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Using micro self-reinforcement learning, NLP and AI gamification in dynamic real-time
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Advances in Robotics & Automation

ISSN: 2168-9695

Open Access

Using micro self-reinforcement learning, NLP and AI gamification in dynamic real-time


6th Global Summit on Artificial Intelligence and Neural Networks

October 15-16, 2018 Helsinki, Finland

Erwin E Sniedzins

Mount Knowledge Inc., Canada

Scientific Tracks Abstracts: Adv Robot Autom

Abstract :

Educators, students, employers and employees are inundated with big data. They are seeking relief. AI provides the bridge between big data and personalized data using NLP and GANN. In recent years Natural Language Processing (NLP) and Genetic Algorithm Neural Networks (GANN) have entered to add, ??Data into Knowledge? (DiK) solutions. Research with NLP and GANN has enabled tools to be developed that selectively filters big data and combine this data into microSelf- Reinforcement and personalized gamification of any DiK in Dynamic real-time. Is the combination of GA, NLP, MSRL and dynamic Gamification that has enabled people to experience relieve in their quest to turn DiK 32% better, faster and easier and with more confidence over traditional learning methods.

Biography :

Erwin E Sniedzins is the President of Mount Knowledge Inc, Toronto, Canada. The company is a Global Leader in AI, neural networks, automatic gamification of any textual data and micro reinforcement learning. He has patented the Knowledge Generator™ (KG), which is an artificial intelligence application that takes any digitized textual content and automatically creates a MicroSelf-Reinforcement Learning and Personalize Gamification of this content into lessons, exercises and tests with scores and marks in Dynamic real-time. The KG technology enables people to turn Data into Knowledge (DiK) 32% better, faster and easier with more confidence and fun. No teacher or course designer is required. He is the author and has published 12 books. He is also a Professor at Hebei University, Canada.

E-mail: esniedzins@gmail.com

 

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Citations: 1275

Advances in Robotics & Automation received 1275 citations as per Google Scholar report

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