The document provides an overview of artificial intelligence, including its history, functions, applications, and types. It discusses: - The origins of AI research in the 1940s with the invention of programmable computers and foundations being laid at the 1956 Dartmouth workshop. - Key early developments including cybernetics, neural networks, and the work of scientists like Turing, Shannon, Pitts, and McCulloch. - Modern applications of AI like natural language processing, virtual agents, computer vision, and robotic process automation. - How AI is used across industries such as healthcare, education, and marketing. - Major tech companies developing AI like Amazon, Google, Apple, Facebook, and

1. ARTIFICIAL
INTELLIGENCE
A SPITRITUAL POTENTIALITY
PROVIDED TO A MACHINE
BY
HUMANS

2. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
1. Artificial intelligence is
an interdisciplinary of science of multiple
approches. Intelligence demonstred by machine,
(AI) is wide ranging branch of computer science
concerned with building smart machine capable
of performing tasks that typically require human
intelligence
The first over task
tendering machine is known as data processor , by
using its several types of programming languages
there are some benefits on respective tasks. As we
consider on AI ….The main aim of AI is performing
on an artificial brain.

3. HISTORY EDGE OF ARTIFICIAL INTELLIGENCE
 The invention of the programming digital computer in the
1940s, a machine based on the abstract essence of
mathematical reasoning. This device and the ideas behind it
inspired a handful of scientists to begin seriously discussing the
possibility of building an electric brain.
 The field of AI research was founded at a workshop held on the
campus of DARTMOUTH COLLEGE during 1956.
 CYBERNETICS AND EARLY NEURAL NETWORKS:
 The research in neurology had shown that the brain was an
electrical networks of
neurons NORBERTWIENER'S CYBERNETICS described
control and stability in electrical networks. CLAUDE
SHANNON'S INFORMATION THEORY described digital signals.
ALAN TURING'S THEORY OF COMPUTATION showed that any
form of computation could be described digitally. The close
relationship between this ideas suggested that it might
be possible to construct an ELECTRONIC BRAIN.
 WALTER PITTS and WARREN MCCULLOCH analyzed networks
of artificial neurons and showed a sample project on simple
logical functions in 1943. A student MARVIN MINSKY

4. SCIENTISTS OF CYBERNETICS
THE THOUGHT OF ARTIFICIAL BRAIN

5. SCIENTISTS OF ARTIFICIAL NEURONS

6. THE FUNCTIONS OF
ARTIFICIAL
INTELLIGENCE
Machine learning
Deep learning

7. AI SERVICES AND APPLICATIONS
 NATURAL LANGUAGE GENERATION: (NLG) a subfield of artificial intelligence, is a software process that automatically
transforms data into plain-english content. A good example of NLG is automated journalism. Where a computer searches the
web for real-time news, scapes the data from different sources and writes a text summary
 VIRTUAL AGENTS: virtual character that serves as an online customer service representative. It leads an intelligent
conversation with users, responds to their questions and performs adequate non- verbal behavior.
 SPEECH RECOGNITION: speech to text, is the ability for a machine or program to identify words spoken aloud and convert
them into readable text. Speech recognition software has limited vocabulary of words and phrases.
 AUGMENTED REALITY: an interactive experience of a real-world environment where the objects that reside in the real
world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including
visual, auditory, haptic, somatosensory and olfactory.
 ROBOTIC PROCESS AUTOMATION: (RPA) is the technology that allows anyone today to configure computer software, or a
robot to emulate and integrate the actions of a human interacting within digital systems to execute a business process.
 DECISION MANAGEMENT: DM is also know as EDM or BDM entails all aspects of designing, building and managing
the automated decision- making systems that an organization uses to manage its interactions with customers, employees and
suppliers.
 IMAGE RECOGNITION: Image recognition is the ability of a system or software to identify objects, people, places, and
actions in images. It uses machine vision technologies with artificial intelligence and trained algorithms to
recognize images through a camera system.

