A constant presence on Google Play and the App Store's top apps charts, it's one of the first apps you'll download when you buy a new phone and almost certainly your biggest source of notification on a typical day. The app has developed dramatically since it emerged almost a decade ago, picking up the ability to transfer files and make voice and video calls – all now core features – along the way. More useful than SMS, slicker than Hangouts and easier to use than Skype, it's no wonder WhatsApp is the number one communications service of choice for hundreds of millions of smartphone users. Even if you use it on a daily basis, WhatsApp is packed with lots of handy, little-known features you might not be familiar with. We've rounded up the best.

Anyone who uses Twitter knows that it can be home to very hostile exchanges, some of which may even constitute cyberbullying. In a world where that kind of danger is a mere click away, what can parents do to keep their children safe without monitoring their every digital move? Analytics and machine learning are offering new options. Bark CEO Brian Bason founded his company after having a couple of kids of his own and finding himself concerned about online safety. Bark offers a machine learning-backed app and service to help parents work together with their children to navigate the dangers of today's digital world.

In this document, we are going to focus specifically on how to use the machine learning capability of Falcon Host to prevent malware. Machine Learning allows Falcon Host to protect against known and unknown malware without using signatures. As a reminder, Falcon Host uses multiple methods to prevent and detect malware. Those methods include Machine Learning, but also exploit blocking, blacklisting and indicators of attack. Once in the App, the default page is the Prevention Policy.

Could 2017 be any more turbulent than 2016? Some would argue that because of this year's unprecedented surprises, fluctuating markets & politics and real-world events, next year couldn't possibly offer any more shocks. In the midst of all of this, the technology world continues unabated, with the FinTech sector holding some exciting new developments for the year ahead. For too long, consumers have had to bear the "Your call is very important to us, please continue to hold" banality, but 2017 could be the year where this unintelligent automation disappears. The advances in Artificial Intelligence have caused a small explosion of sorts in the FinTech sector, but this small bang could expand with the mainstream introduction of chatbots and smartbots, with human help only required for the really tricky questions.

"Nanodegree" is a registered trademark of Udacity. Udacity is not an accredited university and we don't confer traditional degrees. Udacity Nanodegree programs represent collaborations with our industry partners who help us develop our content and who hire many of our program graduates.

This configuration is typically called Level 1 Cache, as it contains one level of cache only. Level 1 caches are normally used for Session and Application state management. Although effective, this approach is not optimal when dealing with applications that move large quantities of data over multiple geographies, which is the scenario that we want to optimise. First of all, large data requires large cache to be effective, which in turn is memory intensive, thus requiring expensive servers with a big allocation of volatile memory. In addition, syncing nodes across regions implies large data transfers, which, again, is expensive and introduces delays in availability of the information in the subordinate nodes.

Artificial intelligence-powered finance chatbot Cleo has picked up $700,000 from angel investors. The London-based company saw participation from a large group of well-known European tech figures including Skype founder Niklas Zennström, Zoople co-founder Alex Chesterman, Wonga co-founder Errol Damelin among others. The startup is an alumnus of the UK's Entrepreneur First and the accelerator's partners Wendy White and Joe White also participated in the round. The investment reportedly closed last year but has not been disclosed until now. Cleo develops an AI-powered financial assistant through which consumers can check their bank account, credit card data as well as track spending, budget and set spending targets.

Researchers from MIT's Computer Science, Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx have demonstrated an artificial intelligence platform that predicts cyber-attacks with 85% accuracy. Named as AI2, this artificial intelligence platform is roughly three times better than present security systems, and also reduces the number of false positives by a factor of 5. The present security systems are either Human or Machine-centric. The human based Security systems rely on the existing rules created by living experts and therefore miss any attacks that don't match the rules. Similarly, the machine reliant systems rely on "anomaly detection," which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway.

As the EMR "space race" peaks, clinical and health leaders are coming to understand that digitising data does not, on its own, drive innovation or transformation. Many are wondering what's next. Looking ahead, the next wave in our journey towards digital transformation is Artificial Intelligence (AI). Simply put, Artificial Intelligence is a collection of systems that sense, comprehend, act and learn. The goal of AI in health is to drive greater "data dividends" than what we are getting from investments already made in EMRs and other systems.

Artificial intelligence and machine learning systems are made up of code and algorithms, and as such, they work as fast as computers can process them. Often this means massive amounts of learning can be accomplished every second without stop 24x7x365. Code doesn't need to take weekends off, holidays, or sick time. It can recognize complex patterns, potentials, areas of improvement, and problems in real-time or digital-time. Given these available computing capabilities and speeds, what are executives to do with AI and machine learning, when we live and operate in relatively slow human-time, and work within organizations that work at an even slower pace of organizational-time.