecture search(NAS) is one of the hottest trends in modern deep learning technologies. Conceptually, NAS methods focus on finding a suitable neural network architecture for a given problem and dataset. Think about it as making machine learning architecture a machine learning problem by itself. In recent years, there have been an explosion in the number of NAS techniques that are making inroads into mainstream deep learning frameworks and platforms. However, the first generation of NAS models have encountered plenty of challenges adapting neural networks that were tested on one domain to another domain.

Since the early days of the Internet, there hasn't been a new technology drawing so much buzz and concern as artificial intelligence. This month, with the help of theologian Ted Peters, we take a closer look at what is at stake as he outlines how our view of AI may color our view of God. It is of course a timely discussion as more of our daily activities are intersecting with AI perhaps with or without our knowledge. The use of computer intelligence is growing, as is our reliance on it to make daily activities easier. Peters though rightly looks at the broad implications from transhumanism to our view of what salvation is.