An Introduction to Vision AI for Business

  • Neena Sathi
  • Arvind Sathi

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Vision AI is a technique that enables digital devices to analyze a large set of images and scenes and detect meaningful objects from them. In business, Vision AI can be used to analyze and synthesize data from documents faster than human can. This video offers background on what Vision AI is and how it can be used in business contexts, providing case studies that illustrate how it can benefit an organization.

Introduction

Vision AI is a technique that enables digital devices to analyze a large set of images and scenes and detect meaningful objects from them.

About The Authors

Neena Sathi

Neena Sathi is Principal at Applied AI Institute. She has 30+ years of experience envisioning, designing, developing and implementing AI solutions associated with enhancing customer experience, back office automation, and risk and compliance for many Fortune 100 organizations. She has worked as Director of AI Technologies at Carnegie Group, Inc, an AI startup, Accenture, KPMG, and IBM. Neena has three masters degrees including an MBA from leading US universities. She is a Master certified integration architect from IBM and Open Group as well as certified Project management professional (PMP) from Project management institute. She is also certified in many Cloud and Cognitive technologies. She has widely presented and published many papers in AAAI, IEEE, WCF, ECF, IBM Information on Demand, IBM Insight, World of Watson, IBM Developer Works and various academic journals.

 
Arvind Sathi

Arvind Sathi is the Director of AI Literacy at KPMG and a faculty member with University of California where he teaches courses on AI and Analytics. Dr. Sathi received his Ph.D. in Artificial Intelligence from Carnegie Mellon University and worked under Nobel Prize-winner Dr. Herbert A. Simon. Dr. Sathi is a seasoned professional with leadership in Artificial Intelligence and Data Science solution development and delivery. Dr. Sathi was a pioneer in developing AI solutions at Carnegie Group (a Carnegie Mellon startup), leading to its successful public offering as a profitable AI company. At KPMG/Bearingpoint, he led the practices for Intelligent Process Automation, Enterprise Integration, & Analytics. At IBM, Dr. Sathi has led several AI and Data Science programs involving IBM products from IBM Watson, and Cloud business units, and has provided technical oversight to IBM’s strategic accounts. He has also delivered numerous workshops and presentations at industry conferences on technical subjects and holds four patents in information technology. He has published four books on analytics - Cognitive (Internet of) Things, Engaging Customers Using Big Data, Big Data Analytics, Customer Experience Analytics. He has also been a contributing author in a number of Data Governance books written by Sunil Soares and has published an article series on Advanced Analytics for IBM Developer Works.

 

About this video

Author(s)
Neena Sathi
Arvind Sathi
DOI
https://doi.org/10.1007/978-3-030-78761-5
Online ISBN
978-3-030-78761-5
Total duration
1 hr 13 min
Publisher
Palgrave Macmillan
Copyright information
© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG, part of Springer Nature 2021

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Video Transcript

Introduction to vision AI for business. Due to explosive growth in data, need for computer vision has grown exponentially to aid in our day to day life. Can we use vision in our common day to day life to expedite our workload processing? Some examples include accounting professionals, deal with mountain of paper load.

These documents often carry a large number of structured tables and forms. Can we use a specialized natural language processing, NLP techniques involving vision AI to analyze and synthesize these documents and to provide meaningful summary to our accounting professions.

Back office business professionals also deal with images such as cheques, driver’s license, license plates, invoices, receipts, and forms. This also requires large image processing. Can we design vision AI solution to analyze these images for classification, tax extraction, and interpretation?

As we deal with social interaction in COVID-19 erra. Compliance for mass querying gets added to a number of other image analysis problems. We have a strong need to analyze and interpret large set of tables, forms, and images using vision AI technology.

So how do you start defining a vision AI solution that offers ability to classify many types of images, including forms and tables to detect objects, to extract entities, and to interpret pictures. This video book offers a set of applications and user examples of vision AI applications and underlying benefit cases.

Introduction. In this introduction section, we will cover video book outline, and its expected outcome. We will also provide brief introduction on instructors Dr. Irwin Sathi and Mr. Nigna Sathi.

Course outline. This video book is divided into multiple sections. After this introduction section to introduce video outline expected outcome, and authors, we will cover drivers and evolution of vision AI in section 2. In order to meet the emerging demand for vision AI, we will also cover how technology is evolving in this area.

In section 3, we will cover vision AI key concept. Vision provides us a unique opportunity to extract objects entities and text from an image. In section 4, we will cover vision AI, tools and techniques. We will provide current state of technology in this area and cover several many techniques like transfer learning, VGG embedding to expedite the vision AI learning process.

In section 5 and 6, we will cover two case studies for vision AI, where vision AI getting used and what benefit does it provide to an organization. As unstructured information grows around business decision makers, vision AI use cases provide mechanisms for synthesizing information from a variety of images, forms, text, and videos.

We provide to use case examples of vision AI solution and associated user personas. We describe vision AI concepts, insights, and benefit cases using these two use case examples. In section 7, we will cover key concept and maturity levels of vision AI technology.

We will cover what tools and technology required for designing a vision AI solution and associated business and technical maturity. In the last section we’ll summarize the video content and will provide pointers for further learning. Audience. We are developing course material in this area targeted to business and IT professionals and executives, who are interested in exploring AI technology for automating their daily or mandate business processes.

We are also targeting this course to graduate students in business, analytics, and information technology, who are interested in data science as a career and are trying to understand the impact of vision AI to their work for developing and managing successful applications and underlying benefit cases.

Audience is primarily interested in understanding the impact of Vision AI to their work and what skills are needed to develop successful Vision AI applications that automate business decisions for their users. Expected outcome. What will you get out of this with video book? You will be able to understand how Vision AI evolved over time.

You will be able to articulate Vision AI concept, you will be able to identify and strategize possible Vision AI use cases. Hi, I am Nina Sathi and I have over 30 years of AI development experience in many industries like, Telecom, Health care, retail, and government.

In Telecom, I had built several AI systems for consultative selling and service engagement. For retail, I have built AI systems for streamlining front end and building business processes. I have widely deployed AI applications in US, Canada, and Latin America. I have widely presented and published AI related papers in many magazines and conferences.

I have worked as director of AI technologies at Accenture CenturyLink, KPMG, and IBM.

Hi, I’m Arvind Sathi. I received my Ph.D from Carnegie Mellon University in the field of artificial intelligence. I’ve worked under Professor Herbert Simon, who received his Nobel Prize in economics, and is also considered to be the father of artificial intelligence.

I work today at University of California Irvine, where I teach courses at graduate level in AI and analytics. And I also work at KPMG, where I lead educational activities associated with high AI literacy for 20,000 advisory consultants.

I have experience in developing and deploying AI systems in Telecom, media, and accounting services. I’ve done this work across the globe in Australia, in Asia, in Europe, as well as in the US. I have written four books in the field of AI and analytics and have several patterns and many papers in the field.