Abstract--Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns between posting and response times, interaction patterns between topics has not been studied. We argue that social media conversations naturally involve interacting rather than independent topics. Modeling such topical interaction patterns can additionally help in inference of latent variables in the data such as diffusion parents and topics of events. We propose the Hidden Markov Hawkes Process (HMHP) that incorporates topical Markov Chains within Hawkes processes to jointly model topical interactions along with useruser and user-topic patterns. We propose a Gibbs sampling algorithm for HMHP that jointly infers the network strengths, diffusion paths, the topics of the posts as well as the topictopic interactions. We show using experiments on real and semisynthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately that state-of-the-art baselines. More interestingly, HMHP finds insightful interactions between topics in real tweets which no existing model is able to do. This can potentially lead to actionable insights enabling, e.g., user targeting for influence maximization. A popular area of recent research has been the study of information diffusion cascades, where information spreads over a social network when a'parent' event from one infected node influences a'child' event at neighboring node [5], [11], [19], [6], [10]. The action of propagating information between two neighboring nodes depends on various factors, such as the strength of influence between the nodes, the topical content of the parent event and the extent of interest of the child node towards that topic. Explosion of social media data has made it possible to analyze and evaluate different models that seek to explain such information cascades. However, many relevant variables such as the network influence strengths, the identity of influencing or parent event for any event, and the actual topics are typically unobserved for most social network data.

The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.

Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation bias exists due to a feedback loop which causes the model to learn to only predict recommendations similar to previous ones. Imbalance in data occurs when systematic societal, historical, or other ambient bias is present in the data. In this paper, we address both biases by proposing a hybrid fairness-aware recommender system. Our model provides efficient and accurate recommendations by incorporating multiple user-user and item-item similarity measures, content, and demographic information, while addressing recommendation biases. We implement our model using a powerful and expressive probabilistic programming language called probabilistic soft logic. We experimentally evaluate our approach on a popular movie recommendation dataset, showing that our proposed model can provide more accurate and fairer recommendations, compared to a state-of-the art fair recommender system.

Liquid democracy is a collective decision making paradigm which lies between direct and representative democracy. One main feature of liquid democracy is the concept of transitive delegations. Indeed, in this setting each voter may decide to vote directly or to delegate her vote to a representative, also called proxy. In liquid democracy this proxy can in turn delegate her vote and the votes that have been delegated to her to another proxy. As a result, a voter who decides to vote has a weight corresponding to the number of people she represents, i.e., herself and the voters who directly or indirectly delegated to her.

Public sector organisations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and AI in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.

I learned a few things from reading an excerpt from Yuval Noah Harari's book, 21 Lessons for the 21st Century, published in the October issue of The Atlantic. One is that it took a Google machine-learning program just four hours to teach itself and master chess, once the pinnacle of centuries of human intellectual effort, easily defeating the top-ranked computer chess engine in the world. Another is that artificial intelligence systems may be inherently anti-democratic and anti-human. New heights of computing power and data processing make it more efficient to centralize systems in authoritarian governments, Harari says, and will render humans increasingly irrelevant. "By 2050," he writes, "a useless class might emerge, the result not only of a shortage of jobs or a lack of relevant education but also of insufficient mental stamina to continue learning new skills."

This essay is the winner of The Economist's Open Future essay competition in the category of Open Progress, responding to the question: "Do the benefits of artificial intelligence outweigh the risks?" The winner is Frank L. Ruta, 24 years old, from America. Upgrade your inbox and get our Daily Dispatch and Editor's Picks. Towards the end of the second world war, a group of scientists in America working to develop an atomic bomb for the Manhattan Project warned that using the weapon would inevitably lead to a geopolitical landscape characterised by a nuclear arms race. This would force America, they said, to outpace other nations in building up nuclear armaments. They recommended that if the military did choose to use the weapon, an international effort for nuclear non-proliferation should promptly be established.

Founded in 1984, the SETI Institute is a non-profit, multi-disciplinary research and education organization whose mission is to explore, understand, and explain the origin and nature of life in the universe and the evolution of intelligence. Our research encompasses the physical and biological sciences and leverages expertise in data analytics, machine learning and advanced signal detection technologies. The SETI Institute is a distinguished research partner for industry, academia and government agencies, including NASA and NSF.

TOKYO (WASHINGTON POST) - Japan is working to develop technology that will fully utilise artificial intelligence (AI) to detect suspicious vessels, according to sources. Aimed at strengthening maritime surveillance capabilities in waters around Japan, the envisioned technology is projected to be used for such purposes as monitoring North Korean ship-to-ship cargo transfers in international waters, the sources said. The government aims to start testing the AI-based technology in fiscal year 2021 using vessels of the Self-Defence Forces. The system will analyse information automatically transmitted by radio from the Automatic Identification System on board many ships. The AI will learn an enormous amount of information on the location and speed of ships, making it possible to automatically detect abnormalities such as ships navigating far away from ordinary routes or in the opposite direction. The Self-Defence Forces will identify suspicious ships by comparing the AI-collected data with information gathered by warning radar, and will dispatch destroyers and patrol aircraft for warning and surveillance activities.

A creepy AI transfers the facial expressions of one person to another to create eerily realistic'deep fake' videos. The software accurately flips a segment of one video - such as the mouth of a character - to the style of another to create life-like fake clips. A video produced by the team transferred the mouth movements of British comedian John Oliver onto the face of US talk-show host Stephen Colbert. Researchers warned the technology could be used to create fake news clips that falsely put words into the mouths of politicians or other powerful figures. An AI transfers the facial expressions of one person to another to create eerily realistic'deep fake' videos.