Skip to main content

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

Comments

  1. Are you trying to move in or out of Jind? or near rohtak Find the most famous, reputed and the very best of all Packers and Movers by simply calling or talking to Airavat Movers and Packers
    good luck
    Ai & Artificial Intelligence Course in Chennai
    PHP Training in Chennai
    Ethical Hacking Course in Chennai Blue Prism Training in Chennai
    UiPath Training in Chennai

    ReplyDelete
  2. Nice! you are sharing such helpful and easy to understandable blog. i have no words for say i just say thanks because it is helpful for me.





    Dot Net Training in Chennai | Dot Net Training in anna nagar | Dot Net Training in omr | Dot Net Training in porur | Dot Net Training in tambaram | Dot Net Training in velachery

    ReplyDelete

Post a comment

Popular posts from this blog

Implement XOR in Tensorflow

XOR is considered as the 'Hello World' of Neural Networks. It seems like the best problem to try your first TensorFlow program.

Tensorflow makes it easy to build a neural network with few tweaks. All you have to do is make a graph and you have a neural network that learns the XOR function.

Why XOR? Well, XOR is the reason why backpropogation was invented in the first place. A single layer perceptron although quite successful in learning the AND and OR functions, can't learn XOR (Table 1) as it is just a linear classifier, and XOR is a linearly inseparable pattern (Figure 1). Thus the single layer perceptron goes into a panic mode while learning XOR – it can't just do that. 

Deep Propogation algorithm comes for the rescue. It learns an XOR by adding two lines L1 and L2 (Figure 2). This post assumes you know how the backpropogation algorithm works.



Following are the steps to implement the neural network in Figure 3 for XOR in Tensorflow:
1. Import necessary libraries
impo…

Understanding Projection Pursuit Regression

The following article gives an overview of the paper "Projection Pursuit Regression” published by Friedman J. H and Stuetzle W. You will need basic background of Machine Learning and Regression before understanding this article. The algorithms and images are taken from the paper. (http://www.stat.washington.edu/courses/stat527/s13/readings/FriedmanStuetzle_JASA_1981.pdf
What is Regression? Regression is a machine learning technology used to predict a response variable given multiple predictor variables or features. The main distinction is that the response to be predicted is any real value and not just any class or cluster name. Hence though similar to Classification in terms of making a prediction, it is largely different given what it’s predicting. 
A simple to understand real world problem of regression would be predicting the sale price of a particular house based on it’s square footage, given that we have data of similar houses sold in that area in the past. The regression so…