Artificial Intelligence

Introduction
AI in Hollywood

1. Metropolis
2. 2001: A Space Odyssey
3. Blade Runner
4. The Terminator
5. The Matrix
6. A.I. Artificial Intelligence
7. I, Robot
8. WALL-E
9. Robot & Frank
10. Her
11. Ex-Machina

https://blog.adext.com/artificial-intelligence-movies/
 What is AI ?
 AI is one of the newest fields in science and engineering
 truly a universal field, How??
 AI - making a machine to act smart
 John McCarthy coined the term AI at Dartmouth conference in
1956

 Artificial
 ????

 Intelligence
 ????

Philosophy
 What is AI ? - Dictionary
 Artificial
 made by human skill; produced by humans (opposed to natural)
 imitation; simulated
 Intelligence
 capacity for learning, reasoning, understanding, and similar
forms of mental activity
 Ability to perceive and act in the world
 Learning: recommend movies, learn traffic patterns
 Reasoning: proving theorem, medical diagnosis,
 Understanding: Text, speech and visual scenes
 Ability to learn, recognize patterns, and solve problems
(Psychologists)
https://www.dictionary.com
 What is AI ? - Dictionary
 artificial intelligence
 the branch of computer science involved with the design of
computers or other programmed mechanical devices having
the capacity to imitate human intelligence and thought.
 operations and tasks analogous to learning and decision
making in humans

https://www.dictionary.com
 Intelligence vs. Humans
 Are humans intelligent?
 replicating human behavior early hallmark of intelligence

 Are humans always intelligent?

 Depends on age, task, situation and other factors
 Can non-human behavior be intelligent?
 Dogs, Dolphins, Snakes, Elephants, Gray Wolf

 How companies test intelligence during placements?

 CGPA, Aptitude, Coding and HR

https://www.scienceabc.com/humans/who-are-some-of-the-people-with-the-highest-iq.html
What is AI?
 Acting humanly
 The Turing Test approach
 Alan Turing (1950)
 A computer passes the test if a human interrogator, after posing some
written questions, cannot tell whether the written responses come from a
person or from a computer.
 The computer would need to possess the following capabilities
 natural language processing to enable it to communicate in English;
 knowledge representation to store what it knows or hears;
 automated reasoning to use the stored information to answer questions and
to draw new conclusions;
 machine learning to adapt to new circumstances and to detect and
extrapolate patterns.

The Loebner Prize

 Acting humanly
 The Turing Test approach
 remains relevant 60 years later
 Yet AI researchers believe that it is more important to study the underlying
principles of intelligence than to duplicate an exemplar.
 Understand this??
 The quest for “artificial flight” succeeded when the Wright brothers and
others stopped imitating birds and started using wind tunnels and learning
about aerodynamics.
 Aeronautical engineering texts do not define the goal of their field as
making “machines that fly so exactly like pigeons that they can fool
even other pigeons.”
 Is Turing Test the right goal?
 Thinking humanly
 The cognitive modeling approach
 If we are going to say that a given program thinks like a human,
we must have some way of determining how humans think.
 need to get inside the actual workings of human minds.

 There are three ways to do this:

 through introspection — trying to catch our own thoughts as
they go by;
 through psychological experiments — observing a person in
action; and
 through brain imaging — observing the brain in action.
 Thinking humanly
 Cognitive Science
 the scientific study of the human mind
 the study of the precise nature of different mental tasks and the
operations of the brain that enable them to be performed
 combining ideas and methods from psychology, computer science,
linguistics, philosophy, and neuroscience.
 The interdisciplinary field of cognitive science brings together
computer models from AI and experimental techniques from
psychology to construct precise and testable theories of the
human mind.
 Thinking rationally
 The “laws of thought” approach
 Rational - agreeable to reason; reasonable; sensible
 Aristotle was one of the first to attempt to codify “right thinking,”
- irrefutable reasoning processes

 These laws of thought were supposed to govern the operation of

the mind; their study initiated the field called logic
 solve any solvable problem described in logical notation - inference
 There are two main obstacles to this approach
 not easy to represent the informal knowledge in the formal terms required by
logical notation
 a problem “in principle” and solving it in practice
 Acting rationally
 The rational agent approach
 An agent is just something that acts
 operate autonomously, perceive their environment, persist over a
prolonged time period, adapt to change, and create and pursue
goals
 A rational agent is one that acts so as to achieve the best outcome
or, when there is uncertainty, the best expected outcome.
 Optimal vs Best solution
 Making correct inferences is sometimes part of being a rational
agent
 There are ways of acting rationally that cannot be said to involve
inference (in human - reflex, in robotics - ???)
 Acting rationally
 In this course,
 Therefore, we concentrate on general principles of rational
agents and on components for constructing them.
 One important point to keep in mind:
 achieving perfect rationality—always doing the right thing—is not
feasible in complicated environments.
 limited rationality — acting appropriately when there is not enough time
to do all the computations one might like.

