Imagine you are an IT executive in a company. As per your role, you have to offer IT solutions to problems that your company is facing, be it related to Finance, Sales, HR, Administration, etc. Maybe you are already playing this role. You have successfully implemented IT solutions to challenging problems. You are praised by your CTO, have also received various awards for successful implementations. You have been promoted in the past for this particular skill. You are living in a perfect world, where most of the problems that you are solving can be solved with programming. You are living in a non-Artificial Intelligence world. Now imagine a world, where the problems that industries are facing today are not solved by programming. Imagine your company is facing problems that look difficult to be solved by any number of lines of code, or any number of current software solutions that exist in the market. You are faced with a dilemma of not able to offer solutions to such proble
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At CereLabs, we are building various image classification systems. While building any kind of classification system one is often challenged to test the trained models. One useful measure to test such models is accuracy, which is the proportion of true results and the total number of images examined. Accuracy thus communicates the essential message of how close one comes to the correct result. In the case of an image classification system, accuracy is how accurately the trained model is able to classify the test image dataset. If we are trying to classify the image of an apple, accuracy will be the measure of how accurately the classifier is able to detect the apple in an image. Consider the following confusion matrix. True Positive (TP) Actual image contains an apple, and is correctly classified as an apple False Negative (FN) Actual image contains an apple but is not classified as an apple False Positive (FP) Actual image does not contain an apple but is class
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