Two associate professors in South Dakota State University's Jerome J. Lohr College of Engineering have received a grant from the National Science Foundation to train artificial intelligence models in making accurate predictions.
Led by associate professor of statistics Semhar Michael, the SDSU project aims to develop ways to make large-scale predictions about a dataset with a large number of categories but few exemplars in each category using the few-shot, or one-shot, learning techniques. Few-shot and one-shot techniques are similar in nature as both are machine learning models, the major difference is one-shot learning relies on a single example, while few-shot relies on a "few" examples.
"The goal of this research is to create a range of models and algorithms that can better handle few-shot learning problems," Michael said.
Another goal of this research will be to provide statistical guarantees backing these machine learning models. Because these methods will be used in identifying forensic evidence, there needs to be an assertation that they are not only correct, but trustworthy and unbiased.
"This will help avoid situations where there is a miscarriage of justice," Michael said.
Michael is collaborating with Chris Saunders, associate professor of statistics in SDSU's Department of Mathematics and Statistics, who has significant experience in forensic identification of source problems and will be providing his expertise on this project.
Once the statistical guarantees are complete, these models can be applied to real world settings. In South Dakota, the researchers believe this work can used to support the criminal justice system or intelligence community. They could also be used to disrupt the illicit economy by helping to identify the source of illicit drugs.
.jpg?upscale=true&width=546&upscale=true&name=construction-271873_1280%20(1).jpg)
.jpeg?upscale=true&width=1120&upscale=true&name=2%20machines%20(1).jpeg)
