Machine Intelligence & Physics

1
Thinking about Thought
Theories of Brain, Mind, Consciousness
May 12, 2015
Piero Scaruffi
www.scaruffi.com
Part 5.
Machine Intelligence
Self-organizing Systems
Relativity and Quantum Theories

2
Video
Paul Wegener: Der Golem (1914) - Golem
Fritz Lang: Metropolis (1927) - Maria
Victor Fleming: The Wizard of Oz (1939) - The Tin Man
Robert Wise: The Day The Earth Stood Still (1951) - Gort
Fred Wilcox: Forbidden Planet (1956) - Robby the Robot
Kurt Neumann: Kronos (1957)
Jean-Luc Godard: Alphaville (1965) - Alpha 60
Stanley Kubrick: 2001 A Space Odyssey (1968) - HAL
George Lucas: Star Wars (1977) - C-3P0

3
Mind and Machines
Is our mind a machine?
Can we build one?
Mathematical models of the mind
Descartes: water fountains
Freud: a hydraulic system
Pavlov: a telephone switchboard
Wiener: a steam engine
Simon: a computer
The computer is the first machine that can be
programmed to perform different tasks

4
A Brief History of Logic
Pythagoras’s' theorem (6th c BC): a relationship between
physical quantities that is both abstract and eternal
Euclides' "Elements" (350 BC), the first system of Logic,
based on just 5 axioms
Aristoteles' "Organon" (4th c BC): syllogisms
William Ockham's "Summa Totius Logicae" (1300 AD) on
how people reason and learn
Francis Bacon’s "Novum Organum" (1620)
Rene' Descartes’ "Discours de la Methode" (1637): the
analytic method over the dialectic method
Gottfried Leibniz ‘s "De Arte Combinatoria" (1676)
Leonhard Euler (1761) how to do symbolic logic with
diagrams
Augustus De Morgan's "The Foundations of Algebra" (1844)

5
Gottfried Leibniz

6
George Boole's "The Laws Of Thought" (1854): the
laws of logic “are” the laws of thought
Propositional logic and predicate logic: true/false!

7
Axiomatization of Thought:
Gottlob Frege's "Foundations of Arithmetic"
(1884)
Giuseppe Peano's "Arithmetices Principia
Nova Methodo Exposita" (1889)
Bertrand Russell's "Principia Mathematica"
(1903)

8
The Axiomatic Method
• David Hilbert (1928)
– Entscheidungsproblem problem: the
mechanical procedure for proving
mathematical theorems
– An algorithm, not a formula
– Mathematics = blind manipulation of symbols
– Formal system = a set of axioms and a set of
inference rules
– Propositions and predicates
– Deduction = exact reasoning
– Logic emancipated from reality by dealing
purely with abstractions

9
• Paradoxes
– ”I am lying"
– The class of classes that do not belong
to themselves (the barber who shaves
all barbers who do not shave
themselves)
– The omnipotent god

10
• Kurt Goedel (1931)
– Any formal system contains an
“undecidable” proposition
– A concept of truth cannot be defined
within a formal system
– Impossible to reduce logic to a
mechanical procedure to prove
theorems (“decision problem”)

11
• Alan Turing (1936)
– Hilbert’s challenge (1928): an algorithm
capable of solving all the mathematical
problems
– Turing Machine (1936): a machine whose
behavior is determined by a sequence of
symbols and whose behavior determines the
sequence of symbols
– A universal Turing machine (UTM) is a Turing
machine that can simulate an arbitrary Turing
machine

12
– Computation = the formal manipulation of
symbols through the application of formal
rules
– Turing machine = a machine that is capable
of performing any type of computation
– Turing machine = the algorithm that Hilbert
was looking for
– Hilbert’s program reduced to manipulation of
symbols
– Problem solving = symbol processing

13
• The Turing machine:
– …an infinite tape (an unlimited
memory capacity)
– … marked out into squares, on each
of which a symbol can be printed…
– The machine can read or write one
symbol at a time
– At any moment there is one symbol in
the machine, the scanned symbol
– The machine can alter the scanned
symbol based on that symbol and on a
table of instructions
– The machine can also move the tape
back and forth

14
– Universal Turing Machine: a Turing
machine that is able to simulate any other
Turing machine
– The universal Turing machine reads the
description of the specific Turing machine
to be simulated
Turing Machine

15
• Turing machines in nature: the ribosome, which
translates RNA into proteins
– Genetic alphabet: nucleotides ("bases"): A, C, G, U
– The bases are combined in groups of 3 to form "codons“
– RNA is composed of a string of nucleotides ("bases")
according to certain rules
– There are special carrier molecules ("tRNA") that are
attached to specific aminoacids (proteins)
– The start codon encodes the aminoacid Methionine
– A codon is matched with a specific tRNA
– The new aminoacid is attached to the protein
– The tape then advances 3 bases to the next codon, and the
process repeats
– The protein keeps growing
– When the “stop” codon is encountered, the ribosome
dissociates from the mRNA

16
Computer Science
1941: Konrad Zuse's Z3 programmable electronic
computer
1943: Warren McCulloch's and Walter Pitts' binary
neuron

17
Computer Science
• World War II:
– Breaking the Enigma code (Bombe)
– Turing worked at Bletchley Park where the
Colossus was built but it was not a universal
Turing machine (not general purpose)
Replica of the Bombe

18
Computer Science
1945: John Von Neumann's computer architecture
Control unit:
•reads an instruction from memory
•interprets/executes the instruction
•signals the other components what to do
•Separation of instructions and data (although
both are sequences of 0s and 1s)
•Sequential processing

19
Computer Science
• The first non-military computer, ENIAC, by John
Mauchly and Presper Eckert (1946)

20
Computer Science
1947: John Von Neumann's self-
reproducing automata
1. A universal constructor A, which can
construct another automaton according
to instruction I.
2. A copier B, which can make a copy of
the instruction tape I.
3. A controller C, which combines A and B
and allows A to construct a new
automaton according to I, B to copy
instructions from I and attach them to
the newly created automaton
4. An instruction tape describing how to
construct automaton A+B+C

