Daniel David Sill

Title: Innovations of Daniel David Sill

Introduction

Daniel David Sill is an accomplished inventor based in Medford, NY (US). He has made significant contributions to the field of machine learning, particularly in the area of data amplification for training algorithms. With a total of 2 patents, Sill's work is paving the way for advancements in artificial intelligence.

Latest Patents

One of Sill's latest patents focuses on the amplification of initial training data. This patent discloses techniques for increasing the amount of training data available to machine learning algorithms. A computer system can access an initial set of training data that specifies a plurality of sequences, each defining a set of data values. The system amplifies this initial set to create a revised set of training data. The amplification process includes identifying sub-sequences of data values within the initial set and using an inheritance algorithm to generate additional sequences. Each of these additional sequences may incorporate sub-sequences from at least two different sequences in the initial set. The computer system then processes these additional sequences using a machine learning algorithm to train a machine learning model.

Career Highlights

Daniel David Sill is currently employed at CA, Inc., where he continues to innovate and develop new technologies. His work has been instrumental in enhancing the capabilities of machine learning systems, making them more efficient and effective.

Collaborations

Sill collaborates with various professionals in his field, including his coworker Michael J Cohen. Their combined expertise contributes to the advancement of technology and innovation within their organization.

Conclusion

Daniel David Sill is a notable inventor whose work in machine learning and data amplification is making a significant impact. His contributions are essential for the future of artificial intelligence and data processing.