From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence

Neuroethics and AI ethics: a proposal for collaboration

The scientific relationship between neuroscience and artificial intelligence is generally acknowledged, and the role that their long history of collaboration has played in advancing both fields is often emphasized. Beyond the important scientific insights provided by their collaborative development, both neuroscience and AI raise a number of ethical issues that are generally explored by neuroethics and AI ethics. Neuroethics and AI ethics have been gaining prominence in the last few decades, and they are typically carried out by different research communities. However, considering the evolving landscape of AI-assisted neurotechnologies and the various conceptual and practical intersections between AI and neuroscience-such as the increasing application of AI in neuroscientific research, the healthcare of neurological and mental diseases, and the use of neuroscientific knowledge as inspiration for AI-some scholars are now calling for a collaborative relationship between these two domains. This article seeks to explore how a collaborative relationship between neuroethics and AI ethics can stimulate theoretical and, ideally, governance efforts. First, we offer some reasons for calling for the collaboration of the ethical reflection on neuroscientific innovations and AI. Next, we explore some dimensions that we think could be enhanced by the cross-fertilization between these two subfields of ethics. We believe that considering the pace and increasing fusion of neuroscience and AI in the development of innovations, broad and underspecified calls for responsibility that do not consider insights from different ethics subfields will only be partially successful in promoting meaningful changes in both research and applications.

The debate over understanding in AI's large language models

We survey a current, heated debate in the artificial intelligence (AI) research community on whether large pretrained language models can be said to understand language-and the physical and social situations language encodes-in any humanlike sense. We describe arguments that have been made for and against such understanding and key questions for the broader sciences of intelligence that have arisen in light of these arguments. We contend that an extended science of intelligence can be developed that will provide insight into distinct modes of understanding, their strengths and limitations, and the challenge of integrating diverse forms of cognition.

On the Contribution of Neuroethics to the Ethics and Regulation of Artificial Intelligence

Contemporary ethical analysis of Artificial Intelligence (AI) is growing rapidly. One of its most recognizable outcomes is the publication of a number of ethics guidelines that, intended to guide governmental policy, address issues raised by AI design, development, and implementation and generally present a set of recommendations. Here we propose two things: first, regarding content, since some of the applied issues raised by AI are related to fundamental questions about topics like intelligence, consciousness, and the ontological and ethical status of humans, among others, the treatment of these issues would benefit from interfacing with neuroethics that has been addressing those same issues in the context of brain research. Second, the identification and management of some of the practical ethical challenges raised by AI would be enriched by embracing the methodological resources used in neuroethics. In particular, we focus on the methodological distinction between conceptual and action-oriented neuroethical approaches. We argue that the normative (often principles-oriented) discussion about AI will benefit from further integration of conceptual analysis, including analysis of some operative assumptions, their meaning in different contexts, and their mutual relevance in order to avoid misplaced or disproportionate concerns and achieve a more realistic and useful approach to identifying and managing the emerging ethical issues.

Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process

The neuropsychological characteristics inside the brain are still not sufficiently understood in previous Gestalt psychological analyses. In particular, the extraction and analysis of human brain consciousness information itself have not received enough attention for the time being. In this paper, we aim to investigate the features of EEG signals from different conscious thoughts. Specifically, we try to extract the physiologically meaningful features of the brain responding to different contours and shapes in images in Gestalt cognitive tests by combining persistent homology analysis with electroencephalogram (EEG). The experimental results show that more brain regions in the frontal lobe are involved when the subject perceives the random and disordered combination of images compared to the ordered Gestalt images. Meanwhile, the persistence entropy of EEG data evoked by random sequence diagram (RSD) is significantly different from that evoked by the ordered Gestalt (GST) images in several frequency bands, which indicate that the human cognition of the shape and contour of images can be separated to some extent through topological analysis. This implies the feasibility to digitize the neural signals while preserving the whole and local features of the original signals, which are further verified by our extensive experiments. In general, this paper evaluates and quantifies cognitively related neural correlates by persistent homology features of EEG signals, which provides an approach to realizing the digitization of neural signals. Preliminary verification of the analyzability of human consciousness signals provides reliable research ideas and directions for the realization of feature extraction and analysis of human brain consciousness cognition.

Vision while the eyes move: Getting the full picture

Visual information is continuously sampled from our environment, even as the eyes move, which helps the visual system create a stable view of the world.

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.