Banking and financial-services providers have more data than any other industry at their disposal – it's time to make the most of this, for the benefit of customers themselves. Digital transformation is changing the way banks talk to customers, but forward-looking financial institutions in the region are looking at how they can deploy artificial intelligence and automation to transform the customer experience, says Danny Drew, Avaya's Managing Director for South Africa. When was the last time you actually visited your bank branch? For most people, the answer is likely to be somewhere in between the last time you had to do a major financial transaction, such as applying for a loan or a mortgage, and… you just can't remember. The way consumers interact with banks has fundamentally changed over the past few years as trends like mobile and online banking have become increasingly important.

Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intelligence applications like image recognition, self-driving cars and most recently the AlphaGo vs. Lee Sedol matches. Recently, Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis. For those of you that are new to the topic of Deep Learning, we have put together a list of ten common terms and concepts explained in simple English, which will hopefully make them a bit easier to understand. We've done the same in the past for Machine Learning and NLP terms, which you might also find interesting. In the human brain, a neuron is a cell that processes and transmits information.

To get started, let's examine 10 fascinating facts about the AI industry. These devices will likely include many connected appliances, cars, wearables, and other gadgets included in the broader Internet of Things market.

Last week, IBM released a public beta of Watson Analytics, a platform for data exploration, visualization and predictive analytics. This product follows on Microsoft's Azure Machine Learning service, which provides cloud-based machine learning solutions. Interested to see how the offerings compare, I set up accounts with both services and set out to explore several datasets. For fairness, I should note that IBM's Watson analytics is in a public beta, while Microsoft's product is a significantly more mature offering. Besides relative maturity, the more striking difference between the products is the fundamentally different use cases they address.

The company showcases HCM Suite innovations to help drive inclusion for better business results. SAP has unveiled plans to use machine learning to help eliminate bias in hiring and employee performance reviews. At the SuccessConnect event in Las Vegas, the company exhibited planned upcoming capabilities within its cloud-based SAP SuccessFactors HCM Suite to help global organisations detect and avoid unconscious bias. As part of its commitment, SAP intends to optimise existing solutions and roll out new functionality in important decision areas that have prevented organisations from using total talent. The German company is designing the new features with a customer advisory group particularly focused on diversity and inclusion issues.

Now if these are not dominant steps toward a world of AI I don't know what is. Front this technology with a robot body and it becomes an intelligent being. They can park themselves, drive themselves, break automatically and navigate based on coordinates. Once they are able to learn and map out routes as well as incorporate traffic reports and weather disturbances to get you quickly and safely from A to B without risk…bingo. Google Assistant, Amazon Echo, Apple Siri and the other personal virtual assistants are bringing all daily rituals, habits and requests together via one central point.

Finnish technology firm Nokia has rolled out major updates in its "Motive Customer eXperience Solutions" (CXS) software portfolio with advanced machine-learning capabilities to reduce costs and improve customer experiences. Developed by Nokia Bell labs, Nokia Motive Service Management Platform (SMP) 7.0 and Motive Care Analytics (CAL) 2.0 use machine-learning algorithms -- advanced capabilities that give computers the ability to learn without being explicitly programmed, the company said in a statement. "By providing the earliest possible detection of network issues and streamlining help desk and self-care interactions, these new Nokia solutions reduce IT and care costs and result in more loyal customers," said Bhaskar Gorti, President of Applications an Analytics at Nokia. It enables service providers to quickly find the optimal remediation to issues when subscribers contact help desk agents or use self-care. "Nokia Motive CAL 2.0 automatically correlates customer help desk calls and self-care actions with network, service and third-party application topologies to identify call anomalies," the company said in a statement.

TechCrunch Disrupt in London is on December 5-6. As well as speakers and panels, we'll be featuring some demos by some amazing tech companies. The first will be by Boston Dynamics. Yes, folks, delegates to Disrupt London will get to see one of those amazing BD robots up close and personal, almost literally in the flesh (if they had any flesh, that is). Marc Raibert, CTO and Founder of Boston Dynamics will be demonstrating one of the amazing robots his team has created, but you'll have to come to find out which one… Raibert was Professor of Electrical Engineering and Computer Science at MIT and a member of the Artificial Intelligence Laboratory from 1986 through 1995.

Academic scientists devote their lives to research, often toiling away on problems that few people outside their discipline fully understand. Perhaps some are driven by pure curiosity or competition, while others have a personal interest in the topic at hand. For Shirley Pepke, a genomics researcher based in Los Angeles, the urgency to find answers comes from her own instinct for survival. Since 2014, she has been working on a tool capable of tailoring ovarian cancer treatment to each patient using genomics data and a machine learning algorithm. The first subject in this DIY precision medicine project was Pepke herself, who was diagnosed with stage IIIC ovarian cancer in September 2013.

This is a long article written by Sebastian Raschka. Sebastian Raschka is the author of the bestselling book "Python Machine Learning." As a Ph.D. candidate at Michigan State University, he is developing new computational methods in the field of computational biology. Sebastian has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning.