Kaggle is the most well known competition platform for predictive modeling and analytics. The company was founded in 2010 in Melbourne, Australia, and a year later, it moved to San Francisco after receiving funding from Silicon Valley. In 2017, it was acquired by Google. The term "data science" has gradually floated and elevated into the English lexicon over the past decade. And so, the words "data science" and "kaggle" have become inextricably linked, and many in the data science community contemplate and debate the utility of the platform: Like many people, I had some preconceived notions about Kaggle competitions.

The eyes really are a window to the soul. The way they move can reveal your personality type – a finding that could help robots better understand and interact with humans. Psychologists have long believed that personality influences the way we visually take in the world. Curious people tend to look around more and open-minded people gaze longer at abstract images, for example.

In December 2017, Amazon, JPMorgan Chase and Berkshire Hathaway announced the formation of a new healthcare company which would use technology to provide high-quality healthcare to patients and families more simply, and at a more reasonable cost. This move and rumors of entry into pharmaceutical distribution shook up stock prices for more established healthcare companies and pharmacy chains. Amazon's entry into healthcare is intriguing because medicine is ripe for disruption. In 2016, U.S. per-person healthcare expenses were $10,348, more than double that of other first-world countries that offer universal health coverage ($4,752 in Canada, $4,600 in France, $4,708 in Australia, and $4,192 in the UK). Despite these costs, U.S. medical care is not altogether accurate or safe; medical errors kill more Americans annually than AIDS and motor vehicle incidents.

Bayesian inference provides a principled approach towards uncertainty quantification of free parameters in geophysical forward models. This provides advantages over optimization methods that provide single point estimates as solutions, which lack uncertainty quantification. Badlands (basin and landscape dynamics model) is geophysical forward model that simulates topography development at various space and time scales. Badlands consists of a number of geophysical parameters that need to be estimated with appropriate uncertainty quantification, given the observed ground truth such as surface topography, sediment thickness and stratigraphy through time. This is challenging due to the scarcity of data, sensitivity of the parameters and complexity of the Badlands model. In this paper, we take a Bayesian approach to provide inference using Markov chain Monte Carlo sampling (MCMC). Hence, we present \textit{BayesLands}, a Bayesian framework for Badlands that fuses information obtained from complex forward models with observational data and prior knowledge. As a proof-of-concept, we consider a synthetic and real-world topography with two free parameters, namely precipitation and erodibility, that we need to estimate through BayesLands. The results of the experiments shows that BayesLands yields a promising distribution of the parameters. Moreover, the challenge in sampling due to multi-modality is presented through visualizing a likelihood surface that has a range of suboptimal modes.

Victoria has threatened to pull out of a state and federal government agreement for the home affairs department to run a facial recognition system because the bill expands Peter Dutton's powers and allows access to information by the private sector and local governments. In October the Council of Australia Governments agreed to give federal and state police real-time access to passport, visa, citizenship and driver's licence images for a wide range of criminal investigations. The identity matching services bill, introduced in February, enables the home affairs department to collect, use and disclose identification information including facial biometric matching. In a submission to the parliamentary joint committee on intelligence and security, the Victorian special minister of state, Gavin Jennings, warned that the bill provided "significant scope" for the home affairs minister to expand his powers beyond what was agreed. This includes the ability to collect new types of identification information and expand identity matching services.

The company rolling out Australia's National Broadband Network (NBN) has announced entering three-year research and development (R&D) partnerships with the University of Technology Sydney (UTS) and the University of Melbourne. Under what it called "major collaborative relationships", NBN said it would work with the two universities on Internet of Things (IoT), robotics, artificial intelligence (AI), smart cities, programmable networks, data analytics and visualisation, wireless technologies, and "technology for social good" R&D projects. "These two new relationships will help NBN Co double down on our strong focus on technology innovation for customer experience and operational excellence," NBN CTO Ray Owen explained. "With these innovative institutions -- UoM and UTS -- we saw a natural fit in helping NBN Co further enable the digital economy." NBN added that the agreements are also expected to cover opportunities such as "student exchanges" and post-doctoral research collaboration by giving the universities "access to real-world telecoms network operational data".

Journal of Parallel and Distributed Computing 104, 19-35.

Are New Zealand road users – and laws – ready for driverless cars? The question has major implications for not just road users - be they drivers, cyclists or pedestrians - but also for police, parking wardens, councils and the people planning and designing parking spaces, towns, cities and roads. A new study funded by the Law Foundation, Realising the potential of driverless vehicles for New Zealand, has investigated the need for legal reforms to cope with driverless vehicles here. "By almost universal consensus, driverless vehicles are coming and represent as big a disruption to the transport sector as the replacement of horses with the automobile over a hundred years ago," study author Michael Cameron said. As part of his research, Mr Cameron went to the US, Europe, Singapore and Australia reviewing international laws and visiting some of the big players designing driverless cars.

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.

In the world of technology, the mantra "innovate or die" is truer for organizations than ever, and artificial intelligence (AI) is redefining industries by providing greater personalization to users, automating processes, and disrupting how we work. Like the adoption of cloud computing five years ago, the adoption of AI and the speed of its deployment varies according to industry. Here we look at some of the places where dispution from AI is already being felt. Alexey Sapozhnikov, co-dounder and CTO of Tel Aviv, Israel-based prooV points out that while virtually every industry is embracing AI, it's the sectors that are stymied by well-worn processes and regulations -- such as healthcare and government -- that are likely to lag in AI adoption. "From the Food and Drug Administration's stringent policies surrounding AI diagnosis software to developing complex proposals for government cybersecurity challenges, these processes can pose a huge stumbling block for organizations.