The document provides a comprehensive overview of artificial intelligence (AI), including its definition, history, types, applications across various sectors such as agriculture, health, and business, and underlying technologies like machine learning and natural language processing. It also discusses the evolution of AI from its inception in the 1950s to current advancements like self-driving cars and smart assistants, highlighting notable milestones and frameworks used in AI development. The importance and impact of AI on efficiency, automation, and decision-making processes in numerous industries are emphasized throughout the document.

1. Artificial Intelligence
Module - 3

2. Table of Contents
Concept of AI
Meaning of AI
History of AI
Levels of AI
Types of AI
Applications of AI - Agriculture, Health, Business (Emerging market), Education
AI Tools and Platforms

3. Artificial Intelligence - Introduction
• Artificial intelligence leverages computers and machines to
mimic the problem-solving and decision-making capabilities of
the human mind
• Artificial intelligence (AI) makes it possible for machines to
learn from experience, adjust to new inputs and perform
human-like tasks
• Computers can be trained to accomplish specific tasks by
processing large amounts of data and recognizing patterns in
the data.
• Example:
 Self Driving Cars
 Chess Playing Computer – Deep Blue
 Chatbot

4. Artificial Intelligence - Introduction

5. Goals of Artificial Intelligence
 Logical Reasoning - IBM Deep Blue
 Knowledge Representation – Smalltalk Programming Language
 Planning and Navigation – Self Driving Vehicles
 Natural Language Processing – Alexa, Siri
 Perception – Human Machine Interaction {Touch, Sense, Sight, Hear}
 Emergent Intelligence - Emotional Intelligence and Moral Reasoning

6. History of Artificial Intelligence

7. 1950: Alan Turing publishes Computing Machinery and Intelligence
1956: John McCarthy coins the term 'artificial intelligence' at the first-ever AI conference at Dartmouth College
1960s: Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning
1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that 'learned' though trial and error
1970s: Defense Advanced Research Projects Agency (DARPA) completed street mapping projects
1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications
1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch)
2002 - The first commercially successful robotic vacuum cleaner was created
2011: IBM Watson beats champions Ken Jennings and Brad Rutter at Jeopardy!
2015: Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and
categorize images with a higher rate of accuracy than the average human
2016: DeepMind's AlphaGo program
2006 – Present: Driverless Vehicles, Chatbots, Automated Robots
History of Artificial Intelligence

8. Types of Artificial Intelligence

9. Types of Artificial Intelligence – Capability Based
Weak AI
 Weak AI outperforms humans
in narrowly defined tasks
 Chatbot that answers customer
service questions
 Facial recognition on Facebook
 Alexa, Google Assistant, and
Siri
Augmented AI
 Helping humans make better
decisions, also boosts their
expertise and improves their
productivity
 IBM Watson for Oncology
 Humans become faster and
smarter at the tasks they’re
performing
Generalized AI
 Form of “Whole Brain
Emulation”, where a machine
can think and make decisions
on many different subjects
 Computers we see on science-
fiction video
 Talking to humans about
many subjects

10. Weak AI

11. Augmented AI

12. Artificial Narrow Intelligence
 Artificial Narrow intelligence or “Weak” AI refers to machines are specialized in
one area and solves one problem
 ANI model can only execute the task for which it was trained - Unable to
perform beyond its area of expertise.
 Apple Siri, which operates on a set of pre-defined functions, is one of the
finest examples of ANI
 The IBM Watson supercomputer, that integrates machine learning and natural
language processing with an expert systems approach
 Playing chess, product recommendations on an e-commerce site, self-driving
vehicles, speech recognition, and image recognition are all examples of
narrow AI

13. Artificial General Intelligence
 Artificial General intelligence or “Strong” AI refers to machines that
exhibit human intelligence
 AGI can successfully perform any intellectual task that a human being
can
 Movies like “Her” or other sci-fi movies in which humans interact with
machines and operating systems that are conscious, sentient, and
driven by emotion and self-awareness
 AGI able to - reason, solve problems, make judgements under
uncertainty, plan, learn, integrate prior knowledge in decision-
making, and be innovative, imaginative and creative

14. Artificial Super Intelligence
 “Any intellect that greatly exceeds the cognitive
performance of humans in virtually all domains of interest” -
Nick Bostrom {Oxford philosopher}
 Artificial Super Intelligence (ASI) will surpass human
intelligence in all aspects — from creativity, to general
wisdom, to problem-solving

