So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, will provide tips on how to be successful in large scale machine learning.

Inside a large, windowless room in an electronics factory in south Shanghai, about 15 workers are eyeing a small robot arm with frustration. Near the end of the production line where optical networking equipment is being packed into boxes for shipping, the robot sits motionless. "The system is down," explains Nie Juan, a woman in her early 20s who is responsible for quality control. Her team has been testing the robot for the past week. The machine is meant to place stickers on the boxes containing new routers, and it seemed to have mastered the task quite nicely. But then it suddenly stopped working. "The robot does save labor," Nie tells me, her brow furrowed, "but it is difficult to maintain."

An historic milestone in artificial intelligence set by Alan Turing - the father of modern computer science - has been achieved at an event organised by the University of Reading. The 65 year-old iconic Turing Test was passed for the very first time by computer programme Eugene Goostman during Turing Test 2014 held at the renowned Royal Society in London on Saturday. 'Eugene' simulates a 13 year old boy and was developed in Saint Petersburg, Russia. The development team includes Eugene's creator Vladimir Veselov, who was born in Russia and now lives in the United States, and Ukrainian born Eugene Demchenko who now lives in Russia. The Turing Test is based on 20th century mathematician and code-breaker Turing's 1950 famous question and answer game, 'Can Machines Think?'.

But those labor savings aren't the only gains to be had from the adoption of driverless trucks. Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driverless truck can drive nearly 24 hours per day. That means the technology would effectively double the output of the U.S. transportation network at 25 percent of the cost. And the savings become even more significant when you account for fuel efficiency gains. The optimal cruising speed from a fuel efficiency standpoint is around 45 miles per hour, whereas truckers who are paid by the mile drive much faster. Further fuel efficiencies will be had as the self-driving fleets adopt platooning technologies, like those from Peloton Technology, allowing trucks to draft behind one another in highway trains.

With technological advancement comes economic adaptation. This is one of the greatest lessons of the next 10 years which will show some pretty dramatic shifts. For the youth, this means preparing for a completely different reality and on-demand economy. Please recognize, your children will also live in a different world. A world where machine intelligence is everywhere.

Everyone knows about the giant skills gap that is haunting the IT sector worldwide. According to IoT company PTC, it is estimated that in the next ten years more than two million IT and communication jobs will be unfulfilled. To address this, several universities have come up with degrees that address the different skills needed in the IoT market, including user interfaces, networks, artificial intelligence, networking, and others. CBR lists ten courses being taught in the UK institutions. Offering both a full time or part time (12 and 24 months respectively) course, University of London's Royal Holloway has built a degree based on computer science, technology and engineering.

China continues to accelerate its bid to grab the initiative in 21st-century robotics and artificial intelligence development as the next emerging global powerhouse. Small wonder that the world's leading manufacturing giant has already raced past the rest of the world as a major user of robots. According to the China Robot Industry Alliance, the country is already a flourishing hub for consumer robotics and is poised for a radical transition from its human-based workforce to an automated Artificial Intelligence or AI-based alternative. Its recent unveiling of the incredibly adept and efficient personal robot BIG-i is a pertinent example. Dubbed "butler," this humanoid is primarily a service robot with the programmed ability to aid homeowners in the performance of a wide variety of household errands.

A slowdown in tech start-up funding has at least one big beneficiary: Walt Disney Co.'s video games division. The entertainment giant is having an easier time finding partners with whom it can develop mobile games, one of its top executives said last week. Why? Up-and-coming companies are losing access to the cash needed to launch games on their own. "As the venture money has dried up and exits have slowed down and valuations have come down, larger game developers that have one or two hits [but not a big stable of them] are now open to work with us in co-development," said Chris Heatherly, senior vice president and general manager at Disney Mobile Games. The comments came during a discussion last week at the L.A. Games Conference with Michael Metzger of investment bank Houlihan Lokey.

Tech giants are active players at the cognitive technologies scene. Alphabet and Facebook proved themselves active acquirers and investors in cognitive tech. Supercomputers, robots and drones are among themes approached by Internet giants. Microsoft, however, seems to be following a differed path: it integrates cognitive technologies into traditional products and makes them smarter. Bill Gates coined the concept of'digital nervous system (DNS)... that ... [provides] a well-integrated flow of information to the right part of the organization at the right time'.

Clustering is a standard technique for exploratory data analysis that finds applications across different disciplines such as computer science, biology, marketing, and social science. The goal of clustering is to group a set of unlabeled data points into several clusters based on some notion of dissimilarity. Inspired by the success of classifier ensembles, consensus clustering has emerged as a research topic [8, 23]. Consensus clustering first generates several partitions of the same dataset. Then it combines the sample partitions to a single consensus partition. The assumption is that a consensus partition better fits to the hidden structure in the data than individual partitions. One standard approach of consensus clustering combines the sample partitions to a mean partition [3, 4, 5, 6, 9, 17, 20, 21, 22].