neering
Artificial Intelligence
for
Engineering
Unit 1
Mr. Ajay KumarDr. Preeti Shrama
Assistant ProfessorAssociate Professor
I.P.E.C
Computer Science & Engineering Department
Dr.Preeti sharma
Unit-1
(Syllabus)
• Various approaches to AI.
• The evolution of AI to the Present.
• What should all engineers know about AI.
• Other emerging technologies.
• AI and ethical concern.
Evolution of AI
Maturation of Artificial Intelligence
(1943-1952)
• Year 1943:
– The first work which is now recognized as AI was done by
Warren McCulloch and Walter pits in 1943. They proposed
a model of artificial neurons.
• Year 1949:
– Donald Hebb demonstrated an updating rule for modifying
the connection strength between neurons. His rule is now
called Hebbian learning.
• Year 1950:
– The Alan Turing who was an English mathematician and
pioneered Machine learning in 1950. Alan Turing publishes
"Computing Machinery and Intelligence" in which he
proposed a test. The test can check the machine's ability to
exhibit intelligent behavior equivalent to human intelligence,
called a Turing test.
The birth of Artificial Intelligence
(1952-1956)
• Year 1955:
– An Allen Newell and Herbert A. Simon created the
"first artificial intelligence program "Which was
named as "Logic Theorist". This program had proved
38 of 52 Mathematics theorems, and find new and
more elegant proofs for some theorems.
• Year 1956:
– The word "Artificial Intelligence" first adopted by
American Computer scientist John McCarthy at the
Dartmouth Conference. For the first time, AI coined as
an academic field.
– At that time high-level computer languages such as
FORTRAN, LISP, or COBOL were invented. And the
enthusiasm for AI was very high at that time.
The golden years-Early enthusiasm
(1956-1974)
• Year 1966:
– The researchers emphasized developing
algorithms which can solve mathematical
problems. Joseph Weizenbaum created the first
chatbot in 1966, which was named as ELIZA.
• Year 1972:
– The first intelligent humanoid robot was built in
Japan which was named as WABOT-1.
The first AI winter
(1974-1980)
• 1974 to 1980 :
– The first AI winter duration. AI winter refers to the
time period where computer scientist deal with a
severe shortage of funding from government for
AI researches.
– During AI winters, an interest of publicity on
artificial intelligence was decreased.
A boom of AI
(1980-1987)
• Year 1980:
– After AI winter duration, AI came back with
"Expert System". Expert systems were
programmed that emulate the decision-making
ability of a human expert.
– In the Year 1980, the first national conference of
the American Association of Artificial
Intelligence was held at Stanford University.
The second AI winter
(1987-1993)
• 1987 to 1993 :
– was the second AI Winter duration.
– Again Investors and government stopped in
funding for AI research as due to high cost but not
efficient result. The expert system such as XCON
was very cost effective.
The emergence of intelligent agents
(1993-2011)
• Year 1997:
– In the year 1997, IBM Deep Blue beats world chess
champion Gary Kasparov, and became the first
computer to beat a world chess champion.
• Year 2002:
– for the first time, AI entered the home in the form of
Roomba, a vacuum cleaner.
• Year 2006:
– AI came in the Business world till the year 2006.
Companies like Facebook, Twitter, and Netflix also
started using AI.
Deep learning, big data and Artificial general intelligence
(2011-Present)
• Year 2011:
– In the year 2011, IBM's Watson won jeopardy, a quiz show, where it had to
solve the complex questions as well as riddles. Watson had proved that it
could understand natural language and can solve tricky questions quickly.
• Year 2012:
– Google has launched an Android app feature "Google now", which was able to
provide information to the user as a prediction.
• Year 2014:
– In the year 2014, Chatbot "Eugene Goostman" won a competition in the
infamous "Turing test.“
• Year 2018:
– The "Project Debater" from IBM debated on complex topics with two master
debaters and also performed extremely well.
Deep learning, big data and
Artificial general intelligence
(2011-Present)
• Google has demonstrated an AI program "Duplex" which
was a virtual assistant and which had taken hairdresser
appointment on call, and lady on other side didn't notice
that she was talking with the machine.
