What is a Chatbot?
You’ve likely talked to a robot already without even knowing it. And you might have even heard the term “chatbot” in the news. But what is a chatbot? How do chatbots work?
Essentially, a chatbot is just a robot chat that imitates human conversations through voice commands, text chats, or both. It’s a virtual conversation in which one party is an online talking robot.
The artificial intelligence feature within talking robots has been used in various industries to deliver information or perform tasks, such as telling the weather, making flight reservations, or purchasing products.
Inside the artificial intelligence of a chatbot is machine learning and what’s known as natural-language processing (NLP). Machine learning can be applied in different fields to create various chatbot algorithms, while NLP has the ability to pick up conversational cadences and mimic human conversation.
The chatbot is trained to translate the input data into a desired output value. When given this data, it analyzes and forms context to point to the relevant data to react to spoken or written prompts. Looking into deep learning within AI, the machine discovers new patterns in the data without any prior information or training, then extracts and stores the pattern.
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.
How Chatbots Got Smarter
With the advancements in artificial intelligence and the rapid growth of messaging apps, chatbots are becoming increasingly necessary in many industries. Although bot technology has been around for decades, machine-learning has been improving dramatically due to the heightened interest from key Silicon Valley powers.
Natural language processing mimics human speech patterns to simulate a human tone in computer-human interaction, which creates more intimate interactions. The predictive analytics within bots uses statistics, modeling, data mining and more to generate information proactively, rather than in response to a prompt.
The sentiment analysis in machine learning uses language analytics to determine the attitude or emotional state of whom they are speaking to in any given situation. This has proven to be difficult for even the most advanced chatbot due to an inability to detect certain questions and comments from context. Developers are creating these bots to automate a wider range of processes in an increasingly human-like way and to continue to develop and learn over time.
An indicator of just how human-like these machines can be was actually developed in the 1950s by British scientist Alan Turing. His Turing Test checks the presence of mind, thought, or intelligence in a machine and if it can fool a human to believe that it is a human as well, then it passes the test.
There was a time when even some of the most prominent minds believed that a machine could not be as intelligent as humans but in 1991, the start of the Loebner Prize competitions began to prove otherwise. The competition awards the best performing chatbot that convinces the judges that it is some form of intelligence. But despite the tremendous development of chatbots and their ability to execute intelligent behavior not displayed by humans, chatbots still do not have the accuracy to understand the context of questions in every situation each time.
Chatbots Uses of Today and Tomorrow
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.
Furthermore, major banks today are facing increasing pressure to remain competitive as challenger banks and fintech startups crowd the industry. As a result, these banks should consider implementing chatbots wherever human employees are performing basic and time-consuming tasks. This would cut down on salary and benefit costs, improve back-office efficiency, and deliver better customer care.
More to Learn
Chatbot technology will continue to improve in the coming years. Chatbot architecture and design will evolve to the point that interactive AI will become standard for customer service. But there are numerous applications for chatbots across a variety of sectors.
That’s why BI Intelligence, Business Insider’s premium research service, has put together a bundle of detailed reports on chatbots:
You can also purchase and download the full reports using the links above.
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