There are two competing forces impacting the world of HR. On one hand, the rate of social and business change is accelerating, resulting in an ever more complex socioeconomic environment. On the other, employees are pushing for even simpler, more engaging and human-like interactions. To strike this balance, leading HR practitioners are starting to leverage emerging technologies such as artificial intelligence (AI), machine learning and chatbots to support rapid business changes. Businesses have long recognised the importance of delivering a memorable experience for customers.

Strategically placed recycled cell phones are combating deforestation by sending notifications to rangers when chainsaw noises are recorded. Algorithms similar to those used by Homeland Security are being developed to provide effective routes for ranger patrols in their battle against poaching. And drones are delivering sylvatic plague vaccines to prairie dog populations in an effort to save the Black Footed Ferret, a highly endangered predator of prairie dogs, from extinction. What happens when wildlife biologists join forces with computer scientists? A new era in wildlife conservation is born!

It may have a history stretching back 3,000 years, but Beijing is fast becoming today's modern'it' city. With amazingly fast internet, access to cutting-edge technology like facial recognition software, significant investment in artificial intelligence and an unrivalled cosmopolitan energy, China's capital is among the most exciting cities for enterprising expats. "'If I can make it there, I'll make it anywhere'," said German expat Clemens Sehi, referencing Frank Sinatra's ode to New York City. "If Sinatra lived today, he would probably sing about a city like Beijing." Sehi, who is creative director at Travellers Archive, says living in the city means you feel like you are "living in the new age" and always up to date.

Anastasia Gnezditskaia is a writer / analyst covering France, Benelux and Morocco. Based in Antwerp, Belgium, she has a background working for trade publications covering markets and their regulation in Washington, D.C., where she lived for 10 years. Following this she managed international development projects in Africa at the World Bank, and worked as a journalist covering Congress, federal government agencies and financial markets, including energy futures.

The idea of Artificial Intelligence has been around since the early 1900s, originally in the form of fictional writing and later seen in films. The minds of these writers and film producers imagined a world where the role of robots in society was elevated from the role of machines in their present society. These individuals imagined technological advances, which would provide a machine with the ability to process sets of information and make a decision based on the information that the machine was taught to process. Although exploration within the field of AI and robotic process automation evolved at a slow pace originally, advancement within this field is currently increasing at an exponential rate. Many ideas that were once considered merely dreams are becoming reality.

Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task. In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels. The proposed deeper model is evaluated on the benchmark treebank and achieves state-of-the-art accuracy with greater than 48% in 11-way and $F_1$ score greater than 50% in 4-way classifications for the first time according to our best knowledge.

Abstract-- Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy. I. INTRODUCTION Robot technology has improved tremendously over the last decade. Consequently, autonomous robot systems have been able to operate in increasingly complex environments and for increasingly long periods of time, i.e. weeks, months, or years. When a fully modelled robot is deployed in a completely known, static environment, the challenge of long-term autonomy (LTA) reduces to one of robustness, i.e. enabling the robot to remain operational for as long as possible. Without these simplifying assumptions autonomous robots face a number of interrelated challenges. The first refers to the application requirements, e.g., the robot platform (hardware and software), environment and tasks to be performed.

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict open types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets. Our data and model can be downloaded from: http://nlp.cs.washington.edu/entity_type

UK company Babylon Health is testing an artificially intelligent medical chatbot in Rwanda.

Scarcity of labeled data is one of the most frequent problems faced in machine learning. This is particularly true in relation extraction in text mining, where large corpora of texts exists in many application domains, while labeling of text data requires an expert to invest much time to read the documents. Overall, state-of-the art models, like the convolutional neural network used in this paper, achieve great results when trained on large enough amounts of labeled data. However, from a practical point of view the question arises whether this is the most efficient approach when one takes the manual effort of the expert into account. In this paper, we report on an alternative approach where we first construct a relation extraction model using distant supervision, and only later make use of a domain expert to refine the results. Distant supervision provides a mean of labeling data given known relations in a knowledge base, but it suffers from noisy labeling. We introduce an active learning based extension, that allows our neural network to incorporate expert feedback and report on first results on a complex data set.