MLecture 1 Introduction to AI . The basics.pptx

Artificial Intelligence
(AI)
Lecture No. 1

Agenda
• Intelligence
• Intelligence of computer
• Artificial intelligence
• Intelligent computing Vs Conventional computing
• Contribution of other fields to AI
• +History of AI
• +Applications of AI
• References
• End
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Intelligence?
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Can Intelligence be defined?
• Intelligence can not be defined abstractly/
precisely.
• There are probably as many definitions of
intelligence as there are experts of this field.
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Intelligence (definition)
• from "Mainstream Science on Intelligence" (1994), an
editorial statement by fifty-two researchers:
• A very general mental capability that, among other
things, involves the ability to reason, plan, solve
problems, think abstractly (conceptually),
comprehend complex ideas, learn quickly and
learn from experience.
• (Gottfredson, L.S., 1997).
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Intelligence
• from "Intelligence: Knowns and Unknowns" (1995), a
report published by the Board of Scientific Affairs of the
American Psychological Association:
• Individuals differ from one another in their ability to
understand complex ideas, to adapt effectively to the
environment, to learn from experience, to engage in
various forms of reasoning, to overcome obstacles by
taking thought.
• (Neisser, 1997) and (Perloff, 1996)
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Other definitions of
intelligence
• capacity for learning, reasoning, understanding, and
similar forms of mental activity; aptitude (ability) in
grasping truths, relationships, facts, meanings, etc.
• the faculty of understanding.
• knowledge of an event, circumstance, etc., received or
imparted; news; information.
• the gathering or distribution of information, especially
secret information
• www. dictionary.com
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Intelligence (summary)
•Intelligence is the ability of:
• abstract thought
• understanding
• communication
• reasoning
• learning
• planning
• problem solving
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Intelligence of computer
• According to the British computer scientist
Alan Turing's test in (1950):
• “a computer would deserves to be called intelligent if it
could deceive a human into believing that it was
human.”
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Artificial Intelligence?
• ???
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• “A branch of a computer science which studies the
development of software and hardware which simulates
human intelligence”
• (Dr. Ghassan Issa)
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• AI is the part of computer science concerned with designing
intelligent computer systems, that is, computer systems that
exhibit the characteristics we associate with intelligence in
human behavior-
• Understanding languages,
• learning,
• reasoning,
• solving problems, and so on.
• (Barr and Feigenbaum, 1981)
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Other Definitions of AI ….
• “AI is the study of how to make computer do
things at which, at the moment, people are
better”
• (Rich and Knight, 1991)
• “AI is study of idea that enable computers to be
intelligent”
• (Patrick H. Winston)
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Intelligent computing Vs Conventional computing
Intelligent Computing Conventional Computing
1 Does not always guarantee
a solution to a given
problem.
1 Always guarantees a
solution to a given
problem.
2 Results may not be reliable
and consistent
2 Results are consistent and
reliable.
3 Programmer does not tell
the system how to solve the
given problem.
3 Programmer tells the
system exactly how to solve
the problem
4 Can solve a range of
problems in a given
domain.
4 Can solve only one
problem at a time in a
given domain March 6, 2
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Intelligent computing Vs
Conventional computing …
• Conventional:
• Based on algorithms whose instructions are
stored in memory and executed in sequential way.
• AI Computing:
• Not based on algorithms but based on:
• Knowledge base (symbolic representation)
• Uses reasoning and inferencing over the
knowledge base to search and perform pattern
matching.
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Intelligent computing Vs Conventional
computing …
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Types of AI
All artificial intelligence systems - real and
hypothetical - fall into one of three types:
• Artificial narrow intelligence (ANI), which
has a narrow range of abilities;
• Artificial general intelligence (AGI), which
is on par with human capabilities; or
• Artificial superintelligence (ASI), which is
more capable than a human.
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What are the 3 types of AI? A guide to narrow, general, and super artificial intelligence |
Codebots

1. Artificial Narrow Intelligence
• Artificial narrow intelligence (ANI), also referred
to as weak AI or narrow AI.
• The only type of artificial intelligence we have
successfully realized to date.
• Narrow AI is goal-oriented, designed to perform
singular tasks - i.e. facial recognition, speech
recognition/voice assistants, driving a car, or
searching the internet - and is very intelligent at
completing the specific task it is programmed to
do.
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2. Artificial General Intelligence
• Artificial general intelligence (AGI), also
referred to as strong AI or deep AI.
• It is the concept of a machine with general
intelligence that mimics human intelligence
and/or behaviours, with the ability to learn and
apply its intelligence to solve any problem.
• AGI can think, understand, and act in a way
that is indistinguishable from that of a human
in any given situation.
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2. Artificial General Intelligence
• Fujitsu-built K, one of the fastest supercomputers, is one of
the most notable attempts at achieving strong AI, but
considering
it took 40 minutes to simulate a single second of neural
activity
, it is difficult to determine whether or not strong AI will be
achieved in our foreseeable future.
• K is upgraded now to Fugaku March 6, 2
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3. Artificial Superintelligence
• Artificial super intelligence (ASI), is the
hypothetical AI that doesn’t just mimic or
understand human intelligence and behaviour;
ASI is where machines become self-aware and
surpass the capacity of human intelligence and
ability.
• Superintelligence has long been a fiction in
which robots overrun, overthrow, and/or enslave
humanity.
• The concept of artificial superintelligence sees AI
will evolve to human emotions and experiences,
that it doesn’t just understand them, it evokes
emotions, needs, beliefs and desires of its own.
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Gary Kasparov, world chess champion in Munich,
Germany(2020)
• we have more to win than lose when it
comes to AI, and that rather than becoming
obsolete, humans are going to be promoted.
• “Jobs don’t disappear, they evolve. Deleting
people from repetitive jobs frees them up to
be more creative. The future of the human
race is there in creativity.
• “The future is about humans and machines
working together. AI will bring you what you
want the most…time.” March 6, 2
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Applications of AI
• Game playing
• General problem solving
• Expert system
• Natural language Processing
• Computer vision
• Robotics
• Education
• Others
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Game Playing
• Game Playing is an important domain
of artificial intelligence.
• Games don't require much knowledge; the only
knowledge we need to provide is the rules, legal
moves and the conditions of winning or losing
the game.
• Generate procedure so that only good moves are
generated
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General problem solving
• In computer science, problem-solving refers
to artificial intelligence techniques, including
various techniques such as forming efficient
algorithms, heuristics, and performing root
cause analysis to find desirable solutions
• A problem consists of five parts: The state
space, an initial situation, actions, a goal test,
and path costs.
• Chess, Tower of Hanoi Problem, Travelling
Salesman Problem, Water-Jug Problem etc.
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Problem Solving Techniques in Artificial Intelligence (AI) | PDF.co

