Over the next few years, society as a whole will need to address what core values it wishes to protect when dealing with technology. Anthropology, a field dedicated to the very notion of what it means to be human, can provide some interesting insights into how to cope and tackle these changes in our Western society and other areas of the world. It can be challenging for social science practitioners to grasp and keep up with the pace of technological innovation, with many being unfamiliar with the jargon of AI. This short guide serves as both an introduction to AI ethics and social science and anthropological perspectives on the development of AI. It intends to provide those unfamiliar with the field with an insight into the societal impact of AI systems and how, in turn, these systems can lead us to rethink how our world operates.

Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users' consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.

Arlo has announced updated versions of two of its wireless security cameras. The $199.99 Pro 4 can capture 2K HDR footage in a 160-degree field of view. It connects directly to WiFi, so it can work as a standalone camera. However, you can integrate the Pro 4 into an existing Arlo setup by connecting it to a SmartHub or base station. The Ultra 2, meanwhile, boasts 4K HDR video capture along with an ultra-wide, 180-degree field of view.

Music has been always with us (I mean, human beings) throughout history. It wouldn't be so necessary to bring out the official definition of music here, but we call any sound that has a rhythmic or melodic feature as music. Probably, it doesn't necessarily give us pleasure but it definitely gives some emotional influence by listening to it. This appears to be borne out by the archaeological evidence. While the first hand axes and spears date back about 1.7 million years and 500,000 years respectively, the earliest known musical instruments are just 40,000 years old. It might be relatively recent compare to the first hand axes and spears, but 40,000 years is already a quite long time ago.

If you have Facebook's Portal TV device, you can now use it to stream Netflix shows and movies. The streaming giant had been a notable omission from a lineup that includes Amazon Prime Video, Showtime, Sling TV and, of course, Facebook Watch. Facebook is following Amazon (which just announced Netflix integration a couple of weeks ago) in bringing Netflix support to its smart displays. The company also revealed a new remote for Portal TV. It has dedicated buttons for quick access to Netflix and Prime Video.

In this article, we first survey the three major types of computer music systems based on AI techniques: (1) compositional, (2) improvisational, and (3) performance systems. Representative examples of each type are briefly described. Then, we look in more detail at the problem of endowing the resulting performances with the expressiveness that characterizes human-generated music. This is one of the most challenging aspects of computer music that has been addressed just recently. The main problem in modeling expressiveness is to grasp the performer's "touch," that is, the knowledge applied when performing a score.

The article introduces the reader to a large interdisciplinary research project whose goal is to use AI to gain new insight into a complex artistic phenomenon. We study fundamental principles of expressive music performance by measuring performance aspects in large numbers of recordings by highly skilled musicians (concert pianists) and analyzing the data with state-of-the-art methods from areas such as machine learning, data mining, and data visualization. The article first introduces the general research questions that guide the project and then summarizes some of the most important results achieved to date, with an emphasis on the most recent and still rather speculative work. A broad view of the discovery process is given, from data acquisition through data visualization to inductive model building and pattern discovery, and it turns out that AI plays an important role in all stages of such an ambitious enterprise. Our current results show that it is possible for machines to make novel and interesting discoveries even in a domain such as music and that even if we might never find the "Horowitz Factor," AI can give us completely new insights into complex artistic behavior.

Following a brief overview discussing why we prefer listening to expressive music instead of lifeless synthesized music, we examine a representative selection of well-known approaches to expressive computer music performance with an emphasis on AI-related approaches. In the main part of the paper we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on TempoExpress, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting on complementing audio information with information of the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This paper is based on the "2011 Robert S. Engelmore Memorial Lecture" given by the first author at AAAI/IAAI 2011.

Abstract: Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide insights into how to improve them. Current inspection methods, however, fail to link modules to their functionality. In this paper, we present a novel method based on learning binary weight masks to identify individual weights and subnets responsible for specific functions. Using this powerful tool, we contribute an extensive study of emerging modularity in NNs that covers several standard architectures and datasets. We demonstrate how common NNs fail to reuse submodules and offer new insights into the related issue of systematic generalization on language tasks.

Riiid, a leader in AI education solutions, just launched a challenge via Kaggle to use the largest educational dataset to build innovative algorithms that track knowledge states of 780K students. The goal is to accurately predict how students will perform on future interactions. If successful, it's possible that any student with an Internet connection can enjoy the benefits of a personalized learning experience, regardless of where they live. With your participation, we can build a better and more equitable model for education in a post-COVID-19 world. The 100K prize competition will run from October 5 to January 2021 and the winning models will have a chance to present at AAAI 2021.