According to a new survey, six out of ten expect that products and services using artificial intelligence will profoundly change their daily life in the next three to five years and half feel that this has already happened. These are some of the findings of a 28-country survey conducted by Ipsos for the World Economic Forum of 19,504 adults under the age of 75 between November 19 and December 3, 2021. "In order to trust artificial intelligence, people must know and understand exactly what AI is, what it's doing, and its impact," said Kay Firth-Butterfield, Head of Artificial Intelligence and Machine Learning at the World Economic Forum. "Leaders and companies must make transparent and trustworthy AI a priority as they implement this technology. At the World Economic Forum, we are focused on multistakeholder collaboration to optimize accountability, transparency, privacy and impartiality to create that trust. With the ability to solve many of society's pressing issues, we are focused on accelerating the benefits and mitigating the risks of artificial intelligence and machine learning. Only then can we gain public trust and benefit from the rewards of emerging tech like AI."

Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the expensive time burden due to both the construction of graphs and eigen-decomposition of Laplacian matrix, and fail to explore the cluster structure of large-scale data. Moreover, they require a post-processing to get the final clustering, resulting in suboptimal performance. Furthermore, rank of the learned view-consensus graph cannot approximate the target rank. In this paper, drawing the inspiration from the bipartite graph, we propose an effective and efficient graph learning model for multi-view clustering. Specifically, our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm, which well characterizes both the spatial structure and complementary information embedded in graphs of different views. We learn view-consensus graph with adaptively weighted strategy and connectivity constraint such that the connected components indicates clusters directly. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size. Extensive experimental results indicate that our method is superior to state-of-the-art methods.

Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.

Constructing benchmarks that test the abilities of modern natural language understanding models is difficult - pre-trained language models exploit artifacts in benchmarks to achieve human parity, but still fail on adversarial examples and make errors that demonstrate a lack of common sense. In this work, we propose gamification as a framework for data construction. The goal of players in the game is to compose questions that mislead a rival AI while using specific phrases for extra points. The game environment leads to enhanced user engagement and simultaneously gives the game designer control over the collected data, allowing us to collect high-quality data at scale. Using our method we create CommonsenseQA 2.0, which includes 14,343 yes/no questions, and demonstrate its difficulty for models that are orders-of-magnitude larger than the AI used in the game itself. Our best baseline, the T5-based Unicorn with 11B parameters achieves an accuracy of 70.2%, substantially higher than GPT-3 (52.9%) in a few-shot inference setup. Both score well below human performance which is at 94.1%.

Recent evidence suggests that SARS-CoV-2, which is the virus causing a global pandemic in 2020, is predominantly transmitted via airborne aerosols in indoor environments. This calls for novel strategies when assessing and controlling a building's indoor air quality (IAQ). IAQ can generally be controlled by ventilation and/or policies to regulate human-building-interaction. However, in a building, occupants use rooms in different ways, and it may not be obvious which measure or combination of measures leads to a cost- and energy-effective solution ensuring good IAQ across the entire building. Therefore, in this article, we introduce a novel agent-based simulator, ArchABM, designed to assist in creating new or adapt existing buildings by estimating adequate room sizes, ventilation parameters and testing the effect of policies while taking into account IAQ as a result of complex human-building interaction patterns. A recently published aerosol model was adapted to calculate time-dependent carbon dioxide ($CO_2$) and virus quanta concentrations in each room and inhaled $CO_2$ and virus quanta for each occupant over a day as a measure of physiological response. ArchABM is flexible regarding the aerosol model and the building layout due to its modular architecture, which allows implementing further models, any number and size of rooms, agents, and actions reflecting human-building interaction patterns. We present a use case based on a real floor plan and working schedules adopted in our research center. This study demonstrates how advanced simulation tools can contribute to improving IAQ across a building, thereby ensuring a healthy indoor environment.

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.