Artificial intelligence is a controversial topic in the technology world. While some argue that it brings with it a host of potential dangers, others say that it can make the world safer, easier, and more efficient. While both sides of the argument have merit, there is no denying that A.I. is here to stay, and will undoubtedly have a tremendous impact on tech startups. However, for now there are still some things that A.I. can't improve upon. In this guest blog post, Designli CEO/Co-Founder and friend of the Founder Institute Keith Shields describes what founders will still have to do on their own that A.I. can't.

The September issue of the Harvard Business Review features a cover story on design thinking's coming of age. We have been applying design thinking within SAP for the past 10 years, and I've witnessed the growth of this human-centered approach to innovation first hand. Design thinking is, as the HBR piece points out, "the best tool we have for … developing a responsive, flexible organizational culture." This means businesses are doing more to learn about their customers by interacting directly with them. Meanwhile, technology is making it possible to know exponentially more about a customer.

The inventor of Android is set to re-enter the phone market - and believes he can take on Apple and Google. Andy Rubin has revealed a glimpse of his firm's new handset with what appears to be a 360 degree camera. It has raised speculation the handset will has a port on top to enable accessories to be plugged in - including the camera, which could be used to create augmented reality apps. Earlier this year it was reported Rubin had recruited over 40 people to his new firm, to be called Essential, according to Bloomberg. It aims to build an entire technology ecosystem using AI - from phones and smart home gadgets to app stores.

Having a complete 360 degree view of each customer is imperative for predictive sales success. Where does the data for this comprehensive customer profile come from? And when should you start creating your profiles? Last week, we looked at what AI-based predictive selling, also known as predictive sales, is doing right now. And it's providing a glimpse into a future that is much more data driven. But what makes predictive selling work?

AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms.

NOTE: The Microsoft Press Guided Tours app has been discontinued and is no longer available in the Windows Store. If you have already installed the app, you can continue to use it for as long as you like. All the tours will remain available for download from within the app. We're happy to announce the release of our newest free ebook, Microsoft Azure Essentials: Azure Machine Learning (ISBN 9780735698178), by Jeff Barnes. This is the third ebook in Microsoft Press's free Microsoft Azure Essentials series. Below you'll find the ebook's Foreword, by Scott Guthrie, Executive Vice President of the Cloud and Enterprise group at Microsoft, as well as a few helpful sections from its Introduction.

Sunil has created this guide to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world. Through this guide, he will enable you to work on machine learning problems and gain from experience. He is providing a high level understanding about various machine learning algorithms along with R & Python codes to run them.

Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects.

The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. This article describes DIRECTOR, a staff scheduling system for contact centers. DIRECTOR is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service-quality metrics. DIRECTOR has successfully been deployed at more than 800 contact centers, with significant measurable benefits, some of which are documented in case studies included in this article.