12 research outputs found
Mentality and Object: Computational and Cognitive Diachronic Emergence
Espousing non-reductive physicalism, how do we pick out the specific relevant physical notion(s) from physical facts, specifically in relation to phenomenal experience? Beginning with a historical review of Gilbert Ryle’s behaviorism and moving through Hilary Putnam’s machine-state functionalism and Wilfrid Sellars’ inferential framework, up to more contemporaneous computationalist- and cognitivist-functionalism (Gualtiero Piccinini), we survey accounts of mentality that countenance the emergence of mental states vide input- and output-scheme. Ultimately arriving at the conclusion that functionalism cannot account for problems such as no-cognition reports, we see any robust defense of physicalism must appeal to other principles. Thus we move on to the question of emergence, not as it pertains to the hard(er) problem, but to the matter of conceptual externalization of mental properties from physical properties. Accordingly, we navigate Karen Bennett’s compatibilist solution to the exclusion argument against mental causation for the non-reductive physicalist position, according to which the physical effects of mental cases are not overdetermined, demonstrating that this backfires by offering a path for the mind-body interactionist Dualist to claim causal closure by appealing to this same schema. We conclude with a series of conceptual musings regarding rationality which take into account our challenges and findings, querying about whether phenomenal consciousness is a fundamentally private, or socially configured, notion
A computational model of focused attention meditation and its transfer to a sustained attention task
How does rumination impact cognition? A first mechanistic model.
Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression
Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In
recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence
Model of models -- Part 1
This paper proposes a new cognitive model, acting as the main component of an
AGI agent. The model is introduced in its mature intelligence state, and as an
extension of previous models, DENN, and especially AKREM, by including
operational models (frames/classes) and will. This model's core assumption is
that cognition is about operating on accumulated knowledge, with the guidance
of an appropriate will. Also, we assume that the actions, part of knowledge,
are learning to be aligned with will, during the evolution phase that precedes
the mature intelligence state. In addition, this model is mainly based on the
duality principle in every known intelligent aspect, such as exhibiting both
top-down and bottom-up model learning, generalization verse specialization, and
more. Furthermore, a holistic approach is advocated for AGI designing, and
cognition under constraints or efficiency is proposed, in the form of
reusability and simplicity. Finally, reaching this mature state is described
via a cognitive evolution from infancy to adulthood, utilizing a consolidation
principle. The final product of this cognitive model is a dynamic operational
memory of models and instances. Lastly, some examples and preliminary ideas for
the evolution phase to reach the mature state are presented.Comment: arXiv admin note: text overlap with arXiv:2301.1355
A computational model of focused attention meditation and its transfer to a sustained attention task
Automated Validation of State-Based Client-Centric Isolation with TLA <sup>+</sup>
Clear consistency guarantees on data are paramount for the design and implementation of distributed systems. When implementing distributed applications, developers require approaches to verify the data consistency guarantees of an implementation choice. Crooks et al. define a state-based and client-centric model of database isolation. This paper formalizes this state-based model in, reproduces their examples and shows how to model check runtime traces and algorithms with this formalization. The formalized model in enables semi-automatic model checking for different implementation alternatives for transactional operations and allows checking of conformance to isolation levels. We reproduce examples of the original paper and confirm the isolation guarantees of the combination of the well-known 2-phase locking and 2-phase commit algorithms. Using model checking this formalization can also help finding bugs in incorrect specifications. This improves feasibility of automated checking of isolation guarantees in synthesized synchronization implementations and it provides an environment for experimenting with new designs.</p
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio