Accession Number:

AD1088914

Title:

Why GBSD Should Consider Machine Learning (ML) and Causal Learning (CL)

Descriptive Note:

Technical Report

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA PITTSBURGH United States

Personal Author(s):

Report Date:

2019-01-01

Pagination or Media Count:

20.0

Abstract:

BLUF. 1. GBSD is definitely a software system. 2. As such, many large streams of sensor and other data will be available. 3. This software system offers many benefits but also faces critical challenges and threats. 4. Machine learning will digest and model these large streams of data, if GBSD captures.and stores the data for later modeling. 5. Causal learning can actually determine cause-effect relationships in data as opposed.to spurious correlation, there by offering results not possible via traditional statistics and machine learning. 6. SEI is poised to contribute to the GBSD Program Office in specifying the data capture and storage needed, and the adoption of these new technologies with oversight and leadership with the contractors. 7. Almost all ilities represent low-hanging fruit opportunities for ML and CL. What is Machine Learning Basically a more sophisticated form of correlation, association and pattern recognition. Can accommodate and needs Big Data, e.g. large volumes of streams of data. Forms include 1. Unsupervised machine learning, e.g. to explore relationships between factors. 2. Supervised machine learning, e.g. to predict outcomes. 3. Deep Learning DL, e.g. a layered network to better identify and learn patterns. 4. Reinforcement Learning RL, e.g. a network that helps learn actions to maximize a reward, sort of an optimization approach. 5. Generative Adversarial Networks GANs, e.g. a set of networks that can interact with each other to generate additional data based on what each network learns from the other.

Subject Categories:

  • Information Science
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE