Abstract: Technologies relating to improving performance of a computer-implemented model that acts as a multi-class classifier are described herein. A chatbot includes the computer-implemented model, and the computer-implemented model receives natural language input from end users. A subset of the natural language inputs are identified as training examples that are to be used to update the computer-implemented model, wherein the natural language inputs are identified as the training examples based upon comparisons between scores for the natural language inputs output by different classifiers of the computer-implemented model. The training examples are labeled by a developer, and the computer-implemented model is updated based upon the labeled training examples.

Abstract: A processor-implemented method includes (i) obtaining raw data and value of a parameter in a column of tabular data, (ii) defining, based on user input, a smart column with tabular data prediction generated from raw data, (iii) validating, based on user input, a first label and a second label corresponding respectively to a first and a second predefined category to obtain a first and a second user-validated label respectively, (iv) detecting error in training set of the predictive AI model when there is a mismatch between a value from predictive AI model and user-validated label, (v) automatically generating a formula for the tabular data prediction to fix the error in training set, (vi) validating the first formula data prediction based on user input to obtain a user-validated formula, and (vii) automatically generating a first tabular data prediction in the smart column using user-validated formula to some of the raw data.

Abstract: A processor-implemented method includes (i) defining a region of interest ranging between a first and second boundary location for each label in the M documents that comprise N labels, (ii) summarizing information, in a selected document, from a first content location to the first boundary location of the region of interest to obtain a first summary that represents context information from the first content location to the first boundary location of the region of interest, (iii) summarizing information, in the selected document, from a second content location to the second boundary location to obtain a second summary that represents context information from the second boundary location to the second content location, (iv) performing training of the AI model including restricting training data from the M documents based on the region of interest, and (v) extracting the target data from the M documents using trained AI model.

Abstract: A processor-implemented method includes (i) selecting initial features using a machine learning algorithm with a training data, (ii) automatically generating selected candidate features for an artificial intelligence (AI) model from the initial features, wherein the selected candidate features are generated from the training data or selected from a repository of curated features, (iii) automatically selecting a subset from selected candidate features and augmenting them to obtain suggested features based on an external knowledge source, (iv) presenting the suggested features to a user based on an improvement in the objective function of the AI model caused by addition of the suggested features to the AI model, (v) enabling the user to validate the suggested features, wherein the suggested features are validated by the user to improve a generalization of the AI model, and (vi) adding validated suggested features to the AI model to improve the generalization of the AI model.

Abstract: Technologies relating to improving performance of a computer-implemented model that acts as a multi-class classifier are described herein. A chatbot includes the computer-implemented model, and the computer-implemented model receives natural language input from end users. A subset of the natural language inputs are identified as training examples that are to be used to update the computer-implemented model, wherein the natural language inputs are identified as the training examples based upon comparisons between scores for the natural language inputs output by different classifiers of the computer-implemented model. The training examples are labeled by a developer, and the computer-implemented model is updated based upon the labeled training examples.