Meaningful research on sports injuries in professionals depends on data being available without restrictions because of ownership, argues Orchard, in a leading article in the British Journal of Sports Medicine. The crux of the argument is that sports professionals are public figures and their injuries are in ….

Walter applies the methodology of neurophilosophy to one of philosophy's centralchallenges, the notion of free will. Neurophilosophical conclusions are based on, and consistentwith, scientific knowledge about the brain and its functioning.

Oxford philosopher Nick Bostrom; in his recent and celebrated book Superintelligence; argues that advanced AI poses a potentially major existential risk to humanity; and that advanced AI development should be heavily regulated and perhaps even restricted to a small set of government-approved researchers. Bostrom’s ideas and arguments are reviewed and explored in detail; and compared with the thinking of three other current thinkers on the nature and implications of AI: Eliezer Yudkowsky of the Machine Intelligence Research Institute ; and David (...) Weinbaum and Viktoras Veitas of the Global Brain Institute. Relevant portions of Yudkowsky’s book Rationality: From AI to Zombies are briefly reviewed; and it is found that nearly all the core ideas of Bostrom’s work appeared previously or concurrently in Yudkowsky’s thinking. However; Yudkowsky often presents these shared ideas in a more plain-spoken and extreme form; making clearer the essence of what is being claimed. For instance; the elitist strain of thinking that one sees in the background in Bostrom is plainly and openly articulated in Yudkowsky; with many of the same practical conclusions. Bostrom and Yudkowsky view intelligent systems through the lens of reinforcement learning – they view them as “reward-maximizers” and worry about what happens when a very powerful and intelligent reward-maximizer is paired with a goal system that gives rewards for achieving foolish goals like tiling the universe with paperclips. Weinbaum and Veitas’s recent paper “Open-Ended Intelligence” presents a starkly alternative perspective on intelligence; viewing it as centered not on reward maximization; but rather on complex self-organization and self-transcending development that occurs in close coupling with a complex environment that is also ongoingly self-organizing; in only partially knowable ways. It is concluded that Bostrom and Yudkowsky’s arguments for existential risk have some logical foundation; but are often presented in an exaggerated way. For instance; formal arguments whose implication is that the “worst case scenarios” for advanced AI development are extremely dire; are often informally discussed as if they demonstrated the likelihood; rather than just the possibility; of highly negative outcomes. And potential dangers of reward-maximizing AI are taken as problems with AI in general; rather than just as problems of the reward-maximization paradigm as an approach to building superintelligence. If one views past; current; and future intelligence as “open-ended;” in the vernacular of Weaver and Veitas; the potential dangers no longer appear to loom so large; and one sees a future that is wide-open; complex and uncertain; just as it has always been. (shrink)

While it seems unlikely that any method of guaranteeing human-friendliness on the part of advanced Artificial General Intelligence systems will be possible, this doesn’t mean the only alternatives are throttling AGI development to safeguard humanity, or plunging recklessly into the complete unknown. Without denying the presence of a certain irreducible uncertainty in such matters, it is still sensible to explore ways of biasing the odds in a favorable way, such that newly created AI systems are significantly more likely than not (...) to be Friendly. Several potential methods of effecting such biasing are explored here, with a particular but non-exclusive focus on those that are relevant to open-source AGI projects, and with illustrative examples drawn from the OpenCog open-source AGI project. Issues regarding the relative safety of open versus closed approaches to AGI are discussed and then nine techniques for biasing AGIs in favor of Friendliness are presented: 1. Engineer the capability to acquire integrated ethical knowledge.2. Provide rich ethical interaction and instruction, respecting developmental stages.3. Develop stable, hierarchical goal systems.4. Ensure that the early stages of recursive self-improvement occur relatively slowly and with rich human involvement.5. Tightly link AGI with the Global Brain.6. Foster deep, consensus-building interactions between divergent viewpoints.7. Create a mutually supportive community of AGIs.8. Encourage measured co-advancement of AGI software and AGI ethics theory.9. Develop advanced AGI sooner not later. In conclusion, and related to the final point, we advise the serious co-evolution of functional AGI systems and AGI-related ethical theory as soon as possible, before we have so much technical infrastructure that parties relatively unconcerned with ethics are able to rush ahead with brute force approaches to AGI development. (shrink)