Whether your interaction with artificial intelligence (AI) is limited to science-fiction or you spend more time in your day talking to Siri and Alexa than actual humans, you can't hide from the fact AI is changing the world. This week, the UK's new digital strategy was launched, which dedicated £17.3m to research and development of robotics and AI. Out of the industries welcoming this technology with open arms, finance and banking is one of the biggest. It's not hard to see why: when companies are dealing with large amounts of data, handing over control to a machine learning system that can analyse and understand information much faster than a human being is an obvious benefit. Where is this new technology having an impact in the finance sector?

On an average, more than 50 percent of projects fail. Without touching on failure factors this staggering number applies across all sorts of project environments – IT, construction, engineering, or transformation projects. One would assume we'd figured out by now how to successfully deliver projects on time, on a budget, and to the desired scope and benefits; But apparently, we haven't. AI is one of the buzzwords du jour, thanks to frequent tech media news about voice-enabled personal assistants and self-driving cars. However, the concept of AI is older than project management itself -- going as far back as 380 BC to Greek philosophers such as Aristotles who described the syllogism, a method of mechanical thought.

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.

Despite Australia "punching above its weight" in artificial intelligence (AI) research, the country is failing to embrace the technology in the business sector, according to an expert. Within the next five to 10 years, Professor Toby Walsh envisions Australian businesses to be using autonomous cars, buses, trucks, and robot advisors. "In research terms, I think we punch above our weight," the University of New South Wales and Data61 AI professor said. However, he is concerned Australia is lagging behind other countries such as the United States and China when it comes to embracing this form of computer intelligence within our businesses. "There's been quite a long history of research into AI in Australia, but I think we've been a bit less successful about translating that into business," he said.

As a combat veteran and more recently an industry technologist and university professor, I have observed with concern the increasing automation--and dehumanization--of warfare. Sarah Underwood's discussion of autonomous weapons in her news story "Potential and Peril" (June 2017) highlighting this trend also reminded me of the current effort to update the ACM Code of Ethics, which says nothing about the responsibilities of ACM members in defense industries building the software and hardware in weapons systems. Underwood said understanding the limitations, dangers, and potential of autonomous and other warfare technologies must be a priority for those designing such systems in order to minimize the "collateral damage" of civilian casualties and property/infrastructure destruction. Defense technologists must be aware of and follow appropriate ethical guidelines for creating and managing automated weapons systems of any kind. Removing human control and moral reasoning from weapons will not make wars less likely or less harmful to humans.

Forgotten memories have been reawakened in mice with Alzheimer's disease, suggesting that the condition may not actually destroy our memories, but instead impair our ability to recall them. It has long been assumed that Alzheimer's disease completely erases memories. The condition involves clumps of proteins known as amyloid plaques and tau tangles accumulating in the brain, where they are thought to destroy the neurons that store our memories. But experiments by Christine Denny at Columbia University and her colleagues suggest that memories may not be wiped by Alzheimer's disease, but instead become harder to access. What's more, these memories can be reawakened by artificially activating the neurons they are stored in. The finding could be revolutionary, says Ralph Martins at Edith Cowan University in Australia.

Data 61 has an exciting opportunity for a Senior Research Scientist to help discover the next generation of machine learning algorithms. The machine learning team is currently building novel technologies for data integration and machine learning on large scale graphs. We are seeking candidates with expertise in machine learning, graph algorithms, deep learning using highly structured data, active learning, online learning, manifold and spectral learning, large scale graph algorithms, distributed algorithms, or frequent graph pattern mining. The Senior Research Scientist will contribute and collaborate with the wider research community by publishing papers in top machine learning forums, as well as contributing to the technological direction of real world projects and finding solutions to some of the most challenging technical problems. Location: Eveleigh Sydney preferred, may consider Canberra ACT Salary: AU$127K - AU$141K plus up to 15.4% superannuation Tenure: 3 year specified term Reference: 43886 At the Commonwealth Scientific and Industrial Research Organisation we do the extraordinary every day.

As self-driving cars are being tested everywhere from the US to South Korea, Germany to Australia, reports today make it clear that it won't be happening in India. The country's transport and highways minister, Nitin Gadkari told reporters today, "We won't allow driverless cars in India. I am very clear on this." Rather, the minister's rejection of self-driving vehicles is about the jobs they would take away from drivers in the country. "We won't allow any technology that takes away jobs. In a country where you have unemployment, you can't have a technology that ends up taking people's jobs," said Gadkari.

Both Microsoft and Google envision a not-so-distant future in which donning smart headgear to repair an elevator or assemble a tractor motor is the norm. Over the past several days, both tech giants have revealed they're each taking concrete and compelling steps to make that happen. Microsoft just revealed that it's working on a new artificial intelligence chip to power its second-generation HoloLens headset. The coprocessor's chief job will be implementing deep neural networks -- a machine learning technique with a structure that loosely resembles the human brain -- into the HoloLens' core processing unit. A dedicated A.I. chip is necessary for a gadget like the HoloLens, says Microsoft, which must be able to comprehend large amounts of complex data gathered by its depth and camera sensors without latency.

In fact, it's about a thousand of them, give or take a few. An experiment using fake ducks to stand in for the real thing has found that when it comes to counting birds, drones beat humans. Jarrod Hodgson and his colleagues at the University of Adelaide in Australia had previously used aerial images from drones to count seabirds and found that the drones had a more comprehensive view of the colonies than the people trying to count them on the ground. However, neither could provide an exact count of the number individual birds. "We couldn't test for accuracy," says Hodgson.