Abstract: A method for simulating a scenario in a computing environment is described. The method includes determining a user intent of a user from an input received from the user for executing at least one intended task by the user, generating a feature set based on analyzing the user intent, and processing, based on the user intent, at least one data feed received from a database to select at least one plan of action for executing the at least one intended task. At least one plan of action is simulated in a graphical user interface as virtual or augmented reality based on the feature set to enable at least one of responding additionally received input from the user and the at least one intended task is performed according to the user intent.

Abstract: Techniques to generate a digitally optimized schedule for a construction activity to meet a construction objective(s) of a construction project are disclosed. An artificial intelligence system receives a plurality of input data sets that impact the construction project. Each of the plurality of input data sets is processed to achieve the construction objective(s). The artificial intelligence system processes the plurality of input data sets using a respective ensemble of machine learning models. The artificial intelligence system generates machine learning validated intermediate output data sets corresponding to each of the plurality of input data sets. The artificial intelligence system implements a supervisory machine learning model to generate an optimized schedule for the construction activity based on the machine learning validated intermediate output data sets and the construction objective(s).

Abstract: Techniques to generate a digitally optimized schedule for a construction activity to meet a construction objective(s) of a construction project are disclosed. An artificial intelligence system receives a plurality of input data sets that impact the construction project. Each of the plurality of input data sets is processed to achieve the construction objective(s). The artificial intelligence system processes the plurality of input data sets using a respective ensemble of machine learning models. The artificial intelligence system generates machine learning validated intermediate output data sets corresponding to each of the plurality of input data sets. The artificial intelligence system implements a supervisory machine learning model to generate an optimized schedule for the construction activity based on the machine learning validated intermediate output data sets and the construction objective(s).

Abstract: Techniques to generate a digitally optimized schedule for a construction activity to meet a construction objective(s) of a construction project are disclosed. An artificial intelligence system receives a plurality of input data sets that impact the construction project. Each of the plurality of input data sets is processed to achieve the construction objective(s). The artificial intelligence system processes the plurality of input data sets using a respective ensemble of machine learning models. The artificial intelligence system generates machine learning validated intermediate output data sets corresponding to each of the plurality of input data sets. The artificial intelligence system implements a supervisory machine learning model to generate an optimized schedule for the construction activity based on the machine learning validated intermediate output data sets and the construction objective(s).