Unravelling the mysteries of 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

There are two approaches to create intelligence in machines. One is to understand how human brain creates intelligence and replicate the methods used by it to create AI. The other is to take a fresh engineering approach to create intelligence. There is an ongoing debate as to which approach is feasible or better. Few companies like Numenta and Vicarious have invested in understanding the neocortex. They have launched successful applications that work on the principles of neocortex. Other big companies including Google, Microsoft and Facebook use deep learning to create applications that can do major classification tasks. But deep learning does not exploit the concepts of neocortex, and hence is not up to the mark to do continuous learning. The deep learning models first go through a training phase and then the models are used to do classification. The idea that makes neocortex truly intelligent is that it is able to do pattern matching and prediction. This has given humans the

There is a growing consensus that the brain uses Bayesian to perform cognition. Our brain is capable of learning using only positive examples, unlike the approach taken in machine learning where there is a need to provide both positive and negative examples. Consider an example where a parent says to a child “Look at that dog!” A child is capable of categorizing all future dogs it looks at from only one or two examples. The brain of that child is generalizing using some form of Bayesian inference. Welcome to the world of One Shot Learning. The discovery that Bayes himself abandoned for unknown reasons, today stands at the forefront of making Artificial Intelligence a reality. Learning from few examples is what we are good at, and any intelligent machine is expected to do. Thanks to Pierre Simon Laplace who rediscovered it and gave Bayes' theorem a mathematical form, cognitive AI research uses Bayesian to make machines learn.