Chandra Ambadipudi, Chief Executive Officer, Clairvoyant discusses the potential for AI and Machine Learning in financial service.
copyright by www.globalbankingandfinance.com
What is the difference between AI and Machine Learning?
AI is the concept of machines performing tasks that are characteristic of human intelligence — it is the all-encompassing phase that is highlighted in multiple Sci-Fi movies like Terminator, Matrix, etc. The concept of AI is to address things like recognizing objects and sounds, learning, planning and problem solving.
Today most of the AI used in a business context is specific to one area, it displays characteristics of the human intelligence in one specific area like sound, image recognition or problem solving. The evolution of AI to replicate multiple aspects of human intelligence is the next stage in its evolution and that is the focus of new emerging AI initiatives across industries.
Machine learning in its most basic form is a way to achieve AI. Machine learning is a way of training an algorithm so that it can learn how to learn. The training here involves feeding large amounts of data into an algorithm and allowing the algorithm to adjust itself and improve. To further expand on this, AI can be achieved without machine learning by writing significant amounts of code or programs, but today machine learning algorithms make the process of creating AI-based applications much easier than through manual processes.
What are some of the common misconceptions about AI and Machine Learning?
People often think that Artificial Intelligence (through machine learning) will replace their job functions completely and perform at a higher level than humans, or that AI has human-level emotions and intelligence. Naturally, these stem from a misunderstanding as to what AI can truly do. While it is capable of processing large amounts of data very quickly, it’s certainly not at human-level in terms of judgement and perception. There’s also a misconception (often fueled by sci-fi movies) that AI applications will suddenly become sentient. AI could certainly become destructive, but not at the level often portrayed in Hollywood. A final misconception is that AI is a silver bullet. AI can solve for a lot of things, but there’s not one magic algorithm that can solve everything. Most AI applications need to be built and implemented for a specific purpose based on the industry and company.
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What are the current applications of AI and Machine Learning in banking, finance and insurance?
More and more institutions are utilizing automated processes, and AI is currently driving some of the biggest industry changes in banking, finance and insurance. By making frequently performed duties automated, AI makes it possible to focus on higher level objectives. You see this in tasks such as document management, where it reads through documents, including forms, contracts, etc.[…]
read more – copyright by www.globalbankingandfinance.com
Chandra Ambadipudi, Chief Executive Officer, Clairvoyant discusses the potential for AI and Machine Learning in financial service.
copyright by www.globalbankingandfinance.com
What is the difference between AI and Machine Learning?
AI is the concept of machines performing tasks that are characteristic of human intelligence — it is the all-encompassing phase that is highlighted in multiple Sci-Fi movies like Terminator, Matrix, etc. The concept of AI is to address things like recognizing objects and sounds, learning, planning and problem solving.
Today most of the AI used in a business context is specific to one area, it displays characteristics of the human intelligence in one specific area like sound, image recognition or problem solving. The evolution of AI to replicate multiple aspects of human intelligence is the next stage in its evolution and that is the focus of new emerging AI initiatives across industries.
Machine learning in its most basic form is a way to achieve AI. Machine learning is a way of training an algorithm so that it can learn how to learn. The training here involves feeding large amounts of data into an algorithm and allowing the algorithm to adjust itself and improve. To further expand on this, AI can be achieved without machine learning by writing significant amounts of code or programs, but today machine learning algorithms make the process of creating AI-based applications much easier than through manual processes.
What are some of the common misconceptions about AI and Machine Learning?
People often think that Artificial Intelligence (through machine learning) will replace their job functions completely and perform at a higher level than humans, or that AI has human-level emotions and intelligence. Naturally, these stem from a misunderstanding as to what AI can truly do. While it is capable of processing large amounts of data very quickly, it’s certainly not at human-level in terms of judgement and perception. There’s also a misconception (often fueled by sci-fi movies) that AI applications will suddenly become sentient. AI could certainly become destructive, but not at the level often portrayed in Hollywood. A final misconception is that AI is a silver bullet. AI can solve for a lot of things, but there’s not one magic algorithm that can solve everything. Most AI applications need to be built and implemented for a specific purpose based on the industry and company.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
What are the current applications of AI and Machine Learning in banking, finance and insurance?
More and more institutions are utilizing automated processes, and AI is currently driving some of the biggest industry changes in banking, finance and insurance. By making frequently performed duties automated, AI makes it possible to focus on higher level objectives. You see this in tasks such as document management, where it reads through documents, including forms, contracts, etc.[…]
read more – copyright by www.globalbankingandfinance.com
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