Analytic software tools and workflows are increasing in capability, complexity, number, and scale, and the integrity of our workflows is as important as ever. Specifically, we must be able to inspect the process of analytic workflows to assess (1) confidence of the conclusions, (2) risks and biases of the operations involved, (3) sensitivity of the conclusions to sources and agents, (4) impact and pertinence of various sources and agents, and (5) diversity of the sources that support the conclusions. We present an approach that tracks agents' provenance with PROV-O in conjunction with agents' appraisals and evidence links (expressed in our novel DIVE ontology). Together, PROV-O and DIVE enable dynamic propagation of confidence and counter-factual refutation to improve human-machine trust and analytic integrity. We demonstrate representative software developed for user interaction with that provenance, and discuss key needs for organizations adopting such approaches. We demonstrate all of these assessments in a multi-agent analysis scenario, using an interactive web-based information validation UI.

Human use language not just to convey information but also to express their inner feelings and mental states. In this work, we adapt the state-of-the-art language generation models to generate affective (emotional) text. We posit a model capable of generating affect-driven and topic focused sentences without losing grammatical correctness as the affect intensity increases. We propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2. The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text. Previous attempts at modelling fine-grained emotions fall out on grammatical correctness at extreme intensities, but our model is resilient to this and delivers robust results at all intensities. We conduct automated evaluations and human studies to test the performance of our model, and provide a detailed comparison of the results with other models. In all evaluations, our model outperforms existing affective text generation models.

There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultaneous non-Euclidean structures, such as hierarchical and cyclic structures. However, existing embedding approaches for temporal KGs typically learn entity representations and their dynamic evolution in the Euclidean space, which might not capture such intrinsic structures very well. To this end, we propose Dy- ERNIE, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data. Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs. Besides, to capture the evolutionary dynamics of temporal KGs, we let the entity representations evolve according to a velocity vector defined in the tangent space at each timestamp. We analyze in detail the contribution of geometric spaces to representation learning of temporal KGs and evaluate our model on temporal knowledge graph completion tasks. Extensive experiments on three real-world datasets demonstrate significantly improved performance, indicating that the dynamics of multi-relational graph data can be more properly modeled by the evolution of embeddings on Riemannian manifolds.

No matter how graciously good customer service is rendered, online reviews are more likely to cite "bad customer service," than the opposite. Problematic service will dominate reviews. People are more likely to angrily pen a disgruntled review than they are a positive one, and the latter acknowledgement can boost a business' morale and bring more people to their business. The coronavirus pandemic caused stress and anxiety among those who were overwhelmed as they faced unprecedented challenges, and sought help in navigating the technology necessitated by social distancing and isolation. Yet, there's a bright light at the end of the tunnel: Issues have been resolved by the judicious use of artificial intelligence (AI)-enabled chatbots and virtual agents, according to a new IBM report, "The value of virtual technology."

Arm DevSummit was one of several virtual tech conferences and launch events taking place over the past month week and it included everything from a fireside chat with the CEOs of Arm and Nvidia about the proposed acquisition to new technology announcements and the deep dive training tracks one would expect. One of the most interesting announcements was around a new platform of cores for SoCs specifically targeting autonomous machines. While there is a growing focus on autonomous vehicles, few companies look at the broader opportunity for autonomous control across a wide variety of platforms that include autonomous systems for materials handling and production for everything from consumer products to agriculture. Arm announced a complete platform that can span a wide range of applications that meet the safety critical requirements of autonomous systems. As Arm expanded its technology offerings in mobile, it began releasing a complete platform of compute cores and related technologies together as a single platform.

Various countries across the Middle East have placed an emphasis on AI. Clear examples of that have been nation-wide strategies around AI and part of wider government digital transformations. In the Gulf Cooperation Council (GCC) region (Saudi Arabia, Qatar, Oman, Bahrain, Kuwait and the United Arab Emirates (UAE), economic development diversifications such as Saudi Vision 2030 has prioritised wider future and innovative economies. Sectors such as fintech and AI-both fintech and non-fintech related- play a strong role in that. It is not just nation-wide economic development diversification strategies such as Vision 2030 but also, complimenting and in parallel, strategies purely around AI.

Machine intelligence is the last invention that humanity will ever need to make.

Machine learning model and Neural Networks helps in extracting archaic information about human civilization. Archaeology is the gateway to our past. It describes events which shaped the world how it is today and the transition that led humans from animal-hunter to a knowledgeable-mosaic. In archaeology, Stone Age holds the key relevance. It establishes the patterns of human behavior and helps in identifying the transitions that hurled humans to the path of development.

Hospitals are likely to increasingly rely on Internet of Things systems as the coronavirus pandemic ... [ ] persists or worsens. The European Commission has launched an €8 million project that aims to use the Internet of Things (IoT) to increase and enhance the remote care provided by hospitals. At a time when the coronavirus pandemic is stretching health systems to their limits, the project is one of several actions the EC is funding with the aim of developing "Next-Generation Internet of Things" tech that could help hospitals and other organisations operate more efficiently. Dubbed IntellIoT, the project is a consortium of 13 participating companies and institutions, including Siemens, Philips, EURECOM, Aalborg University, University of Oulu, Philips, Sphynx Analytics, and the University of St. Gallen. Over the next three years, the 13 partners will trial a range of initiatives and tools intended to autonomously conduct health monitoring and interventions, while also analysing large quantities of medical data.

Agriculture worldwide is a US $5 trillion industry. And artificial intelligence (AI) is revolutionizing this industry every step of the way -- from preparing soils and sowing seeds to getting products to the kitchen table. AI-powered technologies are increasing productivity and reducing costs significantly throughout the production and supply chain. The market value of global AI in the agricultural sector is currently estimated at $852.2 million. In the next decade alone this value is expected to grow more than 10 times, exceeding $8 billion annually.