21
Cybernetics
1943: Beginning of Cybernetics
1946: The first Macy Conference on Cybernetics
– John von Neumann (computer science),
– Norbert Wiener, Walter Pitts (mathematics),
– Rafael Lorente de No, Ralph Gerard, etc (neurology),
– Warren McCulloch (neuropsychiatry),
– Gregory Bateson, Margaret Mead (anthropology),
– Heinrich Kluever, Lawrence Kubie, etc (psychology)
– Filmer Northrop (philosophy),
– Lawrence Frank, Paul Lazarsfeld (sociology)

22
Cybernetics
Norbert Wiener (1947)
• To unify machines and nature, "artificial"
systems and natural systems
• Feedback, by sending back the output as
input, helps control the work of the machine
• A control system is realized by a loop of
action and feedback
• A control system is capable of achieving a
"goal", is capable of "purposeful" behavior
• Living organisms are control systems
• Your brain is a control system
vs

23
Cybernetics
• Paradigm shift from the world of continuous
laws to the discrete world of algorithms
• The effect of an algorithm is to turn time’s
continuum into a sequence of discrete
quanta, and, correspondingly, to turn an
analog instrument into a digital instrument
• A watch is the digital equivalent of a
sundial: the sundial marks the time in a
continuous way, the watch advances by
units.
(Ikea)

24
Cybernetics
Cybernetics
• An analog instrument can be precise, and
there is no limit to its precision.
• A digital instrument can only be approximate,
its limit being the smallest magnitude it can
measure
Titan Octane 9308KM01

25
Cybernetics
Ross Ashby (1952)
– Both machines and living beings tend to
change in order to compensate variations in
the environment, so that the combined
system is stable
– The "functioning" of both living beings and
machines depends on feedback processes
– The system self-organizes

26
Information Theory
Claude Shannon (1949)
– Shannon defines information as chaos
(entropy), Wiener and Brillouin define
information as order (negentropy)

27
Information Theory
Vladimir Kotelnikov (1933)
• The “Nyquist–Shannon” sampling theorem: how to
convert continuous signals (analog domain) into
discrete signals (digital domain) and back without
losing fidelity

29
History of Artificial Intelligence
1950: Alan Turing's "Computing Machinery and
Intelligence" (the "Turing Test")

30
The Turing Test (1950)
• A machine can be said to be “intelligent” if it
behaves exactly like a human being
• Hide a human in a room and a machine in
another room and type them questions: if you
cannot find out which one is which based on
their answers, then the machine is intelligent

31
The “Turing point”: a computer can be said to be intelligent
if its answers are indistinguishable from the answers of a
human being
??

32
The Turing Test
• John Searle’s Chinese room (1980)
– Whatever a computer is computing, the
computer does not "know" that it is
computing it
– A computer does not know what it is doing,
therefore “that” is not what it is doing
– Objection: The room + the machine “knows”

33
The Turing Test
• Hubert Dreyfus (1972):
– Experience vs knowledge
– Meaning is contextual
– Novice to expert
– Minds do not use a theory about the everyday world
– Know-how vs know that
• Terry Winograd
– Intelligent systems act, don't think.
– People are “thrown” in the real world

34
The Turing Test
• Rodney Brooks (1986)
– Situated reasoning
– Intelligence cannot be separated from the body.
– Intelligence is not only a process of the brain, it is
embodied in the physical world
– Cognition is grounded in the physical interactions
with the world
– There is no need for a central representation of
the world
– Objection: Brooks’ robots can’t do math

35
The Turing Test
• John Randolph Lucas (1961) & Roger Penrose
– Goedel’s limit: Every formal system (>Arithmetic)
contains a statement that cannot be proved
– Some logical operations are not computable,
nonetheless the human mind can treat them (at
least to prove that they are not computable)
– The human mind is superior to a computing
machine

36
The Turing Test
• John Randolph Lucas (1961) & Roger Penrose
– Objection: a computer can observe the failure of
“another” computer’s formal system
– Goedel’s theorem is about the limitation of the
human mind: a machine that escapes Goedel’s
theorem can exist and can be discovered by
humans, but not built by humans

37
The Turing Test
• What is measured: intelligence, cognition, brain,
mind, or consciousness?
• What is measured: one machine, ..., all machines?
• What is intelligence? What is a brain? What is a
mind? What is life?
• Who is the observer? Who is the judge?
• What is the instrument (instrument = observer)?
• What if a human fails the Turing test?

38
The ultimate Turing Test
• Build a machine that reproduces my brain,
neuron by neuron, synapses by synapses
• Will that machine behave exactly like me?
• If yes, is that machine “me”?

39
1954: Demonstration of a machine-translation
system by Leon Dostert's team at
Georgetown University and Cuthbert
Hurd's team at IBM
1956: Dartmouth conference on Artificial
Intelligence
Artificial Intelligence (1956): the discipline of
building machines that are as intelligent as
humans

40
1956: Allen Newell and Herbert Simon
demonstrate the "Logic Theorist“, the first
A.I. program, that uses “heuristics” (rules of
thumb) and proves 38 of the 52 theorems
in Whitehead’s and Russell’s “Principia
Mathematica”
1957: “General Problem Solver” (1957): a
generalization of the Logic Theorist but
now a model of human cognition

41
1957: Noam Chomsky's "Syntactic Structures"
S stands for Sentence, NP for Noun Phrase, VP for Verb Phrase, Det for Determiner,
Aux for Auxiliary (verb), N for Noun, and V for Verb stem

42
1957: Frank Rosenblatt's Perceptron, the first
artificial neural network

43
1959: John McCarthy's "Programs with Common
Sense" (1949) focuses on knowledge
representation
1959: Arthur Samuel's Checkers, the world's first
self-learning program
1960: Hilary Putnam's Computational Functionalism
(see chapter on “Philosophy of Mind”)
1962: Joseph Engelberger deploys the industrial
robot Unimate at General Motors

