This whitepaper examines the evolution of AI, how it works, its importance and uses, challenges and limitations.
When it comes to Artificial Intelligence in financial services, there are all kinds of ways in which organizations deploy rules and algorithms. While a majority use it for customer engagement applications or in vertical-specific software, other use cases arise in the form of call center service and support, cybersecurity, supply chain management or asset performance management.
Topics you will find within this White Paper:
A simple way of understanding the importance of AI is to think of it as the addition of intelligence to existing products. It can use rules or decision trees to help people make decisions.
The process depends upon the kind of task, kind of data and amount of data available, all of which then calls for either basic decision trees or clustering, layers of artificial neural networks or deep learning.
Learning by example, something that comes naturally to human beings, is what Deep Learning tries to teach computers. It is a Machine Learning technique responsible for everything from driverless cars to voice control, teaching computers to distinguish being pedestrians and obstructions, or how to recognize stop signs.