ФИЗИКО-ХИМИЧЕСКИЕ ОСНОВЫ НАНОТЕХНОЛОГИИ Профессор Н.Г. Рамбиди I. Исходные предпосылки The term “Nanotechnology” • Coined in 1974 by Nori Taniguchi to mean precision machining with tolerances of a micrometer or less • Popularized by Drexler in 1986 • By analogy with microtechnology – micro = one-millionth (10-6) – nano = one-billionth (10-9) • Actually, 10-100 nm sequential miniaturization From Powers of Ten, by Philip and Phylis Morrison and the office of Charles and Ray Eames. Definition • The creation of functional materials, devices and systems through control of matter (atomic, molecular and macromolecular levels) at the scale of 1 to 100 nanometers, and exploitation of novel properties and phenomena at the same scale. • Nanotechnology lets us fabricate an entire new generation of products that are cleaner, stronger, lighter, and more precise. What is nanotechnology? Nanotechnology involves the manipulation of objects on the atomic level. Products will be built with every atom in the right place, allowing materials to be lighter, stronger, smarter, cheaper, cleaner, and more precise. In order for this science to be realized, positional control must be achieved, and self-replication is necessary to reduce costs. Исходные посылки • Два основных вопроса: Какой все-таки смысл вкладывается в понятие «нанотехнология»? Почему именно сейчас возник нанотехнологический бум? Экономические и социальные корни нанотехнологического бума • Демографические взрывы • Сокращение природных ресурсов • Глобальная информационная система • Глобальная транспортная система • Изменение социальных мотиваций Синтез природного каучука как пример нанотехнологического подхода Полимеризация этилена, стирола, хлорвинила Структурные варианты полимерных систем Синтетические каучуки Синтез природного каучука Синтез природного каучука Синтез природного каучука Корни нанотехнологического бума Исходная и основная причина возникновения нанотехнологии Развитие информационных технологий, вызванное все увеличивающейся значимостью сложных динамических систем, необходимостью понимания их механизмов и управления ими Сложные динамические системы Сложные динамические системы • Многоуровневая структура • Распределенная динамика • Нелинейные динамические механизмы • Развитая система обратных связей Bees Colony cooperation Regulate hive temperature Efficiency via Specialization: division of labour in the colony Communication : Food sources are exploited according to quality and distance from the hive Termites Cone-shaped outer walls and ventilation ducts Brood chambers in central hive Spiral cooling vents Support pillars Ants Organizing highways to and from their foraging sites by leaving pheromone trails Form chains from their own bodies to create a bridge to pull and hold leafs together with silk Division of labour between major and minor ants Question: How can ag systems be quantified & used to understand rural poverty and resource degradation? Hypothesis: Spatial heterogeneity and dynamic properties of these systems are key to understanding their behavior (and linkages to poverty & degradation) • e.g., in understanding non-adoption of conservation techs •A key question is how much detail is needed to answer important policy questions!!! The soil microbial system • More diversity in the palm of your hand than in the mammalian kingdom • Most important and abused ecosystem in the world • Essential features – Species concept not useful – Feedback and feedforward coupling to dynamic environment is central – Functionality – Can’t measure much (anything) Have you ever done something in a crowd you’d never have done alone? If you had to move furniture, would you work better in a group, or alone? What if you were writing a term paper? Have you ever had a bad supervisor? What differentiated that person from a good supervisor? The Nature of Groups • Group • Two or more people who influence each other. • Collections of individuals become increasingly “group like” when they: –Are interdependent –Share a common identity –Have a group structure Crowds and Deindividuation • Deindividuation • The process of losing one’s sense of personal identity, which: • makes it easier to behave in ways inconsistent with one’s normal values. • Example: Anonymous children in Halloween costumes stole more from a candy jar (Beaman et al., 1979) Red circles depict neighbors who are Anti-party Blue circles depict neighbors who are Proparty In an initial vote, the opinions looked like the above. What will happen if everyone checks with his or her immediate neighbors, and goes