We introduce a large scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.

The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires a diverse collection of time series benchmarking data to enable reliable comparisons against alternative approaches. We propose GeneRAting TIme Series with diverse and controllable characteristics, named GRATIS, with the use of mixture autoregressive (MAR) models. We generate sets of time series using MAR models and investigate the diversity and coverage of the generated time series in a time series feature space. By tuning the parameters of the MAR models, GRATIS is also able to efficiently generate new time series with controllable features. In general, as a costless surrogate to the traditional data collection approach, GRATIS can be used as an evaluation tool for tasks such as time series forecasting and classification. We illustrate the usefulness of our time series generation process through a time series forecasting application.

The people of Australia have spoken: they'll only back tech that respects human rights. Developing an AI that supports liberal values could be the best way to gain a competitive advantage.

Too often we're told that if Australia is to compete globally in developing AI products, Australian researchers and companies must not be fettered by human rights concerns, because other countries certainly aren't. China, for example, is investing heavily in AI technology such as facial recognition to support its "social credit score" system, which involves conducting precise and determinative surveillance of its citizens. In the context of a global AI arms race, it is argued, Australia can't compete with one arm tied behind its back.

The costs associated with information technology products and services in Australia are expected to reach A$93 billion in 2019, while 28 per cent of spending within key enterprise IT markets globally is expected to shift to the cloud by 2022. Developments in cloud computing and Artificial Intelligence(AI) have changed what's possible in regard to software services which can be provided to organisations. Each year, solutions powered by the cloud and AI are deployed by enterprise businesses to improve the efficiency of business operations and the productivity of employees. Business communication methods are one area that has and will continue to benefit greatly from improvements in cloud technology and AI. This advance in technology is allowing businesses to seek quicker and more simple communication in an increasingly mobile world.

Could a program detect potential terrorists by reading their facial expressions and behavior? This was the hypothesis put to the test by the US Transportation Security Administration (TSA) in 2003, as it began testing a new surveillance program called the Screening of Passengers by Observation Techniques program, or Spot for short. While developing the program, they consulted Paul Ekman, emeritus professor of psychology at the University of California, San Francisco. Decades earlier, Ekman had developed a method to identify minute facial expressions and map them on to corresponding emotions. This method was used to train "behavior detection officers" to scan faces for signs of deception.

You are head of talent acquisition. You're about to make contact with what recruiters call a cold prospect, someone not actively looking for a job. You know this because their CV isn't on any job boards. Neither have they registered with LinkedIn's Open Candidate tool, which basically says "call me". And yet far from this being the shot in the dark it used to be, you're calling safe in the knowledge that the recipient will be seven times more likely to be interested in what you say.

The growing capabilities of Artificial Intelligence (AI) are changing the world as we know it. Ideas once confined to the imagination are now becoming a reality, with AI technology creating outputs either largely or entirely independent from human intervention. In 2018, an album called I AM AI was the first of its kind to be entirely composed and produced by AI technology, through a music composition software called Amper. Deep learning networks allow Amper to analyse data to learn chords, notes, genres, tempo and song length to independently compose melodies. A qualified person is an Australian citizen or a person resident in Australia.[1]

Gaussian Belief Propagation (BP) algorithm is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network in a finite number of iterations when the underlying Gaussian graph is acyclic. It is also known more recently that the algorithm produces correct marginal means asymptotically for cyclic Gaussian graphs under the condition of walk summability. This paper extends this convergence result further by showing that the convergence is exponential under the walk summability condition, and provides a simple bound for the convergence rate.

In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the above paper, we developed the AI Clinician system, which demonstrated how reinforcement learning could be used to make useful recommendations towards optimal treatment decisions from intensive care data. Since publication a number of authors have reviewed our work (e.g. Given the difference of our framework to previous work, the fact that we are bridging two very different academic communities (intensive care and machine learning) and that our work has impact on a number of other areas with more traditional computer-based approaches (biosignal processing and control, biomedical engineering), we are providing here additional details on our recent publication. We acknowledge the online comments by Jeter et al (https://arxiv.org/abs/1902.03271). The sections of the present document are structured so as to address some of their questions. For clarity, we label figures from our main Nature Medicine publication as "M", figures from Jeter et al.'s arXiv paper as "J" and figures from our response here as "R". Jeter et al. state "the only possible response we can afford is a more aggressive and open dialogue".