A chatbot is a computer program which can converse with a user using auditory or textual methods. There are many popular chatbots brands today such as Siri, Alexa, Google Assistant and Cortana, which can simulate human conversations.
A study by Harvard Business Review and InsideSales concludes that a ten-minute delay in response to queries can reduce the chance of effective contact by 400%. Chatbots allow instant contact and flexibility to their customers to chat anywhere, anytime. Users use chatbots to organize their schedules, get directions, and call people, amongst other things. Gartner has predicted that by 2020, people will have more conversations with chatbots than their spouse!
Machine learning makes smarter chatbots
Ever wondered how Amazon’s Alexa or Apple’s Siri are able to talk fluently with their users? And all this happens while they answer user queries, give reminders and play their favourite music? They are chatbots who have been trained in conversational and basic organizational skills. However, their limitation is obvious when you try to talk to them beyond their basic functionalities. The integration of machine learning using neural networks helps developers come up with neural conversational agents that mimic natural language.
What is machine learning?
Machine learning is the study of algorithms and statistical models used by computer systems. Its purpose is to perform a specific task without being explicitly asked, by relying on past patterns. It allows bots to interact with users via the natural language that can be easily interpreted by the system. This integration of machine learning with bots is known as a dialogue system or simply, chatbots. The aim of this field is to build a model through which a user can interact with a machine and still get a more nuanced ‘human-like’ answer while upholding the framework of the conversation. Machine learning automates the model building for the chatbot development so that the chatbot can learn from its own experience.
How machine learning (ML) integrates with chatbots?
A successful chatbot of the future should be able to hold a natural conversation with its users. Such a conversation will be completely indistinguishable from a human conversation. In reality, this level of finesse has not been reachable so far. Despite the usefulness of chatbots in carrying out basic business-related transactional conversations, they fail to naturally mimic human interactions. This is especially true when the topic of discussion is more evolved, such as an emotional, fun or philosophical topic. Machine learning improves this efficiency of chatbots by developing neural network based conversational models.
Conversational models for chatbots
Creating neural conversational models by use of machine learning algorithms is a time-consuming process and has to carefully performed. But after all the hard work, it does enable a chatbot to interact with customers automatically and naturally. The models are categorised as selective and generative models.
A selective model works by ranking the statements based on their context instead of going sequentially. It does not have to follow a sequential approach and can come up with quicker responses. However, it lacks the accuracy which is offered by the generative models. A generative model is much simpler and is built to function in a sequential manner. It is much slower but also more reliable due to the sequential layer-by-layer approach it follows. Neural conversational models offer a powerful way for machine learning developers to create chatbots which can converse naturally with users.
Today, chatbots are being created to perform evolved cognitive functions. For example, developing a computer vision, convert speech to text, recognise languages, translate, recognise speakers, analyse text etc. With the help of machine learning, the future of chatbots looks promising. Being right at the centre of AI, internet and automation, chatbots can prove to be a highly effective tool.
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