If you think artificial intelligence is something coming in the future, think again: Most people already use artificial intelligence on a daily basis, even if they do not think of it as such. Artificial intelligence or AI simply refers to machine learning put into action toward a goal. When you ask Siri or Cortana to call work or remind you about a dentist appointment, you are relying on artificial intelligence. See what AI is already doing in the modern economy and how future developments in the field of artificial intelligence might impact your life.

But the more articles I read on the topic, the more annoyed I get. It's taken me so long to figure out why! Conversations, writes WIRED, can do things traditional GUIs can't. Matt Hartman equates the surge in text-driven apps as a kind of "hidden homescreen". TechCrunch says "forget apps, now bots take over". The creator of Fin thinks it's a new paradigm all apps will move to. Dharmesh Shah wonders whether the rise of conversational UI will be the downfall of designers. Design, says Emmet Connolly at Intercom is a conversation. Benedict Evans prophecized that the new lay of the land is "all messaging expands until it includes software." "People don't want apps for every single business that you interact with," says David Marcus, head of Facebook Messenger, "…just have a message within a nicely designed bubble … [that's a] much nicer experience than an app." Under his charge, Facebook Messenger has tested this approach, building integrations with high profile partners as well as opening up a bot API. We've even seen avant-garde attempts at taking this idea to its extreme, like Quartz's latest app, which presents the news as a conversation, or the game Lifeline. Apps like Mailtime even promise to save us from our emails by turning them into chats. I guess I might be partially to blame for this, with a few pieces citing a section in a 2014 piece of mine that I literally titled "Chats as Universal UI."

The Friday afternoon news dump, a grand tradition observed by politicians and capitalists alike, is usually supposed to hide bad news. So it was a little weird that Elon Musk, founder of electric car maker Tesla, and Sam Altman, president of famed tech incubator Y Combinator, unveiled their new artificial intelligence company at the tail end of a weeklong AI conference in Montreal this past December. But there was a reason they revealed OpenAI at that late hour. It wasn't that no one was looking. It was that everyone was looking. When some of Silicon Valley's most powerful companies caught wind of the project, they began offering tremendous amounts of money to OpenAI's freshly assembled cadre of artificial intelligence researchers, intent on keeping these big thinkers for themselves. The last-minute offers--some made at the conference itself--were large enough to force Musk and Altman to delay the announcement of the new startup.

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).