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Natural Computing Conclusion Introduction Natural Computing Conclusion Natural Computing Dr Giuditta Franco Department of Computer Science, University of Verona, Italy [email protected] For the logistics of the course, please see the syllabus Dr Giuditta Franco Slides Natural Computing 2017 Introduction Natural Computing Natural Computing Conclusion Natural Computing Natural (complex) systems work as (biological) information elaboration systems, by sophisticated mechanisms of goal-oriented control, coordination, organization. Computational analysis (mat modeling) of life is important as observing birds for designing flying objects. "Informatics studies information and computation in natural and artificial systems” (School of Informatics, Univ. of Edinburgh) Computational processes observed in and inspired by nature. Ex. self-assembly, AIS, DNA computing Ex. Internet of things, logics, programming, artificial automata are not natural computing. Dr Giuditta Franco Slides Natural Computing 2017 Introduction Natural Computing Natural Computing Conclusion Bioinformatics versus Infobiotics Applying statistics, performing tools, high technology, large databases, to analyze bio-logical/medical data, solve problems, formalize/frame natural phenomena. Ex. search algorithms to recover genomic/proteomic data, to process them, catalogue and make them publically accessible, to infer models to simulate their dynamics. Identifying informational mechanisms underlying living systems, how they assembled and work, how biological information may be defined, processed, decoded. New theories and tools to discover general law underlying biology (mental experiments: Darwin theory, Schrodinger - DNA structure, wave equation, cat experiment). Dr Giuditta Franco Slides Natural Computing 2017 Introduction Natural Computing Natural Computing Conclusion Life, information, computation Manca Vincenzo Manca Infobiotics Vincenzo Manca The book presents topics in discrete biomathematics. Mathematics has been widely used in modeling biological phenomena. However, the EMERGENCE, molecular and discrete nature of basic life processes suggests that their COMPLEXITY logics follow principles that are intrinsically based on discrete and AND informational mechanisms. The ultimate reason of polymers, as key element of life, is directly based on the computational power of strings, 1 COMPUTATION ECC and the intrinsic necessity of metabolism is related to the mathematical Infobiotics notion of multiset. The switch of the two roots of bioinformatics suggests a change of perspective. In bioinformatics, the biologists ask computer scientists to assist them in processing biological data. Conversely, in infobiotics mathematicians and computer scientists investigate principles and Infobiotics theories yielding new interpretation keys of biological phenomena. Life is too important to be investigated by biologists alone, and though computers are essential to process data from biological laboratories, Information in Biotic Systems many fundamental questions about life can be appropriately answered by a perspicacious intervention of mathematicians, computer scientists, and physicists, who will complement the work of chemists, biochemists, biologists, and medical investigators. The volume is organized in seven chapters. The first part is devoted to research topics (Discrete information and life, Strings and Genomes, Algorithms and Biorhythms, Life Strategies), the second one to mathematical backgrounds (Numbers and Measures, Languages and Grammars, Combinations and Chances). ISSN 2194-7287 isbn 978-3-642-36222-4 9 7 8 3 6 4 2 3 6 2 2 2 4 › springer.com 123 Dr Giuditta Franco Slides Natural Computing 2017 Introduction Natural Computing Natural Computing Conclusion (Conventional) Computation Turing in ’30s formalized the concept of computation and (silico-based) computer (software), von Neumann in 50s implemented the idea of computational machine (hardware). Main ingredients: 1 physical system (mean to store discrete data, namely representing an optimization combinatorial problem) 2 finite number of states, initial state, final state 3 program: list of instructions (out of a finite number of predefined ones), such as reading, writing (deletion, by a blanck symbol), state changing, bidirectional movement 4 termination criterion to establish which (where) is the output. What “discrete” and “finite” mean? Any computation assumes finite and illimited space1. 1Remark: limited/illimited, finite/infinite Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Unconventional Computing UC is every way to compute beyond Turing’s model. Examples: distributed, cloud, network computing (VPN: virtual private network), machine learning is part of artificial intelligence (ex. computer learning games). Further examples: Light/optical computing (unconventional mean to store data information), DNA/RNA computing (unconventional mean and instructions), quantum computing.. The second ones are some models of natural computing, within a proper subset of unconventional computing. Computational models may be either inspired by nature (ex. cellular automata) or using nature to compute (ex. biomolecular computing). Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Looking for new machine models Information is carried, by a program evolution, from initial state to final one of a mean. Computation is an information flow from input to output, and this may be made up of water, electricity, or something else - one of the motivations to look for new ways to perform computing is the definition of new machine models. Miniaturization is another main motivation. Richard Feynman, nobel laureate in physics (1961): “ There’s plenty of room at the bottom". He founded nanotechnology, having the key idea that molecules (and even atoms) can be machine components. Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Motivation • “In the future, computers may weigh less than 1 ½ tonnes…” • Since then, we have achieved massive miniaturisation of computer components Andrew Hamilton, Brains that Click, Popular Mechanics 91, no. 3, March 1949, pp. 162-167, 256, 258 Slide courtesy of prof. Martin Amos, Manchester Metropolitan University, UK Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Development of computers 60 years ENIAC (Electronic Numeric Cell processor (PS3), Integrator and Calculator), Sony/Toshiba/IBM, c. 2006 University of Pennsylvania, 1946 25.6 gigaflops 230 M2, 3 “calculations” per second Slide courtesy of prof. Martin Amos, Manchester Metropolitan University, UK Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion History Prior to World War 2, most people believed that the future lay with analogue computers Used real, continuous, physical quantities to represent data Spaghetti sort is a realisation of this idea Problem: constructed to solve a specific problem; difficult to generalise Slide courtesy of prof. Martin Amos, Manchester Metropolitan University, UK Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Differential Analyzer Built by Vannevar Bush from 1927, later versions were used to design the “bouncing bomb” Gears, pulleys and rods to represent a set of differential equations Slide courtesy of prof. Martin Amos, Manchester Metropolitan University, UK Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Transistor Fundamental building block of modern computers Semiconductor device, used to amplify or switch electronic signals University of Arizona Slide courtesy of prof. Martin Amos, Manchester Metropolitan University, UK Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Intel Slide courtesy of prof. Martin Amos, Manchester Metropolitan University, UK Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Miniaturisation • “The computing power of processors doubles every 18 months” Gordon Moore, Intel • How true is this, now that miniaturisation is approaching atomic resolutions? • Eventually (10-15 years?), we will hit problems as far as traditional fabrication techniques are concerned • Moore's Wall Slide courtesy of prof. Martin Amos, Manchester Metropolitan University, UK Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Why? Transistors act as gates, channelling electrons around a circuit Their walls are thick enough to contain the electrons, and ensure that they “behave correctly” However, when the gates reach a critical width (c. 5nM), the electrons tunnel through the walls, causing interference Slide courtesy of prof. Martin Amos, Manchester Metropolitan University, UK Dr Giuditta Franco Slides Natural Computing 2017 Introduction Miniaturization Natural Computing A dozen NC trends Conclusion Why? Also, transistor leakage manifests itself as heat “...We find that we are rapidly approaching the point where compromises are forced between device density and switching speed due to thermal constraints.” George
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