Amazon announced today that users of its streaming service Prime Music, which is free with a Prime membership, and its subscription-based Amazon Music Unlimited can now ask Alexa to find tunes appropriate for various activities. As of now, over 500 different activity-based requests are supported including music for meditation, partying and even "getting pumped." The new feature is available immediately to users with Alexa-enabled devices. The new voice controls were geared towards activities that have been requested most often by Alexa users and listeners of Amazon's music streaming services. In the announcement, the company said that 27 percent of all activity requests come from users who want to relax.

These and other findings are from the McKinsey Global Institute Study, and discussion paper, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in) published last month. McKinsey Global Institute published an article summarizing the findings titled How Artificial Intelligence Can Deliver Real Value To Companies. McKinsey interviewed more than 3,000 senior executives on the use of AI technologies, their companies' prospects for further deployment, and AI's impact on markets, governments, and individuals. McKinsey Analytics was also utilized in the development of this study and discussion paper. I am currently serving as Director, Global Cloud Product Management at Ingram Cloud.

Machine Learning, the branch of Artificial Intelligence is based on the idea that machines should be able to learn and adapt through experience. It is increasingly gaining popularity over the last couple of years. Machine learning is one approach to achieve Artificial Intelligence by using algorithms. It is predicted that Machine Learning Algorithms may replace a wealth of jobs in the coming years. Logistic Regression is a powerful statistical way of estimating discrete values (usually binary values) from a set of independent variables.

While EMMA is a relatively simple application, developers are thinking bigger as well: Today's cognitive technologies can track the course, speed, and destination of nearly 2,000 airliners at a time, allowing them to fly safely.4 Over time, AI will spawn massive changes in the public sector, transforming how government employees get work done. It's likely to eliminate some jobs, lead to the redesign of countless others, and create entirely new professions.5 In the near term, our analysis suggests, large government job losses are unlikely. But cognitive technologies will change the nature of many jobs--both what gets done and how workers go about doing it--freeing up to one quarter of many workers' time to focus on other activities.

Regularization does NOT improve the performance on the data set that the algorithm used to learn the model parameters (feature weights). However, it can improve the generalization performance, i.e., the performance on new, unseen data, which is exactly what we want. In intuitive terms, we can think of regularization as a penalty against complexity. Increasing the regularization strength penalizes "large" weight coefficients -- our goal is to prevent that our model picks up "peculiarities," "noise," or "imagines a pattern where there is none." Again, we don't want the model to memorize the training dataset, we want a model that generalizes well to new, unseen data. In more specific terms, we can think of regularization as adding (or increasing the) bias if our model suffers from (high) variance (i.e., it overfits the training data).

Over the past year firms like AggregateIQ and Cambridge Analytica have been credited with using AI-targeted ads on social media to help swing the Brexit referendum and the US presidential election respectively. But a lack of evidence meant we have never known whether the technology exists to make this possible. Now the first study detailing the process from start to finish is finally shedding some light. "This is the first time that I've seen all the dots connected," says Joanna Bryson, an artificial intelligence researcher at the University of Bath, UK. At the heart of the debate is psychographic targeting – the directing of political campaigns at people via social media based on their personality and political interests, with the aid of vast amount of data filtered by artificial intelligence.

Brazil forward Neymar said he needed a new challenge, as he joined Paris St-Germain from Barcelona for a world record fee of 222m euros (£200m). The 25-year-old won seven major trophies in his four seasons at the Nou Camp, including the Champions League once and La Liga twice. He said his father, Neymar Sr, wanted him to stay at Barcelona. "I have won all that a player can win," said Neymar, who will earn 45m euros (£40.7m) a year on a five-year deal. Writing on Instagram, he added: "I have conquered everything an athlete can conquer. I have lived unforgettable moments. But a player [me] needs challenges. "And for the second time in my life, I'll contradict my father." Neymar's transfer smashes the previous record set when Paul Pogba returned to Manchester United from Juventus for £89m in August 2016. His £782,000-a-week wages mean PSG's total outlay is £400m. The French side have called a news conference for 12:30 BST on Friday, and Neymar will be introduced to fans at PSG's first game of the season against Amiens at Parc des Princes on Saturday. PSG reached the last eight of the Champions League last season - knocked out by a Neymar-inspired Barcelona - and were beaten to the French title by Monaco. Neymar said he has joined "one of the most ambitious clubs in Europe". "Paris St-Germain's ambition attracted me to the club, along with the passion and the energy this brings," he added. "I feel ready to take the challenge.

Corp. (OTC-PINK: GOSY) (http://www.GeckoSystems.com/) announced today that their CEO, R. Martin Spencer, has filed RICO counterclaims against Neil T. Wallace seeking damages in excess of $ 47,000,000. For over twenty years, GeckoSystems has dedicated itself to development of "AI Mobile Robot Solutions for Safety, Security and Service … Social Media Analytics News

Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models.You can use the TensorFlow library do to numerical computations, which in itself doesn't seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges. The name "TensorFlow" is derived from the operations which neural networks perform on multidimensional data arrays or tensors! For now, this is all you need to know about tensors, but you'll go deeper into this in the next sections! Today's TensorFlow tutorial for beginners will introduce you to performing deep learning in an interactive way: Also, you could be interested in a course on Deep Learning in Python, DataCamp's Keras tutorial or the keras with R tutorial. To understand tensors well, it's good to have some working knowledge of linear algebra and vector calculus. You already read in the introduction that tensors are implemented in TensorFlow as multidimensional data arrays, but some more introduction is maybe needed in order to completely grasp tensors and their use in machine learning.

Robotic process automation (RPA) is a family of technologies designed to replicate human actions in order to complete a task or series of tasks. Unlike traditional programming constructs, RPA is designed to allow everyday workers to quickly deploy virtual counterparts to learn from and mimic the actions they use to accomplish routine tasks, therefore freeing the worker to handle more complex, and in some cases, more fulfilling work activities. According to a new report from Tractica, the market for RPA is developing rapidly, and the market intelligence firm forecasts that worldwide revenue in the sector will increase from $151 million in 2016 to more than $5.1 billion by 2025. The key industry sectors that are embracing RPA implementations include financial services & banking, utilities & telecommunications, retail & commercial, and healthcare & insurance. The market opportunity for RPA will be largest in Europe during the forecast period, with Asia Pacific and North America not far behind.