Chatbots are computer programs that mimic conversation with people through audio or text, used to communicate information to users.
There are many ways to make a chatbot work, but now three typical methods are logic based on principles, machine learning and artificial intelligence.
Natural language processing ( NLP ) or natural language understanding ( NLU ) is often the first step in the use of artificial intelligence in chatbots – it essentially allows the computer to speak and understand people as they talk, as a language choice.
Most chatbots use NLP in the form of “intention-based” systems, which only allows a chatbot to understand the user’s intentions.
Chatbots go beyond the functionality of interactive voice response systems, which merely help users to progress through a potential option tree.
‘ In marketing, the belief that AI and chatbots are the next big thing is widespread.’
Many marketers believe that chatbots are revolutionizing the business by offering better customer service, keeping customers engaged after the sale and adding “personality” to a company’s brand.
AI chatbots can also help businesses create more personalized customer experiences by customizing responses and content based on user questions and interests.
Most skeptics also believe that AI chatbots will never have a real human touch, and their enthusiastic use can turn against customers.
Chatbots are available in two main types: one based on rules and one based on machine learning.
The AI boosts customer engagement in chatbots, voice experiences and digital assistants such as Google Assistant, Siri, and Amazon Alexa.
Dialog selection is a prediction problem, and the use of heuristics to identify the most suitable response template may include simple algorithms such as keywords matching or more complex processing with automatic learning or deep learning.
Instead of using predefined answers, conversational AI, which uses generative methods, receives a lot of data from conversational training to learn how to generate a new dialogue that looks like it.
In other words, developers should be able to build such conversational AI using only machine learning and training data, and this would not require any technical knowledge or technical expertise.
With adversarial training, intermediaries learn through a miniature Turing Test, in which a generator network creates plausible reactions, while a system of judges whether they are real human conversations or generated by computers.
Many industries around the world, such as healthcare, CPG, banking, finance, financial, IT, customer service, retail, etc ., use chatbots equipped with artificial intelligence ( AI ) to automate some tasks and simplify business processes.
Data encryption, multifactor authentication, behavior analysis, artificial intelligence are some powerful techniques used to protect the use of chatbot.
If companies can integrate this great security practice into the chatbot platform, it will be one of the most robust methods to ensure the security of chatbot.
Although there are several advantages that organizations can enjoy by embracing chatbots and all their vast potential, it is essential first to explore the chatbot and its security capabilities.
Messaging has become an essential way of interacting with smartphones, so companies want chatbots to be part of the maintenance.
Where Microsoft feels it has an advantage, it’s in AI technology and creating chatbots with conservation features.
In addition to the launch of their chatbots and the integration of Cortana, their AI assistant, in most of their products, Microsoft launched the Bot frame at the beginning of 2016 – a toolkit that will help developers create their chatbots.
Chatbots, at least all the useful versions, use what the community calls NLP, which is short for Natural Language Processing.
Chatbots can help internal teams find the right solution based on the intention of the question.
Chatbots are the next step in the evolution of customer communication, and most of us have just begun to understand their meaning or use them.
While basic chatbots may be suitable for most scenarios, some scenarios require more advanced chatbots.
In contrast to the menu – based chatbots, keyword recognition – based chatbots can listen to what users type and respond appropriately, or at least try to do.
Contextual chatbots are by far the most advanced of the three robots discussed here.
In contrast to keyword recognition chatbots, contextual chatbots are smart enough to improve themselves based on what users ask and how they ask for it.
Smart chatbots need an understanding of machine learning, AI and NLP technologies, as well as advanced development skills and in-depth knowledge of various languages and technologies.
With the integrated NLP services, developers can build their tools and platforms to make the chatbot application smarter.
The main advantage of Alexa is its integration with other Amazon Web Services, which include API – based machine learning tools for computer vision, speech and chatbot.