ARTIFICIAL
INTELLIGEN
CE
SURESH RAJPUROHIT
WHAT IS ARTIFICIAL
INTELLIGENCE (AI)?
• Artificial intelligence (AI) refers to the ability
of a machine to learn patterns and make
predictions.
• In its simplest form, Artificial Intelligence is a field
that combines computer science and robust
datasets to enable problem-solving. AI does not
replace human decisions; instead, AI adds value to
human judgment
EXAMPLE OF AI
Understand Language: AI can understand and respond to what you say, like virtual
assistants such as Siri or Alexa.
Recognize Images: AI can look at pictures and recognize what is in them, like identifying
animals in photos.
Make Predictions: AI can analyze data to make predictions, like predicting the weather or
suggesting what movie you might like to watch next.
Play Games: AI can play games and learn to get better at them, like playing chess or video
games.
Drive Cars: AI can help cars drive themselves by sensing the road and making decisions to
stay safe.
Traditional Simple Automation
Rule-Based Systems: Tools: Basic tools like
These machines follow timers or calculators do
set rules without specific tasks but do not
learningfrom data. think or learn.
WHAT IS Mechanical Devices: Fixed-Function
NOT AI? Machines like pulleys or
gears work based on
Hardware: Devices like
microwave ovens
physics but do not learn perform tasks without
or think learning or thinking.
Non-Interactive
Systems: Machines that Basic Sensors: Sensors
do not change based on collect data but do not
new information, like a analyze or understand it.
basic electric fan.
EVOLUTION OF AI
TYPES OF AI
Narrow AI:
Broad AI:
General AI:
NARROW AI:
Rapidly growing in Capable of handling
Focuses on single
consumer applications, specific tasks
tasks like predicting
such as voice-based effectively, but lacks
purchases or planning
shopping and virtual broader
schedules.
assistants like Siri. understanding.
BROAD AI
Often used in businesses
More versatile than
Acts as a midpoint to integrate AI into
Narrow AI, capable of
between Narrow and specific processes,
handling a wider range of
General AI. requiring domain-specific
related tasks.
knowledge and data.
GENERAL AI:
Artificial Superintelligence
Refers to machines that Currently, AI lacks (ASI) may emerge,
can perform any abstract thinking, potentially leading to
intellectual task a human strategizing, and creativity self-aware machines, but
can. like humans. this is far from current
capabilities.
Data Science:
DOMAINS OF Natural Language
AI Processing (NLP):
Computer Vision:
DOMAINS OF AI
Data Science: Data Science deals
with numerical, alphabetical, and
alphanumeric data inputs. It
involves the collection, analysis,
and interpretation of large
volumes of data to extract
insights and patterns using
statistical methods, machine
learning algorithms, and data
visualization techniques.
DOMAINS OF AI
Natural Language Processing (NLP):
NLP focuses on processing text and speech
inputs to enable computers to understand,
interpret, and generate human language. It
involves tasks such as language
translation, sentiment analysis, text
summarization, and speech recognition,
facilitating communication between
humans and machines through natural
language interfaces.
DOMAINS OF AI
Computer Vision: Computer Vision deals
with visual data inputs, primarily images
and videos. It enables computers to
interpret and understand visual information,
and perform tasks such as object
detection, image classification, facial
recognition, and scene understanding,
enabling applications such as autonomous
vehicles, medical imaging, and augmented
reality.
AI TERMINOLOGIES
Artificial intelligence machines don’t think. They
calculate. They represent some of the newest, most
sophisticated calculating machines in human history.
It is a computer system that can perform tasks that
ordinarily require human intelligence or human
interference.
Some can perform what is called machine
learning as they acquire new data. Machine
learning is a subset of artificial intelligence
(AI) that focuses on developing algorithms
and models that enable computers to learn
from data and make predictions or decisions
without being explicitly programmed.
DEEP LEARNING
using calculations arranged in ways inspired by neurons in the human
brain, can even perform deep learning with multiple levels of
calculations. Deep learning is an AI function that imitates the working
of the human brain in processing data and
creating patterns for use in decision making.
The structure of Deep Learning is inspired by the structure of the
neurons and neuron connections in the human brain.
❑ Neural networks, also known as Artificial Neural Networks (ANNs),
are a
❑ subset of Machine Learning and the core heart and concept of
Machine Learning.
❑ They comprise of node layers, containing an input layer, one or
multiple hidden layers, and an output layer.
❑ If the output of any node is above a specified threshold, that node
is activated, sending data to the next layer of the network.
❑ Otherwise, no data is passed along to the next layer of the
network.
❑ If the number of Layers including the Input and Output Layer is
more than three, then it is called a Deep Neural Network.
MACHINE LEARNING VS
DEEP LEARNING
BENEFITS AND
LIMITATIONS OF AI
❑ Increased efficiency and productivity: AI automates tasks,
analyses data faster, and optimizes processes, leading to increased
efficiency and productivity across various sectors.
❑ Improved decision-making: AI analyzes vast amounts of data and
identifies patterns that humans might miss, assisting in data-driven
decision-making and potentially leading to better outcomes.
❑ Enhanced innovation and creativity: AI tools can generate new
ideas, explore possibilities, and automate repetitive tasks, freeing up
human resources for more creative pursuits and innovation.
❑ Progress in science and healthcare: AI aids in drug
discovery, medical diagnosis, and personalized medicine, contributing
to advancements in healthcare and scientific research.
LIMITATIONS:
❑ Job displacement: Automation through AI raises concerns about
job displacement and the need for workforce retraining and
upskilling.
❑ Ethical considerations: Concerns exist around bias in AI
algorithms, potential misuse for surveillance or manipulation, and
the need for ethical guidelines and regulations.
❑ Lack of explainability: Some AI models, particularly complex
ones, lack transparency in their decision-making, making it difficult
to understand how they arrive at their outputs.
❑ Data privacy and security: Large-scale data collection and use
for AI development raise concerns about data privacy and security
vulnerabilities.
