They say the best investors know how an event in one corner of the market impacts a company in another. New mining laws in Colorado require thousands of workers to wear a certain type of hardhat,...

The current technological boom, and the increasing consumerism is doing that every day appear more machines that are playing an essential role in our lives and have already become a vital necessity in every activity that we, or actually they, carry out. Either at home, in the car, at work or anyplace, we will soon have a dependence of these machines. In this post I wanted to reflect the positive aspect of machines in our lives and I have considered that the machines will take the right decisions that will make be better people but without controlling us, but the risks to become too dependent on machines exist and we cannot forget.

Pathologists still do the bulk of their diagnosis of metastatic cancer cells in tissue and lymph nodes by hand, putting slides under a microscope and looking for signature irregularities they're trained to see. Recent advances in computer technology, however, particularly in artificial intelligence (AI), have begun to teach machines to do this kind of detection with growing rates of improvement. Now, a research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School have developed a form of AI that can interpret these pathology images with accuracy levels of 92.5 percent. Moreover, when the two are used in combination, the detection rate approaches 100 percent (approximately 99.5 percent). Their AI method is a form of deep learning, in which the system attempts to replicate the activity of the human neocortex through artificial neural networks.

Like many people, I start most mornings digging myself out of an inbox that often feels like more trouble than it's worth thanks to the seemingly infinite amount of newsletters I receive. And although these newsletters cross verticals as varied as marketing, technology, advertising and the agency world, lately each one has a headline that includes artificial intelligence. Not surprisingly, all of us are curious to know how this growing field of technology is going to infiltrate our professional and personal lives. Across every industry, people are afraid that AI is going to steal jobs. As a marketer I don't think we have any urgent concerns over job security.

When Jason West and Vince Zampella set up Respawn Entertainment in 2010, they had one ambition: to produce a new first-person shooter that would have as massive an impact on the genre as their previous creation: the Call of Duty series. It was a big ask, but when Titanfall arrived three years later, the game was certainly a brilliant attempt. The sci-fi shooter boasted an innovative mechanic allowing players to summon a giant robot into the arena, and an incredibly fluid, free-running movement style – all combined into a set of blisteringly loud and detailed map designs. But one thing many players said about Titanfall was that, beyond the raw speed and inarguable thrill of the highly vertical, highly acrobatic gameplay, there was little in the way of tactical depth. It's something the team says it wants to address. "We learned a lot from the community," says producer Drew McCoy.

In a major breakthrough for artificial intelligence, a computing system developed by Google researchers in Great Britain has beaten a top human player at the game of Go, the ancient Eastern contest of strategy and intuition that has bedeviled AI experts for decades. Machines have topped the best humans at most games held up as measures of human intellect, including chess, Scrabble, Othello, even Jeopardy!. But with Go--a 2,500-year-old game that's exponentially more complex than chess--human grandmasters have maintained an edge over even the most agile computing systems. Earlier this month, top AI experts outside of Google questioned whether a breakthrough could occur anytime soon, and as recently as last year, many believed another decade would pass before a machine could beat the top humans. But Google has done just that.

We hope you find it useful in your machine learning projects.

This post is authored by Sudarshan Raghunathan, Principal Development Lead for modules in the Microsoft Azure ML Studio team based in Cambridge, MA. In his blog post last month, John Langford wrote about the open source Vowpal Wabbit (VW) machine learning (ML) system. He highlighted some of the main advantages of VW, e.g. its performance and ability to handle large sparse datasets, which make it particularly popular both within and outside Microsoft for applications such as sentiment analysis and recommendation systems. When we initially released the public preview of Azure ML in July this year, we exposed a small subset of VW functionality as part of our Feature Hashing module. The latter transforms datasets with text features into binary using the feature hashing algorithm (Murmur hash) implemented in VW.

Virtual agents and chatbots will be the top consumer applications of artificial intelligence over the next five years, according to a consensus poll released today by TechEmergence, a marketing research firm for AI and machine learning. The emphasis on virtual agents and chatbots is in many ways not surprising. After all, the tech industry's 800-pound gorillas have all made big bets: Apple with Siri, Amazon with Alexa, Facebook with M and Messenger, Google with Google Assistant, Microsoft with Cortana and Tay. However, the poll's data also suggests that chatbots may soon be viewed as a horizontal enabling technology for many industries. "The most unexpected result was that so many founders who were not directly involved in the chatbot space or smart home/device space were very excited about these areas," wrote Daniel Faggella, founder of TechEmergence, in an email interview.

AI (or, artificial intelligence) is a topic which is becoming increasingly prevalent. Whilst it is becoming more common within the market research industry, I don't believe it can't totally replace a researcher and how we think and work (thankfully or we'd all be looking for new jobs!); and on top of that I don't think that it should. Companies these days are generating more data than ever before so, don't get me wrong, anything that makes our lives easier is great - and as Helene Protopapas discussed the fact that AI can help speed up research is advantageous to us all. However, there are some reasons why I strongly believe that AI isn't the'produce insight' button we've all secretly been wishing for and that you still need a researcher to deliver the insight that clients want and expect. As Harmony Crawford points out, if you just give someone a load of numbers, they'll drown in them.