Since 2016, 4Tel Pty Ltd of Newcastle, Australia, has been investing in the development of artificial intelligence for application in the rail industry generally. As a part of this activity, 4Tel has a research and development contract with the University of Newcastle Robotics Laboratory known as NUBots, where 4Tel is their Platinum Sponsor. The work is being conducted under Project HORUS, which seeks to develop an Advanced Driver Advisory System (ADAS) using real-time sensors and software to assist a driver in the safe operation of a locomotive. As the technical basis to this work, 4Tel has selectively applied modern autonomous car technology to achieve very sophisticated artificial, intelligence based, ADAS functionality. For safe and efficient operations, a locomotive needs to know exactly where it is, recognise the objects around it, and continuously monitor the authorised route for normal operations.

Since 2016, 4Tel Pty Ltd of Newcastle, Australia, has been investing in the development of artificial intelligence for application in the rail industry generally. As a part of this activity, 4Tel has a research and development contract with the University of Newcastle Robotics Laboratory known as NUBots, where 4Tel is their Platinum Sponsor. The work is being conducted under Project HORUS, which seeks to develop an Advanced Driver Advisory System (ADAS) using real-time sensors and software to assist a driver in the safe operation of a locomotive. As the technical basis to this work, 4Tel has selectively applied modern autonomous car technology to achieve very sophisticated artificial, intelligence based, ADAS functionality. For safe and efficient operations, a locomotive needs to know exactly where it is, recognise the objects around it, and continuously monitor the authorised route for normal operations.

This paper proposes interpretable image synthesis by learning hierarchical AND-OR networks of sparsely connected semantically meaningful nodes. The proposed method is based on the compositionality and interpretability of scene-objects-parts-subparts-primitives hierarchy in image representation. A scene has different types (i.e., OR) each of which consists of a number of objects (i.e., AND). This can be recursively formulated across the scene-objects-parts-subparts hierarchy and is terminated at the primitive level (e.g., Gabor wavelets-like basis). To realize this interpretable AND-OR hierarchy in image synthesis, the proposed method consists of two components: (i) Each layer of the hierarchy is represented by an over-completed set of basis functions. The basis functions are instantiated using convolution to be translation covariant. Off-the-shelf convolutional neural architectures are then exploited to implement the hierarchy. (ii) Sparsity-inducing constraints are introduced in end-to-end training, which facilitate a sparsely connected AND-OR network to emerge from initially densely connected convolutional neural networks. A straightforward sparsity-inducing constraint is utilized, that is to only allow the top-$k$ basis functions to be active at each layer (where $k$ is a hyperparameter). The learned basis functions are also capable of image reconstruction to explain away input images. In experiments, the proposed method is tested on five benchmark datasets. The results show that meaningful and interpretable hierarchical representations are learned with better qualities of image synthesis and reconstruction obtained than state-of-the-art baselines.

Global Artificial Intelligence (AI) for Telecommunication Industry valued approximately USD 651.2 million in 2017 is anticipated to grow with a healthy growth rate of more than 42% over the forecast period 2019-2025. The Artificial Intelligence (AI) for Telecommunication Industry is continuously growing in the global scenario at significant pace. Artificial intelligence (AI) is group of methodology that focus on formation of intelligent machines with the help of human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. The main application of artificial intelligence in telecommunications is for network management. The two key technologies that are widely in telecommunication industry are expert systems and machine learning.

Sampling-based planners are the predominant motion planning paradigm for robots. Majority of sampling-based planners use a global random sampling scheme to guarantee completeness. However, these schemes are sample inefficient as the majority of the samples are wasted in narrow passages. Consequently, information about the local structure is neglected. Local sampling-based motion planners, on the other hand, take sequential decisions of random walks to samples valid trajectories in configuration space. However, current approaches do not adapt their strategies according to the success and failures of past samples. In this work, we introduce a local sampling-based motion planner with a Bayesian update scheme for modelling a sampling proposal distribution. The proposal distribution is sequentially updated based on previous sample outcomes, consequently shaping the proposal distribution according to local obstacles and constraints in the configuration space. Thus, through learning from past observed outcomes, we can maximise the likelihood of sampling in regions that have a higher probability to form trajectories within narrow passages.

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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.