with the local majority? Copyright © 2002 by Allyn and Bacon This individual will change because the majority of her neighbors are “Anti” Copyright © 2002 by Allyn and Bacon But this neighbor will change because the majority of his neighbors are “Pro” With all this changing back and forth, what will happen over the course of a few weeks? Because local majorities draw in people in their vicinity, the neighborhood will eventually stabilize into this pattern. Проблемы «искусственного интеллекта» как подход к пониманию сложных динамических систем AI Defenitions “The automation of activities that we associate with human thinking…” Bellman 1978 “The study of mental faculties through the use of computational models” Charniak & McDermott “The study of how to make computers do things at which at the moment people are better. Rich&Knight “The branch of CS that is concerned with the automation of intelligent behavior.” Lugar&Stubblefield Applied Areas of AI • • • • • • Game playing Speech and language processing Expert reasoning Planning and scheduling Vision Robotics IMPORTANT TO SCIENCE 1. What is the nature of matter? 2. What is the nature of life? 3. What is the nature of mind? IMPORTANT TO ECONOMIC PROSPERITY Toffler's Three Waves 1. Invention of agriculture 2. Invention of steam engine and discovery of electricity 3. Invention of electronics and digital computer IMPORTANT FOR ECONOMIC PROSPERITY Manufacturing Business management and financial services Marketing and customer services Transportation safety and efficiency Communications Construction Waste management and cleanup Health care Physical security Agriculture and food processing Mining and drilling Space and undersea exploration IMPORTANT FOR MILITARY STRENGTH 1. Intelligent systems will enable a new generation of unmanned vehicles and weapon systems that will: -- outperform manned systems -- with fewer casualties -- and lower cost for training and readiness 2. Intelligent systems technologies will enable: -- faster information gathering and processing -- more rapid replanning for real-time events -- and more effective command and control IMPORTANT FOR MILITARY STRENGTH 1. Intelligent systems will enable a new generation of unmanned vehicles and weapon systems that will: -- outperform manned systems -- with fewer casualties -- and lower cost for training and readiness 2. Intelligent systems technologies will enable: -- faster information gathering and processing -- more rapid replanning for real-time events -- and more effective command and control IMPORTANT FOR HUMAN WELL BEING Clean up the environment Improve health care Improve transportation safety and efficiency Improve personal services and security Improve productivity End poverty and create golden age The background of intellectual problems • Recognition of images, scenes and situations • Investigation of the system evolution having complicated behavioral dynamics • Choice of optimal structure or behavior of multifactor systems having complex branching search tree • Control problems Biological roots of intellectual problems Biological roots of intellectual problems сложность Andrey Nikolaevich Kolmogorov • Born: April 25, 1903 Tambov, Russia • Died: Oct. 20, 1987 “Complexity” • Organisms :1000 to 100,000 genes • Boeing 777 : >100,000 subsystems and ... • …many subsystems are highly complex. • Engine: > 10,000 subsystems • Laptop: >1,000,000,000 transistors • Refinery : > 10,000 integral feedback loops • High rise heating-ventilation-AC: > 1000 • Internet, power grid, etc.: ~ Definitions and Properties • Complexity – Non-linear interaction among multiple components – Complicated versus complex systems •Deterministic •Reductionist principle •Dynamic / stochastic •Holistic – Irreducible – Local and distributed – Non-deterministic / unpredictable – Emergence / self-organization Nonlinearity in Spread of Innovation Number of Adopters of Hybrid Seed Corn in Two Iowa Communities 300 250 200 Cumulative 150 number of farmers100 50 19 27 19 28 19 29 19 30 19 31 19 32 19 33 19 34 19 35 19 36 19 37 19 38 19 39 19 40 19 41 0 Year Source: Based on Ryan and Gross (1943) алгоритмическая сложность min l(p) : K(yx) = { (px)=y : pS (px)y Computational Complexity • P: Problems with time complexity of O(nk) where, k is a constant. • P-type problems are classically tractable or easy. • Problems with exponential complexity such as O(NN), O(2N), O(N!) are classically intractable or “hard”. • NP: A solution, if exists, can be verified in polynomial time. • NP-complete: NP and “hard” or intractable classically. Логистическое