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Shane Legg & Marcus Hutter (2007). Universal Intelligence: A Definition of Machine Intelligence.

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  1.  2
    Towards a Unified Framework for Developing Ethical and Practical Turing Tests.Srinivasan Balaji & Shah Kushal - forthcoming - AI and Society:1-8.
    Since Turing proposed the first test of intelligence, several modifications have been proposed with the aim of making Turing’s proposal more realistic and applicable in the search for artificial intelligence. In the modern context, it turns out that some of these definitions of intelligence and the corresponding tests merely measure computational power. Furthermore, in the framework of the original Turing test, for a system to prove itself to be intelligent, a certain amount of deceit is implicitly required which can have (...)
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  2.  48
    Machines and the Moral Community.Erica L. Neely - 2014 - Philosophy and Technology 27 (1):97-111.
    A key distinction in ethics is between members and nonmembers of the moral community. Over time, our notion of this community has expanded as we have moved from a rationality criterion to a sentience criterion for membership. I argue that a sentience criterion is insufficient to accommodate all members of the moral community; the true underlying criterion can be understood in terms of whether a being has interests. This may be extended to conscious, self-aware machines, as well as to any (...)
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  3.  44
    On Potential Cognitive Abilities in the Machine Kingdom.José Hernández-Orallo & David L. Dowe - 2013 - Minds and Machines 23 (2):179-210.
    Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different (...)
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  4. Safety Engineering for Artificial General Intelligence.Roman Yampolskiy & Joshua Fox - 2013 - Topoi 32 (2):217-226.
    Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence and robotics communities. We will argue that attempts to attribute moral agency and assign rights to all intelligent machines are misguided, whether applied to infrahuman or superhuman AIs, as are proposals to limit the negative effects of AIs by constraining their behavior. As an alternative, we propose a new science of safety engineering for intelligent artificial agents based on maximizing for what humans value. In particular, we challenge (...)
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  5. Advantages of Artificial Intelligences, Uploads, and Digital Minds.Kaj Sotala - 2012 - International Journal of Machine Consciousness 4 (01):275-291.
    I survey four categories of factors that might give a digital mind, such as an upload or an artificial general intelligence, an advantage over humans. Hardware advantages include greater serial speeds and greater parallel speeds. Self-improvement advantages include improvement of algorithms, design of new mental modules, and modification of motivational system. Co-operative advantages include copyability, perfect co-operation, improved communication, and transfer of skills. Human handicaps include computational limitations and faulty heuristics, human-centric biases, and socially motivated cognition. The shape of hardware (...)
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    Can Machines Think? An Old Question Reformulated.Achim Hoffmann - 2010 - Minds and Machines 20 (2):203-212.
    This paper revisits the often debated question Can machines think? It is argued that the usual identification of machines with the notion of algorithm has been both counter-intuitive and counter-productive. This is based on the fact that the notion of algorithm just requires an algorithm to contain a finite but arbitrary number of rules. It is argued that intuitively people tend to think of an algorithm to have a rather limited number of rules. The paper will further propose a modification (...)
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    An Object-Oriented View on Problem Representation as a Search-Efficiency Facet: Minds Vs. Machines. [REVIEW]Reza Zamani - 2010 - Minds and Machines 20 (1):103-117.
    From an object-oriented perspective, this paper investigates the interdisciplinary aspects of problem representation as well the differences between representation of problems in the mind and that in the machine. By defining an object as a combination of a symbol-structure and its associated operations, it shows how the representation of problems can become related to control, which conducts the search in finding a solution. Different types of representation of problems in the machine are classified into four categories, and in a similar (...)
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    Minimum Message Length and Statistically Consistent Invariant (Objective?) Bayesian Probabilistic Inference—From (Medical) “Evidence”.David L. Dowe - 2008 - Social Epistemology 22 (4):433 – 460.
    “Evidence” in the form of data collected and analysis thereof is fundamental to medicine, health and science. In this paper, we discuss the “evidence-based” aspect of evidence-based medicine in terms of statistical inference, acknowledging that this latter field of statistical inference often also goes by various near-synonymous names—such as inductive inference (amongst philosophers), econometrics (amongst economists), machine learning (amongst computer scientists) and, in more recent times, data mining (in some circles). Three central issues to this discussion of “evidence-based” are (i) (...)
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