Text to Software Deep Learning

Text to Software Deep Learning includes the following Machine Learning and Artificial Intelligence Capabilities:

Summarizer – A neural network that can read a document and tell you what it is about in a summary. An example use case would be giving it a movie from IMDB and it will tell you the plot. We could use this in Text to Software for reading requirements documents. It would read a document and just tell you what the requirements are.

Natural Language Understanding

  • Find Similar – Take one “thing” and compare it to a set of other things, and tell you which ones are similar. An example of this would be, take an RFP and find other wins that look like this one.
  • Recognition – clustering images, text, words.

Recommender System – Take a list of items and make recommendations on what would get the best result. An example use case would be to come up with a list of related products for a buyer who has just made a product selection (such as “other people have bought these similar products”). Specific example is analyzing sales history to upsell products.

English to Smalltalk language processor for human machine communication (JSON based). Implemented using api.ai

AI from spreadsheet – linear regression, classification, sequence prediction, input form generation, input data validation