44
1963 Irving John Good speculates about
"ultraintelligent machines" (the "singularity")
1964: IBM's "Shoebox" for speech recognition
1965: Ed Feigenbaum's Dendral expert system:
domain-specific knowledge

45
1965: Lotfi Zadeh's Fuzzy Logic
or, better:

46
1966: Ross Quillian's semantic networks

47
1966: Joe Weizenbaum's Eliza
1968: Peter Toma founds Systran to commercialize machine-
translation systems

48
1969: Marvin Minsky & Samuel Papert's
"Perceptrons" kill neural networks
1969: Stanford Research Institute's Shakey the
Robot
1972: Bruce Buchanan's MYCIN
•a knowledge base
•a patient database
•a consultation/explanation program
•a knowledge acquisition program
Knowledge is organised as a series of IF THEN rules

49
1972: Terry Winograd's Shrdlu

50
1972: Hubert Dreyfus's "What Computers Can't Do"
1974: Marvin Minsky's Frame (see chapter on
“Cognition”)
1975: Roger Schank's Script (see chapter on
“Cognition”)
1975: John Holland's Genetic Algorithms
1976: Doug Lenat's AM
1979: Cordell Green's system for automatic
programming
1979: Drew McDermott's non-monotonic logic
1979: David Marr's theory of vision

51
1980: John Searle’s "Chinese Room"
1980: Intellicorp, the first major start-up for Artificial
Intelligence
1982: Japan's Fifth Generation Computer Systems
project

52
1982: John Hopfield describes a new generation of neural
networks, based on a simulation of annealing
1983: Geoffrey Hinton's and Terry Sejnowski's Boltzmann
machine for unsupervised learning
1986: Paul Smolensky's Restricted Boltzmann machine
1986: David Rumelhart’s “Parallel Distributed Processing”
Rummelhart network
Neurons arranged in layers, each neuron
linked to neurons of the neighboring
layers, but no links within the same layer
Requires training with supervision
Hopfield networks
Multidirectional data flow
Total integration between input and output
data
All neurons are linked between themselves
Trained with or without supervision

53
Genealogy of Intelligent Machines
Hydraulic machines
Steam engines
Cybernetics
Neural networks
Logic
Hilbert
Turing Machine
Computers
Expert Systems

54
• Valentino Breitenberg’s “vehicles” (1984)
– Vehicle 1: a motor and a sensor
– Vehicle 2: two motors and two sensors
– Increase little by little the circuitry, and these
vehicles seem to acquire not only new skills, but
also a personality.

55
1985: Judea Pearl's "Bayesian Networks"
1987: Chris Langton coins the term "Artificial Life"
1987: Rodney Brooks' robots
1990: Carver Mead describes a neuromorphic
processor
1992: Thomas Ray develops "Tierra", a virtual world

56
1997: IBM's "Deep Blue" chess machine beats the world's
chess champion, Garry Kasparov
2011: IBM's Watson debuts on a tv show

57
2000: Cynthia Breazeal's emotional robot, "Kismet"
2003: Hiroshi Ishiguro's Actroid, a young woman

58
2004: Mark Tilden's biomorphic robot Robosapien
2005: Honda's humanoid robot "Asimo"

59
2005: Boston Dynamics' quadruped robot "BigDog"
2010: Lola Canamero's Nao, a robot that can show its
emotions
2011: Osamu Hasegawa's SOINN-based robot that learns
functions it was not programmed to do
2012: Rodney Brooks' hand programmable robot "Baxter"

60
• Rod Brooks/ Rethink Robotics (2012)
– Vision to locate and grasp objects
– Can be taught to perform new tasks by moving its
arms in the desired sequence

61
Deep Learning
1998: Yann LeCun 's second
generation Convolutional Neural
Networks
2006: Geoffrey Hinton’s Geoffrey
Hinton's Deep Belief Networks
2007: Yeshua Bengio's Stacked
Auto-Encoders
Deep
Learning

62
Deep Learning mimics the workings of the
brain: the audiovisual cortex works in
multiple hierarchical stages

63
2014: Vladimir Veselov's and Eugene Demchenko's program
Eugene Goostman, which simulates a 13-year-old Ukrainian
boy, passes the Turing test at the Royal Society in London
2014: Li Fei-Fei's computer vision algorithm that can describe
photos ("Deep Visual-Semantic Alignments for Generating
Image Descriptions", 2014)
2014: Alex Graves, Greg Wayne and Ivo Danihelka publish a paper
on "Neural Turing Machines"
2014: Jason Weston, Sumit Chopra and Antoine Bordes publish a
paper on "Memory Networks"
2014: Microsoft demonstrates a real-time spoken language
translation system
2014: GoogLeNet, a 22 layers convolutional network, wins
Imagenet 2014, almost doubling the mean average precision of
the previous year’s winner

66
Knowledge-based Systems
“General problem solver”:
the program capable of
solving all problems
Intelligence =
reasoning about
knowledge
Domain knowledge
and domain experts Knowledge
Representation
Knowledge-based
systems
(expert systems)
Knowledge
Engineering

67
Knowledge-based Systems
• Knowledge representation
– Predicates
– Production rules
– Semantic networks
– Frames
• Inference engine: reasoning mechanisms
• Common sense & heuristics
• Uncertainty
• Learning

68
Information-based System
Data
Base
Who is the
president of
the USA?
Where is
Rome?
OBAMA
ITALY

69
Knowledge-based System
Know
ledge
Base
Who will the
president of
the USA?
Where is
Atlantis?
X
Y

70
Information Processing
vs
Knowledge Processing
A
abaco
ad
addobbo
…
mangia…
zucchero
How
Italian
words
look
like
AAEINPT
AAEINTP
AAEITPN
...
PANIETA
NAPIETA
TANIEPA
PENIATA
...
7!=5000
No
know-
ledge
of Italian
Less than 100
Puzzle: What is the Italian word “PNAATEI”?
1)
2)
PIANETA
PIANETA

