Artificial Intelligence
Module - 1
IV-Sem CSE AIML
• Introduction
– What is AI?
– Foundations of AI
– History of AI
– State-of-the-art
Big Questions
• Can machines think?
• And if so, how?
• And if not, why not?
• And what does this say about human beings?
What is Intelligence?
• Intelligence:
– “the capacity to learn and solve problems” (Websters
dictionary)
– in particular,
• the ability to solve novel problems
• the ability to act rationally
• the ability to act like humans
• Artificial Intelligence
– build and understand intelligent entities or agents
– 2 main approaches: “engineering” versus “cognitive
modeling”
What is artificial intelligence?
There are no clear consensus on the definition of AI
Q. What is artificial intelligence?
A. 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 confine itself to methods
that are biologically observable.
Q. Yes, but what is intelligence?
A. Intelligence is the computational part of the ability to achieve
goals in the world. Varying kinds and degrees of intelligence
occur in people, many animals and some machines.
Exercise
What’s involved in Intelligence?
• Ability to interact with the real world
– to perceive, understand, and act
– e.g., speech recognition and understanding and synthesis
– e.g., image understanding
– e.g., ability to take actions, have an effect
• 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
– our internal models are always being “updated”
• e.g., a baby learning to categorize and recognize animals
AI definitions
AI definitions
• Acting humanly: The Turing Test approach (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.
– Natural Language processing for communication
– Knowledge representation to store information
– Automated Reasoning to use stored information
– Machine Learning to adapt to new circumstances and to
detect and extrapolate (to infer ) patterns
AI definitions
• Thinking humanly: The cognitive modeling approach
– Through Introspection - trying to catch our own thoughts as
they go by.
– Through psychological experiments - observing a person in
action
– Through brain imaging – observing the brain in action
– Once we have a sufficiently precise theory of the mind it
becomes possible to express the theory as a computer program.
• If the program’s input–output behavior matches corresponding human
behavior, that is evidence that some of the program’s mechanisms
could also be operating in humans.
• 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.
• In the early days of AI there was often confusion between the
approaches: an author would argue that an algorithm performs well on
a task and that it is therefore a good model of human performance, or
vice versa.
• Modern authors separate the two kinds of claims; this distinction has
allowed both AI and cognitive science to develop more rapidly.
AI definitions
• Thinking rationally: The “laws of thought” approach
– Aristotle was one of the first to attempt to codify “right Thinking”
which is called Syllogisms
– Example Socrates is a man
– all men are mortal
– therefore Socrates is mortal
– This initiated the field called logic
• Two main obstacles:
1. not easy to take informal knowledge and state it in the formal terms required by
logical notation, particularly when the knowledge is less than 100% certain.
2. there is a big difference between solving a problem “in principle” and solving it in
practice
AI definitions
• Acting rationally: The rational agent approach
– An agent is just something that acts (agent comes from the Latin
agere, to do).
– 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.
THE FOUNDATIONS OF ARTIFICIAL
INTELLIGENCE
• Philosophy
– 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?
• Mathematics
– What are the formal rules to draw valid conclusions?
– What can be computed?
– How do we reason with uncertain information?
THE FOUNDATIONS OF ARTIFICIAL
INTELLIGENCE
• 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?
• Neuroscience
– How do brains process information?
THE FOUNDATIONS OF ARTIFICIAL
INTELLIGENCE
• Psychology
– How do humans and animals think and act?
• Computer engineering
– How can we build an efficient computer?
• Control theory and cybernetics
– How can artifacts operate under their own control?
• Linguistics
– How does language relate to thought?
Types of AI
• Strong AI
– Aims to build machines that can truly reason and solve problems and self
aware and intellectual ability is indistinguishable from that of humans. Eg:
Self driving cars, Speech recognition
• Weak AI
– Deals with creation of computer based AI that cannot truly reason, Eg. A
chess program, Chatbot, Email Spam
• Cognitive AI
– Computers are used to test theories about how human mind works using
computational models. Helps human in decision making. E.g., Computer
Vision
• Applied AI
– Commercially viable smart systems. enabling computers and computer-
controlled robots to execute real tasks.
Optical character recognition
(OCR)
Digit recognition License plate readers
yann.lecun.com http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
Sudoku grabber
Automatic check processing
http://sudokugrab.blogspot.com/
Object Recognition
• Problem: Given an image A, does A contain
an image of a person?
Human Detection
Face Detection: Apple iPhoto,
Facebook, Google..
