Lecture1 AI1 Introduction to artificial intelligence

Introduction Artificial
Intelligence
Lecture 1

Albert Orriols i Puig
http://www.albertorriols.net
htt // lb t i l t
aorriols@salle.url.edu

Artificial Intelligence – Machine Learning
g g
Enginyeria i Arquitectura La Salle
Universitat Ramon Llull

Today’s Agenda

Brainstorming from y
g your “postits”
p
Some Definitions
Prehistory and History of AI
Where are we headed?

Artificial Intelligence Machine Learning Slide 2

Brainstorming
What’s AI?
A
A
…

Do you know of some real-world applications?
A
A
…


What’s Intelligence?
Intelligence (dictionary)
g ( y)
capacity for learning, reasoning, understanding, and similar
forms o mental ac
o s of e a activity; ap ude in grasping truths,
y; aptitude g asp g u s,
relationships, facts, meanings, etc.
In particular, we cou d say
pa cu a , e could say:
Ability to act as human beings
Solve problems
Think rationally

Artificial intelligence …
Building a machine that is (or seems to be at the eyes of the
beholder) intelligent


Can You Be More Formal?
What is artificial intelligence?
g
It is the science and engineering of making intelligent
machines, especially intelligent computer programs.
It is related to the similar task of using computers to
understand human intelligence, but AI does not have
to co
o confine itself to methods that a e b o og ca y obse ab e
e se o e ods a are biologically observable.
Yes, but what is intelligence?
Intelligence i th computational part of the ability t achieve goals i
I t lli is the t ti l t f th bilit to hi l in
the world. Varying kinds and degrees of intelligence occur in people,
many animals and some machines.
Isn't there a solid definition of intelligence that doesn't depend
on relating it to human intelligence?
Not yet. The problem is that we cannot yet characterize in general
what kinds of computational procedures we want to call intelligent. We
understand some of the mechanisms of intelligence and not others.
d t d f th h i f i t lli d t th
See the complete interview at: http://www-formal.stanford.edu/jmc/whatisai/node1.html

What’s Involved in Intelligence?
Ability to interact with the real world
to perceive, understand, and act
e.g., speech recognition and understanding

Searching the best solution
Reasoning and Planning
modeling the external world, given input
solving new problems, planning, and making decisions
ability to deal with unexpected problems, uncertainties
Learning and Adaptation
we are continuously learning and adapting
y g p g
our internal models are always being “updated”
e.g., a baby learning to categorize and recognize animals
g, y g g g


AI Is Not Alone at Home
Crossbreeding of a lot of fields
g

Philosophy Logic, methods of reasoning, mind as physical system,
foundations of learning language rationality
learning, language, rationality.
Mathematics Formal representation and proof, algorithms,
computation, (un)decidability, (in)tractability
Statistics Modeling uncertainty, learning from data
Economics Utility, decision theory, rational economic agents
Neuroscience Neurons as information processing units
Psychology / Neuro How do people behave, perceive, process cognitive
Science information,
information represent knowledge
Computer Engineering Building fast computers
Control Theory
y Design systems that maximize an objective function
g y j
over time
Linguistics Knowledge representation, grammars


Prehistory of AI
Through history, people though of mythic “artificial”
g y, p p g y
robots
golden robots of Hephaestus and Pygmalion s Galatea
Pygmalion's
alchemical means of placing mind into matter
More specific, tangible advances
5th century B.C.
Aristotle invented syllogistic logic, the first formal deductive
reasoning system.
13th century.
Talking heads were said to have been created (Roger Bacon
and Alb t th G
d Albert the Great).
t)
Ramon Lull, Spanish theologian, invented machines for
discovering nonmathematical truths through combinatory.
combinatory


Prehistory of AI
More specific, tangible advances (cont.)
p , g ( )
15th century
Invention of printing using moveable type. Gutenberg Bible
type
printed (1456).
15th 16th
15th-16th century
Clocks, the first modern measuring machines, were first
produced using lathes.
16th century
Clockmakers extended their craft to creating mechanical
animals and other novelties.


Prehistory of AI
p , g ( )
17th century - The revolution of thinking about thinking
Descartes proposed that bodies of animals are nothing
more than complex machines (strong AI).
Variations and elaborations of Cartesian mechanism.

Hobbes published The Leviathan,
containing a material and combinatorial theory of thinking.
Pascal created the first mechanical digital
calculating machine (1642).

Leibniz improved Pascal's machine to do multiplication & division
(
(1673) and envisioned a universal calculus of reasoning by which
) g y
arguments could be decided mechanically.

Prehistory of AI
p , g ( )
18th century – Mechanical toys

Vaucanson’s Duck Von Kempelen’s phony
mechanical chess player


Prehistory of AI
p , g ( )
19th century – Frankenstein’s birth
George Boole developed a binary algebra representing (some)
"laws of thought," published in The Laws of Thought.
Charles Babbage and Ada Byron (Lady Lovelace) worked on
g y ( y )
programmable mechanical calculating machines.

