--Angry Birds is a popular video game in which the player is provided with a sequence of birds to shoot from a slingshot. The task of the game is to destroy all green pigs with maximum possible score. Angry Birds appears to be a difficult task to solve for artificially intelligent agents due to the sequential decision-making, non-deterministic game environment, enormous state and action spaces and requirement to differentiate between multiple birds, their abilities and optimum tapping times. We describe the application of Deep Reinforcement learning by implementing Double Dueling Deep Q-network to play Angry Birds game. One of our main goals was to build an agent that is able to compete with previous participants and humans on the first 21 levels. In order to do so, we have collected a dataset of game frames that we used to train our agent on. We evaluate our agent using results of the previous participants of AIBirds competition, results of volunteer human players and present the results of AIBirds 2018 competition. I NTRODUCTION Angry Birds has been one of the most popular video games for a period of several years. The main goal of the game is to kill all green pigs on the level together with applying as much damage as possible to the surrounding structures.

Actor and equality campaigner Geena Davis has announced that Disney has adopted a digital tool that will analyse scripts and identify opportunities to rectify any gender and ethnic biases. Davis, founder of the Geena Davis Institute on Gender in Media, was speaking at the Power of Inclusion event in New Zealand, where she outlined the development of GD-IQ: Spellcheck for Bias, a machine learning tool described as "an intervention tool to infuse diversity and inclusion in entertainment and media". Developed by the University of Southern California Viterbi School of Engineering, the Spellcheck for Bias is designed to analyse a script and determine the percentages of characters' "gender, race, LGBTQIA [and] disabilities". It can also track the percentage of "non-gender-defined speaking characters". Davis said that Disney had partnered with her institute to pilot the project: "We're going to collaborate with Disney over the next year using this tool to help their decision-making [and] identify opportunities to increase diversity and inclusion in the manuscripts that they receive. We're very excited about the possibilities with this new technology and we encourage everybody to get in touch with us and give it a try."

You might not be aware of it, but there's a quiet arms race going on over our collective reality. The fight is between those who want to subvert it and usher in a world where we no longer believe what we see on our screens and those who want to help preserve the status quo. Up until this point in time, we have largely trusted our eyes and ears when consuming audio and visual media content, but new technological systems that create something known as deepfakes, are changing that. And as these deepfake videos nudge into the mainstream, experts are increasingly worried about the ramifications it will have on the information sharing that underpins society. Dr Richard Nock is the head of machine learning at CSIRO's Data 61 and understands the daunting potential of the technology that powers deepfake videos.

We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the correlations between the observed output, are all time dependent. We perform posterior inference via Hamiltonian Monte Carlo (HMC). We also provide methods for obtaining computationally efficient gradient-based maximum a posteriori (MAP) estimates. We validate our model on synthetic data as well as on electronic health records (EHR) data from Kaiser Permanente (KP). We show that the proposed model provides better predictive performance over a stationary model as well as uncovers interesting latent correlation processes across vitals which are potentially predictive of patient risk.

Chris discusses how rule-based systems are moving to learning-based systems in various enterprise use cases. Sramana Mitra: Let's start by having you introduce yourself and Genesys and what work you're doing around artificial intelligence. Christopher Connolly: I am the Vice President of our Solutions Strategy Group. Genesys is the number one customer experience platform. It enables companies to create exceptional omni-channel experiences and relationships.

"It's been a surprise that the Russians, the GRU, this unit, have felt free to go ahead and carry out this extreme malign activity … That's been a shock," the New York Times quotes an unnamed European security official as saying. Western intelligence agencies only recently became aware of this Russian covert operations unit, according to reports. This is despite Unit 29155 agents engaging in espionage activities for more than a decade. But the pieces have begun to fall into place. And the evidence reveals a Kremlin campaign to convince its people that their troubled nation is back on the path to "greatness" -- all while undermining the Western liberal democratic notion of "rules-based order".

Hummingbird Technologies are a world-leading AI and machine learning business in the crop analysis space. We consolidate data from drones, planes and satellites and deliver value-driven actionable insights for farmers, agronomists and food companies. The Hummingbird platform delivers greater insight into crop health and yield potential with a range of crop-specific AI tools for earlier disease identification, optimum nutrient management, detailed plant counting, crop development modelling and yield prediction. Backed by Sir James Dyson, the European Space Agency, BASF and some of the leading tech VC and large agro businesses, Hummingbird have operations in UK, Brazil, Russia, Ukraine, Australia and North America.

Predicting discomfort glare in open-plan offices is a challenging problem since most of available glare metrics are developed for cellular offices which are typically daylight dominated. The problem with open-plan offices is that they are mainly dependent on electric lighting rather than daylight even when they have a fully glazed facade. In addition, the contrast between bright windows and the buildings interior can be problematic and may cause discomfort glare to the building occupants. These problems can affect occupant productivity and wellbeing. Thus, it is important to develop a predictive model to avoid discomfort glare when designing open plan offices. To the best of our knowledge, we are the first to adopt Machine Learning (ML) models to predict discomfort glare. In order to develop new glare predictive models for these types of offices, Post-Occupancy Evaluation (POE) and High Dynamic Range (HDR) images were collected from 80 occupants (n=80) in four different open-plan offices. Consequently, various multi-region luminance values, luminance and glare indices were calculated and used as input features to train ML models. The accuracy of the ML model was compared to the accuracy of 24 indices which were also evaluated using a Receiver Operating Characteristic (ROC) analysis to identify the best cutoff values (thresholds) for each index for open-plan configurations. Results showed that the ML glare model could predict glare in open-plan offices with an accuracy of 83.8% (0.80 true positive rate and 0.86 true negative rate) which outperformed the accuracy of the previously developed glare metrics.

Non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson-Gamma models are able to derive full posterior distributions of latent factors and are less sensitive to sparse count data. However, current inference methods for these Bayesian models adopt restricted update rules for the posterior parameters. They also fail to share the update information to better cope with the data sparsity. Moreover, these models are not endowed with a component that handles the imbalance in count data values. In this paper, we propose a novel variational auto-encoder framework called VAE-BPTF which addresses the above issues. It uses multi-layer perceptron networks to encode and share complex update information. The encoded information is then reweighted per data instance to penalize common data values before aggregated to compute the posterior parameters for the latent factors. Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values. It also outperformed current models in both reconstruction errors and latent factor (semantic) coherence across five real-world datasets. Furthermore, the latent factors inferred by VAE-BPTF are perceived to be meaningful and coherent under a qualitative analysis.

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications in parameter tuning, experimental design, robotics, and so on. BO usually models the objective function by a Gaussian process (GP), and iteratively samples the next data point by maximizing some acquisition function. In this paper, we propose a new general framework for BO by generating pseudo-points (i.e., data points whose objective values are not evaluated) to improve the GP model. With the classic acquisition function, i.e., upper confidence bound (UCB), we prove a general bound on the cumulative regret, and show that the generation of pseudo-points can improve the instantaneous regret. Experiments using UCB and other acquisition functions, i.e., probability of improvement (PI) and expectation of improvement (EI), on synthetic as well as real-world problems clearly show the advantage of generating pseudo-points.