What do spam discovery, facial acknowledgment, item proposals and credit card fraud detection have common in them? Well, they're all procedures getting to be more quick-witted by the second, all because of machine learning. Putting it better, machine learning is the thing that empowers PCs to pick up insight after some time and as a matter of fact, the product applies few algorithms to the huge chunk of information and distinguishes examples to settling on choices and adjusting in view of what's revealed. With machine learning, organizations can profit by the data that would some way or another require a multitude of individuals to contemplate, translate and follow up on. It does the work which would take an army of people to exploit the data.

People are more into texting than talking. About 65% percent of us would prefer to have a long and painful conversation via Whatsapp than to have one-minute phone call or face-to-face meeting. We text while driving, even though we know it's extremely dangerous. Even when it comes to sacred traditions, families may still sit together at the dinner table, but more and more often, they're texting other people. There is simply too much going on: too many people to talk to, too many contacts to maintain -- it can feel easier to live as a digital introvert, dealing with others at your own pace, than to waste energy on real-world conversations you don't know how to start or how to end, and with frankly zero motivation to explain yourself when you simply are not in the mood.

There is no question that, by their very nature, artificial intelligence (AI) systems are more complex than traditional software systems where the parameters were built in and largely understandable. It was relatively clear how the older rules-based expert systems made decisions. In contrast, machine learning allows AI systems to continually adjust and improve based on experience. Now different outcomes are more likely to originate from obscure changes in how variables are weighted in computer models. This has led some critics to claim that this complexity will result in systemic "algorithmic bias" that enables government and corporate abuse.

Olivia is patient and tireless, with soft brown eyes and a gentle bedside manner. She wears the light blue scrubs and fob watch of an NHS nurse, which in fact she is. But Olivia is also a robot, a virtual avatar accessed on a smartphone app. Currently on trial with the groups responsible for Vanguard in NHS Dudley Clinical Commissioning Group in the West Midlands, the NHS service for non-emergency calls, Olivia will check your symptoms, give advice on treatment and help schedule an appointment, whether that's in English, Spanish or Dutch; she speaks all three, as well as Czech and Japanese. Is it the same as talking to a live nurse?

Will Artificial Intelligence help Big Data deliver on its promise? One of the major trends I have been researching recently, has been the shift in interest towards Artificial Intelligence (AI) in its multiple forms and guises, and the potential it has to analyse vast quantities of data and quickly derive actionable insights. As we all know, AI, Machine Learning and Deep Learning are not new. However, there has been huge investment in the space in recent years and the ability to automatically apply complex mathematical calculations to Big Data – over and over, faster and faster – is a recent development. With steady advances in digitisation and cheap computing power, no wonder people are excited about the possibilities.

If you want the basics of artificial intelligence in the shortest time possible, then grab a cup of coffee and spend two hours reading Tim Urban's brilliant two-parter on the topic. After that, you'll want to look into some of the more specialized areas where AI is already having an impact on our lives. OK, here's a link dump of some of our favorite AI reads, in no particular order. Here are three great books to read on AI. Does this all sound like too much hard work?

Fire up the Emotion Engine and read how your next Kawasaki will be able to "talk" to you while you ride. If you're lucky, Pepper may even sell you it!

So I am nonplussed by word that she was an assigned roaster for a Comedy Central roast of Rob Lowe, just taped for upcoming airing. But other roasters in part turned on her, not Lowe. "Ann is one of the most repugnant, hateful, hatchet-face bitches alive," standup comic Jimmy Carr said. "It's not too late to change, Ann. He was wasting his time.

Subtle changes in what employees think about compensation or product road maps, or how they feel about specific leaders can often be accurate predictors of future productivity or attribution. But annual surveys of workforce sentiment can become outdated quickly. That has inspired a gaggle of startups focused on software for "people analytics," often in the form of short surveys that provide a pulse of what employees think on short notice. One of those companies, Glint, on Tuesday disclosed a 27 million Series C round led by Meritech Capital and Bessemer Venture Partners. The infusion brings Glint's total backing to 50 million, but Glint co-founder CEO Jim Barnett declined to discuss the Redwood City, Calif., company's valuation.

The debate over GMOs has divided activists and Nobel laureates alike, and that is no different when it comes to Monsanto and Vietnam. To Monsanto, genetically modified seeds are a more relevant topic today than the history of Agent Orange associated with a previous version of their company. This agrarian development, it says, serves to benefit farmers and produce greater yields due to the seeds' resistance to insects, herbicides and drought. The company believes that agricultural biotechnology is critical to the sustainability of agriculture in Vietnam and the region, according to Upadyayula. The organization says it believes genetically modified corn is crucial for a country that imported around 6 million metric tons of corn in 2015.