Introduction to artificial intelligence

Introduction to
Artificial Intelligence (AI)
Dr. Albert Fornells Herrera
http://www.linkedin.com/in/afornells

1. To have or not to have mind…this is the question
2. Why AI is interesting for industry?
3. What does “intelligence” mean?
4. What is Artificial Intelligence?
5. Types of AI
6. Why is AI powerful
7. Bases of AI
8. Artificial intelligence history: From 'dark ages' to KBS
9. Artificial intelligence map
10. References
2
Outline
Introduction to Artificial Intelligence - Albert Fornells

 Philosophers have been trying for over two thousand years to understand and
resolve two big questions of the universe:
 how does a human mind work, and
 can non-humans have minds?
 However, these questions are still unanswered
 Some philosophers have picked up the computational approach originated by
computer scientists and accepted the idea that machines can do everything that
humans can do
 Others have an opposed idea, claiming that such highly sophisticated behavior as
love, creative discovery and moral choice will always be beyond the scope of any
machine
 Engineers and scientists have already built machines that can be called
‘intelligent’.
 So what does the word ‘intelligence’ mean? And intelligent machine?
3
To have or not to have mind…this is the question

 Main reasons:
 Economical needs
 Experts highly qualified are expensive and they cannot afford them
 A way for training new experts
 Knowledge preservation
 Computational efficiency
 General decision methods are slowly and inefficient
 Massive information needs to be processed
 Decision support system
 Get fast, efficient and justified decisions
 Autonomous decisions systems
 Summarizing: Companies want to build specific systems to solve specific
problems using the knowledge from domain and the expertise from experts
 They want to introduce our “minds” and “human capabilities” in their systems
4
Why AI is interesting for industry?

 Let us look at a dictionary definition (Essential English Dictionary,Collins, 1990)
 Def 1. Someone’s intelligence is their ability to understand and learn things
 Def 2. Intelligence is the ability to think and understand instead of doing things by
instinct or automatically
 So, it can be defined as ‘the ability to learn and understand, to solve
problems and to make decisions’
 Building a machine that is (or seems to be) intelligent is what is called intelligent
machine.
 However, the intelligence is not the same for everyone (and also for machines),
5
What does “intelligence” mean?
is not the same as

 John McCarthy coined AI term in 1956 as ‘the science and engineering of making
intelligent machines’ at a conference at Dartmouth College. Intelligent machine
terms refer to the capability of performing intelligent human processes as:
 Learning
 Reasoning
 Problem solving
 Perception
 Language understanding
 AI has become an essential part of the technology industry, providing the heavy
lifting for many of the most difficult problems in computer science.
 Prediction
 Classification
 Regression
 Clustering
 Function optimization
6
What is Artificial Intelligence (AI)?

7
So, an intelligent machine is a…
System that acts…
System that thinks…
…like humans ...rationally
Make tasks that,
nowadays, are better
done by humans
(Rich y Knight, 1991)
Exploring and
emulating the intelligent
behavior in
computational process
terms (Shalkoff, 1990)
Study of mental
capabilities through the
study of computational
models (Charniak y
McDermott, 1985)
Computer with minds at
literal sense
(Haugeland, 1985)

 The goal is to build a system that seems to be a human
 At 1950, Alain Turing proposed the Turing test in the article “Computing
machinery and intelligence” in the Mind Journal
 Instead of asking, ‘Can machines think?’, Turing said we should ask, ‘Can
machines pass a behavior test for intelligence?’
 So, Turing defined the intelligent behavior of a computer as the ability to achieve
the human-level performance in cognitive tasks
 A person -acting as a judge- has a conversation for 5 minutes through a text
channel. The judge has to identify if is talking with a computer or a human
 Required capabilities:
 Natural language processing
 Knowledge representation
 Reasoning
 Learning
8
Systems that act like humans

 Systems need to act as a human.
 They have to lie and make mistakes as us!!
 Loebner prize: Turing test competition.
 Total Turing test: The same but also with signal video.
9
More about Turing test
[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
[COMP] Option 2: Rhinoceros can't fly
[INT] What’s the result of 324 x
678?
[COMP] This is too difficult. I’m not
a calculator!

