There are two main debates when it comes to artificial intelligence, by which I mean self-aware thinking machines with a general intelligence equal to or greater than that of humans. The first is when / whether it's coming. The second is what it will mean for humanity when it gets here. The vast majority of smart people who know anything about the topic seem to agree that it is coming, albeit with an ETA ranging anywhere from the 2020s to a few thousand years from now. I'm not a smart enough to weigh in on that. But if we postulate that it's coming eventually, that heralds in the second and far more interesting question of what happens next.

Just a few weeks back, scientists at Google's artificial intelligence division DeepMind announced they were developing a "kill switch" to ensure that intelligent machines do not go all Terminator on us. Now, it seems, Google's AI-related concerns are a bit less dire and a bit more mundane. Right now, Google is not worried that an AI would suddenly become sentient and begin its nefarious plans to take over the world (à la Skynet). It is, however, worried whether the helpful house robot you bought would clean your house without, say, burning it down by sticking a wet mop in an electric socket. In order to address these worries -- or at least put them out there -- Google's computer scientists have now published a paper titled "Concrete Problems in AI Safety."

CIKM is a top-tier ACM conference in the areas of information retrieval, knowledge management and databases. Since 1992, it has successfully brought together leading researchers and developers from the three communities, with the purpose of identifying challenging problems facing the development of advanced knowledge and information systems, and shaping future research directions through the publication of high quality, applied and theoretical research findings. The infographic below describes India's participation at CIKM 2015. Knowledge Discovery and Data Mining (KDD), a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. It was held at Sydney, Australia in 2015.

Note: Before starting Part 2, be sure to read Part 1! When it comes to machine learning, ultimately the most important picture to have is the big picture. Whether it's logistic regression, random forests, Bayesian methods, support vector machines, or neural nets, everyone seems to have their favorite! Unfortunately these discussions tend to truncate the challenges of machine learning into a single problem, which is a particularly problematic misrepresentation for people who are just getting started with machine learning. Sure, picking a good model is important, but it's certainly not enough (and it's debatable whether a model can actually be'good' devoid of the context of the domain, the hypothesis, the shape of the data, and the intended application. In this post we'll discuss model selection in the context of the big picture, which I'll present in terms of the model selection triple, and we'll explore a set of visual tools for navigating the triple.

It was April of 2015 and I had just returned to a burning summer in India from my startup stint in London. I wasn't happy with the heat, but I've never been good with adjusting to climate change. But weather had little to do with my decision to sweat in the humidity of Mumbai. I wanted to see how the burst of smartphone adoption had impacted the lives of those traditionally used to a life without dependency on technology. There were quite a few trends that stuck out, but one in particular.

NIIT, a global leader in skills and talent development, today launched a course in Web App Development with MEAN Stack under the DigiNxt Series. The company has recently ventured into Digital Transformation to offer pioneering programs to young aspirants wishing to enter the digital services industry, as well as to IT professionals wishing to reskill themselves for the new digital world. The cutting-edge program will use the student-centred pedagogy of project-based learning to help them carve a successful career in the emerging digital era. Some of the famous web applications like LinkedIn, Netflix, Uber, Paypal, etc. have been built using MEAN Stack. AngularJS, Node.js (MEAN) represents a group of open source technologies which are known to synergize well together, thereby empowering students to launch their own web and mobile apps.

After great promise in the 1960s that machines would soon think like humans, progress stalled for decades. Only in the past 10 years or so has research picked up, and now there are several popular products on the market that do a decent job of at least recognizing spoken speech. For Björn Schuller, full professor and head of the chair of Complex and Intelligent Systems at the University of Passau, Germany, who grew up watching Knight Rider--a television show about a car that could talk--this is the fulfillment of a childhood fantasy. Schuller is a World Economic Forum Young Scientist who will speak at the World Economic Forum's Annual Meeting of the New Champions in Tianjin, China, from June 26 to 28.He recently spoke about the possibility of machines soon tuning in to human language quirks, behavior and emotion. How did you get interested in machine intelligence and speech recognition?

We need to make data the centre of what we're doing, to be able to achieve the benefits of AI, says Deborah El-Sayed, Director of Digital & Multi-Channel Development at NHS England. And it has to be a collective process - because everyone, whether from academia, a provider organisation or clinician, has a piece of the jigsaw. AI and the NHS might at first glance seem uneasy bedfellows. Are the two an unlikely combination – or a match made in heaven? I think that we've hit a kind of tipping point in healthcare. People are living longer – which is a triumph; our expectations as healthcare consumers are growing and because we're surviving more conditions because we have better healthcare, we've got more complexities and more comorbidities.

Fast reactions can help you drive defensively and avoid collisions. Many of the autonomous features in cars have reactive ability, such as adaptive cruise-control systems that adjust speed by sensing the distance from and the speed of the vehicle ahead. Toyota Motors wants its cars to go beyond reaction to prediction and evasion. To accomplish those more complex functions, Toyota is going to rely heavily on artificial intelligence, according to Reuters. Like Ford and Honda, Toyota is investing heavily in AI and robotics to improve safety and performance in the fast-approaching world of self-driving cars.

Artificial Intelligence has been discussed for a long time and often inappropriately: some say that it represents a great opportunity, while others say it could be the biggest threat to humanity. But many do not even know what it is exactly. Perhaps because it is a discipline by poorly defined contours, debated among scientists and philosophers, which presents theoretical and practical aspects as well as ethical. We can generally define the AI as a computer's ability to perform typical functions and reasoning of the human mind: according to this definition it is clear that today we already are surrounded by examples of AI, for example Google. The search engine of Mountain View, in fact, no longer shows just lists of links, but also direct answers to various questions (try typing "obama birth date" or "David Beckham's wife" and see the results).