Harness has been utilizing neural networks to improve anomaly detection in application log analysis, achieving a 50% reduction in false positives, though with a slight increase in false negatives and performance challenges. Their approach, which initially relied on textual similarity and occurrence frequencies, has evolved to incorporate unsupervised machine learning and natural language processing (NLP) techniques. Despite the noise inherent in log data compared to natural language text, neural networks have shown promise by transforming variable-size log messages into fixed-size vectors, enhancing clustering and anomaly prediction accuracy. While these advancements have improved detection capabilities, challenges remain in real-time processing and maintaining low false negative rates. Harness plans to gradually implement this neural network-based solution with select customers to further refine their models and address existing drawbacks.