By Jutta Stienen -
Part 2 – Machine learning chatbots and deep learning chatbots – how do they work?
Deep learning and machine learning chatbots are often hailed as the best technology to use for the automation of modern customer service delivery.
However, you may not be aware of how these advanced chatbots work.
In this post, we define machine and deep learning as they pertain to a customer service chatbot. We also answer several questions about the mechanics of machine learning and deep learning chatbots to provide clarity on how to get started with these technological tools.
Understanding chatbot technology for customer service optimization.
This blog post is part 2 of a 3-part series. In case you missed the other 2 parts, here they are.
Part 1 – Debunking 9 myths about chatbots in customer service >
Part 3 – How to implement a deep learning chatbot in customer service >
Computer programs, like chatbots, cannot understand human languages the way humans do.
While computers can process data at lightning speed they are not autonomously intelligent and require human intelligence.
Every computer program relies on specific input to deliver a specific output. Humans must provide this input and program a system of parameters, instructions and algorithms to ensure a useful output.
- Artificial Intelligence (AI) refers to a computer program’s ability to mimic human intelligence by completing complex human tasks. This includes exchanging dialogue with another human in a convincing way.
- Furthermore, a computer can only interpret or generate human language, if it is capable of understanding it. While humans use human language; computers use coding languages. Consequently, a computer can only process human words if it is told how to transcribe these words into coding.
One commonly used transcription process in computer programming is called Natural Language Processing (NLP), which is a complex blend of linguistics and computer science. While there are other approaches, NLP effectively breaks down the mechanics of a language so that it makes sense to a computer.
Understanding conversation comes naturally to humans, but not for computers. They depend on programming approaches such as NLU and NLG to understand.
Below are a few of the techniques NPL uses to facilitate more realistic interactions between humans and computers.
Natural Language Understanding (NLU)
- NLU helps computers understand language by enabling them to translate each single human word into a corresponding computer code. It also helps a computer understand the contextual meaning of words in relation to different words within a sentence.
- NLG helps a computer respond to a human by enabling it to tap into its understanding of the natural human language (NLU). Before a chatbot responds to a human, its language (code) is translated back into human words, so it can be understood.
- NLG describes the process a computer uses to compose meaningful humanlike sentences.
How does machine learning help chatbots interact with humans?
Natural Language Processing helps a computer understand human language from a grammatical perspective. It teaches it how and why specific words are used in a human language sentence. However, just like a human learning a new language, a computer needs to learn how to understand different conversations and how to speak to different people.
While you could train a computer in language usage by exposing it to different conversations – like you would with a human – that is extremely labor and time-intensive.
Machine learning lets programmers automate how they train computers. These algorithms teach computers to teach themselves. This code orders a computer to analyze multiple datasets, such as human conversations.
Datasets of human dialogues help computers analyze how words are used in different contexts.
Computers use Natural Language Process & Deep Learning.
For humans, this process is similar to reading books or listening to conversations to develop a solid grasp of a new language. The more books you read and the more often you listen to conversations, the easier it becomes to understand and use this new language.
Machine learning can be viewed as a way of telling a chatbot to read and reread specific books until it develops an understanding of how humans interact with each other.
In customer service, however, these “books” are datasets that are specific to your business. Your chatbot needs to deep-dive into the conversations that pertain to your business, its customers and its offerings.
What is a deep learning chatbot?
When you speak, the meaning of the first words you use is not independent. Their meaning is affected by the other words in your sentence. Similarly, when someone responds to something you have said, your statement created a framework for their response.
This is what makes teaching a computer how to understand language is so complex. Words do not merely have autonomous meanings; they are interconnected.
Deep learning helps computers and chatbots comprehend these interconnected meanings. In fact, deep learning is part of a family of machine learning approaches that mimic the way the human neural network operates. It copies the way brain neurons exchange information in a network of meaning.
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