71
Artificial Intelligence
Artificial
Intelligence
A New Class of
Applications
A New Class of
Technologies

72
A New Class of
Applications
Expert
Tasks
Heuristics Uncertainty
“Complex”
Problem Solving
The algorithm
does not exist
A medical
encyclopedia
is not
equivalent to a
physician
The algorithm is
too
complicated
Design a cruise
ship
There is an
algorithm
but it is
“useless”
Don’t touch
boiling water
The algorithm is
not possible
Italy will win the
next world
cup

73
A New Class of
Applications
Expert
Tasks
Heuristics Uncertainty
“Complex”
Problem Solving
Expert
Systems
Vision
Speech
Natural
Language

74
A New Class of
Technologies
Non-sequential
Programming
Symbolic
Processing
Knowledge
Engineering
Uncertain
Reasoning

75
Expert system
Inference
Engine
Explanation
Subsystem
Knowledge
Base
User
Interface
Knowledge
Acquisition
Module

77
Common Sense
Small minds are concerned with the extraordinary,
great minds with the ordinary"
(Blaise Pascal)

78
Common Sense
• In which cases it is perfectly logical to find a
woman in the men’s restrooms?

79
Common Sense
• How easy is it to build a robot that can make these
trivial inferences?

80
Common Sense
• What is wrong about this picture?
Communication tower
on top of Mt Diablo (2015)

81
Common Sense
• Deduction is a method of exact
inference (classical logic)
– All Greeks are humans and
Socrates is a Greek, therefore
Socrates is a human
• Induction infers generalizations from
a set of events (science)
– Water boils at 100 degrees
• Abduction infers plausible causes of
an effect (medicine)
– You have the symptoms of a flue

82
Common Sense
• Classical Logic is inadequate for ordinary life
• Intuitionism (Luitzen Brouwer, 1925)
• Frederick and Barbara Hayes-Roth (1985):
opportunistic reasoning
• Non- monotonic Logic
• Fuzzy Logic (Lofti Zadeh)

83
Common Sense
The Frame Problem
– Classical logic deducts all that is possible from all
that is available
– In the real world the amount of information that is
available is infinite
– It is not possible to represent what does “not”
change in the universe as a result of an action
("ramification problem“)
– Infinite things change, because one can go into
greater and greater detail of description
– The number of preconditions to the execution of any
action is also infinite, as the number of things that
can go wrong is infinite ("qualification problem“)

84
Common Sense
Uncertainty
“Maybe i will go shopping”
“I almost won the game”
“This cherry is red”
“piero is an idiot”
Probability
Probability measures "how often" an
event occurs
But we interpret probability as “belief”

85
Common sense
Principle of Incompatibility (Pierre Duhem)
The certainty that a proposition is true
decreases with any increase of its
precision
The power of a vague assertion rests in its
being vague (“I am not tall”)
A very precise assertion is almost never
certain (“I am 1.71cm tall)

86
Common Sense
Heuristics
• Knowledge that humans tend to share in a
natural way: rain is wet, lions are dangerous,
politicians are crooks, carpets get stained…
• Rules of thumbs
György Polya (1940s): “Heuretics“ - the nature,
power and behavior of heuristics: where it comes
from, how it becomes so convincing, how it
changes

88
Connectionism
A neural network is a set of interconnected neurons
(simple processing units)
Each neuron receives signals from other neurons and
sends an output to other neurons
The signals are “amplified” by the “strength” of the
connection

89
Connectionism
The strength of the connection changes over time
according to a feedback mechanism (output desired
minus actual output)
The net can be “trained”
Output
Correction
algorithm

90
Connectionism
• Distributed memory
• Nonsequential programming
• Fault-tolerance
• Recognition
• Learning

91
Connectionism
Where are we?
Largest neural network:
– 1,000,000,000,000 neurons
Worm’s brain:
– 1,000 neurons
But the worm’s brain outperforms neural networks on most
tasks
Human brain:
– 100,000,000,000 neurons
– 200,000,000,000,000 connections

92
• Notes…
– How many people can fly and land upside
down on a ceiling? Don’t underestimate the
brain of a fly.
– Computers don’t grow up. Humans do.

94
A Brief History of Bionic Beings
1957: The first electrical implant in an ear (André Djourno
and Charles Eyriès)
1961: William House invents the "cochlear implant", an
electronic implant that sends signals from the ear
directly to the auditory nerve (as opposed to hearing
aids that simply amplify the sound in the ear)
1952: Jose Delgado publishes the first paper on implanting
electrodes into human brains: "Permanent
Implantation of Multi-lead Electrodes in the Brain"
1965 : Jose Delgado controls a bull via a remote device,
injecting fear at will into the beast's brain
1969: Jose Delgado’s book "Physical Control of the Mind -
Toward a Psychocivilized Society"
1969: Jose Delgado implants devices in the brain of a
monkey and then sends signals in response to the
brain's activity, thus creating the first bidirectional
brain-machine-brain interface.