 The goal is to model the human brain and set a theory about how it works.
 Theory should allow the definition of computational models.
 This approach is highly influenced by neuroscience and cognitive sciences.
 The conscious mystery
 Many religions consider that consciousness resides in the soul.
 Can it be included in machines?
 D. Chalmers tackles the conscious topic from two points of view:
 Easy: Distinguish between conscious and unconscious thinking (Freud).
 Heart beat control, muscular movements, planning a day, etc.
 Difficult: Explain how the subjective experience is born from neurons.
 Surprising hypothesis from Francis Crick (1916-2004): brain as a machine
 Our thoughts, sensations and feelings are the result of the physiological activity of
the brain tissues.
 Conscious is a biological product such as blood circulation or the digestion.
10
Systems that think like humans

 Aristotle (384 BC- 322 BC) was the first to codify “the right thinking”, that is,
irrefutable reasoning process.
 Rationally thoughts are based on the logic.
 Formal Logic can be introduced and applied in computers.
 But….
 It is complex formalize the knowledge
 There is a big difference between being able to solve a problem “in principle” and
doing so in practice.
11
Systems that think rationally

 To act rationally means to achieve a set of goals using a previous knowledge
base.
 Approach focuses on rational agents.
 An agent receives and acts taking into account the environment.
 The aim is not to imitate the human model, but to complete a set of actions to
achieve a final goal.
 An agent needs:
 Perception
 Natural language processing
 Knowledge representation
 Reasoning
 Machine learning
12
Systems that act rationally

 The power resides in the combination of disciplines that tackle the same
problems as AI: learn and understand, to solve problems and to make decisions.
 AI is fed from many disciplines
 Philosophy: Logic, methods of reasoning, mind as physical system, foundations of
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 / NeuroScience: How do people behave, perceive, process cognitive
information, represent knowledge.
 Computer Engineering: Building fast computers.
 Control Theory: Design systems that maximize an objective function over time.
 Linguistics: Knowledge representation, grammars.
13
Why AI is powerful?

14
Bases of AI
Philosophy
• Discussion about
the possibility of a
mechanical
intelligence.
Mathematical.
• Philosophic bases
requires formal
rules.
Computational
linguistic
• Understanding
language requires
understanding of
the subject matter
and the context.
Cognitive
psychology
• Behavior theories,
rational behave
bases.
Computational
engineering
• Some mechanism,
hardware and tools
are required for AI.

 Philosophy: Discussion about the possibility of a mechanical intelligence.
 Descartes (1556-1650), Leibniz (1646-1716): mind is linked to the physical world.
 John Locke: In the beginning there was a Mind (1960).
 Hume (1711-1776), Russell (1872-1970): the knowledge comes from the
perception, its acquired by the experience (induction) and its represented by the
logical theories.
 Darwin (1809-1882): Evolutionary theory by natural selection at 1859.
 Mathematical. Philosophic bases require formal rules.
 Boole (1815-1864), Frege (1848-1925): Logical mathematical fundamentals.
 Gödel (1906–1978), Turing (1912–1954): Computability limits.
 Fermat (1601–1665), Bernoulli (1700–1782), Bayes (1702-1761): Probability and
Probabilistic reasoning.
15
Bases of AI

 Cognitive psychology. Behavior theories, rational behave bases.
 William James (1842-1910): Brain possess and processes information.
 Hermann Von Helmholtz (1821-1894): Perception involves a form of unconscious
logical inference.
 Kenneth Craik (1914-1945): Put back the missing mental step between stimulus
and response.
 Stimulus must be translated into an internal representation.
 The representation is manipulated by cognitive process to derive a new
internal representation.
 These are in turn retranslated back into actions.
 Computational linguistic: Understanding language requires understanding of
the subject matter and the context.
 B.F. Skinner (1904-1990) published Verbal Behavior at 1957 about the language
learning.
 Noam Chomsky (1928-present) published Syntactic Structures at 1957 about
knowledge representation and language grammar.
16
Bases of AI

 Computational engineering: Some mechanisms, hardware and tools are
required for AI
 Heath Robinson (1940), first operational modern computing for the single purpose
of deciphering German messages built by Alain Turing
 Z3 (1943), first operational programmable computer built by Konrad Zuse
 ENIAC (1945), first general-purpose, electronic and digital computer built by John
Machly and John Eckert
 EDVAC (1949), followed John Von Neumann’s suggestion to use a stored program
 6000 wave tube, 12000 diodes, 56kw, 45’5m2 and 7850kg. Need 30 workers
17
Bases of AI
ENIAC
EDVAC Microprocessor

