Without the software that powers them, iPhones and Android phones would be high-priced pieces of glass, metal and plastic. So let's see how the new mobile OSes from Apple and Google compare. On one side of the table, iOS 13 includes a systemwide dark mode, more control over privacy settings and a bushel of improvements designed to make the iPhone more secure and easier to use. On the other side, Google's Android 10 also brings on dark mode, a focus on privacy and useful AI enhancements. With the iPhone 11, 11 Pro and 11 Max Pro just announced, we'll soon see this the new iOS software absorbed into hardware.

Data quality rules are the hidden champions of data cleansing tools – these help improve the quality of the master data records in data quality tools. Based on different data quality dimensions, data stewards establish different quality rules to determine if a data set is of good or poor quality. Creating data quality rules can be very time consuming. An easy approach could be to adopt proven algorithms, which already work very well in the context of customer and vendor data within our data sharing community. Today, we would like to introduce one of our virtual employees to you: Our data quality rule number 1297;"DQR-1297", for short.

Artificial intelligence (AI) is here. The technology is already making an impact across many industries. According to IDC, worldwide spending on AI systems is forecast to reach $35.8 billion in 2019 -- a 44% increase over 2018. For any company that hasn't started its AI journey -- it's time to get started. That's because AI is shaking up the customer experience, bringing users and brands closer together than ever before.

While examples of artificial intelligence are numerous across business, AI is still often perceived to be a nascent, still emerging force. In fact, AI is widely deployed. It is critical to the tech platforms of many businesses, across finance and retail and healthcare and media. AI and deep learning examples are so myriad, in fact, that choosing the representative AI example below was a matter of picking among the excess. While the examples of AI below are very different from one another, they all share one common trait: the more data they are fed, the more they learn.

Have you ever been worried on a tour, if you've switched off a light bulb back home? We've all had this confusion at least once, for there was no way you could ensure that everything was perfect. Now, with AI, we don't need to leave anything to chance nor assumptions. AI provides perfect ways to ensure that use of appliances is optimized perfectly when in use and when not in use. Let's imagine some of the ways through which AI and Smart Home Automation will impact and change the way we live: Amazon's Alexa, Google Home, Apple's Siri and Microsoft's Cortana have all optimized and automated living inside a home to a great extent. You can ask Alexa to play a song, while you can also ask Alexa to switch off or switch on the lights in your home.

Whether we know it or not, artificial intelligence (AI) is already steeped into everyday life. It's present in the way social media feeds are organised; the way predictive searches show up on Google; and how music services such as Spotify make song suggestions. The technology is also helping transform the way enterprises do business. Commonwealth Bank of Australia, for instance, has applied AI to analyse 200 billion data points to free up more time so its customer service officers can focus on doing exactly what their title suggests: servicing customers. As a result, the bank has seen a 400% uplift in customer engagement.

Conversational assistants are here to stay, making everything from boiling an egg to making a payment that much easier. And consumers expect more of them day by day. If they meet these growing expectations, conversational assistants are in a position to transform the customer experience landscape. But do organizations have the customer centricity and organizational capabilities necessary to deploy these technologies successfully? In the new report from the Capgemini Research Institute, Smart Talk: How organizations and consumers are embracing voice and chat assistants,we talked to over 12,000 consumers who've used and continue to use voice and/or chat assistants and to 1,000 executives from consumer products and retail, financial services, and automotive, including pure-play digital players.

Today we want to go a step further and implement product recommendation as well. Product recommendation are widely used and are implemented using so called Recommender Systems. There are different ways of implementing recommendations like those we can see on Amazon or Netflix for example. In our case, we will use a multi-class classifier that depending on the answer provided by the user, it will select the product with the highest probability. Using a classifier allows us to avoid having to store past customer behaviour to train the model.

Retail Industry is looking at Artificial intelligence (AI) and machine learning (ML) as a solution to take their organization to the next level of productivity and customer experience. To explain this better, let us look at it this way – Retail companies have access to a massive amount of data about their customers and their shopping preferences. It is difficult for the companies to drill down into these huge mines of useful data and analyze it properly and derive actionable insights in real time. Therefore, massive amounts of this useful information could go waste which would otherwise have helped in increasing sales conversion rates or enhancing the customer satisfaction. With the help of AI and ML, the huge amount of big data could be used in creating web shops that take customer information and turn it into targeted shopping experiences or online chatbots that can easily answer questions and assist customers, or in-store intelligence to make the customer experience even more interactive.

Virtual Assistants have a huge potential in transforming business processes, like cutting down costs, training employees etc. However, it brings some challenges, along with its benefits. Will the CIOs able to overcome it? FREMONT, CA: A virtual wireless assistant is basically an engineered entity embedded in software that communicates with humans in the natural human language. This technology embodies components of communicative voice response, and other advanced artificial intelligence (AI) projects to offer skillful "virtual identities" that interact with users. The list of modern and popular virtual assistants (VA) include Apple's Siri and Microsoft's Cortana among many others.