Online research in philosophy

The realities and myths of long-term care and the challenges it poses for the ethics of autonomy are analyzed in this perceptive work. The book defends the concept of autonomy, but argues that the standard view of autonomy as non-interference and independence has only a limited applicability for long term care. The treatment of actual autonomy stresses the developmental and social nature of human persons and the priority of identification over autonomous choice. The work balances analysis of the ethical concepts associated with autonomy with discussion of the implications of the ethical analysis for long term care. A central chapter involves a phenomenological analysis of four general features of everyday experience (space, time, communication, and affectivity) and explores their practical implications for long term care. This work concludes with a discussion of the advantages associated with a phenomenologically-inspired treatment of actual autonomy for the ethics of long-term care.

Physicians have long tinkered with ways to "improve" the human brain, but as our understanding of that organ's inner workings quickly grows, artificial enhancement is becoming more feasible. Military research is at the forefront of this work, much of it focused on drugs. The goal is to produce a better soldier, but the emerging techniques could just as easily be applied to any individual. The military wants to juice up personnel's brains because the human being is the weakest instrument of warfare. Although for centuries astonishing and terrifying advances have been made in the technology of conflict, soldiers are basically the same. They must eat, sleep, discern friend from foe, heal when wounded, and so forth. The first state (or nonstate) actor to build superior fighters will make an enormous leap in the arms race. In the short run, researchers are trying to devise aids that would overcome a person's inherent limitations, such as mental fatigue. Long-term results could lead to individuals everywhere who are tireless, less fearful or even better speakers.

Our current conception of intelligence as measured by IQ tests is “mind-blind”. IQ tests lack ecological validity because they ignore social cognition – the “mindreading” prowess that enabled one species of social primate to become the most cognitively successful on the planet. In this talk, I shall examine how to correct the ethnocentric and anthropocentric biases of our perspective-taking abilities. What future technologies can enrich our capacity to understand other minds? I shall also discuss obstacles to building empathetic AGI (artificial general intelligence) - and why old-fashioned “autistic AI” may always be vulnerable to the cunning of “Machiavellian apes”. In an era of global catastrophic and existential risks, developing ways to enrich and “de-bias” mankind's capacity for empathetic cognition will be vital. This is because the greatest underlying risk to the wellbeing of life on Earth is the dominance behaviour of other male human primates.

Newell was a founder of artificial intelligence (AI) and a pioneer in the use of computer simulations in psychology. In collaboration with J. Cliff Shaw and Herbert A. <span class='Hi'>Simon</span>, Newell developed the first list-processing programming language as well as the earliest computer programs for simulating human problem solving. Over a long and prolific career, he contributed to many techniques, such as protocol analysis and heuristic search, that are now part of psychology and computer science. Colleagues remembered Newell for his deep commitment to science, his care for details, and his inexhaustible energy.

There are many entry points into the problem of categorization. Two particularly important ones are the so-called top-down and bottom-up approaches. Top-down approaches such as artificial intelligence begin with the symbolic names and descriptions for some categories already given; computer programs are written to manipulate the symbols. Cognitive modeling involves the further assumption that such symbol-interactions resemble the way our brains do categorization. An explicit expectation of the top-down approach is that it will eventually join with the bottom-up approach, which tries to model how the hardware of the brain works: sensory systems, motor systems and neural activity in general. The assumption is that the symbolic cognitive functions will be implemented in brain function and linked to the sense organs and the organs of movement in roughly the way a program is implemented in a computer, with its links to peripheral devices such as transducers and effectors.

This interdisciplinary book presents recent work on emotions in neuroscience, cognitive science, philosophy, computer science, artificial intelligence, and...

Artificial intelligence is a possibility that should not be ignored in any serious thinking about the future, and it raises many profound issues for ethics and public policy that philosophers ought to start thinking about. This article outlines the case for thinking that human-level machine intelligence might well appear within the next half century. It then explains four immediate consequences of such a development, and argues that machine intelligence would have a revolutionary impact on a wide range of the social, political, economic, commercial, technological, scientific and environmental issues that humanity will face over the coming decades.

Artificial Intelligence as a discipline has gotten bogged down in subproblems of intelligence. These subproblems are the result of applying reductionist methods to the goal of creating a complete artificial thinking mind. In Brooks (1987) 1 have argued that these methods will lead us to solving irrelevant problems; interesting as intellectual puzzles, but useless in the long run for creating an artificial being.

A simple exogenous growth model gives conservative estimates of the economic implications of machine intelligence. Machines complement human labor when they become more productive at the jobs they perform, but machines also substitute for human labor by taking over human jobs. At first, expensive hardware and software does only the few jobs where computers have the strongest advantage over humans. Eventually, computers do most jobs. At first, complementary effects dominate, and human wages rise with computer productivity. But eventually substitution can dominate, making wages fall as fast as computer prices now do. An intelligence population explosion makes per-intelligence consumption fall this fast, while economic growth rates rise by an order of magnitude or more. These results are robust to automating incrementally, and to distinguishing hardware, software, and human capital from other forms of capital.

The ethical issues related to the possible future creation of machines with general intellectual capabilities far outstripping those of humans are quite distinct from any ethical problems arising in current automation and information systems. Such superintelligence would not be just another technological development; it would be the most important invention ever made, and would lead to explosive progress in all scientific and technological fields, as the superintelligence would conduct research with superhuman efficiency. To the extent that ethics is a cognitive pursuit, a superintelligence could also easily surpass humans in the quality of its moral thinking. However, it would be up to the designers of the superintelligence to specify its original motivations. Since the superintelligence may become unstoppably powerful because of its intellectual superiority and the technologies it could develop, it is crucial that it be provided with human-friendly motivations. This paper surveys some of the unique ethical issues in creating superintelligence, and discusses what motivations we ought to give a superintelligence, and introduces some cost-benefit considerations relating to whether the development of superintelligent machines ought to be accelerated or retarded.