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.

If you think artificial intelligence was huge in 2016, just wait. While you can't discount the achievements made by AI in the past year -- autonomous driving, AlphaGo's victories, the world's saddest assistant -- 2017 stands to best each of them. And we've started with January off with a bang (and an AI that's now $800,000 richer, sorta). All of this pales in comparison to what's to come. As amazing as current AI seems, it's a compounding effect that uses previous advancements to build upon the last until AI ultimately starts to learn from its mistakes and get smarter than every. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us.

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets.

DAVOS: Open markets and global trade have been blamed for job losses over the last decade, but global CEOs say the real culprits are increasingly machines. And while business leaders gathered at the annual World Economic Forum (WEF) in Davos relish the productivity gains technology can bring, they warned this week that the collateral damage to jobs needs to be addressed more seriously. From taxi drivers to healthcare professionals, technologies such as robotics, driverless cars, artificial intelligence and 3D printing mean more and more types of jobs are at risk. Adidas, for example, aims to use 3D printing in the manufacture of some running shoes. "Jobs will be lost, jobs will evolve and this revolution is going to be ageless, it's going to be classless and it's going to affect everyone," said Meg Whitman, chief executive of Hewlett Packard Enterprise.

Deep neural networks have evolved to be the state-of-the-art technique for machine learning tasks ranging from computer vision and speech recognition to natural language processing. However, deep learning algorithms are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, deep compression significantly reduces the computation and storage required by neural networks. For example, for a convolutional neural network with fully connected layers, such as Alexnet and VGGnet, it can reduce the model size by 35x-49x. Even for fully convolutional neural networks such as GoogleNet and SqueezeNet, deep compression can still reduce the model size by 10x.

Are you considering getting into the field of machine learning and artificial intelligence? For a limited time, StackSocial is offering up a massive discount on four classes that are designed to help you understand the fundamentals of one of the most interesting topics in the world. First off, you'll get a course titled "Artificial intelligence and machine learning training" that delivers 17 hours of content over 91 lessons. This will provide a good starting point, and it'd usually sell for about 300 bucks on its own. Next, you'll have two hours and 10 lessons of "Introduction to machine learning" to help you get up to speed on what machine learning is really capable of handling.

In the simplest terms, cognitive computing simulates human thought through artificial intelligence, Caserta explains. Until recently, cognitive was limited to a small subset of industries, i.e. medical advancements and call center automation. However, self-teaching robots and chatbots have become regular news topics. This year, these technologies will become an integral part of enterprise data analytics by influencing and enhancing the customer experience. In 2016, IBM and Google did huge marketing pushes for their Watson and Brain offerings respectively.