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Model RB is a model of random constraint satisfaction problems, which exhibits exact satisfiability phase transition and many hard instances, both experimentally and theoretically. Benchmarks based on Model RB have been successfully used by various international algorithm competitions and many research papers. In a previous work, Xu and Li defined two notions called i- constraint assignment tuple and flawed i- constraint assignment tuple to show an exponential resolution complexity of Model RB. These two notions are similar to some kind of consistency in constraint satisfaction problems, but seem different from all kinds of consistency so far known in literatures. In this paper, we explicitly define this kind of consistency, called variable-centered consistency, and show an upper bound on a parameter in Model RB, such that up to this bound the typical instances of Model RB are variable-centered consistent. [ABSTRACT FROM AUTHOR]

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 of the above mentioned explication of the notion of machines by quantifying the length of an algorithm. Based on that it appears possible to reconcile the opposing views on the topic, which people have been arguing about for more than half a century. [ABSTRACT FROM AUTHOR]

In their joint paper entitled “ The Replication of the Hard Problem of Consciousness in AI and BIO- AI” (Boltuc et al. Replication of the hard problem of conscious in AI and Bio- AI: An early conceptual framework ), Nicholas and Piotr Boltuc suggest that machines could be equipped with phenomenal consciousness, which is subjective consciousness that satisfies Chalmer’s hard problem (We will abbreviate the hard problem of consciousness as “H-consciousness”). The claim is that if we knew the inner workings of phenomenal consciousness and could understand its’ precise operation, we could instantiate such consciousness in a machine. This claim, called the extra- strong AI thesis, is an important claim because if true it would demystify the privileged access problem of first-person consciousness and cast it as an empirical problem of science and not a fundamental question of philosophy. A core assumption of the extra-strong AI thesis is that there is no logical argument that precludes the implementation of H-consciousness in an organic or in-organic machine provided we understand its algorithm. Another way of framing this conclusion is that there is nothing special about H-consciousness as compared to any other process. That is, in the same way that we do not preclude a machine from implementing photosynthesis, we also do not preclude a machine from implementing H-consciousness. While one may be more difficult in practice, it is a problem of science and engineering, and no longer a philosophical question. I propose that Boltuc’s conclusion, while plausible and convincing, comes at a very high price; the argument given for his conclusion does not exclude any conceivable process from machine implementation. In short, if we make some assumptions about the equivalence of a rough notion of algorithm and then tie this to human understanding, all logical preconditions vanish and the argument grants that any process can be implemented in a machine. The purpose of this paper is to comment on the argument for his conclusion and offer additional properties of H-consciousness that can be used to make the conclusion falsifiable through scientific investigation rather than relying on the limits of human understanding. [ABSTRACT FROM AUTHOR]

What I call semiotic brains are brains that make up a series of signs and that are engaged in making or manifesting or reacting to a series of signs: through this semiotic activity they are at the same time engaged in “being minds” and so in thinking intelligently. An important effect of this semiotic activity of brains is a continuous process of disembodiment of mind that exhibits a new cognitive perspective on the mechanisms underling the semiotic emergence of meaning processes. Indeed at the roots of sophisticated thinking abilities there is a process of disembodiment of mind that presents a new cognitive perspective on the role of external models, representations, and various semiotic materials. Taking advantage of Turing’s comparison between “unorganized” brains and “logical” and “practical” machines” this paper illustrates the centrality to cognition of the disembodiment of mind from the point of view of the interplay between internal and external representations, both mimetic and creative. The last part of the paper describes the concept of mimetic mind I have introduced to shed new cognitive and philosophical light on the role of computational modeling and on the decline of the so-called Cartesian computationalism. [ABSTRACT FROM AUTHOR]

Irony is an effective but challenging mode of communication that allows a speaker to express viewpoints rich in sentiment with concision, sharpness and humour. Creative irony is especially common in online documents that express subjective and deeply-felt opinions, and thus represents a significant obstacle to the accurate analysis of sentiment in web texts. In this paper we look at one commonly used framing device for linguistic irony-the simile-to show how even the most creative uses of irony are often marked in ways that make them computationally feasible to detect. We conduct a very large corpus analysis of web-harvested similes to identify the most interesting characteristics of ironic comparisons, and provide an empirical evaluation of a new algorithm for separating ironic from non-ironic similes. [ABSTRACT FROM AUTHOR]

Given free information and unlimited processing power, should decision algorithms use as much information as possible? A formal model of the decision-making environment is developed to address this question and provide conditions under which informationally frugal algorithms, without any information or processing costs whatsoever, are optimal. One cause of compression that allows optimal algorithms to rationally ignore information is inverse movement of payoffs and probabilities (e.g., high payoffs occur with low probably and low payoffs occur with high probability). If inversely related payoffs and probabilities cancel out, then predictors that correlate with payoffs and consequently condition the probabilities associated with different payoffs will drop out of the expected-payoff objective function, severing the link between information and optimal action rules. Stochastic payoff processes in which rational ignoring occurs are referred to as compressed environments, because optimal action depends on a reduced-dimension subset of the environmental parameters. This paper considers benefits and limitations of economic models versus other methods for studying links between environmental structure and the real-world success of simple decision procedures. Different methods converge on the normative proposition of ecological rationality, as opposed to axiomatic rationality based on informational efficiency and internal consistency axioms, as a superior framework for comparing the effectiveness of decision strategies and prescribing decision algorithms in application. [ABSTRACT FROM AUTHOR]

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in ) monograph, “Representation & Reality”, which if correct, has important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set ( x)”. Then, equating Q ( x) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. [ABSTRACT FROM AUTHOR]

In recent years it has been convincingly argued that the Church-Turing thesis concerns the bounds of human computability: The thesis was presented and justified as formally delineating the class of functions that can be computed by a human carrying out an algorithm. Thus the Thesis needs to be distinguished from the so-called Physical Church-Turing thesis (or Thesis M), according to which all physically computable functions are Turing computable. The latter is often claimed to be false, or, if true, contingently so. On all accounts, though, thesis M is not easy to give counterexamples to, but it is never asked why—how come that a thesis that transfers a notion from the strictly human domain to the general physical domain just happens to be so difficult to falsify (or even to be true). In this paper I articulate this question and consider several tentative answers to it. [ABSTRACT FROM AUTHOR]

It is well understood and appreciated that Gödel’s Incompleteness Theorems apply to sufficiently strong, formal deductive systems. In particular, the theorems apply to systems which are adequate for conventional number theory. Less well known is that there exist algorithms which can be applied to such a system to generate a gödel-sentence for that system. Although the generation of a sentence is not equivalent to proving its truth, the present paper argues that the existence of these algorithms, when conjoined with Gödel’s results and accepted theorems of recursion theory, does provide the basis for an apparent paradox. The difficulty arises when such an algorithm is embedded within a computer program of sufficient arithmetic power. The required computer program (an AI system) is described herein, and the paradox is derived. A solution to the paradox is proposed, which, it is argued, illuminates the truth status of axioms in formal models of programs and Turing machines. [ABSTRACT FROM AUTHOR]