Expert Systems
• In artificial intelligence, an expert system is a
computer system emulating the decision-making
ability of a human expert.
• Expert systems are designed to solve complex
problems by reasoning through bodies of
knowledge, represented mainly as if–then rules
rather than through conventional procedural code.
Wikipedia
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Natural language
processing (NLP)
• Natural language processing (NLP) is a subfield
of linguistics, computer science, and artificial
intelligence concerned with the interactions
between computers and human language, in
particular how to program computers to process
and analyze large amounts of natural
language data.
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Computer Vision
• Computer vision is defined as “a subset of
mainstream artificial intelligence that deals with
the science of making computers or machines
visually enabled, i.e., they can analyze and
understand an image.
• Computer vision is an interdisciplinary scientific
field that deals with how computers can gain high-
level understanding from digital images or videos.
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Robotics
• Robotics is a domain in artificial
intelligence that deals with the study of
creating intelligent and efficient robots.
• Robotics and artificial intelligence are really
two separate things. Robotics involves
building robots physical whereas AI involves
programming intelligence.
• Artificial Intelligence or AI gives robots a
computer vision to navigate, sense and calculate
their reaction accordingly.
• Robots learn to perform their tasks from humans
through machine learning which again is a part of
computer programming and AI.
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Education
• Artificial intelligence can automate basic
activities in education, like grading.
• Educational software can be adapted to student
needs.
• It can point out places where courses need to
improve
• Students could get additional support from AI
tutors.
• AI-driven programs can give students and
educators helpful feedback.
• AI may change where students learn, who
teaches them, and how they acquire basic skills.
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Contributions of other
disciplines to AI
1. Philosophy Logic, methods of reasoning, mind as physical
system, foundations of learning, language,
rationality (wisdom)
2. Mathematics Formal representation and proof of algorithms,
computation, (un)decidability, (in)tractability,
probability
3. Economics utility, decision theory
4. Neuroscience how do brain process information (neuron
operation)
5. Psychology 1- How do humans and animals think and act
2- phenomena of perception and motor control,
experimental techniques
6. Computer engineering building fast computers
7. Control theory 1- How can artifacts (objects) operate under their
own control?
8. 2- design systems that maximize an objective
function over time.
9. Linguistics knowledge representation, grammar
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Abridged history of artificial
intelligence
• 1941 first electric computer was developed
• 1943 McCulloch & Pitts:
• Boolean circuit model of brain
• 1949 first “stored program” computer was introduced
• 1950 Turing proposed his “Turing Test” for
intelligence.
• 1955 early chess playing programs demonstrated
• 1956 in Dartmouth conference birth was given to:
• "Artificial Intelligence"
• 1957 LISP language by John McCarthy at MIT
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intelligence
• 1965 expert system DENDRAL started at Stanford
• 1965 Robinson's complete algorithm for logical
reasoning
• 1966 expert system MACSYMA started at MIT
• 1969—79 Early development of knowledge-based
systems
• 1970 implementation of the Prolog language
• 1972 expert system MYCIN developed at Stanford
• 1972 SHRDLU natural language robot demonstrated at
MIT
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intelligence
• 1980-- AI becomes an industry
• 1981--Commercial NLP system “Intellect” available from NLP
group
• 1986-- Neural networks return to popularity
• 1987--AI becomes a science
• 1995--The emergence of intelligent agents
• 1995-2007 HLAI (Human Level AI):
• AI should return to its roots of striving "machines that think, that learn”
• Hays and Efros (2007)
• discuss the problem of filling in holes in a photograph
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intelligence
• 2008--Artificial General Intelligence or AGI
• AGI looks for a universal algorithm for learning and acting in any
environment
• 2009 :Google builds self driving car
• 2011–2014 : Apple's Siri (2011), Google's Google Now (2012)
and Microsoft's Cortana (2014) are smartphone apps that use
natural language to answer questions, make
recommendations and perform actions.
• 2018: Alibaba language processing AI outscores top humans
at a Stanford University reading and comprehension test,
scoring 82.44 against 82.304 on a set of 100,000 questions.
• 2020: Extensive use of AI in industry and businesses like JD,
Alibaba, Amazon, Daraz etc March 6, 2
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MLecture 1 Introduction to AI . The basics.pptx

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