UNIT-I
Artificial Intelligence (AI) : is a branch of Science which
deals with helping machines find solutions to complex
problems in a more human-like fashion.
This generally involves borrowing characteristics from
human intelligence, and applying them as algorithms in
a computer friendly way.
A more or less flexible or efficient approach can be
taken depending on the requirements established, which
influences how artificial the intelligent behavior appears
Artificial intelligence can be viewed from a variety of
perspectives. From the perspective of intelligence
artificial intelligence is making machines "intelligent" --
acting as we would expect people to act.
o The inability to distinguish computer responses from
human responses is called the Turing test.
o Intelligence requires knowledge o Expert problem
solving - restricting domain to allow including significant
relevant knowledge From a business perspective AI is
a set of very powerful tools, and methodologies for using
those tools to solve business problems. From a
programming perspective, AI includes the study of
symbolic programming, problem solving, and search.
o Typically AI programs focus on symbols rather than
numeric processing. o Problem solving - achieve goals.
o Search - seldom access a solution directly. Search
may include a variety of techniques.
,AI helps people here think more creatively and
solve tough challenges. It concentrates on
delivering high accuracy.
AI looks for connections to understand, disclose
secret information, and find answers.
The decision-making authority of AI reduces the
necessity for humans.
Most of it is employed in industries like
manufacturing, commerce, medical, and banking.
Importance of AI
Game Playing : You can buy machines that can play
master level chess for a few hundred dollars. There is
some AI in them, but they play well against people
mainly through brute force computation--looking at
hundreds of thousands of positions. To beat a world
champion by brute force and known reliable heuristics
requires being able to look at 200 million positions per
second.
Speech Recognition : In the 1990s, computer speech
recognition reached a practical level for limited
purposes. Thus United Airlines has replaced its
keyboard tree for flight information by a system using
speech recognition of flight numbers and city names. It
is quite convenient. On the other hand, while it is
possible to instruct some computers using speech, most
,users have gone back to the keyboard and the mouse
as still more convenient.
Understanding Natural Language : Just getting a
sequence of words into a computer is not enough.
Parsing sentences is not enough either. The computer
has to be provided with an understanding of the domain
the text is about, and this is presently possible only for
very limited domains.
Computer Vision : The world is composed of three-
dimensional objects, but the inputs to the human eye
and computers' TV cameras are two dimensional. Some
useful programs can work solely in two dimensions, but
full computer vision requires partial three-dimensional
information that is not just a set of two-dimensional
views. At present there are only limited ways of
representing three-dimensional information directly, and
they are not as good as what humans evidently use.
Expert Systems : A ``knowledge engineer'' interviews
experts in a certain domain and tries to embody their
knowledge in a computer program for carrying out some
task. How well this works depends on whether the
intellectual mechanisms required for the task are within
the present state of AI. When this turned out not to be
so, there were many disappointing results. One of the
first expert systems was MYCIN in 1974, which
diagnosed bacterial infections of the blood and
suggested treatments. It did better than medical
students or practicing doctors, provided its limitations
were observed. Namely, its ontology included bacteria,
symptoms, and treatments and did not include patients,
doctors, hospitals, death, recovery, and events occurring
, in time. Its interactions depended on a single patient
being considered. Since the experts consulted by the
knowledge engineers knew about patients, doctors,
death, recovery, etc., it is clear that the knowledge
engineers forced what the experts told them into a
predetermined framework. The usefulness of current
expert systems depends on their users having common
sense.
Heuristic Classification One of the most feasible kinds
of expert system given the present knowledge of AI is to
put some information in one of a fixed set of categories
using several sources of information. An example is
advising whether to accept a proposed credit card
purchase. Information is available about the owner of
the credit card, his record of payment and also about the
item he is buying and about the establishment from
which he is buying it (e.g., about whether there have
been previous credit card frauds at this establishment).
The applications of AI
Consumer Marketing :
if so, you have very likely been “input” to an AI algorithm
o All of this information is recorded digitally Companies
like Nielsen gather this information weekly and search
for patterns – general changes in consumer behavior –
tracking responses to new products – identifying
customer segments: targeted marketing, e.g., they find
out that consumers with sports cars who buy textbooks
respond well to offers of new credit cards. o Algorithms
(“data mining”) search data for patterns based on
mathematical theories of learning
Artificial Intelligence (AI) : is a branch of Science which
deals with helping machines find solutions to complex
problems in a more human-like fashion.
