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.

Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency ``event'', such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions. Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable or even better for very short anticipation times ($<0.4$ sec) to a recurrent neural network based method. We perceive our method as a first step towards the interpretability of the results by representing human body motions as linear combinations of ``factors''. In addition, compared to the neural network based methods large training times are not needed. Actually, our methods do not even regress to any other motions than the one to be anticipated and hence is of a generic nature.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.

Managing Marketing is a weekly podcast hosted by TrinityP3. Each one is a conversation with a marketing thought-leader, professional, practitioner or expert on the issues and topics of interest to marketers and business leaders everywhere. In this special series, TrinityP3's Anton Buchner, discusses the rise of Artificial Intelligence and the impact it is having on marketing. Jay Henderson is the Senior Vice President of Product Management at Acoustic (formerly Watson Customer Engagement – purchased from IBM by Centerbridge Partners, and rebranded in 2019 as Acoustic). He talks about how machine learning algorithms should be seen as working together with marketers. Offering options and solutions for marketers to assess and consider, rather than being seen as a distrustful'black box' of solutions running rampant by themselves. Welcome to Managing Marketing, a weekly podcast where we sit down and talk with marketing thought leaders and experts on the issues and topics of interest to marketers and business leaders everywhere. To discuss this I'm sitting down today with Jay Henderson. Jay is the senior vice-president of product management for Acoustic. Thanks, I'm really excited to be here. You've just flown in so you've got over your jetlag? I got here a couple of days ago. We're here today in Sydney to launch the Acoustic brand and the company into the Australian market.