Machine Learning and Artificial Intelligence Advancements for - PowerPoint PPT Presentation

Machine Learning and Artificial Intelligence Advancements for Electrical Inspection SEPTEMBER 5 - 7, 2018

Publicized Milestones • January 2011 Watson beats long standing Jeopardy Champion • Amazon’s Jeff Bezos – “Big trends are not that hard to spot. We are in the middle of one right now. Machine Learning and Artificial Intelligence.” SEPTEMBER 5 - 7, 2018

Facial Recognition SEPTEMBER 5 - 7, 2018

AI is Broadest of Terms SEPTEMBER 5 - 7, 2018

Artificial Intelligence • Mimic Human Intelligence – Using Logic, If-Then Rules, Decision Trees • ML statistical techniques that enable machines to improve a task with experience • Big data is large volumes of data used for computational analysis, to reveal patterns or trends SEPTEMBER 5 - 7, 2018

Neural Network • A computer system designed to work by classifying information similar to the same way a human brain functions • Taught to recognize images – classify the images, including the components or sub- elements – Probability statement, decisions or predictions with a degree of certainty or statistical probability SEPTEMBER 5 - 7, 2018

Glossary of Terms Artificial Intelligence (AI) – A.I. is the broadest of term, applying to any technique that enables computers to mimic human intelligence, using logic, • if-then rules, decision trees and machine learning. Machine Learning (ML) – The subset of A.I. that includes statistical techniques that enable machines to improve at task with experience. • Big Data – Large volumes of data sets that are used for computational analysis, to reveal patterns or trends. • Deep Learning – The subset of machine learning composed of algorithms that permit software to train itself to perform tasks, like speech and image • recognition, by exposing multilayered neural networks to vast amounts of data. Neural Networks – Software constructions modeled after the way adaptable networks of neurons in the brain are understood to work, rather than • through rigid instructions predetermined by humans. Bayesian Networks – A probabilistic graphical model or type of statistical model that represents a set of variables and their conditional • dependencies. For example, a Bayesian network could represent the probabilistic relationships between electric outages and a individual electric grid component anomaly. Given the component anomaly type, the Bayesian Network can be used to compute the probabilities of an outage. Clustering – During the supervised learning phase of inputting data for training, a subject matter expert selects or targets input features to be utilized • in the learning models. In clustering or unsupervised learning, the target features are not given in the training examples. The goal is to construct a natural classification that can be used to cluster the data. The general idea behind clustering is to partition the examples into clusters or classes. Each class predicts feature values for the examples in the class. Each clustering has a prediction error on the predictions. The best clustering is the one that minimizes the error. SEPTEMBER 5 - 7, 2018

Big Box Thought Leaders • IBM (Watson) • Apple • Microsoft (Azure) • Facebook • Amazon (AWS) • Spotify • Intel • Uber • Google • Salesforce SEPTEMBER 5 - 7, 2018

Inherent Advantages • Speed • Accuracy • Repeatability • Lack of bias SEPTEMBER 5 - 7, 2018

Origin of Project SiMON • Scientific American • Century old children’s game: Simon Says – Series of commands to eliminate players • 70’s memory game with sounds that increase in complexity with each successive sequence SEPTEMBER 5 - 7, 2018

Commercial AI & ML Engines • The software developed to query the AI engines consist of a library of Application Programming Interface (API) models • 500 GB per second or million books SEPTEMBER 5 - 7, 2018

Methods of Acquisition SEPTEMBER 5 - 7, 2018

UAS Platform • 40 Minute Endurance • Less than 55 lbs. • Multi Sensor Platform • ~200 ft. AGL • 30 mph Wind Endurance • 15 lbs. Payload • Autorotate for Safety • Made in USA SEPTEMBER 5 - 7, 2018

1 Flight – 4 Datasets SEPTEMBER 5 - 7, 2018

Flight Profile • 50’ Above Structure • 33’ Offset • Left or Right Centerline • Down & Back & Following • Geofence Restrictions • SEPTEMBER 5 - 7, 2018

Thermal Imagery (IR) SEPTEMBER 5 - 7, 2018

Virtual Side-by-Side Analysis SEPTEMBER 5 - 7, 2018

Corona (UV) SEPTEMBER 5 - 7, 2018

Close Range Oblique Still SEPTEMBER 5 - 7, 2018

LiDAR 50 ppsm SEPTEMBER 5 - 7, 2018

General Process Model What? Where? Why? When? Collect Remotely Sensed Datasets Prioritize Corrective Action Generate Anomaly Report Inspection Summary Report SEPTEMBER 5 - 7, 2018

Inputs & Process Process & Procedures • Software • LiDAR Filtering & Analyzing • Large Volumes of Remotely Ultraviolet • Infrared Sensed Data Visible Component Analysis Predictive Analytics SEPTEMBER 5 - 7, 2018

1000’s Images for Training SEPTEMBER 5 - 7, 2018

Anomaly Reports SEPTEMBER 5 - 7, 2018

Current Progress • Supervised Phase – Current and ongoing expected to last 2.5 to 3 years. • Transition Phase – Incremental reliance on AI & ML, with Subject Matter Expert verification. Current and ongoing with a 10% to 15% automated. • Unsupervised Phase – 3 to 5 years to achieve an 85% to 95% automation with Subject Matter Expert providing Quality Assurance. SEPTEMBER 5 - 7, 2018

1,105 Miles & 6,927 Structures SEPTEMBER 5 - 7, 2018

Ground & Aerial Inspection SEPTEMBER 5 - 7, 2018

Issues SEPTEMBER 5 - 7, 2018

Issues SEPTEMBER 5 - 7, 2018

Project Statistics Oblique Still Photos Vertical Nadir Imagery • 254,000+ photos • 169,000+ photos • 3.8 TB • 2.5 TB Oblique Thermal Imagery LiDAR • 254,000+ images • 50 ppsm • .40 TB • 11.5 TB SEPTEMBER 5 - 7, 2018

A I Humor SEPTEMBER 5 - 7, 2018