Introduction to Artificial Intelligence (AI)

Name: Introduction to Artificial Intelligence (AI)
Rating: 4.2 (24 reviews)

Define strategy and engagement for Artificial Intelligence solutions

Created byNeena Sathi

Last updated 4/2022

English

What you'll learn

What Is AI
Key AI Capabilities and Technology
AI technologies and associated case studies
Components of a AI solution

Course content

7 sections • 13 lectures • 2h 32m total length

Introduction6:03
On behalf of the Applied AI Institute, we welcome you to the course Introduction to Artificial Intelligence. AI has become a buzz word all the way from corporate board rooms to dining conversations among family members. As consumer products like Siri and Alexa invade our homes and corporation debate how to use AI as a strategic differentiator, we must first understand what is AI.
In this section, we will provide you with course objectives, expected audience, course outline, and expected outcomes
Instructor's Bio2:26
In this lecture, we will provide brief Bio on course instructors - Dr. Arvind Sathi and Ms. Neena Sathi
Expected Outcome

Key Technologies12:52
In this section, we will introduce you to 4 key AI capabilities and technologies developed by AI researchers in each of those 4 areas. These capabilities roughly follow human intelligence areas of perception, cognition, knowledge and collaboration, but take minor deviations to include learning – an important aspect that drives growth of human intelligence. In each of these areas, we will describe specific technologies which support the corresponding capability.
Key AI Technologies

AI Technology - Sensing10:54
In the section we will provide key characteristics of Sensing technology and why Sensing is important. Sensing include many AI technologies like
Text Understanding
Speech recognition
Edge Analytics
Odor detection
Signal processing
Gesture detection and associated sentiment analysis
Key Sensing technologies
Sensing - Case Study11:56
In this section I will be taking about first case study. around AI Technology - Sensing. This Case study is health care and pediatrics directed. We will discuss how patient analytics is evolving with the AI capabilities in our understanding of vitals, behaviors and interactions.
Windowed aggregates

AI technology - Reasoning18:39
In this lecture, we will cover AI Technologies - Reasoning. Reasoning is the most developed and yet least used area of AI with enormous potential for commercialization and commercial use. In most applications, a fair amount of data science work is focused on sensing, for example in dealing with vision and speech, and learning to improve sensing. This is typically followed with procedural programming for reasoning, making it hard to learn, collaborate and adjust with users in evolving inferencing, alternative generation and decision making. We will explore how a variety of reasoning techniques can be used instead of hard-coded procedural programming to introduce organic and evolving processes for inferencing.
What use case for a Reasoning Engine
Case Study 3 - Reasoning24:57
In this section, we will Case study on Movie Recommendation Engine using AI Technology – Reasoning

AI technology - Learning16:50
In this lecture, we will cover AI technology – Learning or Machine Learning. We will introduce how AI induced techniques like learning can drive significant capabilities.
Key Learning technologies
Case Study 2 - Learning7:53
In this section, we will cover our latest Case study on Payment Protection Plan (PPP) Form Processing / Automation and how we automated the PPP form processing using Machine Learning - Vision AI technology and some commercially available tools.
Key Document extraction AI vendors

AI Technology - Interaction16:36
In this lecture, we will cover 4th tier of AI Technologies – Interaction. As much emphasis on sensing was on dealing with a variety of inputs the way from unstructured to structured, the emphasis in interaction is in dealing with a range of interactions – all the way from structured reports to flexible and meaningful conversations. In this section, we will discuss a number of interaction techniques.
Key capabilities of a Conversational AI engine
Case Study 4 - Interaction15:43
In this section, we will cover the case study for AI Technologies – Interaction build for Hotel industry to support hotel concierge services
Key benefits for customer for a Hotel concierge chatbot

Requirements

None

Description

As we deal with current data explosive world, much of the data is unstructured – forms, tables, images, and video. As we deal with social interactions in Covid-19, compliance for mask wearing gets added to a number of other image analysis problems.

We have a strong need to analyze large set of unstructured and semi-structured data to interpret the meaning using various AI technology. What are the different types of AI capabilities and associated technologies? How do you select an AI use case and associated technology.

In this course, you will understand

What is AI?
Major capabilities of AI
Various AI technologies and associated use cases
Components of an AI solution
Strategize an AI engagement and associated technologies

This course is divided into multiple sections. After this introductory section,

We will cover what is AI and four major tiers of AI capabilities. In each area, we will identify key technologies and how they drive and transform analytics.
First area is sensing - this includes perception capabilities embedded in our ingestion of speech, images, text, and sensors. We will cover this technology and will also include one case study in this area.
Second area is learning – here we discuss the role of adaptive learning in model improvement as seen today in supervised, unsupervised and reinforcement learning. We will cover this technology and will also include one case study in this area.
Third area is reasoning – our discussion here showcases the role of semantic knowledge representation in developing reasoning capabilities. We will cover this technology and will also include one case study in this area.
Four area is interaction – it covers our use of collaboration in human – machine interaction. We will define key characteristics of this technology and will also include one case study in this area.
Next, we will round up the four capabilities – perception, adaptive learning, semantic knowledge representation and collaboration and show how they have collectively shaped various common life use cases

In last summary section, we will review our findings and provide a set of recommended readings.

The course will cover many interactive quizzes to test your understanding on the subject.

Who this course is for:

Business professionals
IT professionals
Senior year undergraduate and graduate students in Business & IT
Vendors, consultants and service providers for AI technology

Introduction to Artificial Intelligence (AI)

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 8min

Key Capabilities and Technologies1 lecture • 13min

AI Technology - Sensing2 lectures • 23min

AI Technology - Reasoning2 lectures • 44min

AI Technology - Learning2 lectures • 25min

AI technology - Interaction2 lectures • 32min

Summary2 lectures • 8min

Requirements

Description

Who this course is for: