This post is by Herain Oberoi, Senior Director of Product Marketing at Microsoft. Just over a year ago, we announced the Microsoft Cortana Intelligence Suite with the goal of helping customers use Big Data, Advanced Analytics and Machine Learning technologies in the cloud to derive insights, predict outcomes and prescribe actions based on their data. Cortana Intelligence provides an end to end set of cloud services and pre-configured solutions to enable line of business departments such as Sales, Marketing, Finance, Operations and more to accelerate their digital transformation projects. A year into the release, Cortana Intelligence has had big impact on customers across healthcare, retail, manufacturing, financial services and government and other industries. The range of creative solutions that our customers have come up with, to address their business needs, has been very impressive.

Microsoft's servers are now powered by optimized custom chips that joined together to translate the entirety of Wikipedia in literally less than a blink of an eye. In a demonstration at Microsoft's Ignite conference on Orlando, Microsoft tapped what it called its "global hyperscale" cloud to translate 3 billion words across 5 million articles in less than a tenth of a second. Microsoft helped custom-design the programmable logic components, or Field Programmable Gate Arrays (FPGAs), that it has added to each of its computing nodes. The company recognizes that smarter, more computationally intensive technologies will require more computing power on the back end, whether those technologies revolve around Microsoft's own Cortana digital assistant--which can now intelligently reschedule your workout to meet your fitness goals--or something that can recognize a distracted drivers, as the automobile manufacturer Volvo is researching. Microsoft's Cortana now includes health-specific information.

Investigators eager to uncover the genetic basis of autism could now have hundreds of promising new leads thanks to a study by Princeton University and Simons Foundation researchers. In the first effort of its kind, the research team developed a machine-learning program that scoured the whole human genome to predict which genes may contribute to autism spectrum disorder (ASD). The results of the program's analyses -- a rogue's gallery of 2,500 candidate genes -- vastly expand on the 65 autism-risk genes currently known. Researchers have recently estimated that 400 to 1,000 genes underpin the complex neurodevelopmental disorder. This newest research provides a manageable, "highly enriched" pool from which to pin down the full suite of ASD-related genes, the researchers said.

As Artificial Intelligence race is on, major tech companies are already developing Chatbots to serve their customer in a better way. Many customer services oriented businesses believe that Artificial Intelligence tool could help their companies. But are not sure if their business is sophisticated enough to implement Chatbots in their systems. While there are some imperatives for implementing an AI-based virtual assistant in your organisation, the entry barrier is much lower than many believe. Chatbots and Service Industry can go together till long extend to solve customer queries efficiently saving human cost and giving customers a pleasant and personalised experience.

Imagine if you could analyze trillions of data points using machine learning algorithms to come up with a list of the absolute best startups to invest in. That's no small task as there are an estimated 23,000 startups in Silicon Valley alone. One startup called SignalFire is doing just that by taking unstructured data from over 2 million data sources and then using machine learning algorithms to pick the best startups to invest in. Wouldn't you be the least bit curious to know which companies they picked? We were extremely interested to know, so we had one of our on-staff PhDs take a look on Crunchbase and lo and behold, 8 startups were listed that SignalFire has invested in so far.

Steam rolled out of the pot, and my kitchen was filled with an intoxicating blend of scents. I'd cooked a turkey ragu before, but as soon as I opened the pot, I knew this would be an entirely different experience. There were the familiar, comforting notes of ground turkey and mushrooms, but there was also earthiness from the beets, the fire of serrano chile, and an unexpected brightness from the coriander seed (coriander seed!). My dinner wouldn't have been out of place at one of San Francisco's trendy food trucks or upscale restaurants, but coming out of my 15 IKEA pot, it was unbelievable. As I mixed in the pasta and ricotta cheese and took the first incredible bite, I realized that A.I. could do a lot more for me than just beating me at Go.

Disruption in the workforce is hardly a new phenomenon. Mechanisation of manufacturing, mass production and the advent of the internet and computers have all changed the way that work is done. Earlier waves of industrialisation have primarily affected low-skilled manual labour and past improvements in technology have typically made jobs at the lower end of the skills spectrum obsolete – for example, flight navigators but not pilots; typists but not data analysts. There is wide acceptance that this has led to productivity improvements and higher economic growth – new jobs were generated that led to improvements in standards of living. The benefits have overwhelmingly outweighed the costs and there has never been a better time to be a human being. The current wave, characterised by automation becoming smarter, machine-to-machine communication, artificial intelligence and continued technological improvements – and otherwise described at the fourth industrial revolution – still brings uncertainty and threatens a broader range of occupations and skill levels.

We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP) data. The method consists of four stages: (1) SNPs with the most discriminating information between the healthy controls and schizophrenia patients are selected to construct a support vector machine ensemble (SNP-SVME). The method was evaluated by a fully validated leave-one-out method using 40 subjects (20 patients and 20 controls). The classification accuracy was: 0.74 for SNP-SVME, 0.82 for Voxel-SVME, 0.83 for ICA-SVMC, and 0.87 for Combined SNP-fMRI. Experimental results show that better classification accuracy was achieved by combining genetic and fMRI data than using either alone, indicating that genetic and brain function representing different, but partially complementary aspects, of schizophrenia etiopathology.

Bank of America Merrill Lynch released a report this week that said that annual global sales of robots has reached a record 10.7 billion in 2014. The authors valued the overall market for robotic technologies (including related software and sensors) at 32 billion for that same year. By 2020, the authors expect the robot market to be worth 83 billion. The autonomous driverless cars, developed by Google, provide one example of how manual tasks in transport and logistics may soon become automated. The combination of AI, machine learning, deep learning, and natural user interfaces (such as voice recognition) is making it possible to automate many knowledge-worker tasks.

Amazon's Echo voice-activated speaker has more "skills" than you may be using. Amazon's Echo has become a sleeper hit thanks to tasks its digital assistant Alexa can carry out. There's probably more you could do with it. In case you haven't played around with an Echo or similar Alexa-driven voice speakers, the 179 cylindrical-shaped, cloud-connected speaker sits in a room and you use your voice to ask a question or give a command. Using the wake word "Alexa," a sensitive microphone picks up the request and dishes up the info you want in a human-like voice.