We propose Disentanglement based Active Learning (DAL), a new active learning technique based on query synthesis which leverages the concept of disentanglement. Instead of requesting labels from the human oracle, our method automatically labels majority of the datapoints, thus drastically reducing the human labelling budget in active learning. The proposed method uses Information Maximizing Generative Adversarial Nets (InfoGAN) to achieve the task where the active learner provides a feedback on the generation of InfoGAN based on which decision is taken about the datapoints to be queried. Results on two benchmark datasets demonstrate that DAL is able to achieve nearly fully supervised accuracy with fairly less labelling budget compared to existing active learning approaches.

This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimension. Specifically, we assume that the columns of a matrix are generated by polynomials acting on a low-dimensional intrinsic variable, and wish to recover the missing entries under this assumption. We show that we can identify the complete matrix of minimum intrinsic dimension by minimizing the rank of the matrix in a high dimensional feature space. We develop a new formulation of the resulting problem using the kernel trick together with a new relaxation of the rank objective, and propose an efficient optimization method. We also show how to use our methods to complete data drawn from multiple nonlinear manifolds. Comparative studies on synthetic data, subspace clustering with missing data, motion capture data recovery, and transductive learning verify the superiority of our methods over the state-of-the-art.

One of the things that really annoys AI researchers is how supposedly "intelligent" machines are judged by much higher standards than are humans. Take self-driving cars, they say. So far they've driven millions of miles with very few accidents, a tiny number of them fatal. Yet whenever an autonomous vehicle kills someone there's a huge hoo-ha, while every year in the US nearly 40,000 people die in crashes involving conventional vehicles. Likewise, the AI evangelists complain, everybody and his dog (this columnist included) is up in arms about algorithmic bias: the way in which automated decision-making systems embody the racial, gender and other prejudices implicit in the data sets on which they were trained.

There's little doubt that artificial intelligence (AI) is having a massive impact on IT budgets, operations, and user experiences. But an area of AI that is receiving increasing attention is ethics. As people and companies become more dependent on the use of algorithms to make and support decisions, the inherent biases of software developers and the data pools they depend on to build their models have come under close scrutiny. Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them.

Artificial intelligence will soon be making a career in the maritime industry: Because specialist personnel and cargo space are scarce and transport costs are high, more and more ship owners are relying on ships with state-of-the-art assistance systems and autonomous driving functions. Autonomous ships will get by completely without captain and crew. When autonomous vessels plough through the waves in the future, the history of ghost ships will have to be rewritten. Legends like the Flying Dutchman and the Marie Celeste have one thing in common. Both vessels had a crew on board before fate befell them in the vastness of the oceans.

Future Life Conference is an opportunity to take a step beyond what you already know about the impact emerging technologies have on Australia's health system, education, job market, housing and climate. Imagine your children or grandchildren being born within the next 20 years. What will their life be like? What will their education look like with the support of emerging technologies? How will they find work when a lot of people fear of losing their jobs to artificial intelligence?

Through the years of its troubles, when it hemorrhaged reputation and people, Wells Fargo never backed off from its determination to innovate. The fourth-largest U.S. bank has been a digital banking powerhouse for years on both the retail and business banking sides. It has 30 million digitally active customers -- about 43% of its total customer base. The bank's Innovation Group, a key element of that success, has been led since May 2018 by Lisa Frazier, EVP and Head of Innovation, making her one of the highest-ranking women in financial technology. Frazier, an Australian, has an unusual background for a digital banking leader.

Twenty IT leaders look into their crystal balls to predict the technologies and trends that will drive the sector in 2020. CIO Australia asked Australian technology bosses about their top line predictions for 2020, the technologies that will have the greatest impact next year, and what top trends will impact the IT and business landscape. Here are the predictions from IT leaders across vendor land to CIOs and CTOs across a host of industries. Intelligent systems (machine learning, artificial intelligence and automation) are the top trends in 2020. Intelligent systems will have a significant impact on increasing situational awareness (insights) and using these insights to enhance decision making – to deliver optimal outcomes for customers. One large impact on the business landscape will be the expanding role of digital twins – extending beyond the optimisation of individual assets/systems to driving improvements at the organisational level. We are introducing a reference to'Digital Twin of Operations (DTO)' – having recently built some proof of concepts. The DTO brings together inputs from a range of different systems and assets onto a common data & analytics platform; is able to process large-scale and real-time data sets to simulate millions of'what if' scenarios through cloud technologies.

Neurons in a human brain have been somewhat of a mystery for scientists. Unlike the traditional electrical circuits, the inner workings of the biological circuitry in the brain have always been less than predictable, apart from the complex biology they exhibit. Scientists at the University of Bath now seem to have decoded the bizarre behavior of our brain cells and replicated it on tiny silicon chips. Researchers from the Universities of Bristol, Zurich & Auckland collaborated on this effort. Designing artificial neurons has been a challenge for medical researchers for decades.

General Game Playing is a field which allows the researcher to investigate techniques that might eventually be used in an agent capable of Artificial General Intelligence.  Game playing presents a controlled environment in which to evaluate AI techniques, and so we have seen an increase in interest in this field of research.  Games of imperfect information offer the researcher an additional challenge in terms of complexity over games with perfect information.  In this article, we look at imperfect-information games: their expression, their complexity, and the additional demands of their players.  We consider the problems of working with imperfect information and introduce a technique called HyperPlay, for efficiently sampling very large information sets, and present a formalism together with pseudo code so that others may implement it. We examine the design choices for the technique, show its soundness and completeness then provide some experimental results and demonstrate the use of the technique in a variety of imperfect-information games, revealing its strengths, weaknesses, and its efficiency against randomly generating samples.  Improving the technique, we present HyperPlay-II, capable of correctly valuing information-gathering moves.  Again, we provide some experimental results and demonstrate the use of the new technique revealing its strengths, weaknesses and its limitations.