18
AI history: From ‘Dark ages’ to KBS
early 1990s-onwards
The failure of expert systems and where AI is going
mid 1980s-early 1990s
AI becomes an industry
early 1970s–mid-1980s
The technology of expert systems
late 1960s–early 1970s
Unfulfilled promises
1956–late 1960s
The era of great expectations
1943–1956
The birth of artificial intelligence

 Situation: There was nothing
 First neuron model and demonstration that any function is computable (Warren
McCulloch and Walter Pits, 1943)
 Hebbian theory describes a basic mechanism for synaptic plasticity (Donald
Hebb, 1949)
 First neural network simulator (Marvin Misky and Dean Edmonds, 1951)
 ENIAC (1946) and EDVAC (1949) are built
 Demonstrating the need to use heuristics in the search for the chess game
(Claude Shannon, 1950)
 Creation of the non numerical reasoning program “Logic Theorist” (Herbert
Newell, Allen Simon, J.C. Shaw, 1955)
 Summer Workshop at Dartmouth College where AI concept is founded
(McCarthy, Minsky, Shannon, Rochester, More, Samuel, Solomonoff, Selfridge,
Newell and Simon, 1956)
19
AI history: Beginnings (1943-1956)

 The early years of AI are characterized by tremendous enthusiasm, great
ideas and very limited success.
 Only a few years before, computers had been introduced to perform routine
mathematical calculations.
 Now AI researchers were demonstrating that computers could do more than that. It
was an era of great expectations.
 Definition of the high-level language LISP (John McCarthy, 1958).
 Advice Taker is proposed to search for solutions to general problems of the
world. Focus on formal logic (John McCarthy, 1958).
 Generate solutions using axioms and new ones can be added.
 1st KBS with the principles of knowledge representation and reasoning.
 Frame theory for knowledge representation and reasoning (Marvin Minsky,
1975).
 Perceptron convergence theorem is proved (Frank Rosenblatt, 1962).
20
AI history: Big expectations (1956-late 1960s)

 Development GPS, a General Problem Solver, to simulate human problem-
solving methods (Herbert Newell and Allen Simon,1961)
 First attempt to separate the problem solving-technique from the data
 Means-ends analysis: Strategy based on states
 GPS fails in complicated problems due to the infinite number of states
 Fuzzy logic is defined (Lofti Zadeh, 1965)
 By 1980 the euphoria about AI was gone and most of government funding for AI
projects was cancelled
 AI researches attempted to simulate the complex thinking process by inventing
general methods for solving broad classes of problems.
 The general purpose search mechanism not work with real problems. They were
called weak methods because they use weak information from domain
 However, it was a time when new ideas such as knowledge representation,
learning and computing concepts were set
21
AI history: Big expectations (1956-late 1960s)

 From the mid-1950s, AI researchers were making promises to build all-purpose
intelligent machines on a human-scale knowledge base by the 1980s and to
exceed human intelligence by the year 2000.
 By 1970, however, they realized that such claims were too optimistic.
 Few AI programs could demonstrate some level of machine intelligence in one or
two toy problems.
 Main difficulties:
 General methods for broad classes of problems. So, NO knowledge about domain
 Search strategies based on try and error. They were not scaled for big problems.
 Easy or tractable problems can be solved in polynomial time, intractable problems
require times that are exponential functions of the problem size.
 So, many problems were NP-hard. “The size matters”
 Eliza, the first chatterbot is created (Joseph Weizenbaum, 1966).
 Probably the most important development in the 1970s was the realization that
the problem domain for intelligent machines had to be sufficiently restricted
22
AI history: A dose or reality (late 60s-early 70s)

 Knowledge from expert is used to tackle real problems
 DENDRAL: A molecular structure interpretation (Feigenbaum,1971)
 The first expert system to solve a real problem
 The major shift in AI: shift from general purpose, knowledge-sparse, weak methods
to domain-specific, knowledge intensive techniques
 A new methodology of expert systems: knowledge engineering, which
encompassed techniques of capturing, analyzing and expressing in rules an
expert’s ‘know-how’. Appears the knowledge acquisition bottleneck problem
 MYCIN: Rule-based expert system for the diagnosis of infectious blood
diseases (Edgar ShortLiffe, 1972)
 Worked as well as experts and better than junior doctors
 Which were the main contributions?
 Knowledge in the form of rules was clearly separated from the reasoning
mechanism
 No general theoretical model existed: Rules are extracted from interviews
 Rules reflect the uncertainty with medical knowledge
23
AI history: Knowledge as key of success (early 70s- mid 80s)