95
A Brief History of Bionics
Jose Delgado

96
1997: Remotely controlled
cockroaches at Univ of Tokyo
1998: Philip Kennedy develops a brain
implant that can capture the "will"
of a paralyzed man to move an
arm (output neuroprosthetics:
getting data out of the brain into a
machine)

97
2000: William Dobelle develops an
implanted vision system that
allows blind people to see outlines
of the scene. His patients Jens
Naumann and Cheri Robertson
become "bionic" celebrities.
2002: John Chapin debuts the
"roborats", rats whose brains are
fed electrical signals via a remote
computer to guide their
movements

98
2002: Miguel Nicolelis makes a monkey's brain
control a robot's arm via an implanted
microchip
2005: Cathy Hutchinson, a paralyzed woman,
receives a brain implant from John
Donoghue's team that allows her to
operate a robotic arm (output
neuroprosthetics)
2004: Theodore Berger demonstrates a
hippocampal prosthesis that can provide
the long-term-memory function lost by a
damaged hippocampus

99
The age of two-way neural transmission…
2006: The Defense Advanced Research
Projects Agency (Darpa) asks
scientists to submit "innovative
proposals to develop technology to
create insect-cyborgs
2013: Miguel Nicolelis makes two rats
communicate by capturing the
"thoughts" of one rat's brain and
sending them to the other rat's brain
over the Internet

100
The age of two-way neural
transmission…
2013: Rajesh Rao and Andrea
Stocco devise a way to send a
brain signal from Rao's brain to
Stocco's hand over the Internet,
i.e. Rao makes Stocco's hand
move, the first time that a
human controls the body part of
another human
2014: An amputee, Dennis Aabo,
receives an artificial hand from
Silvestro Micera's team capable
of sending electrical signals to
the nervous system so as to
create the touch sensation

101
Neuro-engineering?
(http://targetedindividualscanada.com)
(http://its-interesting.com)

102
Which one is the robot and
which is the person?

104
Bibliography
• Dreyfus, Hubert & Dreyfus Stuart: Mind Over
Machine (Free Press, 1985)
• Hofstadter, Douglas: Goedel Escher Bach (Vintage,
1980)
• Holland, John: Emergence (Basic, 1998)
• Kosko Bart: Neural Networks And Fuzzy Systems
(Prentice Hall, 1992)
• Langton, Christopher: Artificial Life (Mit Press, 1995)
• Rumelhart David & Mcclelland James: Parallel
Distributed Processing Vol. 1 (Mit Press, 1986)
• Russell, Stuart Jonathan & Norvig Peter: Artificial
Intelligence (Prentice Hall, 1995)
• Wiener, Norbert: Cybernetics (John Wiley, 1948)

105
Machine Intelligence
"The person who says it cannot be done should not
interrupt the person doing it" (Chinese proverb)

106
Break
"There is more stupidity than hydrogen in the universe,
and it has a longer shelf life"
(Frank Zappa)

107
Self-organization
Or
The Science Of Emergence
Birds
Terra-form.com
Fish
corpusoptima.com

108
Entropy: the Curse of Irreversibility
• Thermodynamics studies macroscopic
systems that cannot easily reduced to
their microscopic constituents

109
Entropy
• Sadi Carnot:
– Heat flows spontaneously from hot to cold
bodies, but the opposite never occurs
– Any transformation of energy has an
energetic cost
– There are no reversible processes in
nature
• “I propose to name the quantity S the entropy
of the system, after the Greek word [τροπη
trope], the transformation” (Rudolf Clausius,
1865)
• Natural processes generate entropy

110
Entropy
• Ludwig Boltzmann’s statistical definition
– Many different microscopic states of a
system result in the same macroscopic state
– The entropy of a macrostate is the logarithm
of the number of its microstates
– Entropy = the number of molecular degrees
of freedom
– Entropy is a probability function
– A system tends to drift towards the most
probable state, which happens to be a state
of higher entropy

111
Entropy: the curse of irreversibility
• The second law of Thermodynamics (1852)
– Some processes are not symmetric in
time
– Change cannot always be bi-directional
– We cannot always replay the history of
the universe backwards
– Some things are irreversible

112
The Second Law
• Entropy can never decrease
• Transformation processes cannot be run
backwards, cannot be "undone"
• Young people age, elderly people cannot rejuvenate
• Buildings don't improve over the years, they decay
• Scrambled eggs cannot be unscrambled and
dissolved sugar cubes cannot be recomposed

113
Entropy
Entropy and Information
• Order is directly related to information
• Entropy is therefore related to information
• Entropy = a measure of disorder = a measure
of the lack of information
• Entropy measures the amount of disorder in
a physical system; i.e. entropy measures the
lack of information about the structure of the
system
Entropy = - Information

114
The Origin of Order
• Classical physics: order can be built only
rationally, by application of a set of
fundamental laws of physics
– Order needs to be somehow created by
external forces
• Darwin: order can build itself spontaneously,
e.g. Evolution
– Spontaneous "emergence" of order.

115
Chaos and Complexity
• Need for a new science:
– A mathematical model of phenomena
of emergence (the spontaneous
creation of order)
– A justification of their dynamics (which
violates the second law of
Thermodynamics)

116
Chaos and Complexity
• Henri Poincare`'s "chaos" theory (1900s): a
slight change in the initial conditions results
in large-scale differences
• Very "disordered" systems spontaneously
"crystallize" into a higher degree of order
• Complexity: a system must be complex
enough for any property to “emerge” out of it
• Complexity can be formally defined as
nonlinearity

117
Dissipative Systems
• Ilya Prigogine's non-equilibrium
thermodynamics (1960s)
– Classical physics: the world is a static and
reversible system that undergoes no
evolution, whose information is constant in
time
– Classical physics is the science of being
– Thermodynamics describes an evolving
world in which irreversible processes
occurs
– Thermodynamics is the science of
becoming

118
Dissipative Systems
• Ilya Prigogine (1960s)
– Order can come either from equilibrium
systems or from non-equilibrium systems that
are sustained by a constant source (by a
persistent dissipation) of matter/energy
– All living organisms are non-equilibrium
systems
– The distance from equilibrium and the
nonlinearity of a system drive the system to
ordered configurations, i.e. create order

119
Complex Systems
• Per Bak (1987)
– The collapse of a pile of sand under the
weight of a new randomly placed grain is
unpredictable
– However, when it happens, the pile
reorganizes itself
– No external force is shaping the pile of
sand: it is the pile of sand that organizes
itself.

120
Complex Systems
• Chris Langton (1989)
– When ice turns into water, the atoms have
not changed, but the system as a whole
has undergone a phase transition
– The state between order and chaos (the
"edge of chaos") is sometimes a very
"informative" state, because the parts are
not as rigidly assembled as in the case of
order and, at the same time, they are not
as loose as in the case of chaos.
– The system is stable enough to keep
information and unstable enough to
dissipate it.
– The system at the edge of chaos is both a
storage and a broadcaster of information.