This generally involves borrowing characteristics from
human intelligence, and applying them as algorithms in
a computer friendly way.
A more or less flexible or efficient approach can be
taken depending on the requirements established, which
influences how artificial the intelligent behavior appears
Artificial intelligence can be viewed from a variety of
perspectives. From the perspective of intelligence
artificial intelligence is making machines "intelligent" --
acting as we would expect people to act.
o The inability to distinguish computer responses from
human responses is called the Turing test.
o Intelligence requires knowledge o Expert problem
solving - restricting domain to allow including significant
relevant knowledge From a business perspective AI is
a set of very powerful tools, and methodologies for using
those tools to solve business problems. From a
programming perspective, AI includes the study of
symbolic programming, problem solving, and search.
o Typically AI programs focus on symbols rather than
numeric processing. o Problem solving - achieve goals.
o Search - seldom access a solution directly. Search
may include a variety of techniques.
,AI helps people here think more creatively and
solve tough challenges. It concentrates on
delivering high accuracy.
AI looks for connections to understand, disclose
secret information, and find answers.
The decision-making authority of AI reduces the
necessity for humans.
Most of it is employed in industries like
manufacturing, commerce, medical, and banking.
Importance of AI
Game Playing : You can buy machines that can play
master level chess for a few hundred dollars. There is
some AI in them, but they play well against people
mainly through brute force computation--looking at
hundreds of thousands of positions. To beat a world
champion by brute force and known reliable heuristics
requires being able to look at 200 million positions per
second.
Speech Recognition : In the 1990s, computer speech
recognition reached a practical level for limited
purposes. Thus United Airlines has replaced its
keyboard tree for flight information by a system using
speech recognition of flight numbers and city names. It
is quite convenient. On the other hand, while it is
possible to instruct some computers using speech, most
,users have gone back to the keyboard and the mouse
as still more convenient.
Understanding Natural Language : Just getting a
sequence of words into a computer is not enough.
Parsing sentences is not enough either. The computer
has to be provided with an understanding of the domain
the text is about, and this is presently possible only for
very limited domains.
Computer Vision : The world is composed of three-
dimensional objects, but the inputs to the human eye
and computers' TV cameras are two dimensional. Some
useful programs can work solely in two dimensions, but
full computer vision requires partial three-dimensional
information that is not just a set of two-dimensional
views. At present there are only limited ways of
representing three-dimensional information directly, and
they are not as good as what humans evidently use.
Expert Systems : A ``knowledge engineer'' interviews
experts in a certain domain and tries to embody their
knowledge in a computer program for carrying out some
task. How well this works depends on whether the
intellectual mechanisms required for the task are within
the present state of AI. When this turned out not to be
so, there were many disappointing results. One of the
first expert systems was MYCIN in 1974, which
diagnosed bacterial infections of the blood and
suggested treatments. It did better than medical
students or practicing doctors, provided its limitations
were observed. Namely, its ontology included bacteria,
symptoms, and treatments and did not include patients,
doctors, hospitals, death, recovery, and events occurring
, in time. Its interactions depended on a single patient
being considered. Since the experts consulted by the
knowledge engineers knew about patients, doctors,
death, recovery, etc., it is clear that the knowledge
engineers forced what the experts told them into a
predetermined framework. The usefulness of current
expert systems depends on their users having common
sense.
Heuristic Classification One of the most feasible kinds
of expert system given the present knowledge of AI is to
put some information in one of a fixed set of categories
using several sources of information. An example is
advising whether to accept a proposed credit card
purchase. Information is available about the owner of
the credit card, his record of payment and also about the
item he is buying and about the establishment from
which he is buying it (e.g., about whether there have
been previous credit card frauds at this establishment).
The applications of AI
Consumer Marketing :
if so, you have very likely been “input” to an AI algorithm
o All of this information is recorded digitally Companies
like Nielsen gather this information weekly and search
for patterns – general changes in consumer behavior –
tracking responses to new products – identifying
customer segments: targeted marketing, e.g., they find
out that consumers with sports cars who buy textbooks
respond well to offers of new credit cards. o Algorithms
(“data mining”) search data for patterns based on
mathematical theories of learning
