15,902 research outputs found
Turing++ Questions: A Test for the Science of (Human) Intelligence
There is a widespread interest among scientists in understanding a specific and well defined form of intelligence, that is human intelligence. For this reason we propose a stronger version of the original Turing test. In particular, we describe here an open-ended set of Turing++ questions that we are developing at the Center for Brains, Minds, and Machines at MIT -- that is questions about an image. For the Center for Brains, Minds, and Machines the main research goal is the science of intelligence rather than the engineering of intelligence -- the hardware and software of the brain rather than just absolute performance in face identification. Our Turing++ questions reflect fully these research priorities
Supermachines and superminds
Abstract. If the computational theory of mind is right, then minds are realized by machines. There is an ordered complexity hierarchy of machines. Some finite machines realize finitely complex minds; some Turing machines realize potentially infinitely complex minds. There are many logically possible machines whose powers exceed the Church-Turing limit (e.g. accelerating Turing machines). Some of these supermachines realize superminds. Superminds perform cognitive supertasks. Their thoughts are formed in infinitary languages. They perceive and manipulate the infinite detail of fractal objects. They have infinitely complex bodies. Transfinite games anchor their social relations
The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence
This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing
The Turing Test and the Zombie Argument
In this paper I shall try to put some implications concerning the Turing's test and the so-called
Zombie arguments into the context of philosophy of mind. My intention is not to compose a review
of relevant concepts, but to discuss central problems, which originate from the Turing's test - as a
paradigm of computational theory of mind - with the arguments, which refute sustainability of this
thesis.
In the first section (Section I), I expose the basic computationalist presuppositions; by
examining the premises of the Turing Test (TT) I argue that the TT, as a functionalist paradigm
concept, underlies the computational theory of mind. I treat computationalism as a thesis that
defines the human cognitive system as a physical, symbolic and semantic system, in such a
manner that the description of its physical states is isomorphic with the description of its symbolic
conditions, so that this isomorphism is semantically interpretable. In the second section (Section
II), I discuss the Zombie arguments, and the epistemological-modal problems connected with them,
which refute sustainability of computationalism. The proponents of the Zombie arguments build their
attack on the computationalism on the basis of thought experiments with creatures behaviorally,
functionally and physically indistinguishable from human beings, though these creatures do not
have phenomenal experiences. According to the consequences of these thought experiments - if
zombies are possible, then, the computationalism doesn't offer a satisfying explanation of
consciousness. I compare my thesis from Section 1, with recent versions of Zombie arguments,
which claim that computationalism fails to explain qualitative phenomenal experience. I conclude
that despite the weaknesses of computationalism, which are made obvious by zombie-arguments,
these arguments are not the last word when it comes to explanatory force of computationalism
On the possible Computational Power of the Human Mind
The aim of this paper is to address the question: Can an artificial neural
network (ANN) model be used as a possible characterization of the power of the
human mind? We will discuss what might be the relationship between such a model
and its natural counterpart. A possible characterization of the different power
capabilities of the mind is suggested in terms of the information contained (in
its computational complexity) or achievable by it. Such characterization takes
advantage of recent results based on natural neural networks (NNN) and the
computational power of arbitrary artificial neural networks (ANN). The possible
acceptance of neural networks as the model of the human mind's operation makes
the aforementioned quite relevant.Comment: Complexity, Science and Society Conference, 2005, University of
Liverpool, UK. 23 page
Minds, Brains and Turing
Turing set the agenda for (what would eventually be called) the cognitive sciences. He said, essentially, that cognition is as cognition does (or, more accurately, as cognition is capable of doing): Explain the causal basis of cognitive capacity and you’ve explained cognition. Test your explanation by designing a machine that can do everything a normal human cognizer can do – and do it so veridically that human cognizers cannot tell its performance apart from a real human cognizer’s – and you really cannot ask for anything more. Or can you? Neither Turing modelling nor any other kind of computational r dynamical modelling will explain how or why cognizers feel
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