8. SUB-FUNCTIONS OF ARTIFICIAL INTELLIGENCE(SIMILAR TYPE)
 MACHINE LEARNING: Machine
learning (ML) is a type of
artificial intelligence (AI) that
allows software applications to
become more accurate at
predicting outcomes without
being explicitly programmed to do
so. Machine learning algorithms
use historical data as input to
predict new output values.
 DEEP LEARNING: Deep
learning is an AI function that
mimics the workings of the human
brain in processing data for use in
detecting objects, recognizing
speech, translating languages, and
making decisions. Deep learning AI
is able to learn without human
supervision, drawing from data that
is both unstructured and unlabeled.

9. ARITIFICIAL INTELLIGENCE IN DIFFERENT SECTOR
 Healthcare and medicine.
 Education.
 Marketing.
 Small business.
 Retail and e-commerce.
 Public relations (PR)
 Recruitment and human resources (HR)

10. ARTIFICIAL INTELLIGENCE IN MAJOR COMPANIES
 AMAZON: the e-commerce company. Amazon uses AI in three of its departments. (1)Alexa,(2)Amazon go
store,(3)Amazon prime(Amazon echo).
 GOOGLE: Google became our basic need and day to day prerequisite site.(1)Google assistant,(2)Google
translate,(3)Google photos,(4)Google duplex,(5)Google lens. Deepmind is also an AI company acquired by Google.
 APPLE: Siri is most executive assistant overall world. Apple's started AI innovation of new phones including a
chip called the A12 which has a neural engine running the neural network software for speech and
image recognition including augmented reality.(1)laserlike and Drive.ai with acquisitions like Silk labs, Asaii.
 FACEBOOK: (1)Facial recognition,(2)Deeptext,(3)Language translation. (FAIR)is an internal AI research working on
computer vision, conversational AI. Its also acquired AI companies like Masquerade, Zurich eye, Fayteq
 MICROSOFT: Cortana is an assistant of Microsoft. AI is used in Microsoft365, Bing search, Bot payments, Azure bot
service and it also acquired by XOXCO.

11. TYPES OF ARTIFICIAL INTELLIGENCE
TYPE-1(BASED ON FUNCTIONALITY)
 Reactive Machine: the oldest type of AI. This type of AI has no memory power.
EX:IBM's chess-playing supercomputer.
 Limited Theory: these type of AI have memory capabilities so they can use past
information/experience to make better future decisions. EX: In self-driving cars
like GPS locations, speed of nearby cars, size/nature of obstructions.
 Theory of Mind: the next level of AI like "work in progress". This type of
AI understands the human needs like, emotions, thought process. EX: a
robot which uses the common sense.
 Self-Aware AI: this is the final stage of AI. It is commonly seen in sci-fic movies,
this type of AI understands the human emotions and also have its own
emotions if it happens AI can get its self-preservation mode it might
consider humans are potential threat and its endeavor to end humanity.
 EX: Elon musk said that AGI should not be the cause of the Eschatology. To
eradicate this he had established a company denominated as Open AI.

12. TYPES OF ARTIFICIAL INTELLIGENCE
TYPE-2(BASED ON CAPABILITITIES)
 Artificial Narrow Intelligence: (ANI) the
applications around us are under categorized
of ANI. This are partialism AI algorithms.
 Artificial General Intelligence: AGI has the
capability to train, learn, understand and
perform functions just like a normal human
does. EX: terminator.
 Artificial Super Intelligence(ASI): ASI will be the
top-most point of AI development. This
is more capable better task tender than
human, the researchers fear that the advent of
ASI will ultimately result in "technological
singularity" and it result an unimaginable
change in human civilization.

13. PROGRAMMING LANGUAGES FOR EXPERT SYSTEM
 Artificial Intelligence: Python
and libraries like numpy,
Pandas, Pybrain and SciPy.
Java, Haskell, Julia, Lisp.
 Machine Learning: Including
A.I languages and R,
Javascript.
 Deep Learning: Including A.I
and M.L languages and
Prolog.