This video was recorded at FTC 2016 – http://saiconference.com/FTC
The problem being addressed in this paper is that using brute force in Natural Language Processing and Machine Learning combined with advanced statistics will only approximate meaning and thus will not deliver in terms of real text understanding. Counting words and tracking word order or parsing by syntax will also result in probability and guesswork at best. Their vendors struggle in delivering accurate quality and this results in ill-functioning applications. The newer generation methodologies like Deep Learning and Cognitive Computing are breaking barriers in the (Big Data) fields of Internet of Things, Robotics and Image/Video Recognition but cannot be successfully deployed for text without huge amounts of training and sample data. In the short term, we believe non-biological Artificial Intelligence will produce the best results for text understanding. Miia applied advanced Linguistic and Semantic Technologies combined with ConceptNet modeling and Machine Learning to successfully cater deep intelligent and cross-language quality to several industries.
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