If artificial intelligence takes over our lives, it probably won't involve humans battling an army of robots that relentlessly apply Spock-like logic as they physically enslave us. Instead, the machine-learning algorithms that already let AI programs recommend a movie you'd like or recognize your friend's face in a photo will likely be the same ones that one day deny you a loan, lead the police to your neighborhood or tell your doctor you need to go on a diet. And since humans create these algorithms, they're just as prone to biases that could lead to bad decisions--and worse outcomes. These biases create some immediate concerns about our increasing reliance on artificially intelligent technology, as any AI system designed by humans to be absolutely "neutral" could still reinforce humans' prejudicial thinking instead of seeing through it. Law enforcement officials have already been criticized, for example, for using computer algorithms that allegedly tag black defendants as more likely to commit a future crime, even though the program was not designed to explicitly consider race.

With so many articles proliferating the media space on how humans are at the cusp of full AI (artificial intelligence), it's no wonder that we believe that the future -- which is full of robots and drones and self-driven vehicles, as well as diminishing human control over these machines -- is right on our doorstep. But are we really approaching the singularity as fast as we think we are? It's not hard to have that impression with the likes of Elon Musk, Stephen Hawking, leading university departments and research centers around the world and more being highly concerned with the potential risks brought about by AI and taking action now to avoid a doomsday scenario in the near future. They predict that by the year 2030 machines will develop consciousness through the application of human intelligence. In fact, Dr. Hawking told the BBC, "The development of full artificial intelligence could spell the end of the human race."

A knowledge of algorithms (maths and stats) is the main differentiator between traditional programming and analytics -based programming. Having said that, it helps to start with programming and approach the maths (initially) through APIs and libraries. I find that this technique works better because more people are familiar with programming than with maths. Techniques used in Data Science such as Data transformations, Exploratory data analysis, Feature engineering, Ensemble strategies, and Visualization (story telling) all involve maths and stats. Future versions of this tutorial will elaborate on this.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

At CES 2017 next week, it will become even more prevalent. Without giving away any secrets, it feels like there is something in the air already. Looking over my schedule, almost every meeting and test has an element related to artificial intelligence and machine learning. I know of one smart home company that is announcing a new AI system that knows when you are home and can adjust the lighting, security cameras, front door alarm, and heat automatically -- no more opening an app and punching a bunch of options. Sure, the Nest Smart Thermostat has some of these features already, but having your entire home benefitting from an AI that works in the background?

Hospitals in Paris are trialling Big Data and machine learning systems designed to forecast admission rates – leading to more efficient deployment of resources and better patient outcomes. It's just one more way in which cutting-edge data science is being applied to real-world problems in healthcare, along with creating personalized medicines, fighting cancer and streamlining pharmaceutical trials.

Artificial intelligence (AI) has spread its wings: from Tesla's self-navigating cars, to Google's DeepMind defeating the world champion at Go (a board game more complicated than chess), to ibm's Watson diagnosing diseases. Software is moving beyond executing black-and-white instructions to making complex and often subjective decisions. In 2017 society will begin to trust machines in much the same way that we trust people. AI introduces a kind of unpredictability not found in traditional software. In solving complex tasks, it is too difficult to build rules to cover every eventuality. Instead, AI systems learn from experience.

BENGALURU: Mediatech startup Dailyhunt, InShorts and ScoopWhoop -through its latest acquisition Touchfone -amongst others are increasingly leveraging machine learning capabilities to improve personalisation and interaction for the end user. From InShorts introducing quizzes to Dailyhunt's personalised news feed, they aim to increase end users' monthly time spent on the app, ultimately targetting higher ad revenue potential. InShorts recently integrated a quiz into its app, which is in its beta phase and has been introduced to 10,000 users. While the user swipes through news content, the quiz emerges as an option. The user can play with a random opponent or share an invite with a friend through WhatsApp.

An increasing number of brands and companies are turning to chatbots as a means of enhancing user experience and reaching customers in new ways. With many such chatbots popping up on the widely used platform of Facebook, the spread of chatbots is gaining considerable speed. Aside from being an obvious step forward for technological advancement, chatbots are surprisingly useful for and centered around enhancing user experience-more so than you might think. Chatbots are a simpler alternative to building an app. Shifts in browsing behavior have made mobile-first accessibility of paramount importance for brands and businesses of all disciplines.

Mark Zuckerberg has just built a digital home assistant (Jarvis). This achievement took over social networks, and every internaut wanted his own Jarvis. This reflects quite well where the world is headed. In a few years' time, robots and artificial intelligence (AI) will undoubtedly be part of people's daily lives. Research about machine learning and neuronal networks are "hot" topics, and intelligent algorithms are improved continuously.