New research explains why so many biological networks, including the human brain (a network of neurons), exhibit a hierarchical structure, and will improve attempts to create artificial intelligence. The study, published in PLOS Computational Biology, demonstrates this by showing that the evolution of hierarchy – a simple system of ranking – in biological networks may arise because of the costs associated with network connections. Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, and metabolic networks. This means they have separate units that can each be repeatedly divided into smaller and smaller subunits. For example, the human brain has separate areas for motor control and tactile processing, and each of these areas consist of sub-regions that govern different parts of the body.

How much should you charge someone to live in your house? Or how much would you pay to live in someone else's house? Would you pay more or less for a planned vacation or for a spur-of-the-moment getaway? And the struggle to do so, my colleagues and I discovered, was preventing potential rentals from getting listed on our site--Airbnb, the company that matches available rooms, apartments, and houses with people who want to book them. In focus groups, we watched people go through the process of listing their properties on our site--and get stumped when they came to the price field. Many would take a look at what their neighbors were charging and pick a comparable price; this involved opening a lot of tabs in their browsers and figuring out which listings were similar to theirs.

Insilico Medicine and Life Extension announced today an exclusive collaboration to identify novel biomarkers of human aging through the use of big-data analytics and AI, with the ultimate goal of discovery and formulation of nutrients to support health and longevity. Insilico Medicine* is a big-data analytics company specializing in applying advances in deep learning to discovery of biomarkers and drugs. Life Extension**, a Florida-based organization established in the early 1980s, is a dietary-supplement innovator dedicated to extending healthy human longevity. Insilico Medicine will focus on applying advanced signaling pathway activation analysis techniques and deep-learning algorithms to find nutraceuticals that mimic the tissue-specific transcriptional response of many known interventions and pathways associated with health and longevity. Life Extension will use this information to develop novel nutraceutical products to support health and longevity, such as "geroprotectors" -- precision natural organic small-molecule formulations that slow down or even reverse age-associated conditions and damage.

This film is brought to you by the World Technology Network. Job displacement due to automation of ever increasing range of professions - from truck drivers and lawyers, to writers and financial analysts - is likely to be one of the greatest challenges of the next couple of decades. A 2013 Oxford study predicts that up to 47% of jobs could be lost in the United States - nearly twice the unemployment rate of the Great Depression. Why wait till it's too late? Let's talk about this elephant in the room now.

Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.

German researchers are experimenting with an "artificial robot nervous system" to teach robots how to feel and react to pain, in what might be one of the saddest displays of robot bullying since Boston Dynamics' Atlas was pushed by a hockey stick. It may seem counter-intuitive at first, but we build robots to go into dangerous situations for us. Pain is a way to protect ourselves, so wouldn't it be beneficial to have something that doesn't have those limitations? Researchers from Leibniz University of Hannover believe that robots can also use these sensations as a form of protection. "Pain is a system that protects us," researcher Johannes Kuehn told Spectrum IEEE.

Hardly a day goes by without word of advancements in artificial intelligence (AI) and machine learning. The technology is rippling through a growing array of industries and transforming the way people live and work. At Atom Bank, an upstart financial services firm in the United Kingdom (UK), machine learning is the fundamental underpinning for the business. "We are an app-based bank," says Chief Operating Officer Stewart Bromley. "There are no branches or massive call centers. The app-only approach is revolutionary, and, if successful, it could prove highly disruptive to the financial industry and beyond. "It's our belief that people in the UK who want to bank digitally are underserved," Bromley explains. "There is no other firm that allows them to do all their banking entirely within an app.

Researchers in Japan have used a machine-learning method to cut the time it takes to predict the catalytic potential of different metals. Binding between a metal surface and an adsorbate mainly depends on the electronic structure of the metal. More energy at centre of the metal's d-band creates a stronger bond between its surface and the adsorbate. Based on this theory, scientists have long regarded a value called the d-band centre as a key indicator of a metal's catalytic activity. Researchers normally compute this value independently for each metal using first-principles calculations.

At the recent LexisNexis Enterprise Solutions' InterAction Share event in London, I shared with delegates my insight and advice in relation to the rise of smart technologies, artificial intelligence (AI) and robots in the legal ecosystem and how these technologies are being, and will be, deployed in the industry. By addressing all of the above, it naturally led to my tackling the challenging question "what is the next legal frontier?" It's important to realise and understand that it is inevitable that the roles of lawyers, general counsel, marketers, business development, social media and CRM specialists etc. are going to change in light of such overwhelming technological advances. The question that is hotly debated today is whether advanced technologies will support or replace lawyers. Well, the research speaks volumes.

After earning two advanced degrees, completing three postdocs, working in three countries, and finally reaching the stage when I am setting up my own lab, I realize that three lessons taught by three great mentors have influenced how I think about doing science. These lessons, each of which came at just the right time in my career, have helped me probe new intellectual territories and enjoy my work. Looking back, I appreciate the way that my mentors supported my development as a researcher and imparted valuable advice that still guides how I approach my work and career. Now, as I am moving into the role of adviser myself, I hope to be able to pass these lessons on to my current and future students. "Three great mentors have influenced how I think about doing science."