Intelligent personal assistants have the real potential to transform our daily lives in the nearest future. At least this is what Gartner says in its report on the Top 10 Strategic Technology Trends for 2017. For businesses, this means an excellent opportunity to refine their offers and improve customer experience, providing smarter and more effective ways to handle routine tasks. The great thing about Intelligent apps is that they can become integrated with almost every area of a customer's life. Over the last few years, more and more smart connected devices have been hitting the market, and all these gadgets are usually augmented with digital conversational interfaces.

One Monday afternoon in April 2013, 19-year-old Jordan Green was working in a liquor store in Adelaide, Australia, when two men in balaclavas holding a shotgun entered the store, jumped the counter, held the gun to his head, and demanded his co-worker open the store's safe. It isn't the typical foundation for a company, but this is how Jemsoft was born. As a pragmatist, Green told ZDNet that he approached the situation by questioning how they entered the store with automatic doors and security cameras. Fortunately, Green was also a programmer involved in robotics. "The question in my head was why is it that someone who so clearly was not here to grab a slab could come into the local bottle-o and threaten my life and the life of my co-worker -- who to my knowledge has not returned to work. You could say that I took a pretty radical career change because I then left uni, left that job, and tried to build a company, which is not something a sane person would do," he said.

John Schrom is an Epidemiologist by training, a Data Scientist by occupation (at Practice Fusion), and a PhD student by hobby. His interests primarily revolve around finding utility in social data, the application and representation of health data, and general data mining and machine learning techniques.

If you're interested in large scale machine learning, then this book is for you. This book doesn't provide deep coverage of theoretical foundations of machine learning (I would recommend to look to other books, like Introduction to Machine Learning (Adaptive Computation and Machine Learning series),Machine Learning in Action or Programming Collective Intelligence: Building Smart Web 2.0 Applications, etc., if you want to get more background), but concentrates on explanation on how to use Apache Mahout ([...]) to solve some of machine learning problems: making recommendations, data clustering & classification. For each of class of these problems, description starts with base things, and continues with more complex examples, including complete solutions, that could be easily adapted for your machine learning problems. All examples that come with book were checked with actual release of Apache Mahout (version 0.5). Book is written in succinct, but understandable language and provides many code snippets that make understanding of topics much easier.

Makes your study time more efficient by focusing on the topics you where need the most help. Proven to help students earn a better grade in their courses. Before You Buy: This is an online third party study guide to accompany AP Physical geography and is not meant for submitting homework assignments. This product does not accept a course key. If one was provided to you, this is not the correct product.

Machine learning techniques generally produce more accurate predictions. Predictive analytics has become an increasingly important tool for businesses as they look to make better use of all the data they're gathering. Machine learning can provide even more punch to analytics, giving enterprises an even more powerful data resource. AI techniques are becoming part of every day computing: here's how they're being used to help online retailers keep up with the competition. Data analysts are increasingly using machine learning techniques for predictive analytics because they "tend to outperform statistical techniques for prediction problems," said Thomas Dinsmore, an independent consultant and author of Disruptive Analytics.

Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. It provides much-needed broad perspective and mathematical preliminaries for software engineers and students entering the field, and serves as a reference for authorities. This is the definitive textbook on deep learning. Written by major contributors to the field, it is clear, comprehensive, and authoritative. If you want to know where deep learning came from, what it is good for, and where it is going, read this book.

To help merchants on its marketplaces efficiently handle growing volumes of consumer enquiries, Alibaba has launched a smart customer service chatbot powered by artificial intelligence (AI) that retailers can customize to suit their individual virtual-storefront operations. Named Dian Xiaomi (store assistant), the text-only chatbot was inspired by Ali Xiaomi, an AI-powered chatbot rolled out by Alibaba in 2015 to handle customer enquiries and complaints coming into the e-commerce company. Encouraged by the success of Ali Xiaomi in understanding and answering questions from human users, Alibaba said it wanted to make the technology available to merchants who sell through its online marketplaces so they can upgrade their customer service. "We noticed the pain points suffered by merchants on our platform," said Liu Jianrong, Dian Xiaomi product manager. "For instance, merchants don't have enough staff to handle enquiries during rush hours in the daytime nor do they have any staff on duty in the evening. When it comes to big promotions like 11.11, they need to hire a large temporary customer-service crew, yet they are still swamped by the overwhelming volume," Liu said.

In the retail business, big data is poised in the coming years to open up huge opportunities in the way stores (both physical and online) fundamentally operate and serve customers. Given the incredibly small margins, Big Data will also provide much-needed efficiency improvements – from tighter supply chain management to more targeted marketing campaigns – that can make a big difference to a retail business of any size. Making data-driven decisions is no longer about learning from the past; it means making changes to the business constantly based on real time input from all data sources across the organisation. Making predictions and applying machine learning is based on traditional data but also on new and innovative sources like connected Internet of Things (IoT) devices and sensors or, going a step further with deep learning, unstructured data from things like static images or cameras monitoring stock in warehouses. Consumers can be fickle, so being able to accurately anticipate what they will do next and quickly react is what puts the most innovative and successful retailers above the rest. Dataiku, recently explored the types of data problems facing retail, the problems they solve, and the steps that any retail organisation can take to become more data driven.

Amazon has been using algorithms to try to sell you extra stuff for years. But the technology to personalise merchandising, much further than recommendations, is advancing rapidly across ecommerce. Companies such as Sentient and Apptus and their AI-powered systems are changing site search functionality, product lists, facets and more, to try to generate more sales. I caught up with Sören Meelby, VP Marketing at Apptus, to get an introduction to the technology (Apptus eSales), and to pose some questions about the user experience in online retail. Sören Meelby: Each and every sort order typically follows a logic or business rules and'most popular' is fairly straight forward.