Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Knowledge Engineering – Definition, Application & Example
In artificial intelligence (AI), knowledge engineering is a branch of the science that develops rules to apply to data in order to simulate the thinking process of a human expert. It examines the structure of a job or a choice in order to determine how a result is arrived at.
A collection of problem-solving techniques, as well as the ancillary information is developed and given up as issues for the system to diagnose. The software developed as a consequence of this research will be used to help humans in diagnosing, troubleshooting, and fixing problems.
Key Points Briefly
Artificial Intelligence (AI) has a subset called knowledge engineering that is creates rules which are used that are followed by experts of those field.
The early stages of knowledge engineering only concerned with problem solving skills, and later on focused on the individual’s ability to solve it given the same data.
Transfer processing has its limits as it never considered analogous rationale based on instinct that could also be correct.
The primary purpose of knowledge engineering is to make decisions just like specialists in fields of finance, health etc.
One could argue that knowledge is already being implemented by most experts to make better choices.
When it comes to finance, what exactly is Knowledge Engineering?
Knowledge engineering in finance is a branch of artificial intelligence that makes use of data to develop rules that mimic the thinking process of a financial specialist. Using a large electronic library, artificial intelligence can recognize the job at hand and choose the best logical result from the options available.
The Application of Machine Learning in Finance
The following are some examples of how artificial intelligence is being utilized in finance today −
DataRobot is a software firm that assists financial institutions and companies in the development of predictive models that may be used to improve specific decision-making situations, such as fraudulent credit card transactions and loan decisions.
Scienaptic Systems — This company provides a credit institution and bank underwriting platform that increases transparency while also lowering the risk of loss.
Kensho – Used by some of the biggest names in the financial world like J.p. Morgan and Bank of America. It mainly provides data analytics, machine learning services.
Alphasense — One of the favorite search engines for financial organizations that gives users interactive features like trends of markets and latest research.
Kavout — This could be the most important tool for financial investors as it provides live, real time financial data powered through its AI engine.
Positions in the Financial Sector that require AI expertise
Businesses are getting more engaged with knowledge management systems and artificial intelligence as time goes on. The following are financial professions that require interpretation of artificial intelligence −
Commercial Credit Product Manager with a high level of responsibility (Capital One)
Applicants must have expertise of artificial intelligence, knowledgeable in automation, and machine learning in order to evaluate possible risk (approximate pay range: $55,000-$105,000 per year).
Experience Designer with a Senior Role (American Express)
Application requirements include expertise of marketing and artificial intelligence in order to develop user-centered experiences for their dynamic programs (estimated pay range: $77,000-$115,000).
Artificial Intelligence Backend Engineer (J.P. Morgan)
Application requirements include expertise of artificial intelligence (AI) and responsibility for developing a back-end AI system that can handle data and respond to machine learning demands (approximate pay range: $90,000 – $130,000).
772 Views
