Abstract: A method for establishing and generating contextual links between data from a plurality of data sources is described. The method includes receiving data and decomposing the received data into a decomposed data set; parsing and analyzing the decomposed data set based on a set of attribute analyzers to associate one or more attributes to the decomposed data set; determining an intent of data from the decomposed data set; generating a semantic graph of the decomposed data set based on the intent of data to evaluate data relatability between the decomposed data set; generating atomic knowledge units (AMUs) based on the parsed decomposed data set and the semantic graph; analyzing the AMUs corresponding to the received data by applying trained machine learning models to generate links between the AMUs and processing the generated links by a model ensemble to establish contextual links between data.

Abstract: A method for generating computational predictions associated with a real-world entity is disclosed. The method comprises analyzing knowledge from a plurality of data sources; determining investment objectives associated with the real-world entity; applying, a plurality of ensembles of machine learning models to each of the plurality of data sources to conduct a combinatorial analysis of the plurality of data sources; generating, by colonies of Artificial Intelligence (AI) colonizers, intermediary outputs; analyzing the intermediary outputs in a weighted combination to generate weighted outputs; and assembling, by a supervisory algorithm, the weighted outputs based on the investment objectives to generate a computational prediction associated with the real-world entity.

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: A method for simulating a product in a computing environment includes analyzing data related to at least constructional representation, determining a requirement of prefabricating at least one custom construction artefact for a construction project based on the analysis of the data, determining at least one of: an intent or at least one objective associated with the at least one custom construction artefact, and enabling simulation of the at least custom construction artefact based on the determination of the intent or the at least one objective to render a digital representation of the product in a graphical user interface.

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).