Artificial intelligences (AI) are increasingly being embodied and embedded in the world to carry out tasks and support decision-making with and for people. Robots, recommender systems, voice assistants, virtual humans - do these disparate types of embodied AI have something in common? Here we show how they can manifest as "socially embodied AI." We define this as the state that embodied AI "circumstantially" take on within interactive contexts when perceived as both social and agentic by people. We offer a working ontology that describes how embodied AI can dynamically transition into socially embodied AI. We propose an ontological heuristic for describing the threshold: the Tepper line. We reinforce our theoretical work with expert insights from a card sort workshop. We end with two case studies to illustrate the dynamic and contextual nature of this heuristic.

Recent years have witnessed the proliferation of fake news, propaganda, misinformation, and disinformation online. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract much more attention, and spread further than simple text. As a result, researchers started targeting different modalities and combinations thereof. As different modalities are studied in different research communities, with insufficient interaction, here we offer a survey that explores the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, audio, video, network structure, and temporal information. Moreover, while some studies focused on factuality, others investigated how harmful the content is. While these two components in the definition of disinformation -- (i) factuality and (ii) harmfulness, are equally important, they are typically studied in isolation. Thus, we argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness, in the same framework. Finally, we discuss current challenges and future research directions.

We review our recent work in the area of autonomous materials research, highlighting the coupling of machine learning methods and models and more problem-aware modeling. We review the general Bayesian framework for closed-loop design employed by many autonomous materials platforms. We then provide examples of our work on such platforms. We finally review our approaches to extend current statistical and ML models to better reflect problem-specific structure including the use of physics-based models and incorporation of operational considerations into the decision-making procedure.

There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", whereby AI algorithms and agents no longer simply learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through embodied physical interactions with their environments, whether real or simulated. Consequently, there has been substantial growth in the demand for embodied AI simulators to support a diversity of embodied AI research tasks. This growing interest in embodied AI is beneficial to the greater pursuit of artificial general intelligence, but there is no contemporary and comprehensive survey of this field. This paper comprehensively surveys state-of-the-art embodied AI simulators and research, mapping connections between these. By benchmarking nine state-of-the-art embodied AI simulators in terms of seven features, this paper aims to understand the simulators in their provision for use in embodied AI research. Finally, based upon the simulators and a pyramidal hierarchy of embodied AI research tasks, this paper surveys the main research tasks in embodied AI -- visual exploration, visual navigation and embodied question answering (QA), covering the state-of-the-art approaches, evaluation and datasets.

The automotive industry is seen to have witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ high performance AI models to enable vehicles to perceive their environment and make driving decisions with little or no influence from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance to regulations. The assessment of these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs.

The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.

Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.

We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas from emotion causation theories to computationally model and determine the emotional state evoked in clips of movies. Affect2MM explicitly models the temporal causality using attention-based methods and Granger causality. We use a variety of components like facial features of actors involved, scene understanding, visual aesthetics, action/situation description, and movie script to obtain an affective-rich representation to understand and perceive the scene. We use an LSTM-based learning model for emotion perception. To evaluate our method, we analyze and compare our performance on three datasets, SENDv1, MovieGraphs, and the LIRIS-ACCEDE dataset, and observe an average of 10-15% increase in the performance over SOTA methods for all three datasets.

Over-parameterization of neural networks benefits the optimization and generalization yet brings cost in practice. Pruning is adopted as a post-processing solution to this problem, which aims to remove unnecessary parameters in a neural network with little performance compromised. It has been broadly believed the resulted sparse neural network cannot be trained from scratch to comparable accuracy. However, several recent works (e.g., [Frankle and Carbin, 2019a]) challenge this belief by discovering random sparse networks which can be trained to match the performance with their dense counterpart. This new pruning paradigm later inspires more new methods of pruning at initialization. In spite of the encouraging progress, how to coordinate these new pruning fashions with the traditional pruning has not been explored yet. This survey seeks to bridge the gap by proposing a general pruning framework so that the emerging pruning paradigms can be accommodated well with the traditional one. With it, we systematically reflect the major differences and new insights brought by these new pruning fashions, with representative works discussed at length. Finally, we summarize the open questions as worthy future directions.

The automotive industry is seen to have witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ high performance AI models to enable vehicles to perceive their environment and make driving decisions with little or no influence from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance to regulations. The assessment of these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have 'seen', done and might do in environments where they operate. In this paper, we provide a comprehensive survey of the existing work in explainable autonomous driving. First, we open by providing a motivation for explanations and examining existing standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and show their perceived need for explanation. Third, we provide a taxonomy of explanations and reviewed previous work on explanation in the different AV operations. Finally, we draw a close by pointing out pertinent challenges and future research directions. This survey serves to provide fundamental knowledge required of researchers who are interested in explanation in autonomous driving.