Video description
Presented by Sangeeta Krishnan – Former Director, Enterprise Data Management & Strategy at Asembia
Artificial Intelligence (AI) is the current buzz word across all industries. Marvin Minsky’s definition of AI describes it as the science of making machines do things that would normally require human intelligence. However, the opinions are split across two camps. On one side we hear about new AI gadgets getting introduced in the market that would empower and improve our lives. At the same time, there are stories of AI companies going bankrupt like the recent closure of AI powered clock – Bonjour.
Any digital technology has its own risks such as Cyber Security, Data Privacy Protection, apart from protecting the interests and careers of the human workforce. When you attempt to transform data into answers, many questions erupt. AI is a combination of techniques that includes Data Analytics and Predictive Analytics. Many organizations start their Analytics and AI journey without implementing the discovery phase of defining clear achievable business values. This results in most of the R&D budget to evaporate in experimental pitfalls with no real gain, resulting in AI being non-productive. It is imperative for any organization to discuss and focus on certain fundamental areas and challenges before embarking on the AI journey.
This presentation would give a detailed overview of the areas that need to be addressed for the AI journey to be successful beyond PoC (Proof of Concept). It would demystify AI from an implementation stand point and would cover multiple aspects to demonstrate the current capabilities of AI technologies. It would also put out a road map for the industries to have a smooth journey to the AI world. The presentation would discuss the entire spread of activities such as budgeting, risk mitigation, training and many more.
Table of contents
- Rocky Journey to AI Adoption 00:29:03
Product information
- Title: Rocky Journey to AI Adoption
- Author(s):
- Release date: September 2019
- Publisher(s): Data Science Salon
- ISBN: None
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