Artificial Intelligence, or AI, is expected to be the next big thing in technology, and many tech companies are investing heavily to make sure they don't lose out on opportunities as and when the technology becomes more mainstream in the coming years. While AI has already started intriguing and exciting industry insiders and tech enthusiasts alike, the increasing chatter surrounding the new technology has given rise to multiple concerns regarding job losses and science fiction-like scenarios of AI-enabled robots starting to exert control over humans, eventually taking over the planet, not unlike the dystopian sci-fi movie'I, Robot'. While Alphabet Chairman, Mr. Eric Schmidt, has already admitted that jobs may be at stake because of artificial intelligence, the founder and CEO of Facebook, Mr. Mark Zuckerberg, recently categorically denied that AI-enabled robots are about to take over the planet any time soon. Whatever be the case, most established technology companies are diving into AI-related research headlong, and while American tech giants like Google and Facebook are already burning the midnight oil on research related to artificial intelligence, South Korean conglomerate, Samsung, is also spending the big bucks by investing in a number of tech startups worldwide, in an effort to increase its presence in the sector. According to reports in the South Korean media, Samsung Venture Investment, which happens to be a subsidiary of the Samsung Group and hence, a sister concern for Samsung Electronics, has already invested a truckload of money in more than ten global tech companies, all of which are deeply involved in AI-related research.

Microsoft has apologized for the conduct of its racist, abusive machine learning chatbot, Tay. The bot, which was supposed to mimic conversation with a 19-year-old woman over Twitter, Kik, and GroupMe, was turned off less than 24 hours after going online because she started promoting Nazi ideology and harassing other Twitter users. The company appears to have been caught off-guard by her behavior. A similar bot, named XiaoIce, has been in operation in China since late 2014. XiaoIce has had more than 40 million conversations apparently without major incident.

It took less than 24 hours. Microsoft had released its latest experiment with artificial intelligence: a Twitter bot named Tay that was designed to research and foster "conversational understanding." But Tay learned too much, much too young. And, as with so many things on the internet, the best of intentions went awry almost immediately. Tay was programmed to edit responses to her on Twitter in order to form new thoughts and sentences.

A report of drone sightings from the Federal Aviation Administration (FAA) shows that despite a new registration scheme, near misses between unmanned and piloted aircraft in American are on the rise. Sightings by pilots and airport officials have steadily increased from less than one a day in 2014, to over 3.5 between August 2015 and January this year, many of them from commercial passenger aircraft. In the most serious incident, the pilot of an American Airlines jet last September had to swerve to avoid a drone. On September 13, flight 475 took off from Atlanta, Georgia en route to Charlotte, North Carolina. It was climbing to 3,500 ft when the pilot of the Airbus had to take evasive action to avoid a collision with an unidentified unmanned aerial system (UAS) or drone.

Every weekend we select a handful of in-depth articles we think are worth a bit of your valuable time, either because they peel back the layers on a compelling business story, or somehow make us look at business in a different light. AI may undermine big-company advantages. Machine learning – software that can improve itself without human intervention – may mean trouble for big companies that depend on their heft to outmaneuver smaller upstarts, writes Howard Yu for the Harvard Business Review. And for a sneak preview of where the world is headed, one need not look further than the success story of AlphaGo, an artificial intelligence that beat a champion of the ancient game of Go, something that was previously thought to be impossible. "It is easy to imagine a world where self-taught algorithms will play a much bigger role in coordinating economic transactions; AlphaGo simply shows us what is possible in the near future. With instantaneous adjustment, automatic optimization, and continuous improvement all quietly managed by unsupervised algorithms, the redundancy of production facilities and wastage in the supply chain should become headaches of the past."

Lately there seems to be a surge in the term machine learning. Much like big data a few years ago, machine learning is the new buzzword -- and the two terms actually go hand in hand. With increasing volumes of data now stored in distributed environments such as Hadoop, it's possible to quickly produce models that can analyze bigger, more complex data, and deliver faster and more accurate results – two critical elements in the battle against fraud. The more time it takes to discover an instance of fraud, the more the victim organization loses. Association of Certified Fraud Examiners (ACFE) estimates fraud costs organizations 5 percent of annual revenues worldwide.

Technology is the future of retail. Digital data, machine learning, cloud-powered analytics and the Internet of Things (IoT) will separate the wheat from the chaff in tomorrow's retail industry. A recent Sector Insights government report (PDF) said that retailers will need to embrace the latest technology trends, such as big data, and have the skills to work with digital systems if they are to be successful in the future. The retail industry is on the whole a voracious adopter of modern data-centric technology, aping the manufacturing world by using big data analytics to streamline supply chains, and using smartphone apps and wireless beacons to harvest customer data to deliver better service. However, Robert Hetu, retail research director at analyst house Gartner, noted that, despite having the technology to collect and access large amounts of digital data, retailers fail to put it to effective use.

Microsoft recently unveiled Tay, an artificial intelligent chat bot developed by Microsoft's Technology and Research and Bing teams to experiment with and conduct research on conversational understanding. The company stated that the more you chat with Tay, "the smarter it gets, learning to engage people through casual and playful conversation." Microsoft launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill". However, pretty soon after Tay launched, people starting tweeting the bot with all sorts of misogynistic, racist, and Donald Trumpist remarks. And as Tay was being essentially a robot parrot with an internet connection, started repeating these sentiments back to users.

When dealing with machine learning problems, sometimes one has to face a huge dataset with hundreds or thousands of features. Machine learning relies on these data to build models for prediction, more information the features contain, more easier to train a good model. However, these variables also contain noise, and most of them might be anonymous or formatted by some kind of hash process due to privacy issue or confidential reasons. So it's hard to figure out the physical meaning and explain the correlation between these variables. Even we could know all variables' meanings, it's still difficult to determine which are more essential than others.

Search on mobile can be a major pain. To start, the keyboard is small. Then there are the load times. Have you ever stared at your 4G smartphone screen and wondered in disbelief how only one bar of 3G was possible in San Francisco – in the country that put the first man on the Moon? How often have you longed for your laptop to be able to "search properly"?