Recent surveys, studies, forecasts and other quantitative assessments of AI highlight the current state of adoption of AI by enterprises worldwide, the future of deep learning, and consumers' attitudes regarding Covid-19 contact tracing. Americans are less willing to trust the government with private data than most other countries. Just half of those in the U.S. say they would be willing to share more data with the government to help track and contain the Covid-19 than they would otherwise. Only France (47%) and Japan (44%) had lower rates of willingness to share. Chinese citizens are most trusting, with 91% saying they would provide more data to the government.

The use of sophisticated machine learning models for critical decision making is faced with a challenge that these models are often applied as a "black-box". This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models. In this paper, we propose a novel approach underpinned by an extended framework of Bayesian networks for generating post hoc interpretations of a black-box predictive model. The framework supports extracting a Bayesian network as an approximation of the black-box model for a specific prediction. Compared to the existing post hoc interpretation methods, the contribution of our approach is three-fold. Firstly, the extracted Bayesian network, as a probabilistic graphical model, can provide interpretations about not only what input features but also why these features contributed to a prediction. Secondly, for complex decision problems with many features, a Markov blanket can be generated from the extracted Bayesian network to provide interpretations with a focused view on those input features that directly contributed to a prediction. Thirdly, the extracted Bayesian network enables the identification of four different rules which can inform the decision-maker about the confidence level in a prediction, thus helping the decision-maker assess the reliability of predictions learned by a black-box model. We implemented the proposed approach, applied it in the context of two well-known public datasets and analysed the results, which are made available in an open-source repository.

Sharing, downloading, and reusing software is common-place, some of which is carried out legally with open source software. When it is not legal, it is unclear just how many copyright infringements and trade secret violations have taken place: does an infringement count for the artefact as a whole or perhaps for each file of the program? To answer this question, it must first be established whether a program should be considered as an integral whole, a collection, or a mere set of distinct files, and why. We argue that a program is a functional whole, availing of, and combining, arguments from mereology, granularity, modularity, unity, and function to substantiate the claim. The argumentation and answer contributes to the ontology of software artefacts, may assist industry in litigation cases, and demonstrates that the notion of unifying relation is operationalisable. Indirectly, it provides support for continued modular design of artefacts following established engineering practices.

Voluntary employee turnover incurs significant direct and indirect financial costs to organizations of all sizes. A large proportion of voluntary turnover includes people who frequently move from job to job, known as job-hopping. The ability to discover an applicant's likelihood towards job-hopping can help organizations make informed hiring decisions benefiting both parties. In this work, we show that the language one uses when responding to interview questions related to situational judgment and past behaviour is predictive of their likelihood to job hop. We used responses from over 45,000 job applicants who completed an online chat interview and also self-rated themselves on a job-hopping motive scale to analyse the correlation between the two. We evaluated five different methods of text representation, namely four open-vocabulary approaches (TF-IDF, LDA, Glove word embeddings and Doc2Vec document embeddings) and one closed-vocabulary approach (LIWC). The Glove embeddings provided the best results with a positive correlation of r=0.35 between sequences of words used and the job-hopping likelihood. With further analysis, we also found that there is a positive correlation of r=0.25 between job-hopping likelihood and the HEXACO personality trait Openness to experience. In other words, the more open a candidate is to new experiences, the more likely they are to job hop. The ability to objectively infer a candidate's likelihood towards job hopping presents significant opportunities, especially when assessing candidates with no prior work history. On the other hand, experienced candidates who come across as job hoppers, based purely on their resume, get an opportunity to indicate otherwise.

We present a novel adaptation of active learning to graph-based semi-supervised learning (SSL) under non-Gaussian Bayesian models. We present an approximation of non-Gaussian distributions to adapt previously Gaussian-based acquisition functions to these more general cases. We develop an efficient rank-one update for applying "look-ahead" based methods as well as model retraining. We also introduce a novel "model change" acquisition function based on these approximations that further expands the available collection of active learning acquisition functions for such methods.

Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent interactions and distributions. Limited research exists on unlabeled categorical data representations, ignores the heterogeneous and hierarchical couplings, underestimates data characteristics and complexities, and overuses redundant information, etc. The deep representation learning of unlabeled categorical data is challenging, overseeing such value-to-object couplings, complementarity and inconsistency, and requiring large data, disentanglement, and high computational power. This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objective function for unsupervised representation learning of heterogeneous and hierarchical value-to-object couplings. Theoretical analysis shows that UNTIE can represent categorical data with maximal separability while effectively represent heterogeneous couplings and disclose their roles in categorical data. The UNTIE-learned representations make significant performance improvement against the state-of-the-art categorical representations and deep representation models on 25 categorical data sets with diversified characteristics.

Spectral clustering has shown a superior performance in analyzing the cluster structure. However, its computational complexity limits its application in analyzing large-scale data. To address this problem, many low-rank matrix approximating algorithms are proposed, including the Nystrom method - an approach with proven approximate error bounds. There are several algorithms that provide recipes to construct Nystrom approximations with variable accuracies and computing times. This paper proposes a scalable Nystrom-based clustering algorithm with a new sampling procedure, Centroid Minimum Sum of Squared Similarities (CMS3), and a heuristic on when to use it. Our heuristic depends on the eigen spectrum shape of the dataset, and yields competitive low-rank approximations in test datasets compared to the other state-of-the-art methods

Knowledge graph embedding, which aims to learn the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge graph embedding methods focus on the structural relationships in fixed triples while ignoring the temporal information. Currently, existing time-aware graph embedding methods only focus on the factual plausibility, while ignoring the temporal smoothness which models the interactions between a fact and its contexts, and thus can capture fine-granularity temporal relationships. This leads to the limited performance of embedding related applications. To solve this problem, this paper presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness. Two major innovations of our paper are presented here. At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding. Via the proposed additional smoothing factor, RTGE can preserve both structural information and evolutionary patterns of a given graph. Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally-aware information, which further improves the adaptive ability of the proposed algorithm and plays an essential role in obtaining superior performance in various tasks. Extensive experiments conducted on multiple benchmark tasks show that RTGE can increase performance in entity/relationship/temporal scoping prediction tasks.

The promise of Artificial Intelligence (AI) is no longer a concept taken from science fiction. Businesses around the world are taking advantage of AI and automation, moving the enterprise right at the center of digital business transformation. Similarly, Government AI is not just a trend but a response from governments around the globe to the current state of Artificial Intelligence evolution, societal implications, enterprise disruption, and industry adoption. National Artificial Intelligence strategies are a response to the need that urges governments to remain in sync with strategic technology trends that have been emerging in recent years, technology trends that are shaping the way we live, socialize, work, and educate the future generations. Despite being a small nation of 5.5 million inhabitants, geographically located far up in the Nordics, bordering with the Polar Arctic Circle, Finland's citizens are internationally renowned for being early adopters of new technologies.

India has now joined the league of leading economies including USA, UK, EU, Italy, Australia, Canada, France, Germany, Japan, Mexico, New Zealand, South Korea, & Singapore to launch the Global Partnership on AI. Global Partnership on Artificial Intelligence (GPAI) is the first of its kind initiative, aimed to guide responsible development and use of AI, grounded in human rights, inclusion, diversity, innovation, and economic growth. This is also the first initiative of its type for evolving a better understanding of the challenges and opportunities around Artificial Intelligence using the experience and diversity of participating countries. To embark on this goal, GPAI will look to close the gap between theory and practice on Artificial Intelligence by leveraging cutting-edge research and applied action on Artificial Intelligence-related objectives. "We, Australia, France, Canada, Italy, Germany, Mexico, India, Japan, the Republic of Korea, New Zealand, Slovenia, Singapore, the United States of America, the United Kingdom, and the European Union, have come together to build the Global Partnership on Artificial Intelligence."