Much has been said on the enabling technologies of Industry 4.0 from a technological point of view. In our opinion, in order for the innovator to effectively address the new, powerful tools that this revolution can offer, it is fundamental to have a broad knowledge also on their implications on society and quality of life. In particular, in this article we are going to focus on Human Resource management and development. The advanteges brought by the new technologies may apply to hiring, training and organisation of personnel under several aspects, let's list three of them. Artificial Intelligence and hiring A controverse and trending aspect of new technologies regards the selection of new employees basing decisions solely on the outcome of big data analysis.

Our previous roundup of machine learning resources touched mlpack, a C -based machine learning library originally rolled out in 2011 and designed for "scalability, speed, and ease-of-use," according to the library's creators. Implementing mlpack can be done through a cache of command-line executables for quick-and-dirty, "black box" operations, or with a C API for more sophisticated work. The 2.0 version has lots of refactorings and new features, including many new kinds of algorithms, and changes to existing ones to speed them up or slim them down. For example, it ditches the Boost library's random number generator for C 11's native random functions. One long-standing disadvantage is a lack of bindings for any language other than C, meaning users of everything from R to Python can't make use of mlpack unless someone rolls their own wrappers for said languages.

When we first began to explore the idea of building a step classifier, we knew we would be constrained to a very limited population of individuals (Jawbone employees) available to us for early development and testing. It seemed certain that the development of the classifier would be very iterative in that, as we tested larger and more varied sets of individuals and behaviors, we would undoubtedly find issues that we needed to quickly correct. So we would need a technical approach that was suited to rapid updates and that those updates would need to be essentially risk free. We could not afford the risk and development time of actually writing new code as we iterated. In short, we needed a step classifier that learned.

I grew up in a family of builders. My father is still in the construction business, and my mother is a practicing designer. We tore old walls down, built new walls, and shaped our environment. I started applying those skills after buying my first home -- a 105-year-old federal style row house in Washington, D.C. that was barely standing. After 543 days, 212 tools, three trips to the doctor, thousands of feet of lumber and drywall, and miles of wire and pipe, we had a new home.

Tweeting under the influence may not get you in as much trouble as drunk driving does, but it can still mean a whole lot of hot water. Now there's an algorithm that can tell when you're drinking while tweeting -- and also figure out where you're imbibing. Using machine learning, researchers at the University of Rochester have created a system that can find alcohol-related tweets and determine whether they were made by someone who was actually drinking at the time. It can also pick out whether those tweeters were drinking at home or somewhere else. Equipped with that knowledge, the researchers compared the results for different locations in New York State. Eventually, they hope to use the technology to study the health implications of alcohol.

This is part 2/3 of a series on deep learning and deep belief networks. This section will focus on artificial neural networks (ANNs) by building upon the logistic regression model we learned about last time. It'll be a little shorter because we already built the foundation for some very important topics in part 1 – namely the objective / error function and gradient descent. We will focus on 2 main functions of ANNs – the forward pass (prediction) and backpropagation (learning). Your sci-kit learn analogues would be model.predict()

By now, we've all heard of the addictive tile-mashing game called 2048. Last week, I picked up 2048 for the first time and -- true to my nature -- I started designing an AI to beat the game for me the following day. It didn't take me long to find out that there's already some pretty good AIs out there, so I picked up the best 2048 AI I could find and fired several instances of it to see what it could do. Much to my surprise, it not only beat 2048… it crushed every human record in 2048 that I could find. Below is a video of the first 20 seconds of the AI hacking away at the game, mashing and merging tiles at superhuman speeds that we only wish we could match.

Artificial intelligence-related research has tremendous potential to become useful in practical, everyday applications and to dramatically increase productivity. The field has been developing rapidly in recent years and is expected to really start taking off in the near future. A few of the cool things happening on the cutting edge in AI are highlighted below. AI Crossword App Could Help Machines Understand Language Researchers have designed a web-based platform that uses artificial neural networks to answer standard crossword clues better than existing commercial products specifically designed for the task. The system, which is freely available online, could help machines understand language more effectively.

Future computers will be smarter than humans, but they'will never be wiser' Internet tycoons have reached a rare consensus on the promise of artificial intelligence following the historic victory earlier this month for Google Inc's AI-powered AlphaGo over its human competitor, South Korean Go master Lee Se-dol. The widely watched five-match series came to a close on Tuesday, with four victories for the machine to the human's one. Mark Zuckerberg, CEO of Facebook Inc, told an audience at the China Development Forum in Beijing on Saturday that he predicted more great advances for AI within the next decade. "Artificial intelligence will understand senses, such as vision and hearing, and grasp language better than human beings over the next five to 10 years," he said. Lei Jun, founder and chairman of Chinese smartphone giant Xiaomi Corp, agreed, describing the win as a breakthrough in artificial intelligence.

Google has a special way to improve the quality of search results: artificial intelligence (AI.) The company uses a type of AI known as machine learning to figure out what its users really want to search for. Machine learning is where a computer gradually teaches itself how to perform a task. One example of that is Google DeepMind, which learned how to play retro arcade games over time. Google's machine learning system for search is called RankBrain, and it tries to figure out what a user is searching for.