Open AccessBook
Computation: Finite and Infinite Machines
Marvin Minsky
- 01 Jan 1967
2.9K
TL;DR: In this article, the authors present an abstract theory that categorically and systematically describes what all these machines can do and what they cannot do, giving sound theoretical or practical grounds for each judgment, and the abstract theory tells us in no uncertain terms that the machines' potential range is enormous and that its theoretical limitations are of the subtlest and most elusive sort.
read more
Abstract: From the Preface (See Front Matter for full Preface)
Man has within a single generation found himself sharing the world with a strange new species: the computers and computer-like machines. Neither history, nor philosophy, nor common sense will tell us how these machines will affect us, for they do not do "work" as did machines of the Industrial Revolution. Instead of dealing with materials or energy, we are told that they handle "control" and "information" and even "intellectual processes." There are very few individuals today who doubt that the computer and its relatives are developing rapidly in capability and complexity, and that these machines are destined to play important (though not as yet fully understood) roles in society's future. Though only some of us deal directly with computers, all of us are falling under the shadow of their ever-growing sphere of influence, and thus we all need to understand their capabilities and their limitations.
It would indeed be reassuring to have a book that categorically and systematically described what all these machines can do and what they cannot do, giving sound theoretical or practical grounds for each judgment. However, although some books have purported to do this, it cannot be done for the following reasons: a) Computer-like devices are utterly unlike anything which science has ever considered---we still lack the tools necessary to fully analyze, synthesize, or even think about them; and b) The methods discovered so far are effective in certain areas, but are developing much too rapidly to allow a useful interpretation and interpolation of results. The abstract theory---as described in this book---tells us in no uncertain terms that the machines' potential range is enormous, and that its theoretical limitations are of the subtlest and most elusive sort. There is no reason to suppose machines have any limitations not shared by man.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Verifiable UML artifact-centric business process models (Extended Version)
TL;DR: In this article, an extended version of Verifiable UML Artifact-Centric Business ProcessModels (VUMLABPM) is presented, with a focus on the verification of UML artifacts.
16
Priced Timed Petri Nets
Richard Mayr,Parosh Aziz Abdulla +1 more
TL;DR: It is shown that the infimum of the costs to reach a given control-state is computable in the case where all place and transition costs are non-negative, and if negative costs are allowed, then the question whether a given Control-State is reachable with zero overall cost becomes undecidable.
16
A family of smallest symmetrical four-state firing squad synchronization protocols for ring arrays
TL;DR: This paper proposes a family of smallest four-state firing squad synchronization protocols that can synchronize any one-dimensional ring cellular arrays of length n = 2k for any positive integer k.
16
Quantitative Pathway Logic for Computational Biology
Michele Baggi,Demis Ballis,Moreno Falaschi +2 more
- 27 Aug 2009
TL;DR: An extension of Pathway Logic, called Quantitative Pathway logic (QPL), which allows one to reason about quantitative aspects of biological processes, such as element concentrations and reactions kinetics and supports the modeling of inhibitors.
16
Interactive Evolving Recurrent Neural Networks Are Super-Turing Universal
Jérémie Cabessa,Jérémie Cabessa,Alessandro E. P. Villa +2 more
- 15 Sep 2014
TL;DR: This work shows that interactive evolving recurrent neural networks are not only super-Turing, but also capable of simulating any other possible interactive deterministic system, irrespective of whether their synaptic weights are rational or real.
16