121
Complex Systems
• Chris Langton (1989)
– The edge of chaos is where the system
can perform computation, can metabolize,
can adapt, can evolve
– In a word: these systems can be “alive”
– Phenomena such as life can only exists in
a very narrow boundary between chaos
and order

122
Synchronized Oscillators
• Clocks and cycles are pervasive in our
universe, from biological clocks to the cycles
of electrons around the nucleus

123
• Populations of synchronized oscillators are
pervasive in nature
– Biological clocks are regulated by
populations of interacting neurons
– The rhythm of the heart is set by the
collective behavior of a group of cells
– The suprachiasmatic nucleus is made of a
large population of independent oscillators
– At low temperatures all bosons behave like
one (Bose-Einstein)
• The population does not need any leader in
order to achieve synchronization

124
• Order emerges from the synchronized
behavior of "coupled oscillators“
• The advantage of having a population of
clocks: fault tolerance

125
• Yoshiki Kuramoto (1984): There is a
threshold value for a chaotic system of
oscillators, below which a solution of
complete synchronization exists and above
which it does not exist
• Steven Strogatz (2003): Synchronicity "will"
emerge at some point in any system that
exhibits partial synchronicity above the
threshold

126
• Self-organization can be achieved in time or
in space.
– Some interacting molecules, cells and
atoms achieve self-organization in space
through spontaneous reorganization
– Some coupled oscillators achieve self-
organization in time through
spontaneous synchrony

127
Questions?
"The beaver told the rabbit as they stared at the Hoover Dam:
No, I didn't build it myself, but it's based on an idea of mine"
(Charles Townes)

128
The Classical World: Utopia
• Aristoteles
• There is a reality that exists apart from
anyone experiencing it, and human
senses are capable of accessing it
• The natural state of things is rest
• The natural state of the heavens is
uniform circular motion
• Heavier bodies of a given material fall
faster than lighter ones

129
• Galileo Galilei
– A body in free motion does not need any
force to continue moving: the natural state
of things is uniform linear motion
– If a force is applied, then what will change
is the acceleration, not the velocity
– All bodies fall with the same acceleration
– Space and time are independent

130
• Isaac Newton
– Differential calculus and Euclid's geometry
– Three fundamental laws
– Exact mathematical relationships
– The dynamic equation that mathematically
describes the motion of a system
– Newton’s world is a deterministic machine

131
The End of Utopia
• Gas
– Gas: a system made of many minuscule
particles in very fast motion and in continuous
interaction
– Classical approach abandoned in favor of a
stochastic approach
– All quantities that matter (from temperature to
heat) are statistical quantities

132
The End of Utopia
• Electromagnetism
– Electric bodies radiate invisible waves of
energy through space (fields)
– James Maxwell (1873): field equations
(distribution of electric and magnetic
charges)
– The number of coordinates needed to
determine a wave is infinite

133
The End of Utopia
• Erwin Schroedinger: classical science relies on
two fundamental postulates:
– object and subject can be separated (i.e., the
subject can look at the object as if it were a
completely disconnected entity);
– the subject is capable of knowing the object
(i.e., the subject can look at the object in a
way that creates a connection, one leading to
knowledge)

134
Relativity: the Primacy of Light
• Special theory of relativity (1905)

135
• Special theory of relativity (1905)
1. Laws of nature must be uniform
2. Laws must be the same in all
frames of reference that are
"inertial"
3. The speed of light is always the
same

136
• To unify Newton’s equations for the
dynamics of bodies and Maxwell’s equations
for the dynamics of electromagnetic waves in
one set of equations
• Different observers see different things
depending on their state of motion
• All quantities are decomposed into a time
component and a space component, but how
that occurs depends on the observer’s state
of motion.

137
• Distortions of space and time
• "Now" is a meaningless concept
• The past determines the future
• Matter = energy
• Nothing can travel faster than light

138
General Relativity: Gravity Talks
• General Relativity is ultimately about the
nature of gravitation
• Relativity explains gravitation in terms of
curved space-time, i.e. Geometry
• Free falls as natural motions, as straight lines
in spacetime

139
• The effect of a gravitational field is to
produce a curvature of spacetime
• The straight line becomes a “geodesic”, the
shortest route between two points on a
warped surface
• Bodies not subject to forces other than a
gravitational field move along geodesics of
spacetime
A B

140
• Spacetime warp

141
• What causes the “warps” is energy-mass
• "Force" becomes an effect of the geometry of
space
• A "force" is the manifestation of a distortion in
the geometry of space
• Clocks are slowed down by “gravity” (i.e. by
spacetime warps)

142
Summarizing Einstein...
• The dynamics of matter is determined by the
geometry of space-time
• Geometry is in turn determined by the
distribution of matter
• Space-time acts like an intermediary device
that relays the existence of matter to other
matter
Matter Spacetime
geometry
Matter

144
Quantum Theory: Enter Probabilities
• Any field of force manifests itself in the form of
discrete particles (“quanta”)
Planck
DeBroglie
Einstein
Born
Bohr

145
• Forces are manifestations of exchanges of
discrete amounts of energy (Max Planck,
1900)
• Equivalent descriptions: energy and mass,
frequency and wavelength (Einstein, 1905)
• Electrons are permitted to occupy only
some orbits around the nucleus (Niels Bohr,
1913)
• Waves and particles are dual aspects
(Louis de Broglie, 1923)
• The wave associated to a particle is a wave
of probabilities (Max Born, 1926)

146
• 1925 equations to replace Newton's (or,
better, Hamilton's) equations and predict the
state of the system at a later time given the
current state
– Werner Heisenberg's equation (based on
matrix algebra): the result of a physical
experiment depends on the order in
which the calculations are performed
– Erwin Schroedinger (based on wave
mechanics): an object is a wave spread
in space

