Europeans are amongst the most distrustful in the world of artificial intelligence companies, according to a survey that reveals a split between the rich and developing world over the technology. Citizens from economically developing countries like China and India are "significantly more likely" than those from the wealthier world to be positive about the future impact of AI, to trust AI companies and to believe they understand the technology. More than three quarters of Chinese respondents said they trusted AI companies as much as other companies. Saudi Arabia at 73% and India on 68% had similar levels of trust. But in Canada (34%), France (34%), the US (35%), the UK (35%) and Australia (36%), only minorities said they trusted AI companies equally. Meanwhile, six in ten respondents expected AI products and services to "profoundly" change their daily lives in the next three to five years.

The uprising of Artificial Intelligence machines (hereinafter referred as "AI") is a popular and intriguing subject for many science fiction works. The advancement of AI machines and their progression with respect to playing a significant role in our lives has increased exponentially in the past few years. The future possibilities of this technology has stirred a hornets' nest of innumerable possibilities. As we witness AI machines overlapping with Intellectual Property Rights (IPR), it gives rise to many questions concerning legal discipline. When the earliest substantial work in the field of Artificial Intelligence was concluded in the mid-20th century by the British logician and computer pioneer, Alan Mathison Turing, nobody could have imagined that there will be an attempt towards an assimilation of technical solutions created by an AI machines into the scope of patent law.

In this paper, we introduce an important yet relatively unexplored NLP task called Multi-Narrative Semantic Overlap (MNSO), which entails generating a Semantic Overlap of multiple alternate narratives. As no benchmark dataset is readily available for this task, we created one by crawling 2,925 narrative pairs from the web and then, went through the tedious process of manually creating 411 different ground-truth semantic overlaps by engaging human annotators. As a way to evaluate this novel task, we first conducted a systematic study by borrowing the popular ROUGE metric from text-summarization literature and discovered that ROUGE is not suitable for our task. Subsequently, we conducted further human annotations/validations to create 200 document-level and 1,518 sentence-level ground-truth labels which helped us formulate a new precision-recall style evaluation metric, called SEM-F1 (semantic F1). Experimental results show that the proposed SEM-F1 metric yields higher correlation with human judgement as well as higher inter-rater-agreement compared to ROUGE metric.

Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of fair machine learning with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of fair machine learning produced by both social and formal (specifically machine learning and statistics) sciences in this field guide. Specifically, in addition to giving the mathematical and algorithmic backgrounds of several popular statistical and causal-based fair machine learning methods, we explain the underlying philosophical and legal thoughts that support them. Further, we explore several criticisms of the current approaches to fair machine learning from sociological and philosophical viewpoints. It is our hope that this field guide will help fair machine learning practitioners better understand how their algorithms align with important humanistic values (such as fairness) and how we can, as a field, design methods and metrics to better serve oppressed and marginalized populaces.

Characterizing the patterns of errors that a system makes helps researchers focus future development on increasing its accuracy and robustness. We propose a novel form of "meta learning" that automatically learns interpretable rules that characterize the types of errors that a system makes, and demonstrate these rules' ability to help understand and improve two NLP systems. Our approach works by collecting error cases on validation data, extracting meta-features describing these samples, and finally learning rules that characterize errors using these features. We apply our approach to VilBERT, for Visual Question Answering, and RoBERTa, for Common Sense Question Answering. Our system learns interpretable rules that provide insights into systemic errors these systems make on the given tasks. Using these insights, we are also able to "close the loop" and modestly improve performance of these systems.

It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks in narrow AI sub areas and there is no unified way to manage and access them. This not only creates unnecessary overheads when training or deploying Machine Learning models but also limits the understanding of the data, which is very important for data-centric AI. In this paper, we present our vision about a unified framework for different datasets so that they can be integrated and queried easily, e.g., using standard query languages. We demonstrate this in our ongoing work to create a framework for datasets in Computer Vision and show its advantages in different scenarios.

Graph problems such as traveling salesman problem, or finding minimal Steiner trees are widely studied and used in data engineering and computer science. Typically, in real-world applications, the features of the graph tend to change over time, thus, finding a solution to the problem becomes challenging. The dynamic version of many graph problems are the key for a plethora of real-world problems in transportation, telecommunication, and social networks. In recent years, using deep learning techniques to find heuristic solutions for NP-hard graph combinatorial problems has gained much interest as these learned heuristics can find near-optimal solutions efficiently. However, most of the existing methods for learning heuristics focus on static graph problems. The dynamic nature makes NP-hard graph problems much more challenging to learn, and the existing methods fail to find reasonable solutions. In this paper, we propose a novel architecture named Graph Temporal Attention with Reinforcement Learning (GTA-RL) to learn heuristic solutions for graph-based dynamic combinatorial optimization problems. The GTA-RL architecture consists of an encoder capable of embedding temporal features of a combinatorial problem instance and a decoder capable of dynamically focusing on the embedded features to find a solution to a given combinatorial problem instance. We then extend our architecture to learn heuristics for the real-time version of combinatorial optimization problems where all input features of a problem are not known a prior, but rather learned in real-time. Our experimental results against several state-of-the-art learning-based algorithms and optimal solvers demonstrate that our approach outperforms the state-of-the-art learning-based approaches in terms of effectiveness and optimal solvers in terms of efficiency on dynamic and real-time graph combinatorial optimization.

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multiview representations in a local-to-global manner. Extensive experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance.

University of South Australia's Habib Habibullah says their algorithm could be applied in many environments, including industrial warehouses where robots are commonly used, for robotic fruit picking, packing and pelletizing, and also for restaurant robots An algorithm developed by researchers at the University of South Australia (UniSA) aims to help robots avoid humans and other obstacles in their path while taking the fastest, safest route to their destination. The researchers based their model on the best elements of existing algorithms and used it to create a TurtleBot able to avoid collisions by adjusting its speed and direction. They performed simulations in nine different scenarios and found their model outperformed the online collision avoidance algorithms Dynamic Window Approach and Artificial Potential Field. Said UniSA's Habib Habibullah, "Our proposed method sometimes took a longer path, but it was faster and safer, avoiding all collisions."

Drone footage of endangered dolphins swimming with paddleboarders in Southland could help artificial intelligence, which is being used for conservation. Rodd Trafford filmed the playful pod in Te Waewae Bay, about an hours drive west of Invercargill, on January 9. "I thought it was a once-in-a-lifetime opportunity. I'll never get a chance to film that again," Trafford said. The footage appears to show at least 16 dolphins. READ MORE: * Dolphin advocates say Government's proposed protections are fundamentally flawed * 'It's right in the middle of their hood': Dolphin researcher fearful old dumpsite could spell disaster for Hector's * DOC proposal could cut red tape to building cycle trails on conservation land Trafford said he technically broke drone-use rules when he got the footage, but the Department Of Conservation had since given him the OK because the department would use the footage to enhance its artificial technology work.