The document discusses an introduction to artificial intelligence lecture. It defines AI as the power of machines to mimic human behavior. It outlines the history, purposes, and foundations of AI, including logic, mathematics, psychology, computer science, linguistics. Examples of AI applications discussed include chatbots, self-driving cars, face recognition, and robotics. The types of AI are described as weak, strong, and super AI. The components of AI discussed are learning, reasoning, problem solving, perception, and language understanding. Challenges and issues with AI like costs, unemployment, and lack of emotions are also covered.
4. Purpose of Artificial Intelligence
To add human capabilities and help us make advanced
decisions with far-reaching consequences.
Artificial Intelligence has the potential to help humans live
more meaningful lives devoid of hard labor, and help
manage the complex web of interconnected individuals,
companies, states and nations to function in a manner
that’s beneficial to all of humanity.
5. Foundations of AI
Philosophy: logic, philosophy of mind, philosophy of science,
philosophy of mathematics
Mathematics: logic, probability theory, theory of computability
Psychology: behaviorism, cognitive psychology –
Computer Science & Engineering: hardware, algorithms,
computational complexity theory –
Linguistics: theory of grammar, syntax, semantics
Now we discuss some detail examples of AI.
6. Chatbot
A chatbot is a type of software that can help
customers by automating conversations and interact
with them through messaging platforms.
How an AI Chatbot works
7. Self-driving car
A self-driving car, also known as an autonomous car, driver-
less car, or robotic car, is a car incorporating vehicular
automation, that is, a ground vehicle that is capable of
sensing its environment and moving safely with little or no
human input
9. nt fields.
Robotics and artificial intelligence
Robotics and artificial intelligence are two related but entirely
differe
Robotics involves the creation of robots to perform tasks without further
intervention, while AI is how systems emulate the human mind to make
decisions and 'learn.
Artificial Intelligence Robot
They usually operate in computer They involved in Physical world
simulation world
The input to AI program is in symbols Input to robot is in the form of speech,
or rules voice and text.
They need general purpose computer They need special hardware with
to operate on sensors and effectors
10. ken from
errors are
Advantages
Reduction in Human Error: With Artificial intelligence, the decisions are ta
the previously gathered information applying a certain set of algorithms. So
reduced and the chance of reaching accuracy. e.g: In Weather Forecasting using AI
they have reduced the majority of human error.
Takes risks instead of Humans: risky limitations of humans by developing an AI
Robot which in turn can do the risky things for us. Let it be going to mars, defuse a
bomb, explore the deepest parts of oceans, mining for coal and oil, it can be used
effectively in any kind of natural or man-made disasters.AI Robots can be used in
such situations where intervention can be hazardous.
Available 24x7: Educational Institutes and Helpline centers are getting many queries
and issues which can be handled effectively using AI.
Digital Assistance: Using AI the organizations can set up a Voice bot or Chatbot
which can help customers with all their queries. We can see many organizations
already started using them on their websites and mobile applications.
Faster Decisions: We all have played Chess games in Windows. It is nearly
impossible to beat CPU in the hard mode because of the AI behind that game. It will
take the best possible step in a very short time according to the algorithms used
behind it.
11. gence is
e deal of
Disadvantages of Artificial Intelligence
High Costs: the ability to create a machine that can simulate human intelli
no small feat. It requires plenty of time and resources and can cost a hug
money. AI also needs to operate on the latest hardware and software to stay updated
and meet the latest requirements, thus making it quite costly.
No creativity
Unemployment: One application of artificial intelligence is a robot, which is
displacing occupations and increasing unemployment (in a few cases). Therefore,
some claim that there is always a chance of unemployment as a result of chatbots
and robots replacing humans.
Make Humans Lazy: AI applications automate the majority of tedious and repetitive
tasks. Since we do not have to memorize things or solve puzzles to get the job done,
we tend to use our brains less and less. This addiction to AI can cause problems to
future generations
Emotion less: Since early childhood, we have been taught that neither computers
nor other machines have feelings. Humans function as a team, and team
management is essential for achieving goals. However, there is no denying that
robots are superior to humans when functioning effectively, but it is also true that
human connections, which form the basis of teams, cannot be replaced by
computers
14. Ethical challenges/Issues
AI decisions are not always intelligible to humans. AI is not
natural: AI-based decisions are susceptible to inaccuracies,
discriminatory outcomes, embedded or inserted bias.
Threat to Privacy. ...
Threat to Human Dignity. ...
Threat to Safety.
15. Types of Artificial Intelligence
There are three main types of AI based on its capabilities
– weak AI, strong AI and super AI.
1.Weak AI :
When an Artificial intelligence works on specific task only in
a closed domain, then this form of AI is called weak AI.
Focuses on one/specific task and cannot perform beyond its
limitations (common in our daily lives) .These AI devices are
designed to perform Specific task.
Examples: Smart home, Google assistant, Alexa Playing Chess
16. Continue….
2. Strong AI :
When an Artificial intelligence works on vast scope of
activities with generalized cognitive abilities ,then this form of
AI is called Strong AI.
Can understand and learn any intellectual task that a
human being can (researchers are striving to reach strong
AI) such as Robots
Other example are if a machine might hear Good Morning
and start to associate with the coffee maker turning on
17. Continue….
3. Super AI :
Surpasses human intelligence and can perform any task
better than a human (still a concept)
18.
19. Components of AI
Artificial Intelligence ‘AI’ has been an integral part of our
lives. Today, AI has become an essential part of everything big
and small ranging from the self-driving cars manufacturing units
to the smallest screens like smart watches that we use; it is
everywhere. T
oday, companies, independent of their shape or
size, rely on Artificial Intelligence to improve their customers’
satisfaction and increase sales.
20. emorizing
Components of Artificial intelligence
1) Learning : The learning component of AI (Trail and error method) includes m
individual items like different solutions to problems, vocabulary, foreign languages, etc.,
also known as rote learning.
2) Reasoning: The reasoning is the mental process of deriving
making predictions from available knowledge, facts, and beliefs.
logical conclusion and
.
3) Problem Solving: ability to comprises data, where the solution
the queries divided into special and general purposes.
needs to find x. Where
4) Perception: It is the process of acquiring, interpreting, selecting, and organizing
sensory information.
5) Language Understanding : It is one’s ability to use, comprehend, speak, and write the
verbal and written language. It is important in interpersonal communication.
21. elligence.
simplest
1.Learning
There are a number of different forms of learning as applied to artificial int
Similar to humans, computer programs also learn in different manners. The
is learning by trial and error.
Like humans, AI needs to learn a task before doing it. The human brain organizes
information so that it can use that data to make rapid decisions when next
encountering the same or similar information.
For example, a simple computer program for solving mate-in-one chess problems
might try moves at random until mate is found. The program might then store the
solution with the position so that the next time the computer encountered the same
position it would recall the solution.
The solution keeps on solving problems until it comes across the right results. This
way, the program keeps a note of all the moves that gave positive results and stores it
in its database to use the next time the computer is given the same problem.
The learning component of AI includes memorizing individual items like different
solutions to problems, vocabulary, foreign languages, etc., also known as rote
learning. This learning method is later implemented using the generalization method.
22. 2.Reasoning
Reasoning plays a great role in the process of artificial Intelligence. Thus
Reasoning can be defined as the logical process of drawing conclusions,
making predictions or constructing approaches towards a particular thought
with the help of existing knowledge.
The reasoning is the mental process of deriving logical conclusion and making
predictions from available knowledge, facts, and beliefs. Or we can say,
"Reasoning is a way to infer facts from existing data." It is a general process of
thinking rationally, to find valid conclusions.
