Give us your feedback Thank you for your feedback. Parker's first day at work at the law firm Norton Rose Fulbright in Australia involved 1,000 conversations with potential clients. Even the most super-energetic young lawyer would normally manage only a fraction of that but Parker is, of course, a chatbot -- a computer program that simulates human conversation. The new recruit is a prime example of how law firms in Asia-Pacific are experimenting with artificial intelligence to improve efficiency. Chatbots, which use AI to answer simple questions from people wanting to learn more about a subject, are already being adopted in industries ranging from banking to medicine.

From robotic process automation to machine learning algorithms, many of today's most influential companies are deploying artificial intelligence (AI) technologies to drive business results. While most decision makers are aware of the business opportunities that emerging technologies present, many are unprepared simply because they fail to understand them. AI includes a variety of technologies and tools, some that have been around for a long time and others that are relatively new. Nevertheless, one thing is clear: businesses are thinking harder about how to prioritize AI in 2018. According to International Data Corporation (IDC), the widespread adoption of artificial intelligence will jump from $8.0 billion in 2016 to more than $47 billion in 2020.

The causes of real-world problems in economics and public health can be notoriously hard to determine. Often, multiple causes are suspected, but large datasets with time-sequenced data are not available. Previous models could not reliably analyze these challenges. Now, researchers have tested the first artificial intelligence model to identify and rank many causes in real-world problems without time-sequenced data, using a multi-nodal causal structure and Directed Acyclic Graphs. When something bad happens, it is natural to try figure out why it happened.

E3, the biggest video game news event of the year, is taking place next week in Los Angeles – and as it is every year, it will be preceded by press conferences and livestreams where Sony, Microsoft, Nintendo and the rest will hype up their forthcoming games. E3 very much concentrates on the Hollywood blockbusters of the video game world; here are the 16 games generating the most buzz. We'll be liveblogging the first three days of E3 2018, including the press conferences. Operating somewhere between Destiny, The Division and Mass Effect, the new game from Bioware sparked interest at last year's E3 with its luscious alien landscapes and its promise of an epic cooperative adventure – with jetpacks. Now delayed until early 2019, we'll be expecting a lot more information about the world, and those cool customisable exosuits, in Los Angeles.

Motion-sensor cameras are increasingly being used to track wildlife across the globe, from tigers in India to aardvarks in Africa. But combing through the millions of images captured by these systems is a time-consuming task. Now, scientists have discovered that artificial intelligence is as effective as human volunteers -- and much faster -- at identifying species in these largely untapped photo repositories. In a new study published this week in the Proceedings of the National Academy of Sciences, a team of researchers, led by computer scientist Mohammad Sadegh Norouzzadeh at the University of Wyoming, tested whether a type of artificial intelligence called deep neural networks could correctly identify and count species, determine animals' ages, and classify their behaviors. They analyzed AI's capabilities using 3.2 million images from the Snapshot Serengeti dataset, which contains photos from 225 camera traps in Tanzania's Serengeti National Park since 2011.

We can't stop Nature when it unleashes its fury in the form of volcanoes, earthquakes, storms and avalanches, but we can use technology to warn us of imminent danger - and save lives. As the sun sets over Lake Victoria, Africa's largest lake, tens of thousands of fishermen ready themselves to head out on the water for the night, fishing mostly for tilapia and Nile perch. As they push off, they know they are risking their lives - some of them may never be seen again. Lake Victoria - a lake so big it straddles Uganda, Tanzania and Kenya - is notorious for its deadly storms. At this time of year, strong winds, rain, lightning and huge waves are a regular occurrence.

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.

A home-based caregiver in a village near Kayar, Senegal, provides basic healthcare services, including malaria treatment for patients living in areas where there are no healthcare facilities. This article is brought to you thanks to the strategic cooperation of The European Sting with the World Economic Forum. To put this into context, the average waste per-person across the top 15 countries is 10-15 times more than the average amount spent by the bottom 50 countries on healthcare, who currently spend an average of around $120 per person. Even more concerning is the fact that the underlying reasons for this waste include preventable and rectifiable system inefficiencies such as care delivery failures, over-treatment, and improper care delivery. Technologies such as artificial intelligence (AI) can help minimise such inefficiencies, ensuring substantially more stream-lined and cost-effective health ecosystems.

The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of widely available remote sensing data for the prediction of infrastructure quality in Africa. We train a convolutional neural network to predict ground truth labels from the Afrobarometer Round 6 survey using Landsat 8 and Sentinel 1 satellite imagery. Our best models predict infrastructure quality with AUROC scores of 0.881 on Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using Landsat 8. These performances are significantly better than models that leverage OpenStreetMap or nighttime light intensity on the same tasks. We also demonstrate that our trained model can accurately make predictions in an unseen country after fine-tuning on a small sample of images. Furthermore, the model can be deployed in regions with limited samples to predict infrastructure outcomes with higher performance than nearest neighbor spatial interpolation.

Neural attention (NA) is an effective mechanism for inferring complex structural data dependencies that span long temporal horizons. As a consequence, it has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization (ADS), machine translation (MT), and video captioning (VC). NA mechanisms perform inference of context vectors; these constitute weighted sums of deterministic input sequence encodings, adaptively sourced over long temporal horizons. However, recent work in the field of amortized variational inference (AVI) has shown that it is often useful to treat the representations generated by deep networks as latent random variables. This allows for the models to better explore the space of possible representations. Based on this motivation, in this work we introduce a novel regard towards a popular NA mechanism, namely soft-attention (SA). Our approach treats the context vectors generated by SA models as latent variables, the posteriors of which are inferred by employing AVI. Both the means and the covariance matrices of the inferred posteriors are parameterized via deep network mechanisms similar to those employed in the context of standard SA. To illustrate our method, we implement it in the context of popular sequence-to-sequence model variants with SA. We conduct an extensive experimental evaluation using challenging ADS, VC, and MT benchmarks, and show how our approach compares to the baselines.