The progressive advance of technology has seen an increase in businesses moving from traditional to digital platforms to transact with consumers. Convenience through technology is being carried out by businesses by implementing Artificial Intelligence (AI) techniques on their digital platforms. One AI technique that is growing in its application and use is chatbots. Some examples of chatbot technology are virtual assistants like Amazon's Alexa and Google Assistant, and messaging apps, such as WeChat and Facebook messenger.
“I believe the dreamers come first, and the builders come second. A lot of the dreamers are science fiction authors, they’re artists…They invent these ideas, and they get catalogued as impossible. And we find out later, well, maybe it’s not impossible. Things that seem impossible if we work them the right way for long enough, sometimes for multiple generations, they become possible.”

This machine learning algorithm, known as neural networks, consists of different layers for analyzing and learning data. Inspired by the human brain, each layer is consists of its own artificial neurons that are interconnected and responsive to one another. Each connection is weighted by previous learning patterns or events and with each input of data, more "learning" takes place.
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Spot is a chatbot developed by Criminal Psychologist Julia Shaw at the University College London. Using memory science and AI, Spot doesn’t just allow users to report workplace harassment and bullying, but is capable of asking personalized, open-ended questions to help you recall details about events that made you feel uncomfortable. The application helps users process what happened, to understand whether or not they experienced harassment or discrimination and offers advice on how they can take matters further.

1. AI-based: these ones really rely on training and are fairly complicated to set up. You train the chatbot to understand specific topics and tell your users which topics your chatbot can engage with. AI chatbots require all sorts of fall back and intent training. For example, let’s say you built a doctor chatbot (off the top of my head because I am working on one at the moment), it would have to understand that “i have a headache” and “got a headache” and “my head hurts” are the same intent. The user is free to engage and the chatbot has to pick things up.
An AI-powered chatbot is a smarter version of a chatbot (a machine that has the ability to communicate with humans via text or audio). It uses natural language processing (NLP) and machine learning (ML) to get a better understanding of the intent of humans it interacts with. Also, its purpose is to provide a natural, as near human-level communication as possible.
Chatbots currently operate through a number of channels, including web, within apps, and on messaging platforms. They also work across the spectrum from digital commerce to banking using bots for research, lead generation, and brand awareness. An increasing amount of businesses are experimenting with chatbots for e-commerce, customer service, and content delivery.
One of the most thriving eLearning innovations is the chatbot technology. Chatbots work on the principle of interacting with users in a human-like manner. These intelligent bots are often deployed as virtual assistants. The best example would be Google Allo - an intelligent messaging app packed with Google Assistant that interacts with the user by texting back and replying to queries. This app supports both voice and text queries.
A chatbot (also known as a spy, conversational bot, chatterbot, interactive agent, conversational interface, Conversational AI, talkbot or artificial spy entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods.[1] Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatbots use sophisticated natural language processing systems, but many simpler ones scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database.
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