What entitles you to rely on information received from others? What entitles you to rely on information retrieved from your own memory? Intuitively, you are entitled simply to trust yourself, while you should monitor others for signs of untrustworthiness. This article makes a case for inverting the intuitive view, arguing that metacognitive monitoring of oneself is fundamental to the reliability of memory, while monitoring of others does not play a significant role in ensuring the reliability of testimony.

Real agents rely, when forming their beliefs, on imperfect informational sources (sources which deliver, even under normal conditions of operation, both accurate and inaccurate information). They therefore face the ‘endorsement problem’: how can beliefs produced by endorsing information received from imperfect sources be formed in an epistemically acceptable manner? Focussing on the case of episodic memory and drawing on empirical work on metamemory, this article argues that metacognition likely plays a crucial role in explaining how agents solve the endorsement problem.

Intelligence can be understood as a form of rationality, in the sense that an intelligent system does its best when its knowledge and resources are insufficient with respect to the problems to be solved. The traditional models of rationality typically assume some form of sufficiency of knowledge and resources, so cannot solve many theoretical and practical problems in Artificial Intelligence (AI). New models based on the Assumption of Insufficient Knowledge and Resources (AIKR) cannot be obtained by minor revisions or extensions (...) of the traditional models, and have to be established fully according to the restrictions and freedoms provided by AIKR. The practice of NARS, an AI project, shows that such new models are feasible and promising in providing a new theoretical foundation for the study of rationality, intelligence, consciousness, and mind. (shrink)

The study of causal inference has seen recent momentum in machine learning and artificial intelligence, particularly in the domains of transfer learning, reinforcement learning, automated diagnostics, and explainability. Yet, despite its increasing application to address many of the boundaries in modern AI, causal topics remain absent in most AI curricula. This work seeks to bridge this gap by providing classroom-ready introductions that integrate into traditional topics in AI, suggests intuitive graphical tools for the application to both new and traditional lessons (...) in probabilistic and causal reasoning, and presents avenues for instructors to impress the merit of climbing the “causal hierarchy” to address problems at the levels of associational, interventional, and counterfactual inference. Finally, this study shares anecdotal instructor experiences, successes, and challenges integrating these lessons at multiple levels of education. (shrink)

Binz et al. propose a general framework for meta-learning and contrast it with built-by-hand Bayesian models. We comment on some architectural assumptions of the approach, its relation to the active inference framework, its potential applicability to living systems in general, and the advantages of the latter in addressing the explanation problem.

Our society is in the middle of the AI revolution. We discuss several applications of AI, in particular medical causality, where deep-learning neural networks screen through big data bases, extracting associations between a patient’s condition and possible causes. While beneficial in medicine, several questionable AI trading strategies have emerged in finance. Though advantages in many aspects of our lives, serious threats of AI exist. We suggest several regulatory measures to reduce these threats. We further discuss whether ‘full AI robots’ should (...) be programmed with a virtual consciousness and conscience. While this would reduce AI threats via motivational control, other threats such as the desire for AI—human socioeconomic equality could prove detrimental. (shrink)