InteliCare has been awarded a $100,000 grant from the New South Wales Smart Sensing Network ("NSSN") to develop its machine learning (ML) capability in conjunction with the University of Sydney (USyd) and Macquarie University (MU). The company is negotiating an agreement with USyd, MU and the NSSN to use these funds to help fund a one-year joint project delivered by the universities' Computer Science Departments. The goal is to build ML algorithms that can predict and prevent chronic disease and mental health deterioration that can lead to a loss of independence and an increased risk of injury. In addition to the NSSN funds, InteliCare will provide a co-contribution of $152,898 in cash and the universities will provide $161,021 of in-kind support. Ongoing development beyond the initial project will require the company to budget from working capital.

The increased digitalisation and monitoring of the energy system opens up numerous opportunities % and solutions which can help to decarbonise the energy system. Applications on low voltage (LV), localised networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known LV level open datasets to encourage further research and development.

Understanding the structure of loss landscape of deep neural networks (DNNs) is obviously important. In this work, we prove an embedding principle that the loss landscape of a DNN "contains" all the critical points of all the narrower DNNs. More precisely, we propose a critical embedding such that any critical point, e.g., local or global minima, of a narrower DNN can be embedded to a critical point/hyperplane of the target DNN with higher degeneracy and preserving the DNN output function. The embedding structure of critical points is independent of loss function and training data, showing a stark difference from other nonconvex problems such as protein-folding. Empirically, we find that a wide DNN is often attracted by highly-degenerate critical points that are embedded from narrow DNNs. The embedding principle provides an explanation for the general easy optimization of wide DNNs and unravels a potential implicit low-complexity regularization during the training. Overall, our work provides a skeleton for the study of loss landscape of DNNs and its implication, by which a more exact and comprehensive understanding can be anticipated in the near future.

Automatically tuning software configuration for optimizing a single performance attribute (e.g., minimizing latency) is not trivial, due to the nature of the configuration systems (e.g., complex landscape and expensive measurement). To deal with the problem, existing work has been focusing on developing various effective optimizers. However, a prominent issue that all these optimizers need to take care of is how to avoid the search being trapped in local optima -- a hard nut to crack for software configuration tuning due to its rugged and sparse landscape, and neighboring configurations tending to behave very differently. Overcoming such in an expensive measurement setting is even more challenging. In this paper, we take a different perspective to tackle this issue. Instead of focusing on improving the optimizer, we work on the level of optimization model. We do this by proposing a meta multi-objectivization model (MMO) that considers an auxiliary performance objective (e.g., throughput in addition to latency). What makes this model unique is that we do not optimize the auxiliary performance objective, but rather use it to make similarly-performing while different configurations less comparable (i.e. Pareto nondominated to each other), thus preventing the search from being trapped in local optima. Experiments on eight real-world software systems/environments with diverse performance attributes reveal that our MMO model is statistically more effective than state-of-the-art single-objective counterparts in overcoming local optima (up to 42% gain), while using as low as 24% of their measurements to achieve the same (or better) performance result.

Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types. In real-world applications, ED typically does not have sufficient labelled data, thus can be formulated as a few-shot learning problem. To tackle the issue of low sample diversity in few-shot ED, we propose a novel knowledge-based few-shot event detection method which uses a definition-based encoder to introduce external event knowledge as the knowledge prior of event types. Furthermore, as external knowledge typically provides limited and imperfect coverage of event types, we introduce an adaptive knowledge-enhanced Bayesian meta-learning method to dynamically adjust the knowledge prior of event types. Experiments show our method consistently and substantially outperforms a number of baselines by at least 15 absolute F1 points under the same few-shot settings.

Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. However, they fail to fully utilize the structure information of the input graph. In this paper, we aim to further improve the performance of the pre-trained language model by proposing a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes the model on Wikipedia before adapting to the graph-to-text generation. In addition to using the traditional token and position embeddings to encode the knowledge graph (KG), we propose a novel tree-level embedding method to capture the inter-dependency structures of the input graph. This new approach has significantly improved the performance of all text generation metrics for the English WebNLG 2017 dataset.

There are 15 Chapters to display the Global Artificial Intelligence in Platform as a Service (PaaS) Market Chapter 1, Overview to describe Definition, Specifications and Classification of Global Artificial Intelligence in Platform as a Service (PaaS) market, Applications [SME & Large Enterprises], Market Segment by Types, Machine Learning Platform, Natural Language Processing Service, Visual Analysis Service, Language Processing Service & Data Insight Service; Chapter 2, objective of the study.

GlobalData has identified ten of the biggest influencers in AI on Twitter during Q3 2019, using its Influencer Platform. GlobalData research has found the top AI influencers based on their performance and engagement online. Using research from GlobalData's Influencer platform, Verdict has named ten of the most influential people in AI on Twitter during Q3 2019. Ronald van Loon is a Big Data expert and Director at Advertisement, a data and analytics consultancy firm. He helps data-driven companies in executing data and analytics strategies to become more successful.

Technological advancements have been influencing our lives for quite some time already and still continue to do the same. Lately, the role and importance of Artificial Intelligence (AI) have risen at significant heights. It has an impact on every industry and every single aspect of our lives. Australia, as well as other countries all around the world, is facing challenges that are not easy to overcome, for example, management of natural hazards, aging population, health, infrastructure, etc, and surprisingly or not, AI has the ability to provide them with the proper help. The term Artificial Intelligence has been used to describe the technique that analyzes the threat or challenge and helps us to solve them.

"Together they created Appen, arguably one of the greatest IT success stories in Australia," said University of Sydney Vice-Chancellor and Principal Professor Stephen Garton AM. "Their company was founded long before artificial intelligence became fashionable and is a testament to the foresight of the Vonwillers." Honorary degrees are awarded to individuals who have made an outstanding contribution to the wider community or who have achieved exceptional academic or creative excellence. Husband and wife team Chris and Julia Vonwiller have been admitted to the degree of Doctor of Engineering (honoris causa). Dr Vonwiller is a respected linguist who studied at Macquarie University and graduated in 1980 with a Bachelor of Arts (Honours). She completed her PhD in linguistics at Macquarie University in 1989 before working as a researcher at the University of Sydney.