In artificial intelligence, the reasoning is essential so that the machine can also
think rationally as a human brain, and can perform like a human.
Types of Reasoning
Deductive reasoning
Inductive reasoning
a.
b.
23. a).Deductive Reasoning
Deductive reasoning starts from the general premises to the specific conclusion,
Example:
Premise-1: All the human eats fruits
Premise-2: John is human.
Conclusion: john eats fruits.
Let’s say that you find yourself at a conference where you know that all the people
present are thirty or older. Y
ou notice Maria in the room. Therefore, Maria is at least
thirty years old. Y
ou’ve taken a general theory, i.e. that all people in the room are thirty
or older, and applied it to one specific person there, i.e. Maria. So, you
used deductive reasoning to determine her age.
Theory Hypothesis Pattern Conclusion
24. b).Inductive Reasoning
Inductive reasoning starts with the series of specific facts or data and reaches
to a general statement.
Example:
Premise: All of the pigeons we have seen in the zoo are white.
Conclusion: Therefore, we can expect all the pigeons to be white.
Let’s imagine that you’re asking all of your friends which countries they’ve traveled
to. The first friend you ask tells you that he’s been to Italy. The second one also
says that he’s been to Italy, and the third one as well. Therefore, you draw the
conclusion that all of your friends have been to Italy. Here, you’ve collected specific
facts about specific people and applied them to a wider group. inductive reasoning.
Observation Pattern Hypothesis Theory
25. 3.Problem Solving
It is the process in which one perceives and tries to arrive at a desired solution
from a present situation by taking some path, which is blocked by known or
unknown hurdles.
Problem solving also includes decision making, which is the process of selecting
the best suitable alternative out of multiple alternatives to reach the desired goal
are available.
26. Cont…
Goal Formulation: It is the first and simplest step in problem-solving. It organizes the
steps/sequence required to formulate one goal out of multiple goals as well as actions
to achieve that goal. Goal formulation is based on the current situation and the agent's
performance measure (discussed below).
Problem Formulation: It is the most important step of problem-solving which decides
what actions should be taken to achieve the formulated goal. There are following five
components involved in problem formulation:
Initial State: It is the starting state or initial step of the agent towards its goal.
Actions: It is the description of the possible actions available to the agent.
Transition Model: It describes what each action does.
Goal Test: It determines if the given state is a goal state.
Path cost: It assigns a numeric cost to each path that follows the goal. The problem-
solving agent selects a cost function, which reflects its performance measure.
Remember, an optimal solution has the lowest path cost among all the solutions.
27. 4.Perception
Perception presumes sensing. In humans, perception is aided by sensory organs.
In the domain of AI, perception mechanism puts the data acquired by the sensors
together in a meaningful manner.
Perception is our ability to see, hear, or become aware of something through the
senses.
However, when I do logical thinking involving physics, mathematics, planning,
calculations, accounts or formulating strategy and tactics, there are very few
patterns from the past. Does my brain deal with patterns differently than with
logical situations?
Simplistic Model of Our Brain
If I were to model the brain based on my observations it would consist of two parts:
Right — Perception based
Left — Rational based
1.
2.
28. Cont…
Our senses — taste, sight, touch, smell, and hearing — provide patterns to the right part of
our brain to generate perceptions. Whereas all our logical interpretations influence the left
part and generate a structured and rational understanding of a situation or a problem.
29. 5. Language Understanding
Natural Language Processing, involves machines or robots to understand and
process the language that human speaks, and infer knowledge from the speech
input. It also involves the active participation from a machine in the form of dialog
i.e. NLP aims at the text or verbal output from the machine or robot. The input and
output of an NLP system can be speech and written text respectively.
30. AI Features
Views of AI fall into four categories
Systems that Acting like humans
Systems that think like humans.
Systems that act rationally
Systems that think rationally
31. 2nd Task to do
Can Machines
Can machines
Think?
Behave Intelligently?
