Simone Giertz is a self-professed maker of crummy robots. She's made a drone that cuts hair, a robot that applies her lipstick and the Wake-Up machine, a DIY wall-mounted robot alarm clock with a rubber arm, that slaps her awake in the morning. While she admits that none of her robots are meant to do their job well, Simone's fascination for technology and electronics have made her a role model for young robot makers worldwide. So what does this queen of bad robots make of our increasingly robotics-reliant society? And does she recognise the possible pitfalls of human-robot interactions in her day-to-day work?

Retail to cloud-computing giant Amazon plans to hire over 1,000 new staff across three sites in the UK, and will open a new office in Manchester next year. "These are Silicon Valley jobs in Britain, and further cement our long-term commitment to the UK," said Doug Gurr, Amazon's UK country manager. A new corporate office in Manchester, due to open next year, will be located in the Hanover Building in the Northern Quarter. The company said the six-storey, 90,000 square-foot site will house at least 600 new staff working on software development, machine learning and R&D. Amazon said it will also expand its development centre in Edinburgh, adding 250 new staff where it already has hundreds of software engineers, machine learning scientists and user experience designers.

In the competitive retail industry, personalization strategies have become table stakes. But for a brand to really connect with a customer, it first has to know that customer. Data science and machine learning are making it easier for brands to get useful insight into their customers based on their behavior. Director of Data and Audience, talked to ZDNet about the different insights it can gain from its customer data -- what decades-old information about a customer can tell you versus the latest updates to their shopping cart. Overstock.com is marketing roughly five-plus million products on a global scale, Robison noted.

In 2018, practically every mundane, household item can possess smart qualities. Your vacuum can move on its own. You can control your coffee maker through an app. There's even a virtual, personal assistant that can order you pizza, play music, and recite the weather forecast. But there are some tasks that are done just as well -- if not, better -- than their fancy, hi-tech alternatives.

It's the ultimate unanswerable question we all face: When will I die? If we knew, would we live differently? So far, science has been no more accurate at predicting life span than a $10 fortune teller. But that's starting to change. The measures being developed will never get good enough to forecast an exact date or time of death, but insurance companies are already finding them useful, as are hospitals and palliative care teams.

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost).

AI is the hottest topic in the HR Tech space, but something critical to making the right hires is getting overlooked: Developing approaches to engage with managers so recruiters can influence the decision-making process. The HRTech space is on fire with AI. CRM, HCM, chatbots, screening, video interviewing, job posting – are trying to inject AI with one thing in common: FIND CANDIDATES. Ironically, the industry is missing the boat on where finding a candidate falls on the scale of importance. Recruiters need to be more focused on creating greater engagement with the person with the pain and the purse: THE HIRING MANAGER.

Payroll, which includes employees and their salaries or wages, can be defined as the amount of money a company pays its workers. However, the process is not that simple. Human resource details like employee benefits, salaries and records all fall under payroll, and organizing each employee's file can be difficult. But as technology advances, payroll is becoming a more seamless process. This is great news for small businesses that struggle to manage human resources.

The underwater ocean world is an ecosystem with lots of different sounds. So naval forces have traditionally relied on so-called "golden ears," or musicians and other individuals with particularly sharp hearing, to detect the specific signals coming from an enemy submarine. But given the overload of data today, distinguishing between false alarms and actual dangers has become more difficult. That's why "Thales is working on "Deep Learning" algorithms capable of recognizing the particular "song" of a submarine, much as the "Shazam" app helps you identify a song you hear on the radio", says Dominique Thubert, Thales Underwater Systems, which is specialized in sonar systems for submarines, surface warships, and aircraft. These algorithms, attached to submarines, surface ship or drones, will help naval forces sort through and classify information in order to detect attacks early on.

We profit from it, we fear it, and we find it impossibly hard to quantify: risk. While not the sexiest of industries, insurance can be a life-saving protector, pooling everyone's premiums to safeguard against some of our greatest, most unexpected losses. One of the most profitable in the world, the insurance industry exceeded $1.2 trillion in annual revenue since 2011 in the US alone. But risk is becoming predictable. And insurance is getting disrupted fast.