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Showing posts from 2017

Building Contents of Watson Chatbots

In today’s world Chatbots are tremendously transforming the way we interact with software by providing a great business opportunity for almost every company. Chatbots are seen in almost all the websites and also in applications. The first question I ask to myself, what is Chatbot? Chatbots are known by different names some call it “conversational gents”, some “Chatter Robot”.
Chatbots are basically a computer program that mimics written or spoken human speech in its natural format using Artificial Intelligence techniques such as Natural Language Processing (NLP) which is used for conversation purpose. In today’s era Chatbots are most commonly used in customer service space, acts as a human face of the brand for support operatives and customer satisfaction reps. We all know virtual assistants like Apple Siri or Amazon Alexa, are two most popular chatbots interacting via voice rather than text. Chatbots engages their customers in the right place, at the right time, with right information…


While working on ReactJS the render() method will be enough in case of ideal and simple conditions of components, But in case of complex situations and conditions, We need some more control over the when, how and where components to execute or update. Lifecycle methods of React help in that case to control and execute things as per requirement only.
Purpose of this document to describes the role of every component in lifecycle and order of execution of those components. Using these lifecycle methods we can control render, update of each section. Knowledge of this lifecycle will help to perform various actions while components are created and destroyed, also in updating components and to react on props or state change.
Component Flow:
React allows to create component invoking the ‘create-Class ()’ which expects   ‘render ()’ and triggers a lifecycle.
There are 4 different scenarios, where react undergo different lifecycles:

A] Initial Rendering: ·Get default props & Get initial state

Where have you seen Machine Learning in your everyday life?

1 –  Google’s AI-Powered Predictions Using anonymized location data from smartphones, Google Maps (Maps) can analyze the speed of movement of traffic at any given time. And, with its acquisition of crowdsourced traffic app Waze in 2013, Maps can more easily incorporate user-reported traffic incidents like construction and accidents. Access to vast amounts of data being fed to its proprietary algorithms means Maps can reduce  commutes by suggesting the fastest routes to and from work. 2 –  Ridesharing Apps Like Uber and Lyft How do they determine the price of your ride? How do they minimize the wait time once you hail a car? How do these services optimally match you with other passengers to minimize detours? The answer to all these questions is ML. Engineering Lead for Uber ATC Jeff Schneider  discussed in an NPR interview how the company uses ML to predict rider demand to ensure that “surge pricing”(short periods of sharp price increases to decrease rider demand and increase driver supply…