One year ago, we wrote about the world's first robot lawyer. It is a website with a chatbot that started off with a single and free legal service: helping to appeal unfair parking tickets. When the article was published, the services was available in the UK, and in New York and Seattle. At the time, it had helped overturn traffic tickets to the value of 4 million dollars. Apart from appealing parking tickets, the website could already assist you, too, in claiming compensation if your flight was delayed.

I recently had the opportunity to travel to South Korea to look over LG's work in both the AI and robotics fields, including some detailed time with its LG CLOi Airport Guide Robot. That's a design that LG has iterated on over time, and I had the chance to sit down for an interview (via a translator) with Hyungjn Choi, LG's Leader of Life support Robot Biz. That's a fancy title to say that he's in charge (in his own words) "of robot business development and product planning" at LG. Robots in industry are nothing new, but people-centric robots are a tough challenge. Mr Choi is quite clear that the first robot was the toughest. "Technically speaking, the most difficult one is the first one that you can see when you arrive (at Seoul's Incheon International Airport), the Airport guide robot. Because we have to deal with a spacious indoor area, the environment is very noisy. There are a lot of people travelling around. The robot needs to autonomously drive around and move around these high traffic areas. Also it needs to interpret and understand different and diverse pronunciations and ways to respond to customers' needs. That was the most technologically challenging area, and in order to take on that challenge, we've specifically taken on airports. But that doesn't mean that other robots were easy, technologically! So for example, the lawnmower robot is different from other lawnmower robots you might see. Our one moves around in a specific pattern, so it can move around in a zig zag motion, so even if your ground is bumpy, it can make a very calculated movement to ensure that every part of your lawn is well managed."

Digital transformation is driving a new breed of retail that will take consumers where they've never been before, writes Microsoft's Christi Olson. The following is a guest post by Christi Olson, head of evangelism for search at Microsoft. The views are the author's own. Like many industries today, retail is in an extreme state of disruption. In five years, 25% of malls will be gone.

For many organisations and their workers, technology automation is an opportunity and a threat. On one hand, using robotic process automation and other tools creates efficiencies by automating manual and repetitive tasks. On the other, it has the potential to make jobs redundant. Tech execs gathered at roundtable events in Sydney and Melbourne recently to discuss the impact automation technologies are having on their organisations. A key issue raised during the discussion was the challenge of ensuring governance policies and procedures are followed as'bots' replace tasks carried out by humans across the business.

Many currently deployed Reinforcement Learning agents work in an environment shared with humans, be them co-workers, users or clients. It is desirable that these agents adjust to people's preferences, learn faster thanks to their help, and act safely around them. We argue that most current approaches that learn from human feedback are unsafe: rewarding or punishing the agent a-posteriori cannot immediately prevent it from wrong-doing. In this paper, we extend Policy Gradient to make it robust to external directives, that would otherwise break the fundamentally on-policy nature of Policy Gradient. Our technique, Directed Policy Gradient (DPG), allows a teacher or backup policy to override the agent before it acts undesirably, while allowing the agent to leverage human advice or directives to learn faster. Our experiments demonstrate that DPG makes the agent learn much faster than reward-based approaches, while requiring an order of magnitude less advice.

Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run on-device, on an arbitrary communication network, without a central coordinator. We propose COLA, a new decentralized training algorithm with strong theoretical guarantees and superior practical performance. Our framework overcomes many limitations of existing methods, and achieves communication efficiency, scalability, elasticity as well as resilience to changes in data and participating devices.

In mammals this incorporates the cortex, the hippocampus, the claustrum, the amygdala, the basal ganglia, and the olfactory bulb. Convergent evolution results in analogous characters with similar appearances or functions, although these were not present in the last common ancestor of the two lineages. Most species are characterized by a high brain-to-body mass ratio, ecological flexibility, and a complex social life, featuring long-term partnerships and dynamic groups structured by social relationships. The term derives from the Greek word hodos which means'road'. Each layer is constituted by distinctive cell populations with unique connectivity patterns. At first glance, neocortical lamination looks uniform (and is therefore sometimes called'isocortical').

Putting aside the concept of'personality' and its disputed nature within psychology, the new platform attempts to group subjects into different personality types on the basis of eye-motion. The research comes from the University of South Australia. Visual exploration is driven by two main factors. First there is the stimuli in our environment; and second, in response to our own individual interests and intentions. It is through the latter that some researchers think that personality traits can be discerned.

Keeping a cool head and discerning what's beneficial and what is not are indispensable considerations to avoid a fiasco Today the role of the CIO is perceived as a strategic business position, tasked with driving change and transformation within their organisation instead of exclusively focusing on IT operations. It's one of the most dynamic executive roles which requires constant development and adaptation to digital ecosystems, business model innovations, technology demands and stakeholders' expectations. Adapting to tight budgets and focusing on internal operations are now just part of a long list of duties expected from a CIO. Driving business operations and leading innovation and transformation are regarded as extremely desired skills. In an interview with our sister title CIO UK, SThree CIO Lance Fisher explained the change in nature of his job: "I split IT into two. You have to learn and understand the business and its competitive advantage, understand how your competitors run their businesses and then look to deliver competitive advantage. I firmly believe as a CIO you are the glue. You need to connect IT to the business, vendors and your clients."

Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we take a step toward explaining such deep learning based models through a case study on a popular neural model for NLI. In particular, we propose to interpret the intermediate layers of NLI models by visualizing the saliency of attention and LSTM gating signals. We present several examples for which our methods are able to reveal interesting insights and identify the critical information contributing to the model decisions.