Face Recognition
Open-Universe Face
Identification
Open-Universe Face
Identification
Facial expression
Fatigue detection
Lip-reading
Eye Tracking
Video Surveillance and
Monitoring
UAVs: Unmanned Aerial Vehicles
(drones)
Tracking (multi-object)
Human Action Recognition
Counting in Extremely Dense Crowd
Images
Mobile visual search: Google
Goggles
Biometrics
How the Afghan Girl was Identified by Her Iris Patterns
Automotive safety
• Mobileye: Vision systems in high-end BMW, GM, Volvo models
– Pedestrian collision warning
– Forward collision warning
– Lane departure warning
– Headway monitoring and warning
36
AutoCars - Uber
Mobile robots
NASA’s Mars Spirit Rover
http://en.wikipedia.org/wiki/Spirit_rover http://www.robocup.org/
Saxena et al. 2008
STAIR at Stanford
Augmented Reality and Virtual
Reality
HoloLens, Oculus, Magic
Leap,
AI in Medical image analysis
Image guided surgery
3D imaging
Grimson et al., MIT
MRI, CT
Games
• IBM’s Deep Blue defeated the reigning • Google’s AlphaGo defeated South Korean
world chess champion Garry Kasparov in professional Go player Lee Sedol in 2012
1997
Mars Rovers
AI Applications: Machine Translation
• Language problems in international business
– e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no
common language
– or: you are shipping your software manuals to 127 countries
– solution; hire translators to translate
– would be much cheaper if a machine could do this
• How hard is automated translation
– very difficult! e.g., English to Kannada
– not only must the words be translated, but their meaning also!
• Nonetheless....
– commercial systems can do a lot of the work very well
– US miltary’s Phraselator to communicate with PoW’s and injured Iraqis
– CMU’s Speechlator for doctor and patient language translator
AI and Web Search
• Monitor user’s task
• Seek needed information
• Learn which information is most useful
Can we build hardware as complex as
the brain?
• How complicated is our brain?
– a neuron, or nerve cell, is the basic information processing unit
– estimated to be on the order of 10 12 neurons in a human brain
– many more synapses (10 14) connecting these neurons
• Conclusion
– YES: in the near future we can have computers with as
many basic processing elements as our brain, but with
• far fewer interconnections (wires or synapses) than the brain
• much faster updates than the brain
Can Computers Talk?
• This is known as “speech synthesis”
• Difficulties
– sounds made by this “lookup” approach sound unnatural
– a harder problem is emphasis, emotion, etc
• humans understand what they are saying
• machines don’t: so they sound unnatural
• Conclusion:
– NO, for complete sentences with emotion
– YES, for individual words
Can Computers Recognize
Speech?
• Speech Recognition:
– mapping sounds from a microphone into a list of words
– classic problem in AI, very difficult
• Recognizing normal speech is much more difficult
– speech is continuous: where are the boundaries between words?
– large vocabularies
– background noise, other speakers, accents, cold, etc
• Conclusion:
– NO, normal speech is too complex to accurately recognize
– YES, for restricted problems (small vocabulary, single speaker)
Can Computers Understand speech?
• Understanding is different to recognition:
– “Time flies like an arrow”
• assume the computer can recognize all the words
• how many different interpretations are there?
1. time passes quickly like an arrow?
2. command: time the flies the way an arrow times the flies
3. command: only time those flies which are like an arrow
4. “time-flies” are fond of arrows
• only 1. makes any sense,
– but how could a computer figure this out?
– clearly humans use a lot of implicit commonsense knowledge in
communication
• Conclusion: NO, much of what we say is beyond the
capabilities of a computer to understand at present
Can Computers Learn and Adapt ?
• Learning and Adaptation
– Machine learning allows computers to learn to do things
without explicit programming
– many successful applications:
• Conclusion: YES, computers can learn and adapt,
when presented with information in the appropriate
way
Can Computers “see”?
• Recognition v. Understanding
– Recognition and Understanding of Objects in a scene
• Why is visual recognition a hard problem?
Challenges: viewpoint variation
Michelangelo 1475-1564
Challenges: illumination
Challenges: scale
Challenges: deformation
Challenges: occlusion
Magritte, 1957
Challenges: background clutter
Challenges: Motion
Challenges: object intra-class
variation
slide credit: Fei-Fei, Fergus & Torra
Can computers plan and make optimal decisions?
• Intelligence
– involves solving problems and making decisions and plans
– e.g., you want to take a holiday in Goa
• you need to decide on dates, flights
• you need to get to the airport, etc
• involves a sequence of decisions, plans, and actions
• What makes planning hard?