15. Types of Artificial Intelligence - Functionality Based

16. Types of Artificial Intelligence - Functionality Based

17. Importance of Artificial Intelligence
 AI automates repetitive learning and discovery through data - AI performs frequent, high-volume, computerized
tasks
 AI adds intelligence to existing products - Automation, conversational platforms, bots and smart machines can be
combined with large amounts of data to improve many technologies
 AI adapts through progressive learning algorithms to let the data do the programming - models adapt when given
new data
 AI analyzes more and deeper data using neural networks that have many hidden layers
 AI achieves incredible accuracy through deep neural networks - AI techniques from deep learning and object
recognition can now be used to pinpoint cancer on medical images with improved accuracy
 AI gets the most out of data – When algorithms are self-learning, the data itself is an asset

18. Artificial Intelligence Frameworks

19. Machine Learning
 Machine learning automates analytical model building. It uses methods from
neural networks, statistics, operations research and physics to find hidden
insights in data without explicitly being programmed for where to look or what
to conclude
 Machine learning is a method of data analysis that automates analytical model
building. It is a branch of artificial intelligence based on the idea that systems
can learn from data, identify patterns and make decisions with minimal human
intervention.
 While artificial intelligence (AI) is the broad science of mimicking human abilities,
machine learning is a specific subset of AI that trains a machine how to learn

20. Deep Learning
Deep learning uses huge neural networks with many layers of processing units,
taking advantage of advances in computing power and improved training
techniques to learn complex patterns in large amounts of data. Common
applications include image and speech recognition
Deep learning is a type of machine learning that trains a computer to perform
human-like tasks, such as recognizing speech, identifying images or making
predictions.
Instead of organizing data to run through predefined equations, deep learning sets
up basic parameters about the data and trains the computer to learn on its own by
recognizing patterns using many layers of processing

21. Neural Network
A neural network is a type of machine learning that is
made up of interconnected units (like neurons) that
processes information by responding to external inputs,
relaying information between each unit. The process
requires multiple passes at the data to find connections
and derive meaning from undefined data.

22. NLP – Natural Language Processing
 Natural language processing (NLP) is the ability of computers to
analyze, understand and generate human language, including
speech. The next stage of NLP is natural language interaction, which
allows humans to communicate with computers using normal,
everyday language to perform tasks.
 Natural language processing (NLP) is a branch of artificial
intelligence that helps computers understand, interpret and
manipulate human language.
 NLP helps computers communicate with humans in their own
language, making it possible for computers to read text, hear
speech, interpret it, measure sentiment and determine which parts
are important.

23. Computer Vision
 Computer vision is a field of artificial intelligence that trains
computers to interpret and understand the visual world. Using
digital images from cameras and videos and deep learning
models, machines can accurately identify and classify objects —
and then react to what they “see.”
 From recognizing faces to processing the live action of a football
game, computer vision rivals and surpasses human visual abilities
in many areas.

24. Commercial Business uses of AI
 Banking Fraud Detection
From extensive data consisting of fraudulent and non-fraudulent transactions, the AI learns to predict if a new
transaction is fraudulent or not.
• Online Customer Support
AI is now automating most of the online customer support and voice messaging systems.
• Cyber Security
Using machine learning algorithms and sample data, AI can be used to detect anomalies and adapt and respond to
threats.
• Virtual Assistants
Siri, Cortana, Alexa, and Google now use voice recognition to follow the user's commands. They collect information,
interpret what is being asked, and supply the answer via fetched data. These virtual assistants gradually improve and
personalize solutions based on user preferences.

25.  Finance sector
Analyzing stock markets to give future trends and keep finances in check
 Manufacturing Sector
Assembling are already done by robotic hands in building complex systems such as electronic goods and automobiles
 Robotics
Automating manual repetitive tasks
 Spam and Malware Filtering
 Automatic Language Translation
 Product Recommendations
 Traffic Prediction
 Driverless Cars
Commercial Business uses of AI

26. AI in Detecting Floods – Natural Calamities
In the flood-prone region of Patna in northern India, the waters were
rising. But thanks in part to an artificial intelligence system, residents of
the region received early warnings on their phones. A flood forecasting
system that Google developed for India’s Central Water Commission is
making a difference! But it can do more than forecast high waters. It’s
also smart enough to avoid false alarms.
Sella Nevo, the head of the flood forecasting unit and a software
engineering manager at Google, notes that “For our high-risk alerts, we
had less than 10 percent false positives [down to regions measuring 64
by 64 meters] ... That’s highly accurate.” The trick is training the system’s
accuracy so that unnecessary evacuations are avoided, and trust can be
built for the alert system.