Mary Shelley published the story of Frankenstein's monster
(1818).
Crossing the century bridge
Behaviorism was expounded by
psychologist Edward Lee Thorndike in
"Animal Intelligence."


Pre-birth of AI
Beginning of the 20th century
g g y
Russell and Whitehead published Principia Mathematica.
Capek s
Capek's play “Rossum's Universal Robots” produced in 1921 (London
Rossum s Robots
opening, 1923). First use of the word 'robot' in English.
McCulloch and Pitts publish "A Logical Calculus of the Ideas Immanent in
A
Nervous Activity" (1943), laying foundations for neural networks.
Rosenblueth, Wiener and Bigelow coin the term cybernetics (1943).
Bush published As We May Think (1945) a prescient vision of the future in
which computers assist humans in many activities.


The 3 Key Ingredients
The first key ingredient: The computer and the program
y g p p g
ENIAC (1945). The first electronic digital computer
EDVAC (1949) Th first stored program computer
(1949). The fi t t d t


The second key ingredient: The TURING TEST.
y g
(Human) judge communicates with a human and a machine
o e e o y channel.
over text-only c a e
Both human and machine try to act like a human
Judge tries to tell hi h is hi h
J d t i t t ll which i which.
Numerous variants.
Loebner prize.
Cu e t programs o e e close
Current p og a s nowhere c ose
to passing this
http://www.jabberwacky.com/
http://turingtrade.org/


The Turing Test
More on Turing test
g
Objective: The machine needs to fool the machine
[INT] I heard that a striped rhinoceros flow on the
Mississippi in a pink balloon this morning. What do
you think about?
[COMP] That sound rather ridiculous to me
[INT] Really? My uncle did this one... Why this sound
ridiculous?
[COMP] Option 1: Rhinoceros don't have stripes
don t
[COMP] Option 2: Rhinoceros can't fly

Try
Tr to change ON for UNDER the Mississipi

Is this unfair for the computer?
[INT] What’s the result of 324 x 678?
[COMP] This is too difficult. I’m not a calculator!

Needs to seem more foolish than it actually is (has to lie!)


The third key ingredient: THE DARMONT CONFERENCE.
y g
People working on building intelligent machines.

J. McCarthy, M. L. Minsky, N. Rochester, and C.E.
Shannon.
Shannon August 31, 1955. "We propose that a 2 month
31 1955 We month,
10 man study of artificial intelligence be carried out
during the summer of 1956 at Dartmouth College in
Hanover, New Hampshire. The study is to proceed on
the basis of the conjecture that every aspect of learning
or any other feature of intelligence can in principle be so
precisely described that a machine can be made to
simulate it."
i l t it "


Brief History of AI
The Golden years (
y (1956 – 1974)
)
‘1960s
Strong funding of AI centers
Building intelligent automata
Searching in complex search spaces
First AI programs that work
Samuel’s checker program (which learns)
S l’ h k ( hi h l )
Newell and Simon’s Logic Theorist
Gelernter’s geometry engine
G l t ’ t i
Robinson’s complete algorithm for logical reasoning
First programming languages for AI
McCarthy - Lisp (1958)


Brief History of AI
The Golden years (
y (1956 – 1974)
)
And the first chatterbots:
(1966).
ELIZA (1966)
It carried out very realistic conversations.
It searched for key words in the conversation and asked
y
information about that


Brief History of AI
The Winter: After expansion, there’s always a contraction
p , y
First doubts on the feasibility of all the approach
Problems:
P bl
Limited computer power
Combinatorial
C bi t i l explosion (exponential time)
l i ( ti l ti )
Commonsense knowledge and reasoning
Moravec’s paradox
M ’ d
The Chinese room argument undermined the goal of building
intelligent machines
END OF FUNDING


Brief History of AI
The Chinese room argument (Searle, 1980)
g ( , )
Person who knows English but not
C ese sits
Chinese s s in room
oo
Receives notes in Chinese
Has
H systematic English rule b k f
t ti E li h l book for
how to write new Chinese characters
based on input Chinese c a acte s, returns his notes
o put C ese characters, etu s s otes
Person=CPU, rule book=AI program, really also need lots of paper
(storage)
Has no understanding of what they mean
But from the outside, the room gives perfectly reasonable
answers i Chinese!
in Chi !
Searle’s argument: the room has no intelligence in it!


