Exploring Design Space For An Integrated Intelligent System

Understanding the trade-offs available in the design space of intelligent systems is a major unaddressed element in the study of Artificial Intelligence. In this paper we approach this problem in two ways. First, we discuss the development of our integrated robotic system in terms of its trajectory through design space. Second, we demonstrate the practical implications of architectural design decisions by using this system as an experimental platform for comparing behaviourally similar yet architecturally different systems. The results of this show that our system occupies a "sweet spot" in design space in terms of the cost of moving information between processing components

Anti-Innovation Norms

Intellectual property (IP) scholars have recently turned their attention to social norms—informal rules that emerge from and are enforced by nonhierarchically organized social forces—as a promising way to spur innovation in communities as diverse as the fashion industry and the open-source software movement. The narrative that has emerged celebrates social norms’ ability to solve IP’s free-rider problem without incurring IP’s costs. But this account does not fully consider the dark side of social norms. In fact, certain social norms, when overenforced, can create substantial barriers to the most socially beneficial creative pursuits. Because IP scholars have left unexplored how social norms can hinder innovation in this way, the harm they cause has gone unmitigated. This Article sheds light on the dark side of innovation norms. It coins the term “anti-innovation norms” to label these counterproductive social forces. Using the double lens of sociology and psychology, it gives a full theoretical account of three types of anti-innovation norms: research priority, methodology, and evaluation norms—all of which interfere with socially beneficial boundary-crossing innovation. Our elucidation of anti-innovation norms has both theoretical and policy implications. On the theory side, it suggests that IP scholars to date have been too focused on addressing the free-rider problem. This has caused them to overlook other barriers to innovation, like those posed by the set of anti-innovation norms we describe here. This focus on free riding may also help explain why innovation and norms scholars have paid little attention to debates within the broader literature on law and social norms concerned with identifying situations in which social norms are welfare reducing. On the policy side, it points to innovation dilemmas that IP is not fully equipped to solve. While changes to the IP doctrines of attribution and fair use in copyright and nonobviousness in patent law can counteract anti-innovation norms at the margin, a comprehensive solution requires innovation scholars to broaden their vision beyond the IP toolkit. We take the first steps in this direction, proposing a number of interventions, including novel funding regimes and tax credits

Fear and Loathing: Shame, Shaming, and Intellectual Property

This paper investigates the relationship between intellectual property protection, shame, and shaming. Although some scholars have examined shame and shaming as they relate to criminal law and behavior, none have considered how shame and shaming govern intellectual property and copying behavior. This paper identifies and focuses on two significant intersections: First, shame shapes the behavior of would-be copiers, who abide by anti-copying norms even in the absence of formal intellectual property protection. Second, public shaming shapes the behavior of intellectual property owners, who refrain from aggressively enforcing their rights to avoid being identified as bullies or trolls. These two shame/shaming effects have opposing results — on one hand, restriction on copying, and on the other, the freedom to copy — but they unite to establish and enforce intellectual property “negative spaces” where innovation and creation thrive without significant formal intellectual property protection or enforcement. In areas beyond the reach of formal intellectual property protection, shame helps define the boundaries of informal or norms-based intellectual property practices. In areas governed by formal intellectual property protection, shaming helps define the boundaries of rights holders’ enforcement forbearance. The result of these effects is an overlay of shame- and shaming-driven behavior that sits atop, and informally adjusts, the boundaries of formal intellectual property protection. This, in turn, requires us to adjust our thinking about the ideal boundaries of formal protection. Shame and shaming are not suitable substitutes for formal law, nor are they miracle cures for law’s failings, but they may act as guideposts for determining where to draw the lines of formal legal protection

Copyrights Without Limits—The Undefeatable Right of Access Control Under §1201(A) of the Digital Millennium Copyright Act

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A Heuristic Approach to Creating Technological Fair Use Guidelines in Higher Education

Higher education has experienced challenges defining and implementing copyright compliance. Confusion among faculty and staff appears to be common regarding copyright and fair use. The original copyright doctrine was drafted over 200 years ago, which predates practically all technological advances that have and will continue to occur. Change is slow and onerous with most legislation; there is not much possibility the small amendments made to the law will be able to keep pace with the continual technological evolution. Further, judges are citing precedents in court rulings of copyright disputes that were made using the best interpretation of the law, even though those earlier adjudicators had nothing concrete upon which to base decisions. The cycle of loose interpretations further exacerbates the copyright and fair use problem involving technology. Moreover, this concern has been magnified due to the digital nature of lesson delivery most learning institutions are adopting today. The rapid, widespread move toward online learning methods creates an entire set of copyright and fair use circumstances that extend beyond the traditional, face-to-face pedagogical issues. Invariably, schools will be left to attempt to decide what will be considered legal and safe, often by trial and error, until clearer, universally accepted guidelines can be created. A group consensus for best practice was achieved over three rounds of surveying with the help of a Delphi panel highly experienced in copyright laws. Opinions converged early during the process, where proper fair use assessment was one of the major themes appearing during the first round. Respondents also agreed future educators will undoubtedly continue to struggle with fully understanding the intricacies of fair use. An overall consensus reached for many questions was sufficient for answering the proposed research questions and drafting a list of recommendations for technological fair use. The outcome should add to the existing knowledge base, given the limited number of studies that have been conducted regarding the complexities of copyright topics in distance and online education. Recommendations for further investigations encourages researchers to continue where this effort ends to remain current and compliant with the ubiquitous changes in technologies

Knowledge Commons

This chapter provides an introduction to and overview of the knowledge commons research framework. Knowledge commons refers to an institutional approach (commons) to governing the production, use, management, and/or preservation of a particular type of resource (knowledge). The research framework supplies a template for interrogating the details of knowledge commons institutions on a case study basis, generating qualitative data that may be used to support comparative analysis

Continual Learing of Hand Gestures for Human Robot Interaction

Human communication is multimodal. For years, natural language processing has been studied as a form of human-machine or human-robot interaction. In recent years, computer vision techniques have been applied to the recognition of static and dynamic gestures, and progress is being made in sign language recognition too. The typical way to train a machine learning algorithm to perform a classification task is to provide training examples for all the classes that need to be identified by the model. In a real-world scenario, such as in the use of assistive robots, it is useful to learn new concepts from interaction. However, unlike biological brains, artificial neural networks suffer from catastrophic forgetting, and as a result, are not good at incrementally learning new classes. In this thesis, the HAnd Gesture Incremental Learning (HAGIL) framework is proposed as a method to incrementally learn to classify static hand gestures. We show that HAGIL is able to incrementally learn up to 36 new symbols using only 5 samples for each old symbol, achieving a final average accuracy of over 90%. In addition to that, the incremental training time is reduced to a 10% of the time required when using all data available