Unravelling the mysteries of Artificial Intelligence

Imagine a scenario - You are on a website, and on the bottom right you see a chat window. You realize that it is a virtual assistant, some kind of a chatbot. The look of that window tempts you to experiment. You have interacted with various chatbots previously, a kind of a hobby you have developed, and have seen how miserable the chatbots are. So you start your usual conversation with a Hello. The chatbot replies with “Hello. How can I help you?” . You think yourself as a grand experimenter. You have heard of the Turing Test, and you feel like testing this bot. You say “Hello” again, expecting a similar reply that you got earlier, but to your surprise you get “Hey you. I guess we have exchanged greetings. How can I help you?” You are not satisfied, you want to test it further. You say “Hi, what’s up?” . Will the chatbot get irritated, you ask yourself. The chatbot replies “So you want to play the hello game? I am all for it, as long as you don’t get tired.” You get sho...

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 ...