Building and Training Self-Learning Chatbots

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Chatbots form firms like Convertobot are one of the most fascinating results of the development of Artificial Intelligence (AI). Despite their rapid yet extensive development, completely autonomous self-learning chatbots are yet to become reality.

The majority of chatbots are currently designed to perform only simple, repetitive functions. As more data is added to them, chatbots will become very intelligent.

As of today, most chatbots in existence are supervised. Before they can become autonomous, chatbots need to be trained just as any intelligent human would.

As developers learn to build chatbots on the correct framework and power them using appropriate technologies, only then will chatbots become self-learning.

Self-Learning Chatbots

To develop self-learning chatbots, developers will have to use programming approaches which describe their goals to the chatbots and let them achieve them on their own as opposed to telling them how to achieve said goals.

For self-learning bots to become reality rather than merely a possibility, the following components have to be considered:

Interfaces

Majority of chatbots have in-built interfaces that are integrated into original channel. Such is the case with Facebook’s Messenger.

When creating such an interface, text, voice and visuals all present a level of complexity. To be more specific, the complexity involves the chatbot’s ability to provide the appropriate user interface interaction within the correct context.

Natural Language Processing (NLP)

The ability to understand human language and communicate with people in an organic way is a challenge. NLP is the tool that may help developers overcome that challenge.

NLP is powerful but it can only assist software to understand intent or sentences. Using NLP to enhance the chat experience is the goal for most organisations.

Context

To truly mimic human conversations, bots need to understand context from the start to finish of a conversation. Chatbots should use the history/ memory of chats with a user to offer a personalised experience to users.

Loops, Splits and Recursions

The lion’s share of complexity regarding chatbots is included here. Bots need the ability to split from one conversation to another or loop back to another conversation seamlessly when having open ended chats. Many chatbots do not currently have this ability.

Legacy System Integration

When building a chatbot, many developers have to work with an existing system either as a source of information or as a record system. Integrating the chatbot with such legacy systems will vastly improve their ability.

Analytics

Analytics are crucial in chatbot development. They are what enable the chatbot to identify and comprehend interactions which ultimately result in more personalised experience for the user.

Hand-Offs

When interactions with chatbots get too complicated, there will be a hand-off between the bot and the user. The above is the case especially when developing a chatbot for customer service.

Tone and Character

The little details usually make the biggest difference and the same is the case with chatbots. The tone of speech and character of conversation are what make an interaction with a chatbot feel more human. Giving the bot a gender, formal or informal will give the bot human characteristics.