147
• The state of a particle is a “superposition”
of alternatives
• The state of a particle is described by a
“wave function” which summarizes
(superposes) all the alternatives and their
probabilities
• Erwin Schroedinger's equation describes
how this wave function evolves in time
• The wave function describes a set of
possibilities

148
Quantum Theory: Enter Uncertainty
• An observable quantity can assume a
range of values (its “eigenvalues”), each
one with a given probability
• An observer can measure at the same
time only observables which are
compatible: indeterminacy of incompatible
observables
• Werner Heisenberg’s "uncertainty
principle": there is a limit to the precision
with which we can measure quantities
• The degree of uncertainty is proportional
to the Planck constant

149
• The degree of uncertainty is proportional
to the Planck constant
• Below a quantity proportional to the Planck
constant the physical laws stop working
altogether
• This implies that there is a limit to how
small a physical system can be

150
• The Planck constant
– Newton and Einstein physics assumed that
the most fundamental units of the universe
were the point and the instant
– Quantum Theory introduces a fundamental
unit that is bigger than a point and an instant
– There is a limit to how small an object can be
and to how short an interval can be
– Einstein had warped space and time,
Quantum Theory it turned them into grids.
– The Planck constant has a size: a length,
height and width of 10-33 centimeters and a
time interval of 10-43 seconds

151
• The vacuum
– Paul Dirac (1930)
– If you remove all particles, i.e. you have
absolute certainty about the position of
particles, then you know nothing about
momentum and therefore about energy
– Kinetic and potential energy cannot
both be zero at the same time
– There has to be a minimum amount of
energy: the zero-point energy
– The vacuum is not empty
– Hendrik Casimir (1948): the vacuum
generates a force

152
• Randomness
• Schroedinger's cat
• Non-locality
upscale.utoronto.ca

153
The Measurement Problem
• A measurement causes a “collapse” (reduction) of
the wave function: only one eigenvalue is possible
after the measurement, the one that is measured
• A measurement introduces irreversibility in nature:
collapse cannot be undone

154
• The collapse of the wave:
afriedman.org

155
• John von Neumann (1932)
– The flow of time is mysteriously altered by
measurements
– The classical world emerges from the
quantum world thanks to measurement
– A continuous process of the probabilistic kind
gives rise to a discontinuous process of the
deterministic kind

156
• John von Neumann
– Measurement of a system consists in a
chain of interactions between the
instrument and the system, whereby the
states of the instrument become dependent
on the states of the system
– Eventually, states of the observer’s
consciousness are made dependent on
states of the system, and the observer
“knows” what the value of the observable is
– Somewhere between the system and the
observer’s consciousness the “collapse”
occurs

157
• Is it something that only humans have?
• If not, what is the smallest object that can
collapse a wave?
• What is the measuring apparatus in quantum
physics?
• How does nature know which of the two
systems is the measurement system and
which one is the measured system?
• A privilege of the human mind?

158
Interpretations of Quantum Theory
• Niels Bohr: only phenomena are real
• Werner Heisenberg: the world "is" made of
possibility waves (particles are merely
"potentialities")
• Paul Dirac: a description of our knowledge
of a system
• Hugh Everett: multiverse
• etc

159
Quantum Theory: Enter Entanglement
• Albert Einstein (1935)
– Paradox: once entangled, the states of
two particles would be entangled
forever, no matter how far from each
other (EPR paper: 5 citations in 20
years, more than 300 in 2014)
• Schroedinger (1935)
– Entanglement is the most fundamental
feature of Quantum Mechanics
• John Bell (1964)
– Yep, that’s the way it is!

160
Quantum Theory: Enter Entanglement
• A brief history of teleportation
– Impossible according to Heisenberg’s uncertainty
principle (the very act of measuring its state
destroys it so it is impossible to make an exact
replica)
– William Wootters: Entanglement offers a way
around it (1993)
– (“Teleporting an unknown quantum state via dual
classical and Einstein-Podolsky-Rosen channels”,
1993). In 1998 the team led by US physicist Jeff
Kimble teleports a photon for about one meter
(1993)
– Nicolas Gisin teleports a photon to 2 kms (2003)
– Anton Zeilinger teleports a photon to 143 kms
(2012)

161
The Quest for Certainty
• Hugh Everett's “multi-verse” (1957)
– If something physically can happen, it
does: in some universe
– Each measurement splits the universe in
many universes

162
• Hugh Everett's “multi-verse” (1957)
– We exist in one copy for each universe
and observe all possible outcomes of a
situation
– No collapse of the wave function
– The universe can unfold in an infinite
number of ways
– Quantum theory becomes deterministic
– The role of the observer is vastly reduced

163
Michael Lockwood (1986)
– Each measurement splits the
observer, not the universe

164
• David Bohm (1952)
– The quantum "wave" is a real wave, due to a
real field, that acts upon particles the same way
a classical potential does
– This field is due to a potential that permeates
the universe
– Position and momentum of a particle are no
longer incompatible
– Bohm's wave is a real wave that guides the
particle (the "pilot-wave")
– Everything is both a particle and a wave, and is
acted upon by both a classical potential and a
quantum potential

165
• David Bohm (1952)
– Bohm's potential plays the role of Einstein's
"hidden variables"
– Bohm's quantum potential acts beyond the 4-
dimensional geometry of spacetime
– The quantum field is, in turn, affected by all
particles
– Everything in the universe is entangled in
everything else
– The universe is one indivisible whole

166
• Eugene Wigner (1963) following Von
Neumann:
– Reality is just the content of
consciousness
– The evolution of the universe changed
after the appearance of human beings
(there was no collapse anywhere
before mind appeared)

167
• Transactional Interpretation
– John Wheeler and Richard Feynman (1945)
• Maxwell's equations of electromagnetism have
two possible solutions: a retarded solution and
an advanced one
• A charged particle generates both waves that
will arrive after the emission as well as other,
perfectly symmetrical, waves that will arrive
before the emission.
• A radiation process is a "transaction" in which
the emitter of the radiation and the absorber of
the radiation exchange waves

168
• John Wheeler (1994)
– Ours is a "participatory" universe, one in
which consciousness participates in
creating reality
– The observer and the phenomenon are
engaged in a creative act that yields reality

169
• Relativity, Quantum Physics and
Thermodynamics: three disciplines that
happen to pose limitations on the
speed, amount and quality of
information that can be transmitted in a
physical process.