– the world is not predictable:
• your flight is canceled or there’s a backup
– there are a potentially huge number of details
• do you consider all flights? all dates?
• Conclusion: NO, real-world planning and decision-making is still beyond the
capabilities of modern computers
– exception: AI systems are only successful in very well-defined, constrained problems
History of AI
• AI has roots in a number of scientific disciplines
– computer science and engineering (hardware and software)
– philosophy (rules of reasoning)
– mathematics (logic, algorithms, optimization)
– cognitive science and psychology (modeling high level
human/animal thinking)
– neural science (model low level human/animal brain activity)
– Linguistics, economics, etc.
• The birth of AI (1943 – 1956)
– Pitts and McCulloch (1943): simplified mathematical model of
neurons (resting/firing states) can realize all propositional logic
primitives (can compute all Turing computable functions)
– Allen Turing: Turing machine and Turing test (1950)
– Claude Shannon: information theory; possibility of chess playing
computers
• Early enthusiasm (1952 – 1969)
– 1956 Dartmouth conference
John McCarthy (Lisp);
Marvin Minsky (first neural network machine);
Alan Newell and Herbert Simon (GPS) General Problem Solver;
– Emphasize on intelligent general problem solving
GSP (means-ends analysis);
Lisp (AI programming language);
Resolution by John Robinson (basis for automatic theorem proving);
heuristic search (A*, AO*, game tree search)
• Emphasis on knowledge (1966 – 1974)
– Domain specific knowledge is the key to overcome existing
difficulties
– Knowledge representation (KR) paradigms
– Declarative vs. procedural representation
• Knowledge-based systems (1969 – 1979)
– 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)
EMYCIN: an ES shell
– PROSPECTOR: first knowledge-based system that made significant
profit (geological ES for mineral deposits)
• AI became an industry (1980 – 1989)
– wide applications in various domains
– commercially available tools
• Current trends (1990 – present)
– more realistic goals
– more practical (application oriented)
– distributed AI and intelligent software agents
– resurgence of neural networks and emergence of genetic algorithms
History
Summary
• Artificial Intelligence involves the study of:
– automated recognition and understanding of signals
– reasoning, planning, and decision-making
– learning and adaptation
• AI has made substantial progress in
– recognition and learning
– some planning and reasoning problems
– …but many open research problems
• AI Applications
– improvements in hardware and algorithms => AI applications in industry,
finance, medicine, and science.
Some projects to work on….
• Personality prediction
• Fake product review monitoring
• Twitter trend analysis
• Price negotiator chatbot system
• Personal nutritionalist using FatSecret API
• Online book/music recommendation using collaborative filtering
• Smart city traveler in Android
• Movie rating
• Stock market analysis
• Heart disease prediction
• Gold rate prediction
* source: www.experiment.withgoogle.com, www.nevonprojects.com and
www.crazyengineers.com
THE STATE OF THE ART
• Robotic vehicle - A driverless robotic car named STANLEY sped
through the rough terrain of the Mojave dessert at 22 mph, finishing the 132-
mile course first to win the 2005 DARPA Grand Challenge
• Speech recognition - A traveler calling United Airlines to book a flight
can have the entire conversation guided by an automated speech recognition
and dialog management system
• Autonomous planning and scheduling -A hundred million miles
from Earth, NASA’s Remote Agent program became the first on-board
autonomous planning program to control the scheduling of operations for a
spacecraft (Jonsson et al., 2000).
• Game playing: IBM’s DEEP BLUE became the first computer program to
defeat the world champion in a chess match when it bested Garry Kasparov by
a score of 3.5 to 2.5 in an exhibition match (Goodman and Keene, 1997).
THE STATE OF THE ART
• Spam fighting: Each day, learning algorithms classify over a billion
messages as spam, saving the recipient from having to waste time deleting
what, for many users, could comprise 80% or 90% of all messages, if not
classified away by algorithms
• Logistics planning: During the Persian Gulf crisis of 1991, U.S. forces
deployed a Dynamic Analysis and Replanning Tool, DART (Cross and Walker,
1994), to do automated logistics planning and scheduling for transportation.
• Robotics: The iRobot Corporation has sold over two million Roomba
robotic vacuum cleaners for home use
• Machine Translation: computer program automatically translates from
Arabic to English, The program uses a statistical model built from examples of
Arabic-to-English translations and from examples of English text totaling two
trillion words (Brants et al., 200)
