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Anomaly Detection based on Prediction - A Step Closer to General Artificial Intelligence

Anomaly detection refers to the problem of finding patterns that do not conform to expected behavior [1]. In the last article "Understanding Neocortex to Create Intelligence", we explored how applications based on the workings of neocortex create intelligence. Pattern recognition along with prediction makes human brains the ultimate intelligent machines. Prediction help humans to detect anomalies in the environment. Before every action is taken, neocortex predicts the outcome. If there is a deviation from the expected outcome, neocortex detects anomalies, and will take necessary steps to handle them. A system which claims to be intelligent, should have anomaly detection in place.

Recent findings using research on neocortex have made it possible to create applications that does anomaly detection. Numenta’s NuPIC using Hierarchical Temporal Memory (HTM) framework is able to do inference and prediction, and hence anomaly detection. HTM accurately predicts anomalies in real world applications such as server anomalies, stock anomalies and geospatial tracking.

All the anomaly detection techniques such as simple threshold, statistical, distance based or supervised methods do not exploit prediction, and hence need a lot of manual intervention. Also those techniques don’t have the capability of neocortex to do continuous learning on streaming data.

Frameworks like HTM are taking anomaly detection closer or even surpassing human like capabilities. This is indeed a step closer to General Artificial Intelligence.

REFERENCES:

[1] Chandola V., Banerjee, A. and Kumar A., 2009.  "Anomaly detection: A survey." ACM computing surveys (CSUR), 41(3), p.15.

[2] J. Hawkins, S. Ahmad, and D. Dubinsky. (2014) The Science of Anomaly Detection [Online technical report]. Redwood City, CA: Numenta, Inc. Available: http://numenta.com/#technology

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