A large, crowdsourced evaluation of gesture generation systems on common data : the GENEA Challenge 2020

Co-speech gestures, gestures that accompany speech, play an important role in human communication. Automatic co-speech gesture generation is thus a key enabling technology for embodied conversational agents (ECAs), since humans expect ECAs to be capable of multi-modal communication. Research into gesture generation is rapidly gravitating towards data-driven methods. Unfortunately, individual research efforts in the field are difficult to compare: there are no established benchmarks, and each study tends to use its own dataset, motion visualisation, and evaluation methodology. To address this situation, we launched the GENEA Challenge, a gesture-generation challenge wherein participating teams built automatic gesture-generation systems on a common dataset, and the resulting systems were evaluated in parallel in a large, crowdsourced user study using the same motion-rendering pipeline. Since differences in evaluation outcomes between systems now are solely attributable to differences between the motion-generation methods, this enables benchmarking recent approaches against one another in order to get a better impression of the state of the art in the field. This paper reports on the purpose, design, results, and implications of our challenge.Part of Proceedings: ISBN 978-145038017-1QC 20210607</p

Speaking Music: A Historical Study of Edwin Gordon\u27s Music Learning Theory

Music Learning Theory, conceived, researched, and developed by Dr. Edwin Elias Gordan, has been on the periphery of music education for decades and is the only extant comprehensive theoretical framework that fully addresses the development of music literacy from early childhood through maturity. The concurrent research gap suggests that a Fordist approach may exist throughout music education – one that insists upon behavioral goals, direct instruction, and educational, artistic, and ideological exclusivity. This historical study elucidates Gordan’s work in order to understand the stages and processes that are like spokes of a wheel between his idea of audiation at the core and Music Learning Theory on the outer rim. Conclusions bring Gordon’s concepts within Music Learning Theory to the fore to address this potential gap in practice and exclusion in music education by revealing the theory’s usefulness in explaining how learning occurs while guiding instruction individual student project. The information gleaned is practical and displays Music Learning Theory as a possibility for all forms of music education but particularly for instrumental instruction. It represents possibilities in music instruction beyond those associated with traditional teaching and application of musical concepts and skills

Bounded Evaluation: Cognition, Incoherence, and Regulatory Policy

Cass Sunstein, Daniel Kahneman, David Schkade, and Ilana Ritov have recently advanced a cognitive explanation for incoherence in legal decisionmaking, showing how decision makers tend to make micro-level judgments that make little sense when viewed from a broader perspective. Among other things, they claimed to have discovered striking incoherence in regulatory policy evidenced by varied penalty levels across different statutes, with less serious violations sometimes backed up with higher penalties than more serious violations. This paper comments on Sunstein et al.\u27s treatment of incoherence in regulatory policy, arguing that the same cognitive limitations that Sunstein et al. argue lead to incoherence in the design of regulatory policy also affect judgments about the existence of incoherence itself. Due to cognitive effects, individuals may have a tendency to see incoherence in the legal system when on closer examination there is none. Specifically, observable variations in regulatory policies will sometimes be sensible and justifiable, even though people may at first glance think they are obviously incoherent. When it comes to regulatory penalties, these penalties could quite sensibly be higher for less serious violations if other considerations discussed in this paper are taken into account. The same kind of bounded evaluation problem arises when regulations are judged to be incoherent based on variation in their cost-effectiveness. Regulatory policies that appear incoherent when compared along one dimension or evaluated with only one purpose in mind will not necessarily be properly viewed as incoherent once other dimensions or purposes are taken into account. Indeed, because the conditions underlying regulatory policy making are both varied and complex, judgments about the incoherence of regulatory policies will be unavoidably difficult and even sometimes incoherent themselves

Large Language Model Alignment: A Survey

Recent years have witnessed remarkable progress made in large language models (LLMs). Such advancements, while garnering significant attention, have concurrently elicited various concerns. The potential of these models is undeniably vast; however, they may yield texts that are imprecise, misleading, or even detrimental. Consequently, it becomes paramount to employ alignment techniques to ensure these models to exhibit behaviors consistent with human values. This survey endeavors to furnish an extensive exploration of alignment methodologies designed for LLMs, in conjunction with the extant capability research in this domain. Adopting the lens of AI alignment, we categorize the prevailing methods and emergent proposals for the alignment of LLMs into outer and inner alignment. We also probe into salient issues including the models' interpretability, and potential vulnerabilities to adversarial attacks. To assess LLM alignment, we present a wide variety of benchmarks and evaluation methodologies. After discussing the state of alignment research for LLMs, we finally cast a vision toward the future, contemplating the promising avenues of research that lie ahead. Our aspiration for this survey extends beyond merely spurring research interests in this realm. We also envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.Comment: 76 page

In defense of voting—reinterpreting the terms of the voting calculus with a view toward election law and policy

Democratic voting is desperately in need of defense. Contemporary institutions of mass electoral participation are undertheorized, and there is a critical disconnect between conceptions of voting in democratic theory and election laws and policies that implicate participatory values and interests. This dissertation fills some of these gaps between the theories and practices of modern democracy by examining the factors that motivate individual decisions to vote or abstain and the electoral institutions that structure and respond to such decisions. With a primary focus on elections in the United States, this work explores how normative conceptions of voting not only influence individual participation decisions, but also provide foundations for electoral rules and procedures that impact turnout levels, both in the aggregate and for distinct demographic groups. As an analytical framework, the rational choice calculus of voting is utilized to parse the varied motivations for turnout, with the four elements of the calculus providing the outline for the four main chapters of the dissertation. The voting calculus has often been interpreted in ways that minimize the value of voting and provide reasons that explain why individuals do not—and perhaps even should not—participate in elections. This dissertation critically examines those views, and it reinterprets the terms of the calculus in a manner that demonstrates how the act of voting can in fact be highly valued, which explains why individuals do—and indeed generally should—participate in democratic elections. The analysis proceeds by first redefining the expected probability of one vote having a casual effect on an election outcome (Chapter 1), then by reevaluating the normative significance of the instrumental benefits of voting (Chapter 2) and the various types of voting costs (Chapter 3), and finally by reconsidering the theoretical and practical implications of non-instrumental motivations for participation, especially the notion of a civic duty to vote (Chapter 4). Each chapter further derives policy, legal, and broader ethical implications associated with these new interpretations of the terms of the calculus and makes specific reform proposals designed to increase participation in American elections at federal, state, and local levelsPublic Polic

Using Gaussian Processes for Rumour Stance Classification in Social Media

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted