In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. (Wikipedia)
There is no longer any doubt that artificial intelligence (AI) is advancing biological discovery and biomanufacturing operations. In biological discovery, AI systems such as AlphaFold and the Atomic Rotationally Equivariant Scorer are celebrated for their uncanny ability to predict tertiary structures for proteins and RNA molecules. In biomanufacturing, AI systems usually enjoy less fanfare. Yet they can provide valuable functions such as pattern recognition, real-time assessment of batch quality, multivariable control for continuous manufacturing, prediction/optimization of critical process parameters, and anomaly detection. Such functions are critical to the success of gene and cell therapy operations.
Alexander Tong GRD '23, a computer science graduate student, and Smita Krishnaswamy, professor of genetics and computer science, won the award for best paper at the annual 2020 Machine Learning for Signal Processing conference, hosted by the Institute of Electrical and Electronics Engineers. From Sept. 21 to Sept. 24, the MLSP conference was hosted virtually at Aalto University in Espoo, Finland. Tong and Krishnaswamy's paper, "Fixing bias in reconstruction-based anomaly detection with Lipschitz discriminators," won the best student paper award alongside two other teams. The paper identified problems present in many machine learning based outlier detection models. The researchers found some cases where these systems do not work -- and this occurs quite often for data types found in bigger data sets.
Today, a fresh generation of technologies, fuelled by advances in artificial intelligence based on machine learning, is opening up new opportunities to reassess the upper bounds of operational excellence across these sectors. To stay one step ahead of the pack, businesses not only need to understand machine learning complexities but be prepared to act on it and take advantage. After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behaviour by recognising complex data patterns and uncovering the precise signatures of degradation and failure. They can then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences. The leading software constructs are autonomous and self-learning.