Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: An embodiment of the present invention is directed to combining natural language processing with constrained grammar defined pattern matching to advantageously allow a system to translate natural language questions into any number of underlying technologies. By using a unique intermediate parse tree representation, the disclosed embodiments are able to instantiate a corresponding data store adapter for each given query, which may be for a relational database or a no-SQL database, for example. The ability to abstract underlying storage technology advantageously allows the management of disparate database systems, which is not possible using existing methods and technology.

Abstract: Systems and methods are provided for self-learning natural language predictive searching including receiving a first input, the first input being related to the desired outcome; retrieving a first information related to the first input; determining a first output based on at least the first input and the first information; outputting the first output; receiving a second input based on the outputted first output in response to the first output being different from the desired outcome, the second input being related to the desired outcome; retrieving, by the processor, a second information related to the second input; determining a second output based on at least the second input, the second information, the first input and the first information; and outputting the second output.

Abstract: An embodiment of the present invention is directed to combining natural language processing with constrained grammar defined pattern matching to advantageously allow a system to translate natural language questions into any number of underlying technologies. By using a unique intermediate parse tree representation, the disclosed embodiments are able to instantiate a corresponding data store adapter for each given query, which may be for a relational database or a no-SQL database, for example. The ability to abstract underlying storage technology advantageously allows the management of disparate database systems, which is not possible using existing methods and technology.

Abstract: Systems and methods are provided for self-learning natural language predictive searching including receiving a first input, the first input being related to the desired outcome; retrieving a first information related to the first input; determining a first output based on at least the first input and the first information; outputting the first output; receiving a second input based on the outputted first output in response to the first output being different from the desired outcome, the second input being related to the desired outcome; retrieving, by the processor, a second information related to the second input; determining a second output based on at least the second input, the second information, the first input and the first information; and outputting the second output.