Wickham and Grolemund have produced an excellent book that would help a beginning R user become very efficient in explanatory analysis. Unsurprisingly the approach that they expound utilises the "hadleyverse" a collection of packages (ggplot2 for visualisation, tidyr for reshaping, dplyr for selecting and filtering, purrr for functional programming, broom for linear models etc) that dramatically speed up most of the common steps involved in an analysis. One benefit of Wickham's involvement in these packages has been a coherent philosophy that sits behind them. It can be a little tricky when learning this philosophy, but the long term benefits are enormous. The book is broken up into a number of sections that effectively builds up the ability to ingest, transform, visualise and model datasets.

Here's an analogy using a concept that we can all relate to: a supermarket. Picture the scene: Shopping list in one hand, shopping basket in the other, you're ready to tackle your weekly shopping in your local supermarket. Your items range from fruit and vegetables to washing detergent, perhaps with some free-range eggs thrown in for good measure. Quite the eclectic mix, but you know that you'll be able to find all you need under one roof. The fact that this is possible is in itself quite remarkable.

Watson captured the public imagination about artificial intelligence after defeating two world champions of Jeopardy in 2011 and bringing home a $1 million prize. Since then, Watson has gained new cognitive capabilities through APIs like Alchemy (for sentiment analysis), Tone Analyzer (for personality and emotional analysis), and Conversation (a chatbot builder) and has been embedded in hundreds of applications across financial services, healthcare, retail, and digital. "Chances are, you've interacted with Watson without realizing it," says Alyssa Simpson, Program Director at IBM Watson. "Many companies hide their use of the technology for competitive reasons. They don't want a competitor to buy it too."

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software--the pattern, colour and texture-without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in ecommerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software--the pattern, colour and texture-- without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in ecommerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

"We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems," Rastogi said.Rajeev Rastogi, who heads the Machine Learning team at Amazon, explains how the global ecommerce giant employs Artificial Intelligence to improve the online shopping experience.Edited excerpts: In which areas does Amazon use AI? We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems, product recommendations, product search, forecasting future product demand, among others. We have used Deep Learning to do better speech recognition. We use neural networks to convert speech (spoken by users) to text with very high accuracy. The speech recognition and understanding technology in Alexa (Amazon's voice-controlled virtual assistant) is powered by Deep Learning.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software - the pattern, colour and texture-without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in e-commerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

WASHINGTON/BOSTON One of two current members of the U.S. Securities and Exchange Commission raised questions on Thursday for companies like Snap Inc that offer shareholders unequal voting rights, saying the agency should "focus on how some innovations may prove detrimental to investors."

While personalisation remains the most popular place for innovation among US retailers, there is clear evidence that AI has started to gain traction. According to a new report from SailThru called the Digital Retail Innovation Report, "Although marketers have been able to leverage artificial intelligence (AI) for some time, adoption is still limited." "Of the two thirds of marketers (66 per cent) who leverage AI for marketing purposes, the most common applications are in search (37 per cent) and product recommendations (33 per cent), which are employed by at least one third of marketers," the report said. Among the other common responses were application through programmatic advertising (26 per cent) and data science (26 per cent). The authors said, "AI use alone wasn't enough to guarantee marketing success: Marketers leveraging AI in one or more of the listed channels/methods weren't significantly more likely to have met or exceeded their 2016 marketing goals, suggesting that not all applications and methods are equally useful, and that simply employing AI-based solutions is not sufficient for marketing success."

I read Artificial Intelligence for Humans, Volume 1 and then ordered volumes 2 and 3. What I like about this series is the same thing I like about Volume 2, that it's very readable. For someone without a math background, and limited programming prowess, I can understand the concepts. The only things about the book that I don't like are: 1) Some of the context is missing. For instance, I can understand Genetic Algorithms, Partical Swarm Optimization, and Ant Colony Optimization as concepts and I think I could basically code them if I needed to. I would say his forte is explaining the ideas and the math in plain language.