27. AI in Health Care
 AI applications can provide personalized medicine and X-ray
readings. Personal health care assistants can act as life
coaches, reminding you to take your pills, exercise or eat
healthier
 Cardiologists often work in fast-paced healthcare
environments where inefficiency or delays can affect their
ability to deliver high-quality care
 cardiologists and their teams can streamline workflows to
make their cardiovascular service line more efficient, cost-
effective and patient-centered

28. AI in Education
Education at any time
Education adapts to
student needs
Virtual mentors
Personalization
Curriculum automatic formulation
Ability to detect weakness
Better engagement
Example: Little Dragon, Brainly, ThinkerMath, CTI etc..

29. AI in Agriculture
Analyzing Market Demand
AI can simplify crop selection and help farmers identify what produce will be
most profitable.
Managing Risk
Farmers can use forecasting and predictive analytics to reduce errors in business
processes and minimize the risk of crop failures.
Breeding Seeds
By collecting data on plant growth, AI can help produce crops that are less prone
to disease and better adapted to weather conditions.
Monitoring Soil Health
AI systems can conduct chemical soil analyses and provide accurate estimates of
missing nutrients.
Protecting Crops
AI can monitor the state of plants to spot and even predict diseases, identify and
remove weeds, and recommend effective treatment of pests.
Feeding Crops
AI is useful for identifying optimal irrigation patterns and nutrient application
times and predicting the optimal mix of agronomic products.
Harvesting
With the help of AI, it’s possible to automate harvesting and even predict the
best time for it.

30. AI in Agriculture
 Using AI and machine learning-based surveillance systems to monitor every crop field's real-time video feeds identifies
animal or human breaches, sending an alert immediately
 AI and machine learning improve crop yield prediction through real-time sensor data and visual analytics data from drones
 The UN, international agencies and large-scale agricultural operations are pioneering drone data combined with in-ground
sensors to improve pest management
 Shortage of agricultural workers, making AI and machine learning-based smart tractors, agribots and robotics a viable
option for many remote agricultural operations that struggle to find workers
 Improving the track-and-traceability of agricultural supply chains by removing roadblocks to getting fresher, safer crops to
market is a must-have today
 Optimize the right mix of biodegradable pesticides and limiting their application to only the field areas that need treatment
to reduce costs while increasing yields is one of the most common uses of AI and machine learning in agriculture today
 Monitoring livestock’s health, including vital signs, daily activity levels and food intake, ensures their health, is one of the
fastest-growing aspects of AI and machine learning in agriculture

31. AI in Business
Retail
AI provides virtual shopping capabilities that
offer personalized recommendations and discuss
purchase options with the consumer. Stock
management and site layout technologies will also
be improved with AI.
Manufacturing
AI can analyze factory IoT data as it streams from
connected equipment to forecast expected load
and demand using recurrent networks, a specific
type of deep learning network used with
sequence data.

32. AI/ML across Retail Value Chain

33. AI in Banking
Artificial Intelligence enhances the speed,
precision and effectiveness of human efforts.
In financial institutions, AI techniques can be
used to identify which transactions are likely to
be fraudulent, adopt fast and accurate credit
scoring, as well as automate manually intense
data management tasks

34. AI Platforms
 Google Cloud AI
 Amazon AI services
 Microsoft Azure AI
 H2O.ai
 IBM Watson Studio
 TensorFlow
 DataRobot
 Wipro Holmes AI and automation platform
 Salesforce Einstein
 Infosys Nia

35. AI Tools
Scikit Learn
Tensorflow
Auto ML
Theano
Caffe
MxNet
Keras
PyTorch
CNTK
Google ML Kit

36. Artificial Intelligence for Enterprise
Choice and Flexibility
Deploy your AI applications on
the cloud environment that best
supports your business needs
Security and Trust
Take advantage of built-in
security capabilities and AI
model monitoring
Deep Industry Capabilities
Choose from a wide range of AI
products, built for the specific
needs of your industry

37. Thank You

38. Appendix