Search for: All records

The physical world evolves. The cyber world evolves and grows with big data, with social media as a major component of information growth. Classic ML models are limited by their static training data with implicit Complete and Timeless Knowledge assumptions. In an evolving world, static training data suffer from knowledge obsolescence due to truly novel timely information. Knowledge obsolescence introduces a widening distance between static ML models and the evolving world, called cyber-physical gap. Periodic retraining of new models may restore their accuracy temporarily, but subsequently their performance will deteriorate with widening cyber-physical gap. Knowledge obsolescence affects statically trained models of any size, including LLMs. Two major research challenges arise from cyber-physical gap: (1) collection and incorporation of space-time aware ground truth training data, and (2) understanding and capturing of the varying speed of information and knowledge evolution when the physical and cyber worlds evolve. 

Machine learning models with explainable predictions are increasingly sought after, especially for real-world, mission-critical applications that require bias detection and risk mitigation. Inherent interpretability, where a model is designed from the ground-up for interpretability, provides intuitive insights and transparent explanations on model prediction and performance. In this paper, we present COLABEL, an approach to build interpretable models with explanations rooted in the ground truth. We demonstrate COLABEL in a vehicle feature extraction application in the context of vehicle make-model recognition (VMMR). By construction, COLABEL performs VMMR with a composite of interpretable features such as vehicle color, type, and make, all based on interpretable annotations of the ground truth labels. First, COLABEL performs corroborative integration to join multiple datasets that each have a subset of desired annotations of color, type, and make. Then, COLABEL uses decomposable branches to extract complementary features corresponding to desired annotations. Finally, COLABEL fuses them together for final predictions. During feature fusion, COLABEL harmonizes complementary branches so that VMMR features are compatible with each other and can be projected to the same semantic space for classification. With inherent interpretability, COLABEL achieves superior performance to the state-of-the-art black-box models, with accuracy of 0.98, 0.95, and 0.94 on CompCars, Cars196, and BoxCars116K, respectively. COLABEL provides intuitive explanations due to constructive interpretability, and subsequently achieves high accuracy and usability in mission-critical situations. 

A rapidly evolving situation such as the COVID-19 pandemic is a significant challenge for AI/ML models because of its unpredictability. The most reliable indicator of the pandemic spreading has been the number of test positive cases. However, the tests are both incomplete (due to untested asymptomatic cases) and late (due the lag from the initial contact event, worsening symptoms, and test results). Social media can complement physical test data due to faster and higher coverage, but they present a different challenge: significant amounts of noise, misinformation and disinformation. We believe that social media can become good indicators of pandemic, provided two conditions are met. The first (True Novelty) is the capture of new, previously unknown, information from unpredictably evolving situations. The second (Fact vs. Fiction) is the distinction of verifiable facts from misinformation and disinformation. Social media information that satisfy those two conditions are called live knowledge. We apply evidence-based knowledge acquisition (EBKA) approach to collect, filter, and update live knowledge through the integration of social media sources with authoritative sources. Although limited in quantity, the reliable training data from authoritative sources enable the filtering of misinformation as well as capturing truly new information. We describe the EDNA/LITMUS tools that implement EBKA, integrating social media such as Twitter and Facebook with authoritative sources such as WHO and CDC, creating and updating live knowledge on the COVID-19 pandemic.