Depth of AI determines value for predictions


Many AI companies and tools refer to Intent - Context - Relevance. So, why does one tool get better prediction results, offer greatest value and improve outcomes more than others?


It comes down to the depth of application of AI, NLU, NLP techniques and understanding of domain. When models are generic, context gets lost. For example, timeout can mean very different in Customer Support, IT, Marketing, Sales or Product context.


So how does Ascendo deal with texts?

Ascendo uses advanced NLP and NLU techniques to not only process the text in its purest forms, but also pragmatically understand the context, iintent and importance of individual components of it.




Understanding

First, to start with, Ascendo's engine completely understands the structure of the given input text lexically, semantically and syntactically. What that means is that a text is processed to understand how different words are co-aligning to devise the meaning behind it. This alignment of different words in the text also gives us an understanding of how tokenized words form meaning when clubbed together as phrases.


For example, Computer, Software as individual words vs Computer Software as a phrase. This part of understanding involves number of parallel operations like sentence dependency parsing, word tokenization, part-of-speech tagging and more. This information is then further supplied with Ascendo's model engines to be used in several other steps and use cases.


Processing

Once a text is processed and understood by its structure, we then look at how a text is represented in real world. For this purpose, Ascendo uses Advanced Transformers and Attention networks to depict the text in the vector space. An advanced vector representation of this text helps us to understand the underlying context, intent and sentence importance as a whole.


This enables Ascendo to dive even deeper to understand Root Cause, Sub Root Cause and Symptoms of the text using custom algorithms consisting of Multilayered Neural Networks with Attention Units, Unsupervised Clustering and word importance scores.


Applying to context

Once our Natural Language Engine is able to understand the underlying problem within the text, it is able to incorporate how this text is similar to texts never seen before and predict solutions on the fly. With advanced scoring methodology and multiple metrics like BLEU, Discounted Cumulative Gains, Precision, Recall and F1 proving the credibility of our solutions - we are able to present the best answer from multiple sources to the user.



Ascendo's predictions can also be made better if an expert decided to integrate his knowledge with the product. Using inferential and reinforced learning policies, our predictions are continuously improving with every interaction.