New technology, the evolving business landscape and the demands of 21st century life are impacting the habits of workers and the kinds of tasks they are expected to complete. The physical space of most modern workplaces is also radically different to those of the past. Gone are the days of individual offices with names and job titles engraved on the door. As technology has a greater impact upon the way we work and employees increasingly seek flexible ways of working, what is the future of the workplace, and are these changes necessarily for the best? In this two part series, D/SRUPTION looks at what we can expect from the future of work in both the short and long term.

There's a saying that the eyes are the window into the soul. While at first glance this seems like a piece of idiomatic frivolity, a new algorithm developed by an international team of researchers from Australia and Germany shows that there is some truth to this, showing that the movements of one's eyes and the size of one's pupils can predict certain aspects of personality. The findings, which were published in Frontiers in Human Neuroscience, suggest there is a link between the way we move our eyes and personality traits. For instance, people who are more curious in nature tend to move their eyes more, while more open-minded people will find themselves staring at abstract images for longer periods of time. "Personality traits characterize an individual's patterns of behavior, thinking, and feeling," wrote the researchers in their paper.

There's no denying that human intelligence makes our species stand out from other life on Earth. Our modern brain is an evolutionary feat more than 520 million years in the making, and it is the key to everything that makes us human. But while human brains are extraordinary, we don't have a monopoly on intelligence. "Reserving the term'cognition' for typically human problem-solving abilities ... and dismissing simpler behavior as mechanistic, reflexive, and hard-wired does not do justice to the behavioral complexities of even the simplest of organisms," University of Gronigen psychologist Marc van Duijn and his colleagues write in a widely cited 2006 paper on cognition. You might think of tool use as an exclusively human activity, but macaques on an island off Thailand have learned to use stones as tools to shuck oysters.

Nvidia has developed a method to train robots to carry out actions by first observing human activity. In initial applications, robots learned to pick up and move colored boxes and a toy car in a lab environment, using a Baxter robot. Learnings from such research will be used to retrain robots and create robots that can work safely alongside people in industrial settings and homes. "In the manufacturing environment, robots are really good at repeatedly executing the same trajectory over and over again, but they don't adapt to changes in the environment, and they don't learn their tasks," Nvidia principal research scientist Stan Birchfield told VentureBeat in an interview. "So to repurpose a robot to execute a new task, you have to bring in an expert to reprogram the robot at a fairly low level, and it's an expensive operation. What we're interested in doing is making it easier for a non-expert user to teach a robot a new task by simply showing it what to do."

NVIDIA is talking up some new AI techniques that well help robots to more efficiently work alongside humans. The research was led by NVIDIA researchers Stan Birchfield and Jonathan Tremblay. The duo was able to develop a deep learning-based system that is said to be the first of its kind that can teach a robot to complete a task simply by observing the actions of a human. NVIDIA says that the research is meant to enhance communications between humans and robots while furthering research that allows people to work alongside robots seamlessly. "For robots to perform useful tasks in real-world settings, it must be easy to communicate the task to the robot; this includes both the desired result and any hints as to the best means to achieve that result," the researchers stated in their research paper.

RoboFly is only slightly bigger than a real fly. A new type of flying robot is so tiny and lightweight -- it weighs about as much as a toothpick -- it can perch on your finger. The little flitter is also capable of untethered flight and is powered by lasers. This is a big leap forward in the design of diminutive airborne bots, which are usually too small to support a power source and must trail a lifeline to a distant battery in order to fly, engineers who built the new robot announced in a statement. Their insect-inspired creation is dubbed RoboFly, and like its animal namesake, it sports a pair of delicate, transparent wings that carry it into the air. But unlike its robot precursors, RoboFly ain't got no strings to hold it down.

The development of Artificial General Intelligence (AGI) promises to be a major event. Along with its many potential benefits, it also raises serious safety concerns (Bostrom, 2014). The intention of this paper is to provide an easily accessible and up-to-date collection of references for the emerging field of AGI safety. A significant number of safety problems for AGI have been identified. We list these, and survey recent research on solving them. We also cover works on how best to think of AGI from the limited knowledge we have today, predictions for when AGI will first be created, and what will happen after its creation. Finally, we review the current public policy on AGI.

Insect-sized flying robots could help with time-consuming tasks like surveying crop growth on large farms or sniffing out gas leaks. These robots soar by fluttering tiny wings because they are too small to use propellers, like those seen on their larger drone cousins. Small size is advantageous: These robots are cheap to make and can easily slip into tight places that are inaccessible to big drones. But current flying robo-insects are still tethered to the ground. The electronics they need to power and control their wings are too heavy for these miniature robots to carry.

Here's a question: do you consider yourself to be a trusting person? Or let me put it another way: would you put your life in the hands of a total stranger? This morning I woke up. I switched on the light -- trusting that I wouldn't be electrocuted by a faulty lamp, or cord, or socket. I prepared my breakfast -- trusting that I wouldn't be poisoned by salmonella in my factory-processed muesli.

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.