Hybrid Intelligent Interface (Machine Learning)
The Hybrid Intelligent Interface is a software system that bridges human and complex environments as a user-friendly interface for convenience and service. Ideal Human Computer Interfaces need a hybrid solution with ontology, natural language understanding, and machine learning tools. This work investigates how to combine understanding and learning in to a system of principles. Of course, each of these tasks has very high complexity, so we need to look for partial restricted solutions. Current solutions can be reduce complexity so each subject area may accomplish function while adding user experience and a better understanding of what General AI is and what architecture it should have. This presentation presupposes the existence of examples of concepts processed by Machine Learning methods and the ability to use them in ontology and related procedures.
Date and Time
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- Date: 21 Feb 2019
- Time: 06:00 PM to 09:00 PM
- All times are (GMT-07:00) MST
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- 2155 East Wesley Avenue
- Denver, Colorado
- United States 80208
- Building: Ritchie School of Engineering
- Room Number: 301
- Click here for Map
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- Co-sponsored by James Gowans
- Starts 15 January 2019 12:13 PM
- Ends 21 February 2019 05:00 PM
- All times are (GMT-07:00) MST
- No Admission Charge
Speakers
Alexey Egorov
Hybrid Intelligent Interface (Machine Learning)
The Hybrid Intelligent Interface is a software system that bridges human and complex environments as a user-friendly interface for convenience and service. Ideal Human Computer Interfaces need a hybrid solution with ontology, natural language understanding, and machine learning tools. This work investigates how to combine understanding and learning in to a system of principles. Of course, each of these tasks has very high complexity, so we need to look for partial restricted solutions. Current solutions can be reduce complexity so each subject area may accomplish function while adding user experience and a better understanding of what General AI is and what architecture it should have. This presentation presupposes the existence of examples of concepts processed by Machine Learning methods and the ability to use them in ontology and related procedures.
Biography:
Alexey Egorov holds a MS in Engineering and BS in Computer Science from The Ural University of Russia. Alexey is a senior level software engineer with over 25 years of programming experience. Alexey is currently working for Cognifield LLC. Alexey’s research interests include artificial intelligence development, machine learning, and natural language understanding.