Dark matter in the universe evolves through gravity to form a complex network of halos, filaments, sheets and voids, that is known as the cosmic web. Computational models of the underlying physical processes, such as classical N-body simulations, are extremely resource intensive, as they track the action of gravity in an expanding universe using billions of particles as tracers of the cosmic matter distribution. Therefore, upcoming cosmology experiments will face a computational bottleneck that may limit the exploitation of their full scientific potential. To address this challenge, we demonstrate the application of a machine learning technique called Generative Adversarial Networks (GAN) to learn models that can efficiently generate new, physically realistic realizations of the cosmic web. Our training set is a small, representative sample of 2D image snapshots from N-body simulations of size 500 and 100 Mpc. We show that the GAN-produced results are qualitatively and quantitatively very similar to the originals. Generation of a new cosmic web realization with a GAN takes a fraction of a second, compared to the many hours needed by the N-body technique. We anticipate that GANs will therefore play an important role in providing extremely fast and precise simulations of cosmic web in the era of large cosmological surveys, such as Euclid and LSST.

"This is something that can provide us with huge opportunities within our industry development. "Yes, people will be fearful of it, but we should be learning to embrace that change." The group, modelled on the UK version, will examine the economic and social effects of artificial intelligence.

This paper presents a new multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We propose linear and non-linear methods to develop the MODRL framework that includes both single-policy and multi-policy strategies. The experimental results on a deep sea treasure environment indicate that the proposed approach is able to converge to the optimal Pareto solutions. The proposed framework is generic, which allows implementation of different deep reinforcement learning algorithms in various complex environments. Details of the framework implementation can be referred to http://www.deakin.edu.au/~thanhthi/drl.htm.

A Netflix-style recommendation engine on steroids, a Google AdWords beater and a souped-up game simulator are among a number of machine learning models at work behind the scenes at online bookmaker Sportsbet, the company has revealed. "Our goal is to improve the customer experience at every customer interaction," said Tony Gruebner, the company's general manager, analytics, insights and modelling. Speaking at the Gartner Data and Analytics Summit in Sydney last week, Gruebner explained: "One of the best ways of doing that is to build great data products utilising the company's wealth of data – which is exponentially growing – and also machine learning and artificial intelligence capabilities." Sportsbet helped make gambling mainstream in Australia, was bought out by Paddy Power in 2010 and quickly became the country's biggest corporate online bookmaker, a title it still holds. It makes around 1.7 billion price updates, offers punters 11 million markets and takes 240 million bets every year.

This list of Data Science tools for people who aren't so good at Programming was compiled by Aarshay Jain, from Analytics Vidhya. Programming is an integral part of data science. Among other things, it is considered that a mind which understands programming logic, loops, and functions has higher chances of becoming a successful data scientist. So, what about people who never studied programming subject in their school or college? RM covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment.

Four out of five employees see significant opportunity for artificial intelligence (AI) to create a more engaging and empowering workplace experience, yet employees admit a lack of transparency from their employers is a primary driver of fear and concern. This is according to a worldwide survey of nearly 3,000 employees across eight nations conducted by The Workforce Institute at Kronos Incorporated. "The Engaging Opportunity: Working Smarter with AI" survey conducted with Coleman Parkes Research explores how employees – both hourly and salaried from a variety of industries in Australia, Canada, France, Germany, Mexico, New Zealand, the United Kingdom and the U.S. – believe emerging technologies should be used to improve the future of work. Employees around the world say they will embrace AI to make work easier and fairer. A lack of communication leaves employees feeling apprehensive.

To remain livable and economically competitive, rapidly growing cities need to embrace high-tech solutions to solve their many practical problems. However, how willing are citizens to sacrifice their privacy for the benefits of smart cities, and can government regulations keep up with new tech? This places a significant burden on vital infrastructure, such as transport, housing, energy supply, health care and waste management. Livability in the megacities of tomorrow will largely be determined by the smart solutions being developed today. The term "smart city" is popular among policymakers worldwide.

Humans cannot see, smell or taste airborne asbestos fibers. Identifying them through a microscope requires the eye of a trained analyst -- but perhaps not for long. Australian engineer Jordan Gruber is working on technology that can automatically detect asbestos from the air around a worksite. Exposure to airborne asbestos fibers is the primary cause of mesothelioma, an aggressive form of cancer. The past use of asbestos in building materials has led to great suffering among Americans and Australians alike.

Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development.

What does a student coding in a white mask have in common with a New Zealand man struggling with a passport photo? Both individuals found themselves on the wrong side of computational decisions. The man of Asian descent erroneously had his eyes registered as closed by facial recognition software that analyzed his photo during an interaction with an automated passport renewal system. In my case, I wore a mask because my bare face was not consistently detected by facial recognition software. We both experienced exclusion from seemingly neutral machines programmed with algorithms -codified processes.