Unit-1: �Introduction

Artificial Intelligence (AI)

CT601-N

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Looping

Outline

• Introduction to Artificial Intelligence (AI)
• The AI Problems
• Why do we need to study AI
• AI Techniques
• Applications of AI
• The Underlying assumption

What is Artificial Intelligence (AI)?

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• AI is a branch of computer science dealing with the simulation of intelligent behavior in computers.
• AI is the study of how to make computers do things which, at the moment, people do better.
• AI is, the study and design of intelligent agents where an intelligent agent is a system that perceives its environment and takes actions.

AI is the science and engineering of making intelligent machines, especially intelligent computer programs (1956).

John McCarthy

(the father of Artificial Intelligence)

• Why do we need to study Artificial Intelligence

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1. The Skill of the Century

Artificial Intelligence is one of the emerging technologies making its mark in every industry ranging from fashion to finance. In fact, AI jobs account for an average of 18% of jobs in most companies. 

2. AI is Everywhere

The Automobile Industry, Music Recommendations, Smart Home Devices, Online Customer Support, Security Surveillance, Retail, and Healthcare are just a few industries that depend on AI.

3. Data, Data, More Data

We generate more than 2.5 quintillion bytes every single day. We feed the collected data to machine learning algorithms to retrieve a behavioral pattern. companies are literally running towards AI in the hope to make more sales and win the race ultimately. 

4. Big Bright Career

5. AI is versatile

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AI Techniques

• There are three important AI techniques:
• Search –
• Provides a way of solving problems for which no direct approach is available.
• It also provides a framework into which any direct techniques that are available can be embedded.
• Use of knowledge –
• Provides a way of solving complex problems by exploiting the structure of the objects that are involved.
• Abstraction –
• Provides a way of separating important features and variations from many unimportant ones that would otherwise overwhelm any process.
• Example: using sqrt function to find the square root of a number without knowing what is the implementation details in that function.

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Task Domains of AI

History of AI

Application Domains of AI

Natural Language Processing

Neural Network

Email Spam Filter in Gmail

Image Processing

Deep Learning

Face Detection in Camera

Speech Recognition

Deep Learning

Voice Technology in Virtual Agents

Data Mining

Product recommendation

Market Basket Analysis

Expert System

Reinforcement Learning

IBM Watson

Robotics

Deep Learning

Home Automation

Scheduling

Aurora - Advanced Intelligent Planning and Scheduling Solution

Resource Scheduling

Optimization

Google map path planner

Shortest Path

Game Playing

Deep Neural Network

Alpha Go

Virtual Agents

Conversational AI

Chatbots

Personalized Recommender Systems

Machine Learning

Online Shopping

Automated Control Systems

Fuzzy Logic

Washing Machine

Security

Machine Learning

NVIDIA Metropolis

AI – ML – DL and Data Science

AI

Technique that enables machines to mimic human behavior

Subset of AI which uses statistical methods to enable machine to learn and improve with time

Machine Learning

Deep Learning

Data Science

Subset of ML that includes algorithms and enables system to train itself

The Underlying Assumption

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• Underlying means Cause of something
• Assumption means to accept something to be sole truth without any proof.
• EX: Moon is seen in the night.
• Underlying assumption is the core of AI research and physical symbol system hypothesis.
• Hypothesis consist of knowledge of symbol reality.
• Physical symbol system consist of set of entities and symbol.
• Underlying assumption is process of creation,modification , production and destruction.

Thank You!