Brief History of AI
But in parallel… expert systems rise and grow
p p y g
MYCIN(1972):
Diagnosed infection blood diseases.
diseases
It had a set of about 600 rules and started to ask questions.
In some cases, better than human experts.
cases experts
XCON (1980):
Production-rule-based system that assisted the ordering of a
P d ti l b d t th t i t d th d i f
type of computers systems by automatically selecting the
computer systems components based on the customers
requirements.
Saving $40 million dollars to the company.
2500 rules and processed 80000 orders with 95%-98% accuracy.
The gain in money was because it reduced the need to give free
components when the technicians made errors, by speeding
errors
the assembly process and by increasing customer satisfaction

Brief History of AI
But in parallel… expert systems rise and grow
p p y g
PROSPECTOR (1981)
A computer-based consultation system for mineral
exploration.
Recommending exploratory drilling
g p y g
And many others. Search the web for more!
New funding due to this success
AI groups were formed in many large companies to develop
expert systems.
t t
1986 sales of AI-based hardware and software were $425
million.
illi


Brief History of AI
Q
Quick pace in the ‘90s
p
NCSA releases the first web browser, Mosaic
Deep Bl b t G
D Blue beats Gary K
Kasparov
Robotic soccer players in RoboCup
Sony corporation introduced the robotic dog AIBO
Remote age t auto o ous y d e deep space 1
e ote agent autonomously drive
Even moving faster in the 00’s
iRobot introduces the vacuum cleaning robot Roomba
DARPA grand challenge (we’ll see it in a minute)
A Touareg R5 won the challenge


Some Cool Applications
Three cool applications among hundreds
pp g
Deep Blue
DARPA G d Ch ll
Grand Challenge
Robotics Cog
Loebner Prize
Roomba
oo ba
Rob-Cup
ASIMO
Data mining
Stock Market
Medical Diagnosis


Deep Blue

Origins at CMU
It was a massively parallel,
RS/6000 SP Thin P2SC-based
system with 30-nodes
Deep Blue took Gary Kasparov
to the cleaners


DARPA Grand Challenge
Grand Challenge
Cash prizes ($1 to $2 million) offered to first robots to
complete a long course completely unassisted
Stimulates research in vision robotics planning machine
vision, robotics, planning,
learning, reasoning, etc
2004 Grand Challenge:
150 mile route in Nevada desert
Furthest any robot went was about 7 miles
… but hardest terrain was at the beginning of the course
2005 Grand Challenge:
G d Ch ll
132 mile race
Narrow t
N tunnels, winding mountain passes, etc
l i di t i t
Stanford 1st, CMU 2nd, both finished in about 6 hours


http://cs.stanford.edu/group/roadrunner/
p g p


The challenge: a driverless car competes for wining the
g p g
race

150 mile off-road robot race
across the Mojave desert
Natural and manmade hazards
No driver, no remote control
N di t t l
No dynamic passing
Fastest vehicle wins the race
(and 2 million dollar prize)


The architecture


Robotics - Cog
Humanoid intelligence requires humanoid interactions
g q
with the world


Loebner Prize
Prizes the chatterbots considered to be the most human-like
The
Th contest begun in 1990
t tb i
$25,000 is offered for the first
chatterbot that judges cannot
j g
distinguish from a real human
and that can convince judges
that the human is the
computer program
$100,000 is the reward
for the first chatterbot that
judges cannot distinguish
from a real human in a
Turing test that includes
deciphering and
understanding text, visual,
and auditory input


Roomba
Go around “smartly” to clean up a house
y p


RobCup
First official Rob-Cup soccer match (1997)
p ( )


ASIMO

Advanced Step in Innovative
Mobility
y
Able of
Moving
Interacting with human beings
Help people


Data Mining Explosion
Data mining: Extract novel, useful, and interesting
g , , g
information from data

Why so a big deal?
Companies are generating lots of data about the business
They want to process these data and obtain useful information

Why no
Wh now, not before?
Computers have a lot of power nowadays


Modeling the Stock Market
Modeling market traders
g
LETS project: Evolving artificial traders for successful market
trading (Sonia Sc u e bu g et a , 2007)
ad g (So a Schulenburg al, 00 )

Evolutionary economics:
Create trend followers
and value investors
Let them interact
Evolve a population of
strategies


Medical Diagnosis
Data mining
An important application domain of artificial
intelligence
John H. Holmes
Epidemiologic study by means of LCSs
Hidden relationships among variables
discovered by LCSs

Xavier Llorà et al.
Better than Human Capability in Diagnosing
Prostate Cancer Using Infrared Spectroscopic
imaging


But… Slow it down!

There are no castles in the sky
All these applications rely on:
Search & Optimization
Knowledge representation
Learning
Planning

These are the four topics that we’ll see in this course.
And we will start for the beginning


Detailed Outline AI1
2. Solving search problems
1. Introduction to search problems
2. Blind search
3. Informed/heuristic search
4. Adversary search (first project)
5. Constraint satisfaction problems

3.
3 Knowledge representation
1. Introduction to knowledge representation
2.
2 Knowledge representation based on logics
3. Knowledge and uncertainty
4. Fuzzy Logics
F L i

4. Lisp


Detailed Outline AI2
5. Machine learning
1. Introduction to machine learning

2. Supervised learning
1. Decision trees, Instance-based learning, Bayesian decision theory, Support
vector machines and Neural networks

3.
3 Unsupervised learning – association rules

4. Unsupervised learning – clustering

5. Reinforcement learning
g

6. New challenges in data mining

6. Planning
1. Introduction to planning
p g

2. STRIPS

3. Search through the state world
g

4. Search through the plan world

Lecture1 AI1 Introduction to artificial intelligence

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