Strong AI vs Weak AI ????

 Basic Component of AI
 The main unifying theme is the idea of an intelligent agent.
 define AI as the study of agents that receive percepts from the
environment and perform actions.

sensors
?
?
environment
agent ?
actuators

model

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 The Foundations of AI
 brief history of the disciplines that contributed ideas,
viewpoints, and techniques to AI
 Computer engineering (hardware and software)
 Philosophy (rules of reasoning)
 Mathematics (logic, algorithms, optimization)
 Neuroscience (model low level human/animal brain activity)
 Cognitive Science and Psychology (modeling high level human/animal
thinking)
 Linguistics
 Economics
 Control theory and cybernetics
 The Foundations of AI
 Computer engineering (hardware and software)
 How can we build an efficient computer?
 OS, Prog. Languages, Tools and packages world’s first programmer??

 For artificial intelligence to succeed, we need two things:

intelligence and an artifact.
 the computer has been the artifact of choice.
 provides the artifact that makes AI application possible
 The power of computer makes computation of large and
difficult problems more easily
 AI has also contributed its own work to computer science,
including:
 time sharing, interactive interpreters, personal computers with windows
and mice, rapid development environments, the linked list data type,
automatic storage management,
 The Foundations of AI
 Philosophy (rules of reasoning)
 Can formal rules be used to draw valid conclusions?
 How does the mind arise from a physical brain?
 Where does knowledge come from?
 How does knowledge lead to action?
 At that time, the study of human intelligence began with no
formal expression
 Initiate the idea of mind as a machine and its internal
operations
 The Foundations of AI
 Mathematics (logic, algorithms, optimization)
 What are the formal rules to draw valid conclusions?
 What can be computed?
 How do we reason with uncertain information?
 Mathematics formalizes the three main area of AI: computation,
logic, and probability
 Computation leads to analysis of the problems that can be computed
 complexity theory
 Probability contributes the “degree of belief” to handle uncertainty in AI
 Decision theory combines probability theory and utility theory
(“preferred outcomes” / bias)
 The Foundations of AI
 Economics
 How should we make decisions so as to maximize payoff?
 How should we do this when others may not go along?
 How should we do this when the payoff may be far in the future?
 Most people think of economics as being about money, but
economists will say that they are really studying how people
make choices that lead to preferred outcomes.

 Control theory and cybernetics

 How can artifacts operate under their own control?
 the science of communication and automatic control systems
 The artifacts adjust their actions
 To do better for the environment over time
 Based on an objective function and feedback from the environment
 The Foundations of AI
 Neuroscience (model low level human/animal brain activity)
 How do brains process information?
 Study of the nervous system, esp. brain
 A collection of simple cells can lead to thought and action
 Cognitive Science and Psychology (modeling high level
human/animal thinking)
 How do humans and animals think and act?
 The study of human reasoning and acting
 Provides reasoning models for AI
 Strengthen the ideas
 humans and other animals can be considered as information processing machines
 Despite advances, we are still a long way from understanding how
cognitive processes actually work.
 The Foundations of AI
 Linguistics
 How does language relate to thought?
 computational linguistics or natural language processing
 History of AI
 The birth of AI (1943 – 1956) - The gestation of AI
 McCulloch and Pitts (1943): drew on three sources
 knowledge of the basic physiology and function of neurons in the brain;
 a formal analysis of propositional logic due to Russell and Whitehead; and
 Turing’s theory of computation
 simplified mathematical model of neurons (resting/firing states) can
realize all propositional logic primitives (can compute all Turing
computable functions)
 Alan Turing: Turing machine and Turing test (1950)
 article “Computing Machinery and Intelligence.”
 machine learning, genetic algorithms, and reinforcement learning
 Claude Shannon: information theory; possibility of chess playing
computers
 History of AI
 Early enthusiasm (1952 – 1969)
– 1956 Dartmouth conference
 John McCarthy (Lisp);
 Marvin Minsky (first neural network machine);
 Alan Newell and Herbert Simon (GPS);
– Emphasis on intelligent general problem solving
 General Problem Solver: it could find solutions to a wide range of fairly
structured problems - mathematical word problems - “thinking humanly”
 Lisp (AI programming language) - 1958
 Advice Taker, a hypothetical program that can be seen as the first complete AI system
 Arthur Samuel; heuristic search (A*, AO*, game tree search)
 Emphasis on knowledge (1966 – 1974)
 making predictions; domain specific knowledge is the key to overcome
existing difficulties
 knowledge representation (KR) paradigms; declarative vs. procedural
representation
 machine evolution (now called genetic algorithms)
 Dartmouth workshop (McCarthy et al., 1955)

