Chatbots – Hands-On Python Deep Learning for the Web


If you have ever wondered how some web pages provide 24/7 live help through chat on their websites, the answer would almost always be a chatbot is answering your queries from the other end. When in 1966 Joseph Weizenbaum's ELIZA chatbot created waves across the world by beating the Turing Test, we would never have thought of the impact chatbots would create in the World Wide Web (a reason for this, though, could be that ARPANET was itself only created in 1969).

Today, chatbots are everywhere. Many Fortune 500 companies are pursuing research in the domain and have come out with implementations of chatbots for their products and services. In a recent survey done by Oracle, featuring responses from 800 top executives of several companies and startups, it was found that nearly 80% of them said they had already used or were planning to use a chatbot in their customer-facing products by 2020.

Before AI began powering chatbots, as in the case with ELIZA (and its successor ALICE), chatbots were mostly about a fixed set of responses mapped to several input patterns. Coming across the word mother or father in a sentence entered by the user would almost certainly produce a response asking about the family of the user or their well-being. This clearly wasn't the response desired if the user wrote something like "I do not want to talk about XYZ's family".

And then, there is the famous "sorry, I did not get that" response of such rule-based chatbots, which made them appear quite stupid at times. The advent of neural-network-based algorithms saw chatbots being able to understand and customize responses based on user emotion and the context of the user input. Also, some chatbots scrape online data in case of encountering any new query and build up answers in real time about the topics mentioned in the new, unknown queries. Apart from that, chatbots have been used to provide alternative interfaces to business portals. It is now possible to book hotels or flights over a chatbot platform provided by WhatsApp.

Facebook Messenger's bot platform saw over 100,000 bots created in the first 17 months of its being opened to the public. Hundreds of pages on the social networking giant today have automated responses for users who send messages to their pages. Several bots are running on Twitter that can create content, closely mimicking a human user, and can respond to messages or comments made on their posts.

You can chat with an online version of ELIZA at