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Melbourne, AU, Sept 2019 – Companies are now embracing Artificial Intelligence (A.I.), not just a tool to improve service efficiency but as means to forge a deeper relationship with customers. It is now used to augment processes across the business value chain, resulting in increased productivity and more informed and effective decision making. There is however still a space for Human Intelligence – H.I. In the context of Conversation Analytics, A.I. is deployed to allow us to do things quicker, faster and smarter. Take Quality Assurance (QA) as an example – long the bastion of QA staff listening to calls to assess risk, misconduct, Customer Experience (CX) opportunities and missed sales. Using this approach, most QA functions in a business AT BEST, listen to and assess 1% of their customer interactions.

V arious factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which suffer from two drawbacks. First, they have to make a tradeoff between the expressiveness of higher-order cross features and the computational cost, resulting in suboptimal predictions. Second, enumerating all the cross features, including irrelevant ones, may introduce noisy feature combinations that degrade model performance. In this work, we propose the Adaptive Factorization Network (AFN), a new model that learns arbitrary-order cross features adaptively from data. The core of AFN is a logarithmic transformation layer to convert the power of each feature in a feature combination into the coefficient to be learned. The experimental results on four real datasets demonstrate the superior predictive performance of AFN against the start-of-the-arts. 1 Introduction Feature engineering is typically recognized as central to successful machine learning tasks, such as recommender systems (Lian et al. 2017), computational advertising (He et al. 2014) and search ranking (Lian and Xie 2016). Except for exploiting raw features, it is usually crucial to find effective transformations of raw features to boost the performance of predictive models. Cross features are a major type of feature transformations, where multiplication is performed over sparse raw features to form new features (Cheng et al. 2016). However, handcrafting useful cross features is inevitably expensive and time-consuming, and the results may not generalize to unseen feature interactions.

The same artificial intelligence (AI) drone technology that the Little Ripper Group used for its shark detection drones is now being used to spot crocodiles in Queensland. Little Ripper Group co-founder Paul Scully-Power said the company was approached by the Queensland government to help keep beachgoers safe in the water and on land from crocodiles. "The Queensland government said, 'Hey do we have a challenge for you and asked can you spot crocodiles for us?' Crocodiles are slinky people that like dark, muddy water, so we took on that challenge," he said. The launch of the crocodile-spotting drones follows on from a trial that was carried out between Surf Life Saving Queensland and the Little Ripper Group to identify, monitor, and track the movement of crocodiles in November. The drone technology, dubbed the Little Ripper and designed together with the University of Technology Sydney, uses an AI system that was originally designed to detect sharks in real-time.

Australian workers could end up thousands of dollars richer each year by quitting their jobs and reskilling to enter the technology industry, new research has revealed. The nation is to poised to undergo a tech jobs boom over the next five years, a report launched by Treasurer Josh Frydenberg on Thursday claimed. The news comes as Australia's economy goes from bad to worse, posting the slowest annual growth since the year 2000, with the prospect of a jobs boom offering a sliver of hope for workers frustrated by continuing wage stagnation. An estimated 100,000 new information technology (IT) roles will be created by 2024, bringing the total to about 792,000, the report titled Australia's Digital Pulse 2019 and commissioned by the Australian Computer Society (ACS) said. While reskilling into the IT industry could give the average Australian worker an $11,000 salary increase, the nation is likely to struggle to find workers with the skills to meet the oncoming tech jobs tsunami, the report warned.

If a picture is worth a thousand words, but still missing valuable location data, then why not use artificial intelligence (AI) and machine learning (ML) to fill in the gaps? This graphic below shows vast data sets containing buildings, green spaces, roads to travel, and parking lots. Drill down even further and see rooftops, solar panels, fire hydrants, gas lines, and many other objects. And all this data constantly changes over time. City residents move, purchase new developments for their homes, drive roads with new potholes, and build new construction.

Companies around the world are expecting to apply artificial intelligence (AI) within their organisations in the next few years but are lagging in discussions of the ethics around it, new research has found. More than half of the employers questioned in a multi-country opinion survey say their companies do not currently have a written policy on the ethical use of AI or bots, although 21% expressed a definite concern regarding their companies and a potential for the unethical use of AI. Nearly two-thirds (64%) of the employers surveyed expect their companies to be using AI or advanced automation by 2022 to support efficiency in operations, staffing, budgeting or performance, although only 25% are using it now. Yet in spite of this growing trend, 54% of employers questioned say they are not troubled that AI could be used unethically by their companies as a whole or by individual employees (52%). Employees appear more relaxed than their bosses, with only 17% expressing concern about their companies.

Chi Thang Duong 1 Hongzhi Yin 2 Thanh Dat Hoang 3 Truong Giang Le Ba 3 Matthias Weidlich 4 Quoc Viet Hung Nguyen 5 Karl Aberer 1 1 EPFL 2 The University of Queensland 3 HUST 5 Griffith University 4 Humboldt-Universit at zu Berlin Abstract Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints. We show how to distribute any existing embedding technique by first splitting a graph for any given set of constrained compute nodes and then reconciling the embedding spaces derived for these sub-graphs. We also propose a new way to evaluate the quality of graph embeddings that is independent of a specific inference task. Based thereon, we give a formal bound on the difference between the embeddings derived by centralised and parallel computation. Experimental results illustrate that our approach for parallel computation scales well, while largely maintaining the embedding quality. 1 Introduction Graphs are a natural representation of relations between entities in complex systems, such as social networks or information networks. To enable inference on graphs, a graph embedding may be learned.

Lisa Cornish is a Devex Reporter based in Canberra, where she focuses on the Australian aid community. Lisa formerly worked with News Corp Australia as a data journalist for the national network and was published throughout Australia in major metropolitan and regional newspapers, including the Daily Telegraph in Melbourne, Herald Sun in Melbourne, Courier-Mail in Brisbane, and online through news.com.au. Lisa was awarded the 2014 Journalist of the Year by the New South Wales Institute of Surveyors.

The Cacophony Project's broad vision is to bring back New Zealand's native birds using the latest technology to monitor bird populations and humanely eliminate the introduced predators that are endangering them. The project started in our founder's backyard to measure the effectiveness of his efforts to protect the birds on his property. From this simple beginning, the project has quickly grown into a system that includes two edge devices, a cloud server, and automatic identification of animals using machine learning. The project has been completely open source from the beginning and sees regular contributions from a wide variety of volunteers. In New Zealand, our birds are our taonga, our precious things.