SACRED HEART CONVENT INTERNATIONAL SCHOOL, LUDHIANA
GRADE:VII&VIII
ARTIFICIAL INTELLIGENCE
The term Artificial intelligence was coined by John McCarthy in his proposal for the
1956 Dartmouth conference, the first artificial intelligence conference.
He defined Artificial Intelligence as “the science and engineering of making
intelligent machines”
Artificial intelligence: the ability of machines to mimic human behaviour.
What are Domains of AI?
Domains of AI refers to the main Branches of Artificial Intelligence for ex : Data,
Computer Vision and Natural Language Processing
Computer Vision
Computer Vision in simple words is identifying the symbols from the given object
(pictures) and learn the pattern and alert or predict the future object using the
camera.
What is NLP?
NLP stands for Natural Language Processing, which is a part of Computer Science, Human
language, and Artificial Intelligence. It is the technology that is used by machines to understand,
analyse, manipulate, and interpret human's languages
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Applications of Computer Vision
Applications of NLP (Natural Language Processing)
There are the following applications of NLP 1. Question Answering
Question Answering focuses on building systems that automatically answer the questions
asked by humans in a natural language.
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2. Spam Detection
Spam detection is used to detect unwanted e-mails getting to a user's inbox.
3. Sentiment Analysis
Sentiment Analysis is also known as opinion mining. It is used on the web to analyse the
attitude, behaviour, and emotional state of the sender. This application is implemented
through a combination of NLP (Natural Language Processing) and statistics by assigning the
values to the text (positive, negative, or natural), identify the mood of the context (happy, sad,
angry, etc.)
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4. Machine Translation
Machine translation is used to translate text or speech from one natural language to another
natural language.
Example: Google Translator
5. Spelling correction
Microsoft Corporation provides word processor software like MS-word, PowerPoint for the
spelling correction.
Artificial Intelligence (AI) can be understood as an umbrella that consists of both Machine
learning and deep learning.
What is Artificial Intelligence (AI)?
Artificial Intelligence is defined as a field of science and engineering that deals with
making intelligent machines or computers to perform human-like activities.
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APPLICATIONS OF ARTIFICIAL INTELLIGENCE
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Language Translations
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AI in healthcare
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Speech recognition, text recognition, and image recognition
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AI in astronomy
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AI in gaming
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AI in finance
o
AI in data security
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AI in social media
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AI in travel and transport
o
AI in Automotive Industry
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AI in robots
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AI in Entertainment, agriculture, E-commerce, education, etc.
What is Machine Learning?
Machine Learning is defined as the branch of Artificial Intelligence and computer science that
focuses on learning and improving the performance of computers/machines through past
experience by using algorithms.
AI is used to make intelligent machines/robots, whereas machine learning helps those
machines to train for predicting the outcome without human intervention.
What is Deep Learning?
"Deep learning is defined as the subset of machine learning and artificial intelligence that is
based on artificial neural networks". In deep learning, the deep word refers to the number
of layers in a neural network.
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Applications of deep learning
Deep learning can be applied in various industries such as:
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Self-driving vehicles
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Fraud detection
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Natural language processing
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Virtual personal assistance
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Text, speech, and image recognition
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Healthcare, infrastructure, banking & finance, marketing
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Entertainment
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Education
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Automatic game playing
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Auto handwriting generation
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Automatic language translation
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What is an Expert System?
An Expert System is a piece of software which uses database of expert knowledge
to offer advice or make decisions in such as medical diagnosis, accounting, code or
gaming.
An Expert system can be a software/application or a machine which gives expert
system.
1. User Interface
With the help of a user interface, the expert system interacts with the user, takes
queries as an input in a readable format, and passes it to the inference engine. After
getting the response from the inference engine, it displays the output to the user.
2. The inference engine
It is known as the brain of the expert system as it is the main processing unit of the
system.it takes the advice from the knowledge base and give it back to the user as
an output,
3. Knowledge Base
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The knowledgebase is a type of storage that stores knowledge acquired from the
different experts of the particular domain.
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DENDRAL: It was an artificial intelligence project that was made as a chemical analysis
expert system. It was used in organic chemistry to detect unknown organic molecules with
the help of their mass spectra and knowledge base of chemistry.
MYCIN: It was one of the earliest backward chaining expert systems that was designed to
find the bacteria causing infections like bacteraemia and meningitis. It was also used for
the recommendation of antibiotics and the diagnosis of blood clotting diseases.
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PXDES: It is an expert system that is used to determine the type and level of lung cancer.
To determine the disease, it takes a picture from the upper body, which looks like the
shadow. This shadow identifies the type and degree of harm.
Several Issues & Ethical Concerns of Surrounding AI
a) Fairness and Bias: Sometimes, AI systems can be unfair because they learn
from old data that might have unfair stuff in it, like stereotypes. This can make
them treat people differently based on things like race or gender, which isn't
right.
b) Privacy: AI often uses a lot of our personal information to work better, like what
we do online or where we go. But sometimes, this can be a problem because it
might invade our privacy and make us feel like someone's watching everything
we do.
c) Job Displacement: Automation powered by AI can lead to job displacement in
certain industries. The impacts including unemployment and income
inequality, poses ethical questions.
d) Responsibility: AI can do things by itself, but who's responsible if something
goes wrong? It's like when a robot makes a mistake – should we blame the robot,
or the people who made it?
e) Fake Stuff: Sometimes, AI is used to make fake videos or messages that look
real. This can trick people into believing things that aren't true, which can cause
a lot of problems.
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Difference between Machine learning and Deep Learning
Machine learning is AI that can
automatically adapt with minimal
human interference.
Deep learning is a subset of machine
learning that uses artificial neural
networks to mimic the learning process
of the human brain.
Shorter training and lower accuracy
Longer training and higher accuracy
Machine learning deals with less
amount of data
Deep learning deals with huge amount
of data
Examples: Netflix, spotify, Virtual
Personal Assistants: Siri, Alexa etc
Self-driving cars, Autonomous drones,
Medical Sciences(Diagnosing tumors),
high order facial recognition
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