The concept of artificial intelligence has been around for a long time. In written fiction, AI characters show up in stories from writers like Philip K. Dick, William Gibson and Isaac Asimov. Sometimes it seems like it's touched on by every writer who has written sci-fi. While many predictions and ideas put forward in sci-fi have come to life, artificial intelligence is probably the furthest behind. We are nowhere near true artificial intelligence as exemplified by the characters mentioned above. Sometimes it seems like we've been waiting forever.

What are the traits of a good Chatbot? Situational awareness is a key feature in Bots. It should respond to user's minimum amount of input by leveraging stored information and entertain the user's needs with minimal friction. Bots should be able to automate the daily life tasks for us such as reserving a hotel, ordering grocery, etc. It should save our time and reduce human efforts and pain.

Using Azure, gave them an immediate global reach in a way unthinkable just a few years earlier. Their mission is to bring big data analytics to utilities and smart cities, and one of their focus areas is electric utilities and smart grids. It is a story that combines drones with intelligent software to prevent power blackouts, or as eSmart puts it "making Azure intelligence mobile". The economic impact of blackouts is massive, and the scale of power grids is huge. According to Grid Resilience Report, US Department of Energy, 2011, the average annual cost of power outages caused by severe weather in the USA is anywhere between $18-33 billion.

As the future veers toward artificial intelligence, robotics, and biometric security measures ripped straight from a Sci-Fi novel, you might be amused to know plastic sunglasses are the key to their undoing. Researchers at Carnegie Mellon University developed a 3D-printed pair of sunglasses that cost all of 22 cents to manufacture. The sunglasses, according to an accompanying study, fools advanced facial recognition software by altering small bits of color information in a face. Your questions answered by founders, experts and thought leaders in business, design and tech. The software relies on neural networks to compare pixel coloration in specific areas of the face to match one image to another.

"Artificial intelligence is not just the next step in innovative learning," says Rose Luckin, a self-described learning scientist at University College London. "This technology can identify emotional states of students as well as their meta-cognitive states," she says, "and tailor learning accordingly." Previous forms of technology fell far short of this kind of capability. Rose recently co-authored a paper titled "Intelligence Unleashed: An argument for AI in Education.") Rose says artificial intelligence in education, also called AIed, can also further facilitate deeper collaboration between learners--and help teachers differentiate their instruction in order to meet every learner's needs.

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There is no shortage of attention lately on the "Internet of Things". As a case in point, see the "Developing Innovation and Growing the Internet of Things Act" or "DIGIT Act", i.e., S. 2607, a bill introduced in the Senate on March 1, 2016 and amended on September 28, 2016, "to ensure appropriate spectrum planning and inter-agency coordination to support the Internet of Things" – A companion bill, H.R. 5117, was introduced in the House of Representatives on April 28, 2016. However, since there is no "internet" dedicated to "things", it is fair to state that the Internet of Things does not exist as such. We are left with a definitional vacuum, but it is hammering the obvious to acknowledge that there is no dearth of attempts around the world to fill the gap. Perhaps as a helpful shortcut, we could view the expression as a metaphor that captures the arrival of almost anything and everything, until now out of scope, into the communications space.

Bayesian inference is a way to get sharper predictions from your data. It's particularly useful when you don't have as much data as you would like and want to juice every last bit of predictive strength from it. Although it is sometimes described with reverence, Bayesian inference isn't magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer.

Natural Language Processing (NLP) is a messy and difficult affair to handle. Word embeddings/representations – ever since they came in with great work of Mikolov et al, they have been revolutionary to say the least. The concept itself is very intuitive and motivates deeper understanding fora wide range of applications. The main advantage of the distributed representations is that similar words are close in the vector space, which makes generalization to novel patterns easier and model estimation more robust. Distributed vector representation is showed to be useful in many natural language processing applications such as Named Entity Recognition (NER), Word Sense Disambiguation (WSD), parsing, tagging and machine translation.

And as if all of this were not enough, we find ourselves at the dawn of a host of new technologies and applications. Many of us have become accustomed to the artificial intelligence (AI) within our devices. This can take the form of our increasingly accurate GPS systems, the voice recognition ability and immediate response of personal assistants such as Apple's Siri or Amazon's Echo. Now children's toys are being shipped in time for the holiday season with AI built in. From Hello Barbie to Dino the dinosaur, who happens to be connected to IBM's Watson, the world of kid's playthings is being radically reshaped.