The Internet of things (IoT) is the inter-networking of physical devices (also termed as connected devices or smart devices), vehicles, buildings and other objects (which could be smart wearable, diagnostic device, kitchen appliances etc.) embedded with electronics, software, sensors, actuators, and network connectivity that enables these "smart objects" to collect and exchange data. In other words, Internet of things is a global infrastructure for the information society. IoT allows advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies. For example, the smart refrigerator in your kitchen (at home) can send you an alert (or notification) on your smartphone (while you are leaving office) when you're out of milk or gas. Your wearable or smart watch can warn you if there is something wrong with your pulse or heart-rate. Additionally, all these information gets recorded.

Could you ever imagine yourself in a heated argument with a chatbot? Like, really passionate, deeply reasoned position-taking--argument, counterargument, countercounterargument, countercountercounterargument. Could you imagine a chatbot convincing a jury of a defendant's guilt? And if you could imagine it, what would that mean? Questions like these are at the core of a paper published recently in AI Matters by Samira Shaikh, a computer science-slash-psychology researcher at the University of North Carolina-Charlotte.

Robot Academy is an online platform that provides free-to-use undergraduate-level learning resources for robotics and robotic vision. The content was developed for two 6-week Massively Open Online Courses (MOOCs) that Corke taught in 2015 and 2016. This content is now available as individual lessons (over 200 videos, each less than 10 minutes long) or in masterclasses (collections of videos, around 1 hour in duration, previously a MOOC lecture). Unlike a MOOC, all lessons are available all the time. While the content is typically designed for undergraduate-level students, around 20% of the lessons require no more than general knowledge.

My personal experience with Neural Networks began some time ago. Reading about the amazing things a neural network could do made me eager to explore this problem-solving approach that has attracted so much attention during the past few years. I remember myself impressed by a model that generates natural language descriptions of images and their regions, developed at the Stanford University in 2015, thinking that I would like to be able to do similar things at some point. From my experience in other machine learning related topics, very detailed mathematical explanations, full of derivatives and equations make understanding difficult. So, I decided to ignore them for the time being.

Want to get started with data science and artificial intelligence, but lack the skilled personnel to do it? You could be a candidate for machine learning software company H2O.ai's The Mountain View, California company today announced the beginning of beta testing for Driverless AI. The new product combines H2O.ai's automated machine learning and deep learning products, AutoML and AutoDL respectively, which provide automatic training and tuning of models on GPU-accelerated hardware. It's all about reducing complexity and making the most of what data science skill sets users already have, the company says.

On 1st of August 2017, we will start a free online training program for Machine Learning, called Machine Learning Army Camp. The goal for me is to study and present 20 top books in Machine Learning, in 6 months, and to share my process with the community. We will integrate the knowledge from 20 books into a single big knowledge network, and you will get to see how it will grow over time. I show you here a network (click to see image) that I have built before for foundations in science in general, after having read around 80 books (philosophy of science, logic, math, computer science, machine learning, etc.). In ML Army Camp we will build one big network specifically for Machine Learning.

Artificial Intelligence – the word conjures up the Hollywood sanctioned image of terminators taking over our world. Most people think of AI as something a bit scary that might occur in the future. But the truth is that we are surrounded by Artificial Intelligence even now! Every time you see an advertisement pop up selling exactly what you want – that's AI! From movie recommendations and Facebook feeds to Virtual PAs like Siri and Alexa – AI is already here and is set to become more and more enmeshed in our daily lives. There is no doubt about the benefits Artificial Intelligence can bring to our lives – it makes things smarter, faster and cheaper – and in areas like healthcare, it can even save lives.

In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established body of literature studying the value distribution, thus far it has always been used for a specific purpose such as implementing risk-aware behaviour. We begin with theoretical results in both the policy evaluation and control settings, exposing a significant distributional instability in the latter. We then use the distributional perspective to design a new algorithm which applies Bellman's equation to the learning of approximate value distributions. We evaluate our algorithm using the suite of games from the Arcade Learning Environment. We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning. Finally, we combine theoretical and empirical evidence to highlight the ways in which the value distribution impacts learning in the approximate setting.

We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.

We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before. We apply our method to learning maximum entropy policies, resulting into a new algorithm, called soft Q-learning, that expresses the optimal policy via a Boltzmann distribution. We use the recently proposed amortized Stein variational gradient descent to learn a stochastic sampling network that approximates samples from this distribution. The benefits of the proposed algorithm include improved exploration and compositionality that allows transferring skills between tasks, which we confirm in simulated experiments with swimming and walking robots. We also draw a connection to actor-critic methods, which can be viewed performing approximate inference on the corresponding energy-based model.