What are Headai’s ML & NLP benefits compared to Deep Learning applications?
Digital Self – The Core Model Behind Simulations
An interoperable machine learning data model of any entity’s skill assets. It is based on Self-Organizing Maps (SOM) type of unsupervised learning, which keeps it up-to-date. A digital self can model e.g. skills supply, skills demand, skills forecasts, an individual’s professional profile, educational curriculums, SDGs (UN’s Sustainable Development Goals), and more. Everything can be simulated against each other. Headai Digital Self is interoperable with major labor market standards like ISCO (UN), ESCO (EU), SOC, and O*NET (US). It enables cognitively complex tasks like reasoning with controversial and/or incomplete information (Deep Learning models won’t enable cognitively complex operations). The training of the model can be done in multiple ways with sources like: Global Labour Market Standards,
Global Business Information System, Economical News & Financial Media, Job Ads & Work Foresight Reports, Patents, Research & Innovations.
Headai Dynamic Ontology
The dynamic machine learning model for words, semantics, and meanings is based on self-organizing maps (SOM) type of unsupervised learning. It can be used to build always up-to-date and detailed language models for different situations.
It is based on terabytes of open textual data acquired from the real world: scientific articles, reports, curriculums, course descriptions, job descriptions, and job vacancies. Enables cognitively complex tasks such as reasoning with controversial and/or incomplete information (most deep learning models do not allow cognitively complex functions). Outperforms DL models in computational speed and performance relative to computational capacity.
The general language model is a core component in Headai technology.
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Founder & Chairman