Autonomous Artificial Intelligence Systems Research Articles

Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process

Artificial intelligence (AI) describes systems capable of making decisions of high cognitive complexity; autonomous AI systems in healthcare are AI systems that make clinical decisions without human oversight. Such rigorously validated medical diagnostic AI systems hold great promise for improving access to care, increasing accuracy, and lowering cost, while enabling specialist physicians to provide the greatest value by managing and treating patients whose outcomes can be improved. Ensuring that autonomous AI provides these benefits requires evaluation of the autonomous AI's effect on patient outcome, design, validation, data usage, and accountability, from a bioethics and accountability perspective. We performed a literature review of bioethical principles for AI, and derived evaluation rules for autonomous AI, grounded in bioethical principles. The rules include patient outcome, validation, reference standard, design, data usage, and accountability for medical liability. Application of the rules explains successful US Food and Drug Administration (FDA) de novo authorization of an example, the first autonomous point-of-care diabetic retinopathy examination de novo authorized by the FDA, after a preregistered clinical trial. Physicians need to become competent in understanding the potential risks and benefits of autonomous AI, and understand its design, safety, efficacy and equity, validation, and liability, as well as how its data were obtained. The autonomous AI evaluation rules introduced here can help physicians understand limitations and risks as well as the potential benefits of autonomous AI for their patients.

Functionally Effective Conscious AI Without Suffering

Insofar as consciousness has a functional role in facilitating learning and behavioral control, the builders of autonomous Artificial Intelligence (AI) systems are likely to attempt to incorporate it into their designs. The extensive literature on the ethics of AI is concerned with ensuring that AI systems, and especially autonomous conscious ones, behave ethically. In contrast, our focus here is on the rarely discussed complementary aspect of engineering conscious AI: how to avoid condemning such systems, for whose creation we would be solely responsible, to unavoidable suffering brought about by phenomenal self-consciousness. We outline two complementary approaches to this problem, one motivated by a philosophical analysis of the phenomenal self, and the other by certain computational concepts in reinforcement learning.

2D group 6 transition metal dichalcogenides toward wearable electronics and optoelectronics

Wearable electronic and optoelectronic devices accommodable to various mechanical deformations are becoming increasingly ubiquitous in various realms spanning electronic skins, electronic eyes, intelligent wristbands, lenses, smartphones/watches/glass, robotics, foldable screens, autonomous artificial intelligence systems, etc., which, however, are beyond the scope of conventional bulky semiconductors. Fortunately, 2D group 6 transition metal dichalcogenides (TMDCs) emerge as alternatives, having advantages of strong mechanical strength, high conformability, semitransparency, high biocompatibility, large surface-to-volume ratio, and favorable bandgaps. Highlighting an indispensable physical foundation for reliable wearability, this perspective begins with an elaboration on the structural and mechanical attributes of 2D TMDCs. Then, the latest research advancements of wearable electronic and optoelectronic devices built on them, including field-effect transistors, gas/ion/bio/tactile sensors, photodetectors, and proof-of-concept novel applications, such as electronic noses, flexible displays, and the underlying working mechanisms, are compactly surveyed. This leads us to come up with the predominant challenges standing in the way of further development, and strategies addressing them, along with associated physical principles that are comprehensively outlined.

A Motivational Model of BCI-Controlled Heuristic Search.

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.

Should fully autonomous artificial intelligence systems be granted legal capacity?

The aim of this article is to address the issue of granting legal capacity to artificial inteligence systems. In order to approach the solution to the problem addressed, the article includes several aspects, relevant in order to achieve it. To begin with, the general concept of legal capacity is introduced. Following this aspect, the main features of both natural and juridical persons are addressed, in order to become familiar with the content of legal capacity, or in other words, to be aware – what features do these individuals, which are at present granted legal capacity, possess. Furthermore, the different topic of artificial intelligence is explained, having the aim to clarify, what are the features of artificial intelligence, and whether these features conform with the criteria, which are needed in order to be granted legal capacity. Following this, the additional questions are being discussed, which are relevant while deciding the present issue. Finally, the conclusions of the research have been made, regarding the presented problem.

Liability Issues in Relation to Autonomous AI Systems

This paper explores issues relating to two fundamental questions, how mitigate the risks associated with a constant expansion in autonomy of artificial intelligence (AI) and how to make people liable for their AI creations. The aim of this paper is to only highlight issues fundamental to the constituents of the legal regulatory structure for autonomous AI systems at following levels: (i) desirable attributes of AI, (ii) legal approach and principles to determine liability, and (iii) contractual arrangement governing relationship between developers and users of AI.