Empathetic Computers: The Problem of Confusing Persons and Things

As computers become both more intelligent and ubiquitous we increasingly rely on them for forms of companionship. We are relational beings, instinctively drawn to those who relate back to us, an instinct that is rooted in our creation in the image of a triune, and thus relational, God. Relationships with computers, which necessarily displace relationships with other humans, have so far been shown to be dissatisfying. This dissatisfaction arises because a computer cannot be truly empathetic. It cannot feel emotion due to its lack of a body; it can only simulate emotion. This makes relationship with a computer similar to relationship with a sociopath and can isolate us from both others and our true selves.     

老年人智力与年龄关系研究述评

介绍关于老年人智力发展的三种理论观点，以及影响老年人智力发展的因素。讨论了三种常见的研究方法。最后，指出了当前老年人智力研究领域中存在的一些问题，并对将来的研究进行了展望。     

Development of the Greek version of the Spiritual Intelligence Self-Report Inventory-24 (KAPN): factor structure and validation

ABSTRACT

The Spiritual Intelligence Self-Report Inventory 24 (SISRI-24) is widely used to assess spiritual intelligence (SI) in general population samples. The current study explored the Greek version SISRI-24 factor structure in a convenience sample of 1777 adults. A translation of the original scale was performed in different stages, so as to obtain a fully comprehensible and accurate equivalent. Psychometric properties were analyzed at the level of item. The four-factor solution proposed in the original SISRI-24 was not confirmed. Instead, an alternative model, in which the SISRI-24 structure was revised and trimmed to a final three-factor, 17-item short-form version (KAPN), produced an instrument of sound construct validity [fit indices: CFI?=?.92, TLI?=?.91, RMSEA?=?.06, SRMR?=?.06] and robust internal consistency. The results are sufficient for endorsing the suitability of KAPN in Greek speaking populations, and extend cross-cultural support for the SI model. Implications and recommendations for future directions of research are discussed.     

Applying Deutsch’s concept of good explanations to artificial intelligence and neuroscience – An initial exploration

Artificial intelligence has made great strides since the deep learning revolution, but AI systems remain incapable of learning principles and rules which allow them to extrapolate outside of their training data to new situations. For inspiration we look to the domain of science, where scientists have been able to develop theories which show remarkable ability to extrapolate and sometimes even predict the existence of phenomena which have never been observed before. According to David Deutsch, this type of extrapolation, which he calls “reach”, is due to scientific theories being hard to vary. In this work we investigate Deutsch’s hard-to-vary principle and how it relates to more formalized principles in deep learning such as the bias-variance trade-off and Occam’s razor. We distinguish internal variability, how much a model/theory can be varied internally while still yielding the same predictions, with external variability, which is how much a model must be varied to predict new, out-of-distribution data. We discuss how to measure internal variability using the notion of the Rashomon set and how to measure external variability using Kolmogorov complexity. We explore what role hard-to-vary explanations play in intelligence by looking at the human brain, the only example of highly general purpose intelligence known. We distinguish two learning systems in the brain – the first operates similar to deep learning and likely underlies most of perception while the second is a more creative system capable of generating hard-to-vary models and explanations of the world. We make contact with Popperian epistemology which suggests that the generation of scientific theories is a not an inductive process but rather an evolutionary process which proceeds through conjecture and refutation. We argue that figuring out how replicate this second system, which is capable of generating hard-to-vary explanations, is a key challenge which needs to be solved in order to realize artificial general intelligence.