The 4th Industrial revolution is upon us; driven by technology that can mimic human intelligence and undertake functions at a speed and scale needed to cope with the huge amounts of data we now create daily. Opinions on what this all means for humankind are divided but it's clear how we work, live and relate to each other will fundamentally change. Digital disruption is here and driving this new revolution; experts agree it is distinct in its "velocity, scope and systems impact." This revolution is exponential in its evolution; disrupting cross industry and in every country. Connectivity, the commoditisation of processing power and storage, data creation and access to knowledge is driving innovation at the speed of light.

Now that energy storage technologies are coming close to commercial reality, decades of work should result in artificial intelligence (AI) emerging as the third key technology in the transformation of the electricity sector. Combined with scalable generation and storage, it will blur the distinction between suppliers and consumers, with excess local generation being fed into the grid so that entities from individual homeowners to business and municipalities will become "producer-consumers" or "prosumers". Demand management systems will also have a role to play. The introduction of multiple players of widely varying consumption and production patterns connecting into a single nationwide grid is impossible until we have software able to predict and manage energy flows to ensure that supply and demand balance at all times. There are obvious drivers also for energy storage at small scale, particularly for remote locations. Apart from the potential for autonomy, and the ability to smooth draw from the grid (avoiding or at least reducing demand-based charges), local storage could relieve grid congestion and add flexibility to power generation requirements, potentially improving network stability.

The University of Skövde have research within the field of intelligent systems. The research is organized in the Skövde Artificial Intelligence Lab (SAIL). SAIL have research in applied Artificial Intelligence in close cooperations with the industry.

Slate is currently running a feature called "Future Tense," which claims to be the "citizens guide to the future." Two of their recent articles, however, are full of inaccuracies about AI safety and the researchers studying it. While this is disappointing, it also represents a good opportunity to clear up some misconceptions about why AI safety research is necessary. The first contested article was Let Artificial Intelligence Evolve, by Michael Chorost, which displays a poor understanding of the issues surrounding the evolution of artificial intelligence. The second, How to be Good, by Adam Elkus, got some of the concerns about developing safe AI correct, but, in the process, did great disservice to one of today's most prominent AI safety researchers, as well as to scientific research in general.

First of all, tell me how many machines have helped you today? Most of you use your mobile devices and laptops every single day, cooking and cleaning is unimaginable without the so-called "modern appliances". Electronic gadgets monitor home security, air conditioning and all smart home devices. It's faster, safer and saves our energy. And now imagine that your toaster or microwave can think!

Goal In this ongoing summary we give a first introduction to the theano library, its basic functionality and usage in th field neural networks. Motivation The need for such a library is based on easily handling tensorial objects, like multi-array input data and weights in neural networks. Of particular interest are symbolic operations that can perform differentiation as need for the backpropagation algorithm in order to update the models according to the seen training data. A core functionality of libraries nowadays is to make use of GPUs in order to efficiently distribute the huge amount of computing operations. Steps The theano library is an open source project lead by a machine learning group associated with a university.

Guest blog by Sebastian Raschka, originally posted here. If we tackle a supervised learning problem, my advice is to start with the simplest hypothesis space first. I.e., try a linear model such as logistic regression. If this doesn't work "well" (i.e., it doesn't meet our expectation or performance criterion that we defined earlier), I would move on to the next experiment. I would say that random forests are probably THE "worry-free" approach - if such a thing exists in ML: There are no real hyperparameters to tune (maybe except for the number of trees; typically, the more trees we have the better).

Summary: What do you need to do to get an entry level job in data science? This article is written for anyone who is considering becoming a data scientist. That includes young people just starting their bachelor's degrees and folks in the first two or three years of their careers who want to make the switch. It's not for folks who know they are going to pursue one of the new Master's in Data Science or Ph.D. candidates. It's for folks looking for entry level jobs that are specifically on the data science career ladder.

Cornell University wants to help whales avoid getting hit by ships, so it is working on an algorithm that uses audio recordings to alert ships to the whales' whereabouts. Dassault Systèmes is creating a 3D model of a human heart that will allow surgeons to test the performance of pacemakers before opening up patients. Sure, machine learning has already had a significant impact on the worlds of science and culture, and in life, but it will be years before CIOs need to start worrying about enterprise machine learning applications ... right? "If CIOs invested in machine learning three years ago, they would have wasted their money," Olley says. "But if they wait another three years, they will never catch up."

Google wants to put its artificial intelligence technology to use in top hospitals. Earlier this week, the search giant announced it would work with the U.K.'s National Health Service, or NHS, to alert staff to patients at risk of serious complications due to kidney failure. Details about the technology are fairly thin on the ground at this stage. But it is known that Google DeepMind recently acquired an app called Hark, which is a task management app that aims to replace paper-based systems and pagers. Hark was developed over four years by a team at Imperial College London, which is one of the U.K.'s top medical schools.