 PROSPECTOR . A computer-based consultation system for mineral exploration
(Duda, 1981)
 It used a combined structure that incorporated rules and a semantic network
 It had a sophisticated support package including a knowledge acquisition system
 It incorporated Bayes’ rules of evidence to propagate uncertainties through the
system
 Many applications showed that AI technology could move successfully from the
research laboratory to the commercial environment
 Most expert systems were developed with special AI languages, such as LISP,
PROLOG and OPS, based on powerful workstations
 Expensive hardware and complicated programming language means few
researchers
 Only in 1980, with the arrival of PCs and easy-to-use expert system development
tools made possible the expansion to more modest laboratories
 Knowledge representation and reasoning were the key for success
24
AI history: Knowledge as key of success (early 70s- mid 80s)

 A survey found nearly 200 expert systems, most of them applications in the field
of medical diagnosis (Waterman, 1986)
 7 years later, the survey found 2500 developed expert systems (Durkin, 1994).
The new growing area was business and manufacturing
 Expert system technology had clearly matured, but are the key of success in
any field? It would be a mistake to overestimate the capability of this technology
 Expert systems are restricted to a very narrow domain of expertise
 Expert systems are not as robust and flexible as a user might want
 Expert systems have limited explanation capabilities
 Expert systems are also difficult to verify and validate
 Expert systems, especially the first generation, have little or no ability to learn from
their experience
 Despite all these difficulties, expert systems have made the breakthrough and
proved their value in a number of important applications
25
AI history: AI becomes an industry (mid 80s-begin90s)

 Disillusion about the applicability of expert system technology
 Building an expert system required much more than just buying a reasoning system or
expert system shell and putting enough rules in it
 Expert systems were too expensive to maintain
 They were difficult to update, they could not learn, they were "brittle“ (i.e. they
could make grotesque mistakes when given unusual inputs)
 They proved useful, but only in a few special contexts
 So, the next step was to evolve the Expert System to Knowledge Base Systems
26
AI history: The failure of expert systems (begin 90s – onwards)
Emulate the resolution capability of an
expert human
 Building through knowledge engineering
Mainly based on rules
Reduced learning capabilities
Use the knowledge from domain to solve
problems
Include automatic acquisition process
inside the knowledge engineering process
 Heterogeneous methodologies and
architectures (rules, cases, models, etc.)
Extended learning capabilities

27
A possible map of the current AI
• Robotics and control
• Natural Language Processing
• Artificial vision
• Speech recognition
• Logic
• Multiagents systems
• Decision Support System
• Knowledge management
• Knowledge representation
• Ontology and semantic web
• Computer-Human interaction
• Evolutionary Computation
• Case-Based Reasoning
• Reinforcement Learning
• Neural Network
• Data Analysis
• Non monotonic reasoning
• Model based reasoning
• Constraint satisfaction
• Qualitative reasoning
• Uncertain reasoning
• Temporal reasoning
• Heuristic search
Reasoning
Machine
Learning
Robotics,
perception
and natural
language
processing
Knowledge
Management

1. For each of the following, give reasons why:
a) A dog is more intelligent than a worm
b) A human is more intelligent than a dog
c) An organization is more intelligent than an individual human
Based on these, give a definition of what “more intelligent” may mean
2. Choose a particular word, for example, what is on some part of your desk at
the current time
i. Get someone to list all of the things that exist in this world (or try it yourself as a
thought experiment)
ii. Try to think of ten things that they missed. Make these as different from each other
as possible
iii. Try to find a thing that can’t be described using natural language
iv. Choose a particular task, such as making the desk tidy, and try to write down all of
the things in the world at a level of description that is relevant to this task
28
Exercises

3. Look for the “Chinese room argument” significance (Searle, 1980). Which
was its goal?
29
Exercises

 S. Russell, P. Norvig. Artificial Intelligence: a Modern Approach, 3rd ed.
Pearson Higher Education, 2010 (*).
 Also available in Spanish.
 M. Negnevitsky, Artificial Intelligence, A Guide to Intelligent Systems. Addison
Wesley, 2002 (*).
 N. J. Nilsson, Artificial Intelligence: a new synthesis, The Morgan Kaufmann
Series in Artificial Intelligence Series. Morgan Kaufmann, 1998 (*).
(*) Partially published in Google Books
30
References

Introduction to artificial intelligence

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