Cognitive Machine Learning (ML) is all about machines and other “things” learning concepts from data (and from humans in its “Supervised Machine Learning” mode). But where is the “learned” ML knowledge stored and what can actually be accomplished with cognitive ML?
Here’s the Overview of a presentation at Cognitive Computing Experts describing Cognal’s Machine-Learning driven technology as it applies to different markets:
A. Cognal’s SayQLtm: converting Natural Language to SQL through Machine Learning;
- How Machine Learning (ML) can learn the structure and natural of relational databases and Hadoop file systems (Cognal’s “cognalization” process).
- How Cognal’s ML-driven Natural Language product (SayQL) can use Cognal ML’s data knowledge to convert a user’s natural language information request into SQL and return the correct answer immediately over the internet to the user’s smartphone or PC.
- How Cognal’s patent-pending Supervised Machine Learning allows non-technical users to “Teach the Computer” new semantic and contextual phrases to rapidly increase SayQL’s level of understanding of user requests.
B. Cognal’s Thlinkertm and Thlingotm technology applied to the Internet of Things:
- How Cognal’s Machine Learning stores its “learned” concepts and phrases at the proper SubjectArea Ontology in the new Cognal’s Internet of Things platform, “Thlinker”.
- How Cognal’s “Thlingo” technology will be the “go to” Natural Language product for IoT.
- How Cognal’s Dynamic Data Visualization can show Thlinker-linked information from disparate IoT data sources, delivered in realtime through Thlingo.