The application of cognitive neuroscience to judicial models: recent progress and trends

Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the ambiguity of legal language and the diversity of value functions. The imprecision and procedural knowledge inherent in law makes judicial issues difficult to be expressed in a computer symbol system. Current semantic processing and machine learning technologies cannot fully capture the complex nature of legal relations, thereby raising doubts about the accuracy of legal predictions and reliability of judicial models. Cognitive computing, designed to emulate human brain functions and aid in enhancing decision-making processes, offers a better understanding of legal data and the processes of legal reasoning. This paper discusses the advancements made in cognitive methods applied to legal concept learning, semantic extraction, judicial data processing, legal reasoning, understanding of judicial bias, and the interpretability of judicial models. The integration of cognitive neuroscience with law has facilitated several constructive attempts, indicating that the evolution of cognitive law could be the next frontier in the intersection of AI and legal practice

The Role of Practical Reasoning and Typification in Consumer Analytics Work: An Ethnomethodological Study

Traditional scholarship views quantitative people-categorization in the workplace—i.e. the use of big data to group consumers and categorize their cultures—as primarily a problem of technical and statistical optimization. By contrast, my thesis emphasizes a very different research dimension: namely, the role that practical reasoning plays as workers organize themselves locally to categorize and apply data-based groups. Drawing on the ethnomethodological understanding of practical reasoning, I focus on the way the locally organized talk accomplishes people-categorization as a self-contained activity. Specifically, I will argue that practical reasoning shapes the way workers, through their talk, combine technology, conversation, and everyday practice to render scenes as reasonable and accountable in their attempt to anticipate, understand, and apply consumer preferences, behaviors, and so on. To do this, analysts should go beyond standard empirical methods to adopt a more radically reflexive stance toward workplace discourse. Next, I will argue that the benefits of adopting such an interpretive methodological stance in this setting are threefold: first, this approach will help market researchers and design professionals rethink how they conduct market segmentation and persona development, two important techniques debated in academia, but used extensively in professional settings to design products, processes, and marketing plans. I will show that “practical” actors, through their locally organized practices, make and find in ordinary taken-for-granted ways “market segmentation” and “persona development” as reasonable ways of assembling the world of people-categorization in the workplace. Second, this approach broadens arguments about the “social life of methods” to include professions outside of the academy that apply statistical methods to big data, and to radically consider our relationship with technology. Furthermore, I will argue that part of understanding practical reasoning in the workplace includes identifying the hold that the unquestioned commitment to expanding technology has on discourse. For the latter, I adopt a radical interpretive perspective in order to reveal the irony of our focus on expanding our human powers through technology. To support my claims, I have divided my argument into four main sections, each one given its own chapter. Chapter 1 reviews how digital advertising workers combine big data about groups of people and their culture with other resources to build to a finished technical product. Chapter 2 outlines how these same workers rely on interpretive methods during the conceptual development of big data people segments. Chapter 3 demonstrates how analysts rely on interpretive methods and background expectancies during the process of accessing, extracting, and analyzing big data about groups of people and their culture. These methods can help professionals achieve a richer understanding of consumer culture, and consequently, can help them make better big-data application decisions throughout the design cycle. Chapter 4 takes a radical interpretive case study format and demonstrates how treating digital advertising worker dialogue as discourse reveals important methods for designers, for workers and for social inquirers. In this final Chapter, I show how a very particular example of a stretch of talk about a piece of technology can be examined as a cultural expression of the desire to expand human powers, and I show how the abstract idea of the desire to expand human powers can be critically addressed as a possibility and actualization in its own right. The analysis in Chapter 4 reveals the seen but unnoticed assumption embedded in the culture concerning the unquestioned commitment to expanding technology, which, it can be argued, has undermined our capacity to talk about purpose or point; instead, the talk takes for granted the assumption that there is only one purpose: expanding our human powers. The principle of expanding our human powers through technology does not just have to be assumed; it can and should be critically engaged. This engagement is accomplished by drawing on radical interpretive approaches to modernity, including Grant (1969), and Arendt (1958)

PaLM: Scaling Language Modeling with Pathways

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies

Economics and the Complexity Vision: Chimerical Partners or Elysian Adventurers?

This work began as a review article of: "Complexity and the History of Economic Thought", edited by David Colander, Routledge, London,UK, 2000; & "The Complexity Vision and the Teaching of Economics", edited by David Colander, Edward Elgar, Cheltenham, UK, 2000. It has, in the writing, developed into my own vision of complexity economics

Instrumentalization in the Public Smart Bikeshare Sector

This thesis is concerned with understanding how smart technologies are conceived, created and implemented, and explores the ways these processes are shaped by historical, geo-political, economic and technical contexts. At its core the thesis is concerned with understanding how technical citizenship and democracy can be preserved within the design process against a backdrop of increasing neoliberalism and technocracy. This is investigated by means of a comparative study of smart public bikeshare schemes in Dublin, Ireland and Hamilton, Canada. These schemes are configured and systemized using a variety of technical and ideological rationales and express the imaginaries of place in significantly different ways. Utilising a conceptual framework derived from Andrew Feenberg’s critical theory of technology, the thesis unpacks and problematizes the innovation process in order to understand how the outcomes of these schemes support the way of life of one or another influential social group. The philosophical orientation of the study is critical constructivism which combines a form of constructivism with more systematic and socially critical views of technology. The axis of comparison between the schemes is democratization and the manner in which the rationalizations and embedded cultural assumptions characterizing particular places operate to support or resist more egalitarian forms of participation. Methodologically, Feenberg’s critical framework is supported both by theory-driven thematic coding and critical hermeneutics which is an interpretative process that compliments the theoretical framework and positions issues of power and ideology within a wider, macro-level context. Data sources supporting the research comprise interviews, a variety of documentary sources and the architectures and technical specifications of both smart bikeshare systems. The findings from the research illustrate that despite the pervasiveness of a neoliberal orthodoxy conditioning technology production, citizen-centric design is still possible within a climate of consensus building and cooperation. As such, the thesis adds to the body of knowledge on philosophy of technology, critical urbanism, smart city development, democratic engagement and collaborative infrastructuring. In addition, the conceptual framework, developed in response to the empirical cases, represents an elaboration of Feenberg’s work and so the thesis also makes an important contribution to the analytic and methodological potential of critical theory of technology

Play Among Books

How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books