• Now AI has developed to a remarkable level. The concept
of Deep learning, big data, and data science are now
trending like a boom. Nowadays companies like Google,
Facebook, IBM, and Amazon are working with AI and
creating amazing devices.
• The future of Artificial Intelligence is inspiring and will come
with high intelligence
Types of Artificial Intelligence
• Artificial Intelligence can be divided in various
types.
• There are mainly two categories
– based on capabilities (Type 1) and
– based on functionality (Type 2)of AI.
AI Type-1:
Based on Capabilities
1. Weak AI or Narrow AI
• Narrow AI is a type of AI which is able to perform a dedicated task
with intelligence. The most common and currently available AI is
Narrow AI in the world of Artificial Intelligence.
• Narrow AI cannot perform beyond its field or limitations, as it is only
trained for one specific task. Hence it is also termed as weak AI.
Narrow AI can fail in unpredictable ways if it goes beyond its limits.
• Apple Siris a good example of Narrow AI, but it operates with a limited
pre-defined range of functions.
• IBM's Watson supercomputer also comes under Narrow AI, as it uses
an Expert system approach combined with Machine learning and
natural language processing.
• Some Examples of Narrow AI are playing chess, purchasing suggestions
on e-commerce site, self-driving cars, speech recognition, and image
recognition.
2. General AI
• General AI is a type of intelligence which could perform any
intellectual task with efficiency like a human.
• The idea behind the general AI to make such a system
which could be smarter and think like a human by its own.
• Currently, there is no such system exist which could come
under general AI and can perform any task as perfect as a
human.
• The worldwide researchers are now focused on developing
machines with General AI.
• As systems with general AI are still under research, and it
will take lots of efforts and time to develop such systems.
3. Super AI
• Super AI is a level of Intelligence of Systems at which
machines could surpass human intelligence, and can
perform any task better than human with cognitive
properties. It is an outcome of general AI.
• Some key characteristics of strong AI include capability
,the ability to think, to reason , solve the puzzle, make
judgments, plan, learn, and communicate by its own.
• Super AI is still a hypothetical concept of Artificial
Intelligence. Development of such systems in real is still
world changing task.
AI Type-2:
Based on
functionality
1. Reactive Machines
• Purely reactive machines are the most basic types of
Artificial Intelligence.
• Such AI systems do not store memories or past experiences
for future actions.
• These machines only focus on current scenarios and react
on it as per possible best action.
• IBM's Deep Blue system is an example of reactive
machines.
• Google's AlphaGo is also an example of reactive machines.
2. Limited Memory
• Limited memory machines can store past
experiences or some data for a short period of
time.
• Self-driving cars are one of the best examples
of Limited Memory systems. These cars can
store recent speed of nearby cars, the
distance of other cars, speed limit, and other
information to navigate the road.
3. Theory of Mind
• Theory of Mind AI should understand the
human emotions, people, beliefs, and be able
to interact socially like humans.
• This type of AI machines are still not
developed, but researchers are making lots of
efforts and improvement for developing such
AI machines.
4. Self-Awareness
• Self-awareness AI is the future of Artificial
Intelligence. These machines will be super
intelligent, and will have their own consciousness,
sentiments, and self-awareness.
• These machines will be smarter than human mind.
• Self-Awareness AI does not exist in reality still and
it is a hypothetical concept.
Task Classification of AI
The domain of AI is classified into
Formal tasks, Mundane tasks, and Expert tasks.
Mundane (Ordinary) Tasks Formal Tasks Expert Tasks
•Perception Computer •Mathematics •Engineering
Vision •Geometry •Fault Finding
•Speech, Voice •Logic •Manufacturing
•Integration and •Monitoring
Differentiation
• Natural Language •GamesGo • Scientific
Processing Understanding •Chess (Deep Blue) Analysis
•Language Generation •Ckeckers
•Language Translation
• Common Sense • Verification • Financial Analys
• Reasoning • Theorem Proving • Medical
Diagnosis
• Planing • Creativity
•Robotics Locomotive
