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One week ago, I asked Facebook's Messenger to send me the weather forecast every morning. It has yet to do so. Messenger is supposed to deliver me the weather through a free service called Poncho, one of the first "chat bots" to live inside Facebook's messaging app behemoth. Instead of checking the weather through an app like Dark Sky or even Poncho's own iPhone app, Poncho's playful Messenger bot is designed to chat with me about the weather like a human being. Poncho's bot has not only failed to send me the weather like I asked, but its bot has so far proven to be the most complicated method of getting the weather imaginable.

Deep Learning is a shallow course that is akin to reading CliffsNotes instead of a textbook: you'll learn some terminology and be exposed to some interesting concepts but its abbreviated coverage is likely to confuse students who are new to neural networks while leaving more experienced students unsatisfied. This course seems like a rushed attempt to capitalize on the hottest buzzword in the hottest tech industry, which is a shame because it could have been a good course if it took the time to cover the topics in adequate detail. I give Deep Learning 2 out of 5 stars: Disappointing.

Research funded by the National Science Foundation may have found an ingenious solution to poaching: applying game theory and computer science to real-life situations. One of the biggest factors in why there are so many endangered animals today is poaching – a centuries-old problem. The dwindling tiger population is one of the most glaring examples of this. Whether for sport, medicine, pelts or other body parts, poaching remains a huge threat to wildlife. Patrols have long been the most direct form of human intervention in wildlife protection.

Computer chip giant Nvidia has developed a "miracle" chip that is expected to significantly accelerate breakthroughs in artificial intelligence research. Nvidia's Tesla P100 chip crams in 15 billion transistors within its 610-square-millimeter frame, around three-times more than most processors or graphics chips on the market. According to the company's CEO, this makes the Tesla P100 the largest computer chip ever made. "Three years ago, we dedicated ourselves on the single greatest endeavour in the history of our company," Jen-Hsun Huang, CEO of Nvidia, said at the GPU Technology Conference earlier this month. "We decided to be all in on AI. For the first time, we would design [a chip] that is dedicated to this field of work. Dedicated to accelerating AI; dedicated to accelerating deep learning. "I think we are going to realize looking back that one of the biggest things that ever happened is AI." The Tesla P100 is the product of around 2.5 billion worth of research and development at the hands of thousands of computer engineers. The Tesla P100 chip contains more than 15 billion transistors and is described by Nvidia's CEO as "a beast of a machine." "The odds of this working at all is approximately zero," said Huang. "We are changing so many things in one project.

There is more value in using multistage machine-learning analysis and actual data in an effort to determine which machine learning model will work best for detecting real security events on any one particular network. Processing data streams from various subsystems (data transmission frequency measurements over time, for instance, or protocols in a network stream that identify affiliated applications and infrastructure devices) using a variety of machine learning models, and then comparing the learned data to the original raw data, lets an enterprise grade each data stream to reveal which models provide the highest predictability of anomaly detection for that distinct network. Machine learning models may run the gamut from associated rules learning, to sparse dictionary learning, to Bayesian fields and artificial neural networks.

According to logistics company DHL, artificial intelligence (AI) - robot and other technology - is set to become a major part of the industry. Logistics Manager reports that the company has predicted 26 trends, which span the future five to ten years of logistics, in its Trend Radar paper. Outlining transformational trends in the paper, DHL has found that AI and personalisation have been the main drivers of these expected changes to the industry. One of the main ones includes intelligent supply chains, which use either self-learning or machine learning systems. As the industry becomes slowly more autonomous, there are going to be new heights of optimisation in manufacturing, logistics, warehousing and last-mile delivery.

The traditional approach to science involves collecting data, analyzing the data and publishing the findings in a paper. Microsoft, Google, Facebook and Amazon have been making remarkable progress developing artificial intelligence systems. Neither the motivations of DARPA nor OpenAI explain exactly why these commercial technology companies are open sourcing their AI code. Open-sourcing AI serves these companies' broader goals of staying at the cutting edge of technology.

The world's biggest technology companies are handing over the keys to their success, making their artificial intelligence systems open-source. Traditionally, computer users could see the end product of what a piece of software did by, for instance, writing a document in Microsoft Word or playing a video game. But the underlying programming -- the source code -- was proprietary, kept from public view. Opening source material in computer science is a big deal because the more people that look at code, the more likely it is that bugs and long-term opportunities and risks can be worked out. Openness is increasingly a big deal in science as well, for similar reasons.

Years ago when I first developed a website that allows for an easy access and reviews of scientific literature one of my long-term goals was to create recommendation system that would actually be useful for the users of this vast literature. Since my primary background is in Physics and not computer science or one of the related fields, this goal had only remained a dream. The technical difficulties seemed overwhelming and daunting, and I did not have either the time or the resources to invest in all the necessary tools and learning. However, it seems that in the intervening years there has been a great progress in developing a set of "consumer" level tools that are relatively easy to implement. "Practical Machine Learning" is a short e-book intended to introduce the readers to some of the latest machine learning tools and packages.