LVMH-owned beauty retailer Sephora is doubling down on augmented reality and artificial intelligence sales tactics by enabling shoppers to virtually try on false lashes, watch tutorials using their own image and engage via a chatbot to trial and purchase lip color. With Sephora's customers virtually trying on more than 70 million lip shades using the Virtual Artist in-app functionality that was introduced earlier this year, false lashes are being added to expand the program. Users of the Sephora application can also now experience live step-by-step makeup application tutorials using their own uploaded images and augmented reality technology. "This is a significant expansion because we are adding elements that we know will help empower and educate our clients' purchase making decisions, and they're done in a way that is fun and engaging," said Bridget Dolan, vice president of Sephora Innovation Lab. "The new Live Tutorials especially are a game changer for our users," she said.

It's been called the'sexiest job of the 21st century', the'hottest job of the decade', and is the fastest-growing field in tech at the moment – the impact of Data Science in today's world cannot be overstated. As a discipline, data science involves the collection and study of data – both structured and unstructured – to gain insights and information that can be used by organizations to devise effective strategies. By collating data over a period of time, patterns can be identified that enable companies to find new market opportunities, enhance efficiency, reduce costs, and place themselves at a competitive advantage in their industry. Due to rapid technological advances, especially in areas like mobile advertising, social media, and website personalization, a massive amount of data is being generated on a daily basis. These data volumes have resulted in industries having to become data-savvy & adapt to the new landscape – or risk falling behind the competition.

The growing popularity of messaging platforms, paired with rapid advances in A.I., is driving a new era of meaningful interaction with chabots. Just as browsers like Chrome and Firefox provide access to every web page, messaging platforms have begun to mirror that functionality to access branded conversational bots. A massive number of people -- 1.4 billion monthly users -- are now on messaging platforms and are spending an average of 23 minutes and 23 seconds a day on chat. Together, the top four messaging platforms -- Facebook Messenger, WhatsApp, WeChat, and Viber -- have more registered users, higher retention, and higher engagement than the top four social networks. With 80 percent of U.S. teens on chat platforms, Gen Z is at the center of a major disruption in the evolving ecosystem of communication innovation.

In a discovery that raises fundamental questions about human behavior, researchers at the University of Virginia School of Medicine have found that the immune system directly affects -- and even controls -- our social behavior, such as our desire to interact with others. That finding could have significant implications for neurological diseases such as autism-spectrum disorders and schizophrenia, the researchers suggest. "The brain and the adaptive immune system were thought to be isolated from each other, and any immune activity in the brain was perceived as sign of a pathology. And now, not only are we showing that they are closely interacting, but some of our behavior traits might have evolved because of our immune response to pathogens," explained Jonathan Kipnis, chair of UVA's Department of Neuroscience. "It's crazy, but maybe we are just multicellular battlefields for two ancient forces: pathogens and the immune system. Part of our personality may actually be dictated by the immune system."

AI (artificial intelligence) personal assistants like Apple's Siri and Microsoft's Cortana have gained significant popularity in recent years. Those voice assistants, available on a variety of different tech devices, can help with a number of different tasks from driving directions to general questions. In fact, the Pew Research Center recently conducted a study where some experts claimed that AI technology like those assistants will be integrated into nearly every aspect of people's daily lives by 2025. So how many people currently use AI personal assistants? We asked 1,000 respondents what they think about the technology and how it has impacted their lives.

The Turing test, the quintessential evaluation designed to determine if something is a computer or a human, may have a fatal flaw, new research suggests. The test currently can't determine if a person is talking to another human being or a robot if the person being interrogated simply chooses to stay silent, new research shows. While it's not news that the Turing test has flaws, the new study highlights just how limited the test is for answering deeper questions about artificial intelligence, said study co-author Kevin Warwick, a computer scientist at Coventry University in England. "As machines are getting more and more intelligent, whether they're actually thinking and whether we need to give them responsibilities are starting to become very serious questions," Warwick told Live Science. "Obviously, the Turing test is not the one which can tease them out."

An artificial intelligence programmed to fly fighter jets has defeated several air combat experts in a simulation, according to a paper published in the Journal of Defense Management. The AI, called ALPHA, was built by Psibernetix, Inc. with assistance from the Air Force Research Laboratory. ALPHA's purpose was to be better than highly trained fighter pilots, and so far it appears up to the task. The AI has gone up against its predecessor, the AFRL's previous AI program, and a series of human opponents. It emerged victorious each time. One of those opponents, Gene Lee, is a retired Air Force colonel with extensive flight experience both as a pilot and an instructor.

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a 'selection function' to reveal the relevant latent variables, and use this to obtain a compact approximation of the posterior distribution for EM; this can make inference possible where the number of possible latent states is e.g. exponential in the number of latent variables, whereas an exact approach would be computationally unfeasible. We learn the selection function entirely from the observed data and current EM state via Gaussian process regression. This is by contrast with earlier approaches, where selection functions were manually-designed for each problem setting. We show that our approach performs as well as these bespoke selection functions on a wide variety of inference problems: in particular, for the challenging case of a hierarchical model for object localization with occlusion, we achieve results that match a customized state-of-the-art selection method, at a far lower computational cost.

Technology and data made possible advances in...technology and data. JL The Economist reports: New techniques have made training deep networks feasible. This takes a lot of number-crunching power, which became available when several AI research groups realised that graphical processing units (GPUs), the specialised chips used in PCs and video-games consoles to generate fancy graphics, were also well suited to running deep-learning algorithms. HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver.

In this capstone project, you will leverage what you've learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. You will first define the problem you want to solve and investigate potential solutions and performance metrics. Next, you will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it. You will then implement your algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, you will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values.