Transcript Слайд 1 - Polymer

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(yx) = {
(px)=y
 :  pS (px)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
xn1  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
BrO3
Fe2  Fe3
Br 
Fe3  Fe2
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
2
 y 32 
 1  y1  f ( y , w1 )
 2
2
3
y 12  f ( x 2 , w12 ) 1  y 2  2
2
1
2
y   1  y 2  f ( y , w2 ) y   y3  yOut  f ( y , w1 )
 2 
y 31  f ( x3 , w31 )
 y3  y 2  f ( y1 , w 2 )
y3 

1
3
3

 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