Social psychologists have long known that humans make snap judgements about each other based on nothing more than the way we look and, in particular, our faces. We use these judgements to determine whether a new acquaintance is trustworthy or clever or dominant or sociable or humorous and so on. These decisions may or may not be right and are by no means objective, but they are consistent. Given the same face in the same conditions, people tend to judge it in the same way. And that raises an interesting possibility.

I have been using Lean Six Sigma (LSS) to improve business processes for the past 10 year and am very satisfied with its benefits. Recently, I've been working with a consulting firm and a software vendor to implement a machine learning (ML) model to predict remaining useful life (RUL) of service parts. The result which I feel most frustrated is the low accuracy of the resulting model. As shown below, if people measure the deviation as the absolute difference between the actual part life and the predicted one, the resulting model has 127, 60, and 36 days of average deviation for the selected 3 parts. I could not understand why the deviations are so large with machine learning. After working with the consultants and data scientists, it appears that they can improve the deviation only by 10% through data cleansing.

Sergey Levine is an assistant professor at UC Berkeley whose research is focused on the thing our parents used to make such a fuss over, whenever we made stupid mistakes or should have known to avoid this or that and how on earth we could be so clueless. He has a keen interest in teaching common sense. It might sound frivolous or a fool's errand, but it's in line with the pursuit you begin to hear a lot about these days among experts focused on machine learning and computer vision and the like. Okay, so we want to get robots to a state where they're useful and able to operate and mingle among humans and be an everyday fixture of everyday life. Those machines don't have the benefit of decades of acquired knowledge -- schooling, social skills acquired through interactions and so forth -- which means someone needs to program responses that fit scenarios.

Even if self-driving cars aren't part of our daily lives yet, vehicles are becoming internet-connected at a rapid pace. Gartner predicts that one fifth of all autos on the road, and great majority of new vehicles being produced worldwide will have wireless network connectivity by 2020. Yet, few organizations have access to use the data generated by these vehicles today. That's where Otonomo, a startup based in Herzliya, Israel comes in. The company's systems gather up driver and vehicle data from disparate automakers and original equipment manufacturers.

DB: Imagine what the next 5 years will bring: The term "mobile computing" will eventually become a dated term, replaced by "ubiquitous computing" as the internet will be everywhere. These changes include the transportation we take on land, in the air, and at sea; the clothes and devices we wear, sensors at work, at home, in our environment, and (if we chose) in us for medical purposes as well. DB: Also right behind and coupled with the Internet of Everything: 3D mass fabricators enabling individuals to affordably "print" and modify at the molecular level tangible substances based on digital designs. Maker Faires around the world already exist showcasing the early stages of what 3D fabricators can do in the hands of artists, engineers, and hobbyists. As Co-Chair of the IEEE Committee focused on Artificial Intelligence and Innovative Policies, I firmly believe exponential changes like the era we're in offer great opportunities for society -- as well as great challenges.

AdviceRobo combines the worlds of artificial intelligence scoring and robo-advisory to create a solution for the next-generation of financial institutions. The Dutch firm, with its London office at Level39, uses data and machine learning to support lending for millennials and SMEs. The tech is white-label and backed by specialists in finance, AI, behavioural science and software development. The Seedrs funds will be used to grow the firm's UK presence – find out more and take part in the campaign here. The data explosion and the use of new technologies such as Machine Learning can help to predict risk at an individual customer level.' says Diederick van Thiel, CEO of AdviceRobo,.

This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. The paradox of automation: Earlier this year, Facebook exorcised those pesky human editors who were introducing political bias into its Trending news list and left the job to algorithms. Now, reports Caitlin Dewey in The Washington Post, the Trending news isn't biased, but some of it is fake. Turns out the algorithms can't tell a real news story from a hoax. Facebook says it can improve its algorithms, but errors of judgment aren't the only pitfall in transferring human tasks to machines.

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Symantec has launched Endpoint Protection 14, a new security solution which harnesses artificial intelligence to protect clients. Announced on November 1, the new security offering is powered by AI and machine learning on the endpoint and in the cloud. Symantec says that by harnessing machine learning to collate data and detect patterns and anomalies which may indicate a cyberattack, AI provides "a multi-layered solution able to stop advanced threats and respond at the endpoint regardless of how the attack is launched." Symantec Endpoint Protection combines machine learning, memory exploit mitigation, and threat intelligence provided by Symantec and Blue Coat, which combined their research and security operations in October after Symantec completed the acquisition of Blue Coat for $4.6 billion. The company also says that the solution is capable of 99.9 percent efficacy, low false positives, and a 70 percent carbon footprint reduction in comparison to past endpoint software.