Securing your digital assets is a clear need for any business and individual, whether you are looking to protect your personal photos, your company's intellectual property, your customers' sensitive data, or anything else that can harm your reputation or business continuity. Although billions of dollars are spent on cybersecurity, the number of reported cyberattacks and the magnitude of breaches keep rising. There are many frontiers where harnessing the predictive power of AI might give the upper hand to security vendors -- and to us all, including individuals and businesses. Cisco forecasts that the number of connected devices worldwide will rise from 15 billion today to 50 billion by 2020. A high percentage of these devices do not have basic security measures due to limited hardware and software resources.

Machine learning and Artificial Intelligence are two of the most important developments of the past 10 years within businesses. They have been at the core of the success of several companies, from Facebook's advertising policies through to how American Airlines monitors wear of their plane engines. We wanted to take a look at three companies who are doing some impressive work in the area who are often overlooked for their efforts, either because they are known for other areas or because their competitors sit in the data science limelight. When you think machine learning, you don't naturally go to'short term letting'. Renting out rooms and flats doesn't seem like it would be an especially data-driven enterprise, but this couldn't be further from the truth.

Many important problems can be represented and studied using graphs -- social networks, interacting bacterias, brain network modules, hierarchical image clustering and many more. If we accept graphs as a basic means of structuring and analyzing data about the world, we shouldn't be surprised to see them being widely used in Machine Learning as a powerful tool that can enable intuitive properties and power a lot of useful features. Graph-based machine learning is destined to become a resilient piece of logic, transcending a lot of other techniques. This post explores the tendencies of nodes in a graph to spontaneously form clusters of internally dense linkage (hereby termed "community"); a remarkable and almost universal property of biological networks. This is particularly interesting knowing that a lot of information can be extrapolated from a node's neighbor (e.g. So how can we extract this kind of information?

There is a pervasive underlying fear from generations raised on dystopian science fiction that artificial intelligence and robotics will be the undoing of humankind. Eventually, the conventional thinking goes -- even the likes of Elon Musk and Stephen Hawking are on board here -- artificial intelligence will become smarter than the organic variety and terrible things will happen as machines take over the planet. In reality, however, it's much more likely AI isn't going to destroy us -- or even take our jobs. In fact, it's very likely going to help us do our jobs better. Think about that for a moment.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

You can find many additional references here (Python, Excel, Spark, R, Deep Learning, AI, SQL, NoSQL, Graph Databses, Visualization, etc.) as well as here, here, and here.

Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before.

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. More posts by this contributor: Escaping the trough of disillusionment for virtual and augmented reality The intelligent app ecosystem (is more than just bots!) How to join the network Daniel Li is an investor with Madrona Venture Group. More posts by this contributor: The intelligent app ecosystem (is more than just bots!) How to join the network You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. While everyone agrees on the importance of machine learning to their company and industry, few companies have adequate expertise to do what they wanted the technology to do. Here are some insights into what we can expect in the coming years around ML and AI.

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. 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. You want to control the lights, heating, music, TV, or order the shopping, these tools are evolving to allow you to do any of the above firstly from an initial request, but then by learning what your habits are and when you require things. We are surrounded by AI, is it just a matter of time?