312 research outputs found
Is Turing's Thesis the Consequence of a More General Physical Principle?
We discuss historical attempts to formulate a physical hypothesis from which
Turing's thesis may be derived, and also discuss some related attempts to
establish the computability of mathematical models in physics. We show that
these attempts are all related to a single, unified hypothesis.Comment: 10 pages, 0 figures; section 1 revised, other minor change
Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test
The Turing Test (TT) checks for human intelligence, rather than any putative
general intelligence. It involves repeated interaction requiring learning in
the form of adaption to the human conversation partner. It is a macro-level
post-hoc test in contrast to the definition of a Turing Machine (TM), which is
a prior micro-level definition. This raises the question of whether learning is
just another computational process, i.e. can be implemented as a TM. Here we
argue that learning or adaption is fundamentally different from computation,
though it does involve processes that can be seen as computations. To
illustrate this difference we compare (a) designing a TM and (b) learning a TM,
defining them for the purpose of the argument. We show that there is a
well-defined sequence of problems which are not effectively designable but are
learnable, in the form of the bounded halting problem. Some characteristics of
human intelligence are reviewed including it's: interactive nature, learning
abilities, imitative tendencies, linguistic ability and context-dependency. A
story that explains some of these is the Social Intelligence Hypothesis. If
this is broadly correct, this points to the necessity of a considerable period
of acculturation (social learning in context) if an artificial intelligence is
to pass the TT. Whilst it is always possible to 'compile' the results of
learning into a TM, this would not be a designed TM and would not be able to
continually adapt (pass future TTs). We conclude three things, namely that: a
purely "designed" TM will never pass the TT; that there is no such thing as a
general intelligence since it necessary involves learning; and that
learning/adaption and computation should be clearly distinguished.Comment: 10 pages, invited talk at Turing Centenary Conference CiE 2012,
special session on "The Turing Test and Thinking Machines
Evolving robot software and hardware
This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve design and optimization problems in software. Thereafter, I discuss the Evolution of Things, that is, the possibility of evolving physical artefacts and zoom in on a (r)evolutionary way of creating 'bodies' and 'brains' of robots for engineering and fundamental research
Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition
This paper assumes the hypothesis that human learning is perception based,
and consequently, the learning process and perceptions should not be
represented and investigated independently or modeled in different simulation
spaces. In order to keep the analogy between the artificial and human learning,
the former is assumed here as being based on the artificial perception. Hence,
instead of choosing to apply or develop a Computational Theory of (human)
Perceptions, we choose to mirror the human perceptions in a numeric
(computational) space as artificial perceptions and to analyze the
interdependence between artificial learning and artificial perception in the
same numeric space, using one of the simplest tools of Artificial Intelligence
and Soft Computing, namely the perceptrons. As practical applications, we
choose to work around two examples: Optical Character Recognition and Iris
Recognition. In both cases a simple Turing test shows that artificial
perceptions of the difference between two characters and between two irides are
fuzzy, whereas the corresponding human perceptions are, in fact, crisp.Comment: 5th Int. Conf. on Soft Computing and Applications (Szeged, HU), 22-24
Aug 201
Membrane Systems and Hypercomputation
We present a brief analysis of hypercomputation and its relationship
to membrane systems theory, including a re-evaluation of Turingâs
analysis of computation and the importance of timing structure,
and suggest a âcosmologicalâ variant of tissue P systems that is capable
of super-Turing behaviour. No prior technical background in hypercomputation
theory is assumed
Computing with and without arbitrary large numbers
In the study of random access machines (RAMs) it has been shown that the
availability of an extra input integer, having no special properties other than
being sufficiently large, is enough to reduce the computational complexity of
some problems. However, this has only been shown so far for specific problems.
We provide a characterization of the power of such extra inputs for general
problems. To do so, we first correct a classical result by Simon and Szegedy
(1992) as well as one by Simon (1981). In the former we show mistakes in the
proof and correct these by an entirely new construction, with no great change
to the results. In the latter, the original proof direction stands with only
minor modifications, but the new results are far stronger than those of Simon
(1981). In both cases, the new constructions provide the theoretical tools
required to characterize the power of arbitrary large numbers.Comment: 12 pages (main text) + 30 pages (appendices), 1 figure. Extended
abstract. The full paper was presented at TAMC 2013. (Reference given is for
the paper version, as it appears in the proceedings.
Unsolvability of the Halting Problem in Quantum Dynamics
It is shown that the halting problem cannot be solved consistently in both
the Schrodinger and Heisenberg pictures of quantum dynamics. The existence of
the halting machine, which is assumed from quantum theory, leads into a
contradiction when we consider the case when the observer's reference frame is
the system that is to be evolved in both pictures. We then show that in order
to include the evolution of observer's reference frame in a physically sensible
way, the Heisenberg picture with time going backwards yields a correct
description.Comment: 4 pages, 3 figure
Measurement in biological systems from the self-organisation point of view
Measurement in biological systems became a subject of concern as a
consequence of numerous reports on limited reproducibility of experimental
results. To reveal origins of this inconsistency, we have examined general
features of biological systems as dynamical systems far from not only their
chemical equilibrium, but, in most cases, also of their Lyapunov stable states.
Thus, in biological experiments, we do not observe states, but distinct
trajectories followed by the examined organism. If one of the possible
sequences is selected, a minute sub-section of the whole problem is obtained,
sometimes in a seemingly highly reproducible manner. But the state of the
organism is known only if a complete set of possible trajectories is known. And
this is often practically impossible. Therefore, we propose a different
framework for reporting and analysis of biological experiments, respecting the
view of non-linear mathematics. This view should be used to avoid
overoptimistic results, which have to be consequently retracted or largely
complemented. An increase of specification of experimental procedures is the
way for better understanding of the scope of paths, which the biological system
may be evolving. And it is hidden in the evolution of experimental protocols.Comment: 13 pages, 5 figure
Mode transitions in a model reaction-diffusion system driven by domain growth and noise
Pattern formation in many biological systems takes place during growth of the underlying domain. We study a specific example of a reactionâdiffusion (Turing) model in which peak splitting, driven by domain growth, generates a sequence of patterns. We have previously shown that the pattern sequences which are presented when the domain growth rate is sufficiently rapid exhibit a mode-doubling phenomenon. Such pattern sequences afford reliable selection of certain final patterns, thus addressing the robustness problem inherent of the Turing mechanism. At slower domain growth rates this regular mode doubling breaks down in the presence of small perturbations to the dynamics. In this paper we examine the breaking down of the mode doubling sequence and consider the implications of this behaviour in increasing the range of reliably selectable final patterns
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