Sas Institute Inc.

Pelin Cay

Varunraj Valsaraj

Kedar Shriram Prabhudesai

Fang Liang

Julia Moreno

Brett Alexander Vogelsang

Joshua David Griffin

Jinxin Yi

Bryan C Harris

Steven Joseph Gardner

Alexius Kofi Ameyaw Boakye, Jr

Natalia Summerville

Nabaruna Karmakar

William Lloyd Wisotsky

Justin Joseph Lukas

Antony Nicholas Cooper

Charlotte Kay Crain

Sertalp Bilal Cay

A computer-implemented method includes receiving a natural language input including a natural language request for executing an analytical task and processing the natural language input by a language model, where the processing may include translating the natural language input to an analytical function call for calling an analytical function of a set of distinct analytical functions of an analytics computing server. Additionally, the computer-implemented method includes calling the analytical function at the analytics computing server using the analytical function call, receiving a technical output in response to calling the analytical function, and outputting a response to the natural language input that includes the technical analytical output.

Systems, methods, and graphical user interfaces for secure execution of analytical tasks using natural language

A system and method include reformulating an optimization program as a Quadratic Unconstrained Binary Optimization (QUBO) model and in a single iteration, solving the optimization program by inputting the QUBO model into a quantum computing solver, instructing the quantum computing solver to generate a plurality of solutions to the optimization program based on the QUBO model, receiving the plurality of solutions from the quantum computing solver, inputting each of the plurality of solutions into a nonquantum computing solver, wherein the nonquantum computing solver uses each of the plurality of solutions as a starting point to continue solving the optimization program, and outputting an optimal solution to the optimization program from the nonquantum computing solver.

Hybrid quantum/nonquantum approach to NP-hard combinatorial optimization

Manufacturing processes can be optimized using machine learning models. For example, a system can execute an optimization model to identify a recommended set of values for configurable settings of a manufacturing process associated with an object. The optimization model can determine the recommended set of values by implementing an iterative process using an objective function. Each iteration of the iterative process can include selecting a current set of candidate values for the configurable settings from within a current region of a search space defined by the optimization model; providing the current set of candidate values as input to a trained machine learning model that can predict a value for a target characteristic of the object or the manufacturing process based on the current set of candidate values; and identifying a next region of the search space to use in a next iteration of the iterative process based on the value.

Optimizing manufacturing processes using one or more machine learning models

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Raleigh, NC, United States of America

Pelin Cay