уравнение xn1 rxn 1 xn Проблема «Коммивояжер» { Vin,1,2,3..N, Vout } fixed Vin ,Vout P{1,2,3..N} N! paths Проблема «Коммивояжер» Combinatorial Explosion A 10 city TSP has 181,000 possible solutions A 20 city TSP has 10,000,000,000,000,000 possible solutions A 50 City TSP has 100,000,000,000,000,000,000,000,000,000,00 0,000,000,000,000,000,000,000,000,000,000 possible solutions There are 1,000,000,000,000,000,000,000 litres of water on the planet Mchalewicz, Z, Evolutionary Algorithms for Constrained Optimization Problems, CEC 2000 (Tutorial) Проблема «Коммивояжер» исходная система (100 точек) Проблема «Коммивояжер» после 500 итераций Проблема «Коммивояжер» после 4000 итераций Реакционно-диффузионная среда u i F (u1 , u2 ,..., u N ) Dij ui j u i F (u1 , u2 ,..., u N ) Среда Белоусова-Жаботинского 3 Fe 2 BrO 3CH 2 (COOH ) 2 2 H Fe 2 BrCH (COOH ) 2 3CO2 4 H 2O Fe 2 BrO3 Fe Br 3 Fe3 Fe 2 Нейросетевой подход как инструмент эффективного решения задач высокой вычислительной сложности Принципы парадигмы фон Неймана • Вводимая извне программа • Последовательное выполнение операций • Программа записывается теми же кодами, что и данные, что позволяет изменять программу в ходе вычислений (разветвления, циклы) • Простейшие двоичные элементарные операции, позволяющие создать универсальный вычислитель The brain • The cortex – 1.3-1.4kg (2% of the body weight) … [13,14] – 2,500 cm2 (rat: 6 cm2, elephant: 6,300 cm2) [14] – 1,300-1,500 cm3 • 2 hemispheres connected by corpus callossum (250 mill. nerve fibers) • Inputs: – – – – spinal cord optic nerve (1.2 mill.) cranial nerves (12) auditory system, … Neurons • 100 billion neurons (children) – loss: ~1/sec 31 million/year – Octopus: 300 million • Diameter: 4 – 100 microns • Weight: 10-6 grams • Length: <1 mm – 4 feet (in the leg) [15] – Length of Giraffe primary afferent axon: 15 feet The brain as a computational system • The brain is – biological – de-central (plasticity) – non-digital – highly parallel • What does this mean? The Biological Neuron • 10 billion neurons in human brain • Summation of input stimuli – Spatial (signals) – Temporal (pulses) • Threshold over composed inputs • Constant firing strength 6 • 10 billion synapses in human brain • Chemical transmission and modulation of signals • Inhibitory synapses • Excitatory synapses Biological Neural Networks • 10,000 synapses per neuron • Computational power = connectivity • Plasticity – new connections (?) – strength of connections modified Artificial Neuron • Abstract away from almost everything except connectivity… Binary Neurons hard threshold 1.2 Stimulus output 1 0.8 ui wij x j on 0.6 Response yi f urest ui j 0.4 0.2 input 0 -0.2 -10 -8 -6 -4 -2 0 2 4 6 8 10 “Hard” threshold -0.4 -0.6 heaviside -0.8 -1 -1.2 off z ON f z else OFF • ex: Perceptrons, Hopfield NNs, Boltzmann Machines • Main drawbacks: can only map binary functions, biologically implausible. = threshold Analog Neurons sigmoid 1.2 Stimulus output on 1 ui wij x j 0.8 0.6 Response yi f urest ui j 0.4 0.2 input 0 -0.2 -10 -0.4 -8 -6 -4 -2 0 2 4 6 8 2/(1+exp(-x))-1 10 “Soft” threshold -0.6 -0.8 -1 -1.2 off f z 2 1 1 e z • ex: MLPs, Recurrent NNs, RBF NNs... • Main drawbacks: difficult to process time patterns, biologically implausible. Artificial Neural Networks Output layer fully connected Hidden layers Input layer sparsely connected Neural network mathematics Inputs Output y11 2 1 y1 f ( y1 , w12 ) y32 2 2 3 y 12 f ( x 2 , w12 ) y1 y 2 2 2 1 2 y f ( y , w y y y 2 f ( y , w2 ) Out 1) 3 1 2 y 31 f ( x3 , w31 ) y3 2 1 2 y3 1 y3 f ( y , w3 ) y4 y 14 f ( x 4 , w14 ) y11 f ( x1 , w11 ) Когда аппаратные возможности компьютера сравняются с возможностями человеческого мозга? The most powerful experimental supercomputers in 1998 composed of thousands or tents of thousands of the fastest microprocessors and costing tens of millions of dollars can do a few million MIPS. They are within striking distance of being powerful enough to match human brainpower, but are unlikely to be applied to that end. Why tie up a rare twenty-million-dollar asset to develop one erzatz-human, when millions of inexpensive original-model humans are available. Such machines are needed for high-value scientific calculations, physical simulations, having no cheaper substitute. AI research must wait for the power to become more affordable. H. Moravec “When will computer hardware match the human brain?” Некоторые перспективы Ферменты – принцип «ключ-замок» Механизм ферментативной реакции Three historical trends in manufacturing • More flexible • More precise • Less expensive