As travelers take to the beaches in Australia in the summer, they'll now have the benefit of drones to warn them if a shark is close to shore. A link has been posted to your Facebook feed. As travelers take to the beaches in Australia in the summer, they'll now have the benefit of drones to warn them if a shark is close to shore.

A classification algorithm, called the Linear Centralization Classifier (LCC), is introduced. The algorithm seeks to find a transformation that best maps instances from the feature space to a space where they concentrate towards the center of their own classes, while maximimizing the distance between class centers. We formulate the classifier as a quadratic program with quadratic constraints. We then simplify this formulation to a linear program that can be solved effectively using a linear programming solver (e.g., simplex-dual). We extend the formulation for LCC to enable the use of kernel functions for non-linear classification applications. We compare our method with two standard classification methods (support vector machine and linear discriminant analysis) and four state-of-the-art classification methods when they are applied to eight standard classification datasets. Our experimental results show that LCC is able to classify instances more accurately (based on the area under the receiver operating characteristic) in comparison to other tested methods on the chosen datasets. We also report the results for LCC with a particular kernel to solve for synthetic non-linear classification problems.

Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL techniques within tree search has not been thoroughly studied yet. In this paper we re-examine in depth this close relation between the two fields; our goal is to improve the cross-awareness between the two communities. We show that a straightforward adaptation of RL semantics within tree search can lead to a wealth of new algorithms, for which the traditional MCTS is only one of the variants. We confirm that planning methods inspired by RL in conjunction with online search demonstrate encouraging results on several classic board games and in arcade video game competitions, where our algorithm recently ranked first. Our study promotes a unified view of learning, planning, and search.

As a result, there is a need to change the way they operate to succeed across processes, people and systems. A sign of any good business is the provision of excellent customer service; it is vital to listen and respond to the customer's direct needs. In the field of technology, this has meant a shift change in what is delivered. Enterprise technology has drastically transformed in the last five years and will continue to do so as we enter 2018. With an increasing number of organisations adopting the likes of bots, digital assistants, IoT, artificial intelligence and machine learning in order to boost productivity amongst staff, achieving exemplary customer service in a timely manner will be a must.

Good advice on how NOT to be an academic when you finish your PhD is pretty thin on the ground. Many supervisors have never done anything else, and/or are not well enough connected with industry to know what is'hot'. Careers centres at universities tend to shape their offerings around the huge undergraduate cohort, who have very different needs. If you want to leave academia at the end of you PhD it's likely you will face some kind of career transition. While we train astrophysicists, we don't have any astrophysics companies in Australia.

While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we propose a modular deep reinforcement learning method capable of transferring models trained in simulation to a real-world robotic task. We introduce a bottleneck between perception and control, enabling the networks to be trained independently, but then merged and fine-tuned in an end-to-end manner to further improve hand-eye coordination. On a canonical, planar visually-guided robot reaching task a fine-tuned accuracy of 1.6 pixels is achieved, a significant improvement over naive transfer (17.5 pixels), showing the potential for more complicated and broader applications. Our method provides a technique for more efficient learning and transfer of visuo-motor policies for real robotic systems without relying entirely on large real-world robot datasets.

There are several aspects of the R language that make it hard to learn, and repeating a model for groups in a data set used to be one of them. Here I briefly describe R's built-in approach, show a much easier one, then refer you to a new approach described in the superb book, R for Data Science, by Hadley Wickham and Garrett Grolemund. The gapminder data set contains a few measurements for countries around the world every five years from 1952 through 2007. Let's create a simple regression model to predict life expectancy from year. We'll start by looking at just New Zealand.

An Australian man was taken into custody Saturday for allegedly acting as an economic agent for North Korea and attempting to sell missile parts, military intelligence and coal on the black market. The Australian Federal Police arrested Chan Han Choi, 59, in Sydney and charged him with brokering sales of weapons of mass destruction, according to the Australian Broadcasting Corporation. It is the first time a charge of this kind has been leveled against anyone in Australia. The sales would violate Australian and United Nations sanctions. "We believe this man participated in discussions about the sale of missile componentry from North Korea to other entities abroad as another attempt to try and raise revenue for the government in North Korea, again in breach of the sanctions," said Australian Federal Police Assistant Commissioner Neil Gaughan in a statement.

Neural Networks is one of Australia's leading innovators in wholesale cloud, communications and data services. Since 2007 we've been providing some of the highest quality services available in our industry and have gone from strength to strength ever since.

The first Institute for Machine Learning will be implemented at the former Royal Adelaide Hospital site. The institute will build on world-leading research in machine learning and artificial intelligence, helping to deliver what is known as the Fourth Industrial Revolution -- the transformation of the global economy through artificial intelligence. The University of Adelaide has signed a long-term lease which will see the old Women's Health Centre used for the site. The facility will become home to more than 200 creative thinkers, researchers and start-ups attracting the world's best and brightest to come to work and study in South Australia. "The University of Adelaide is already a world leader with the Australian Centre for Visual Technologies, and the transition to this new institute will build further on the work being done as well as Adelaide's reputation as an incubator of innovation," said Science and Information Economy Minister Kyam Maher.