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.

Artificial intelligence can perform feats that seem like sorcery. AI can drive cars and fly drones. It can compose original music, write poetry that isn't too awful, and design recipes that do sound awful (blueberry and spinach pizza, anyone?). AI can do some things better than humans: lip reading, diagnosing diseases such as pneumonia and some cancers, transcribing speech, and playing Jeopardy!, Go, Texas Hold'em, and a variety of video games. AI software can even learn to make its own AI software.

Artificial intelligence may be able to work out the maximum lifespans of extinct species and early humans. The technique relies on analysing specific regions of DNA that are linked to ageing. Benjamin Mayne at CSIRO, a research organisation in Australia, and his colleagues built an AI to predict the lifespan of different animals. To this the team first trained an AI on the known genomes of 252 species from five classes of animals, including mammals, reptiles and fish, and their maximum lifespans. The AI then narrowed down almost 30,000 DNA regions to just 42 that related to lifespan. This was then used to create a formula that can convert these 42 regions into a prediction of maximum lifespan.

In its 4 year existence the Israeli start-up Taranis has seen huge growth. Taranis started as a tool to provide farmers with the information to detect and prevent crop disease, weeds and insect damage based on weather forecasts gathered from aerial surveillance. The technology was then further developed by adding visual layers from satellites, planes and drones and leveraged with AI capabilities. Taranis also created a one-of-a-kind, patented hardware that can capture accurate images at a high resolution from a plane flying at 160 km/h, such as a specific insect on a leaf from 200 feet above ground. Taranis CEO Ofir Schlam says the future of the precision farming industry is looking bright, with thousands of start-ups emerging within the last 10 years. According to him, smart farming is projected to create a massive impact on the agricultural economy in the near future and will be dependent on precision technologies, such as the adoption of automated practices and indoor urbanised farming.

New centre aspires to be an international think-tank that promotes inter-disciplinary research into the interactions between technology and the law. The new Centre for Technology, Robotics, Artificial Intelligence & the Law (TRAIL), a research unit under the National University of Singapore Faculty of Law (NUS Law), was launched today by Mr Edwin Tong, Senior Minister of State for Law and Health, at the 8th Asian Privacy Scholars Network (APSN) Conference. Leveraging NUS Law's preeminent position amongst the top law schools in the world, TRAIL aspires to be an international think-tank that enables inter-disciplinary communities to research into legal, ethical, policy, philosophical and regulatory questions associated with the use and development of information technology (IT), artificial intelligence (AI), data analytics and robotics in the practice of law. The Centre plans to conduct research into the interactions between technology and the law in a more integrated and holistic manner. TRAIL also aims to provide a forum for legal and non-legal scholars interested in various aspects of technology law, to collaborate and advance inter-disciplinary research.

The months seem to be whipping by in a whirlwind and before long we will be into the new year. While it can be easy to get caught up in plans for marketing for the holidays, casting your eye further is necessary. Because 2020 is more than just a milestone, it is an opportunity to start implementing the hottest digital marketing trends that are going to be dominating the industry. Digital marketing has a wider scope and greater applications than ever before. Statista projected that by the close of 2019, active email accounts would be increasing to an astonishing 5.6 billion.

A new research project in Australia is using motion detectors and muscle sensors to track sheep shearers in an effort to minimize on the-job-injuries. Sheep shearers are six times more likely to be injured in the workplace than the average Australian worker. Data from sensors attached to sheep shearers will be used to model worker movement throughout the workday and test new ways of doing the job without risking injury. The study, a joint project between University of Melbourne and the trade group Australian Wool Innovation, uses sensors to measure electrical activity in muscles. These sensors are placed directly on the skin of the lower back and upper thighs, the ABC reported, while motion detectors are placed around the joints to track a worker's posture and shearing motions.