Fintech Limeglass has launched to'atomise' research by using technology to tag reports in real-time so that they can be easily and quickly searched by the recipient at a granular level. Rowland Park, chief executive and co-founder of Limeglass, said in a report this week that the research market needs innovation as the majority of reports are still being consumed by the buy side as multiple page PDF and HTML documents sent by email, despite advances in technology. Park has more than 30 years' experience in the research industry and founded and grew start-ups IDEA Global and 4CAST, which focused on macroeconomics, policy and financial markets intelligence. Park wrote in the report that financial decision making rests on a three-legged stool – market data; breaking news; and research, which provides wider context for decision making. "The development of tools to better handle market data and breaking news have transformed, and continue to transform, the way activity in the financial markets is conducted," he added.

AI holds the greatest promise for eliminating bias in hiring for two primary reasons. It can eliminate unconscious human bias, and it can assess the entire pipeline of candidates rather than forcing time-constrained humans to implement biased processes to shrink the pipeline from the start. Like any new technology, artificial intelligence is capable of immensely good or bad outcomes. The public seems increasingly focused on the bad, especially when it comes to the potential for bias in AI. This concern is both well-founded and well-documented. It is the simulation of human processes by machines.

The political drumbeat could help create a national consensus around the critical nature of AI, says Jon Bateman, a fellow at the Carnegie Endowment for International Peace and former Pentagon strategist. Still, there is an "asymmetry between the United States and China," says Tarun Chhabra, a senior fellow at Georgetown's Center for Security and Emerging Technology. "[T]he Chinese Communist Party's whole technology worldview is driven, not merely charged, by the imperative of consolidating social control and emerging dominant in geopolitical competition." That means the Chinese government can direct companies to work on a problem it decides is pressing, while the U.S. has to convince companies the problem is worthy of their investment. The political drumbeat could help create a national consensus around the critical nature of AI, says Jon Bateman, a fellow at the Carnegie Endowment for International Peace and former Pentagon strategist.

Having recently published our Top 20 Global Thought Leaders and Influencers on Artificial Intelligence (September 2019), we asked a selection of our Thinkers360 global influencers about their predictions for AI in 2020. Here's what they told us… When data and algorithms are king, then moral and ethical integrity is queen. In light of the Cambridge Analytica scandal, the New York Times ran a headline: "Don't Fix Facebook. In the intelligent age, organizations will win in the long-term when their leadership is based on a moral and ethical foundation – something only humans can provide. The European Commission's ‚ethics for trustworthy AI'-initiative marks a milestone on the road towards the responsible development and deployment of human-centric AI.

The three Canadian federal research funding agencies and UK Research and Innovation (UKRI) are pleased to announce their intention to launch the Canada-UK Artificial Intelligence Initiative. The Canadian agencies include the Canadian Institutes of Health Research; the Natural Sciences and Engineering Research Council; and the Social Sciences and Humanities Research Council .

A referring expression (RE) is a description that identifies a set of instances unambiguously. Mining REs from data finds applications in natural language generation, algorithmic journalism, and data maintenance. Since there may exist multiple REs for a given set of entities, it is common to focus on the most intuitive ones, i.e., the most concise and informative. In this paper we present REMI, a system that can mine intuitive REs on large RDF knowledge bases. Our experimental evaluation shows that REMI finds REs deemed intuitive by users. Moreover we show that REMI is several orders of magnitude faster than an approach based on inductive logic programming.

Large-scale trends in urban crime and global terrorism are well-predicted by socio-economic drivers, but focused, event-level predictions have had limited success. Standard machine learning approaches are promising, but lack interpretability, are generally interpolative, and ineffective for precise future interventions with costly and wasteful false positives. Here, we are introducing Granger Network inference as a new forecasting approach for individual infractions with demonstrated performance far surpassing past results, yet transparent enough to validate and extend social theory. Considering the problem of predicting crime in the City of Chicago, we achieve an average AUC of ~90\% for events predicted a week in advance within spatial tiles approximately $1000$ ft across. Instead of pre-supposing that crimes unfold across contiguous spaces akin to diffusive systems, we learn the local transport rules from data. As our key insights, we uncover indications of suburban bias -- how law-enforcement response is modulated by socio-economic contexts with disproportionately negative impacts in the inner city -- and how the dynamics of violent and property crimes co-evolve and constrain each other -- lending quantitative support to controversial pro-active policing policies. To demonstrate broad applicability to spatio-temporal phenomena, we analyze terror attacks in the middle-east in the recent past, and achieve an AUC of ~80% for predictions made a week in advance, and within spatial tiles measuring approximately 120 miles across. We conclude that while crime operates near an equilibrium quickly dissipating perturbations, terrorism does not. Indeed terrorism aims to destabilize social order, as shown by its dynamics being susceptible to run-away increases in event rates under small perturbations.

In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation. The proposed method combines model-based signal filtering and anomaly detection methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following model. Using the car-following model the subject vehicle (i.e., the following vehicle) utilizes the leading vehicle's information to detect sensor anomalies by employing previously-trained One Class Support Vector Machine (OCSVM) models. This approach allows the AEKF to estimate the state of a vehicle not only based on the vehicle's location and speed, but also by taking into account the state of the surrounding traffic. A communication time delay factor is considered in the car-following model to make it more suitable for real-world applications. Our experiments show that compared with the AEKF with a traditional $\chi^2$-detector, our proposed method achieves a better anomaly detection performance. We also demonstrate that a larger time delay factor has a negative impact on the overall detection performance.

Racial and ethnic disparities in access to healthcare in the United States is well-known and documented [1]. Health disparities are defined to be differences in health ou tcomes and causes among different groups of people. Health equity is achieved when everyone has the same opportunity to be as healthy as possible. We have a very good handle on the types of health disparities i n the US healthcare system, but the causes for these disparities are complex [2, 3] - such as inco me, education, socioeconomic conditions, neighborhood and community influence, public policy, and so cietal structure. Achieving health equity also necessitates a complex set of programs and interventions, a nd several public and private initiatives have tried to address this problem over the past decades.

Unsupervised text embedding has shown great power in a wide range of NLP tasks. While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding. To close this gap, we propose a spherical generative model based on which unsupervised word and paragraph embeddings are jointly learned. To learn text embeddings in the spherical space, we develop an efficient optimization algorithm with convergence guarantee based on Riemannian optimization. Our model enjoys high efficiency and achieves state-of-the-art performances on various text embedding tasks including word similarity and document clustering.