171
The Physics of Elementary Particles
community.emc.com

172
• Quantum Electrodynamics (Paul Dirac,
1928) and Quantum Chromodynamics
(Murray Gellman, 1964)

173
• Forces are mediated by discrete packets of
energy (quanta: photon, w-/w+/z, graviton,
gluons)
• Particles that make up matter belong to two
families of fermions: leptons and quarks
• The world is made of six leptons, six times
three quarks, four bosons for leptons and
eight gluons for quarks (48 fermions and 12
bosons)
• The whole set of equations for these particles
has 19 arbitrary constants

174

175
Unification: In Search of Symmetry
• Peter Higgs (1964)
– The top quark is about 100,000 times
“heavier” than the electron, and more
than one trillion times heavier than the
electron-neutrino
– Mass measures the interaction with the
all-pervasive Higgs field, mediated by
the Higgs boson

176
Grand Unification
• The universe of Relativity is curved and
continuous
• The universe of Quantum Theory is flat and
granular
• Relativity prescribes that matter warps the
continuum of spacetime, which in turns affects the
motion of matter
• Quantum Theory prescribes that matter interacts
via quanta of energy in a flat spacetime
• Quantum Theory does not explain gravity

177
Black Holes and Entropy
• Jakob Bekenstein (1972): Black holes store a
huge amount of entropy
• Stephen Hawking (1974): The area of the
event horizon can never decrease
• The entropy of a black hole is proportional to
the area of its event horizon.
• Hawking (1974): Black holes evaporate
• Leonard Susskind and Gerard ’t Hooft (1993):
A black hole is a hologram: information about
what has been lost inside the black hole is
encoded on the surface of the black hole

178
Black Holes and Wormholes
• Karl Schwarzschild (1916): The "black hole"
• Einstein (1935): Black holes imply the
existence of a "bridge" between our universe
and a mirror universe which is hidden inside
the black hole, and in which time runs
backwards
• Kurt Goedel (1949): Space-time can curve to
the point that a particle will return to a previous
point in time; I.e., "wormholes" can connect
two different points in time of the same
universe
• John Wheeler (1950s): Two points in space
can be connected through several different
routes, because of the existence of spatial
wormholes

179
Black Holes and Wormholes
• Stephen Hawking (1988): Wormholes can
connect different universes altogether
• Lee Smolin (1992): Black holes are the
birthplaces of offspring universes
• Kip Thorne (1994): A time machine capable
of exploiting such time wormholes
• Andrew Strominger (1996): Black holes
decay into strings and strings decay into
black holes

180
The Holographic Universe
• Gerard't Hooft (1993)
– The horizon of a black hole (a two
dimensional surface) stores all the
information that ever fell into the hole
– Generalizing: the informational content of a
region of space can always be equivalently
expressed by a theory that lives on the
boundary of that region
– The three-dimensional universe which we
perceive might be encoded on a two-
dimensional surface, like a hologram.

181
Status
Science cannot account for:
Dark matter (perhaps 23% of the mass-energy of
the universe and five times as abundant as
matter) does not interact with ordinary matter,
does not emit and does not reflect light (its
gravitational effect has been inferred by
observing the motion of galaxies)
Dark energy (about 73% of the mass-energy of the
universe) is needed in order to account for the
acceleration of the expansion of the universe

182
Summary
• Classical Physics is continuous and objective
• Thermodynamics introduces entropy
• Entropy is related to order and information
• Darwin's theory was really a theory of how to create
order
• Complex systems create order (self-organize) in
special cases and yield new properties
• Self-organizing systems only happen at the edge of
chaos
• Oscillators too can self-organize, not in space but in
time
• Electromagnetism introduces fields

183
Summary
• Relativity is based on the observer: even space and
time are relative
• Quantum Mechanics is based on the observer: it is
the act of measuring that creates reality
• There is a limit (uncertainty) to what we can know in
Quantum Mechanics
• The result of a measurement (the collapse of the
wave) is random
• The vacuum
• Entanglement
• Relativity and Quantum Physics contradict each other
• Physics is almost certainly incomplete (dark matter,
dark energy)

185
Bibliography
Bohm, David: WHOLENESS AND THE IMPLICATE ORDER (1980)
Close, Frank: NOTHING (Oxford Univ Press, 2009)
Davies, Paul: ABOUT TIME (Touchstone, 1995)
Davies, Paul: GOD AND THE NEW PHYSICS (Penguin, 1982)
Deutsch, David: THE BEGINNING OF INFINITY (Viking, 2011)
Gell-Mann, Murray: THE QUARK AND THE JAGUAR (1994)
Heisenberg, Werner: PHYSICS AND PHILOSOPHY (1958)
Kaku, Michio: HYPERSPACE (Oxford University Press, 1994)
Kauffman, Stuart: THE ORIGINS OF ORDER (1993)
Langton, Christopher: ARTIFICIAL LIFE (1989)
Penrose, Roger: THE EMPEROR'S NEW MIND (1989)
Prigogine, Ilya & Stengers Isabelle: ORDER OUT OF CHAOS
(1984)
Price, Huw: TIME'S ARROW AND ARCHIMEDES' POINT (1996)
Randall, Lisa: WARPED PASSAGES (2006)
Weinberg, Steven: DREAMS OF A FINAL THEORY (1993)

186
Modern Physics
"Time is the substance of which we are made"
(Borges)

187
Piero Scaruffi
www.scaruffi.com

Machine Intelligence & Physics

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