 Why couldn’t all the work done in AI have taken place

under the name of control theory or operations research or
decision theory??? which, after all, have objectives similar
to those of AI? Or why isn’t AI a branch of mathematics?
 Dartmouth workshop (McCarthy et al., 1955)
 Why couldn’t all the work done in AI have taken place
under the name of control theory or operations research or
decision theory??? which, after all, have objectives similar
to those of AI? Or why isn’t AI a branch of mathematics?

 The first answer is that AI from the start embraced the idea
of duplicating human faculties such as creativity, self-
improvement, and language use.
 None of the other fields were addressing these issues.
 The second answer is methodology.
 AI is the only one of these fields that is clearly a branch of computer
science (although operations research does share an emphasis on
computer simulations)
 History of AI
 Knowledge-based systems (1969 – 1979) - The key to power?
– DENDRAL: the first knowledge intensive system (determining 3D
structures of complex chemical compounds)
– MYCIN: first rule-based expert system (containing 450 rules for
diagnosing blood infectious diseases)
– Winograd’s SHRDLU system for understanding natural language had
engendered a good deal of excitement
 AI became an industry (1980 – 1989(present))
– successful commercial expert system, R1, began operation at the Digital
Equipment Corporation - configure orders for new computer
– wide applications in various domains
– AI industry boomed from a few million dollars in 1980 to billions of
dollars in 1988
 History of AI
 The return of neural networks (1986–present)
 back-propagation
 AI adopts the scientific method (1987–present)
 Hidden Markov models (speech recognition);
 NN - data mining technology has spawned a vigorous new industry
 The emergence of intelligent agents (1995–present)
 most important environments is the Internet - search engines,
recommender systems, and Web site aggregators
 The availability of very large data sets (2001–present)
 Current trends (1990 – present)
 more realistic goals; more practical (application oriented)
 distributed AI and intelligent software agents - resurgence of natural
computation - neural networks
 dominance of machine learning - Reinforcement Learning - Deep
Learning Optimization
 History of AI
 What can AI do today?
 Robotic vehicles – Auto Cars
 Speech recognition
 Autonomous planning and scheduling
 Game playing
 Spam fighting
 Logistics planning
 Robotics
 Machine Translation
 Image Processing – emotion detection
 Assistant
 Banking – Fraud detection
 Attempt
 Define in your own words: (a) intelligence, (b) artificial intelligence, (c)
agent, (d) rationality, (e) logical reasoning
 Read Turing’s original paper on AI (Turing, 1950). What he predicted, by
the year 2000? What do you think AI will produced in next 15 years.
 Are reflex actions (such as flinching from a hot stove) rational? Are they
intelligent?
 Is AI a science, or is it engineering? Or neither or both?
 Examine the AI literature to discover whether the following tasks can
currently be solved by computers:
 Playing a decent game of table tennis (Ping-Pong)
 Composing music / Orchestra
 Buying a week’s worth of groceries at the market / web
 Writing an intentionally funny story
 Giving competent legal advice in a specialized area of law.
 Miss Universe Judge
Interesting Q from Q..!!
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The End…

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The Jetsons - 1962
 The Foundations of AI
 Computer engineering (hardware and software)
 Philosophy (rules of reasoning)
 Mathematics (logic, algorithms, optimization)
 Neuroscience (model low level human/animal brain activity)
 Cognitive Science and Psychology (modeling high level
human/animal thinking)
 Linguistics
 Economics
 Control theory and cybernetics