Chatbots are a form of human-computer dialog system that operates through natural language processing using text or speech, chatbots are automated and generally run 24/7. It is mainly used to drive conversion and designed to handle millions requests at a time.
A group of intelligent, conversational software algorithms called chatbots are triggered by input in natural language. They have the capacity to understand commands, comprehend input, and carry out tasks. Despite the fact that chatbots have been around for a while, they are becoming more advanced because of the availability of data, increased processing power, and open-source development frameworks. These elements have prompted the widespread use of chatbots across a variety of sectors and domains. We frequently come with chatbots in a variety of settings, from customer service, social media forums, merchant websites to availing banking services, alike.
In general, chatbots are made to accomplish specific tasks. For instance, customer care chatbots are created specifically to meet the needs of customers who request assistance, whereas conversational chatbots are created to engage in conversation with users. It is really possible to train with a large dataset and archive human level interaction but organizations have to rigorously test and check their chatbot before releasing into production.
In this article, we will learn more about the workings of chatbots and machine learning algorithms are used in teaching AI chatbots.
Table of contents:
- How do AI chatbots really work?
- Algorithms for AI chatbots
- Programming languages for an AI chatbot
- Creating effective chatbots and limitations of chatbot
How do AI chatbots really work?
A chatbot mimics human speech by carrying out repetitive automated actions based on predetermined triggers and algorithms. A bot is made to speak with a human using a chat interface or voice messaging in a web or mobile application, just like a user would do. A type of conversational AI, chatbots are similar to virtual assistants.
A question-answer bot is the most basic sort of chatbot; it is a rules-based programme that generates answers by following a tree-like process. These chatbots, which are not, strictly speaking, AI, use a knowledge base and pattern matching to provide prepared answers to particular sets of questions. The bot, however, becomes more intelligent and human-like when artificial intelligence programming is incorporated into the chat software. Deep learning, machine learning, natural language processing, and pattern matching are all used by chatbots that are driven by AI (NLP).
Algorithms for AI chatbots
Number of algorithms are used in developing AI chatbots. Among all of them, natural language processing-based algorithms are widely used. As chatbot gets input in natural language, so text processing, classification and interpretation becomes really important when it comes to quality chatbots
Popular chatbot algorithms include the following ones:
- Naïve Bayes Algorithm
- Support vector Machine
- Natural language processing (NLP)
- Recurrent neural networks (RNN)
- Long short-term memory (LSTM)
- Markov models for text generation
- Grammar and Parsing Algorithms
- Naïve Bayes algorithm:
The e Bayes algorithm tries to categorise text into different groups so that the chatbot can determine the user's purpose, hence reducing the range of possible responses. It is crucial that this algorithm functions well because intent identification is one of the first and most important phases in chatbot discussions. Because the algorithm is based on commonality, certain terms should be given greater weight for specific categories based on how frequently they appear in those categories. It allows the classification of intent, phrasing of text data.
- Support vector machine:
The Structural Risk Minimization Principle serves as the foundation for how SVMs operate. Due to the high dimensional input space created by the abundance of text features, linearly separable data, and the prominence of sparse matrices, SVMs perform exceptionally well with text data and Chatbots. It is one of the most widely used algorithms for classifying texts and determining their intentions.
- Natural Language processing algorithms:
For chatbots, NLP is especially crucial because it controls how the bot will comprehend and interpret the text input. The ideal chatbot would converse with the user in a way that they would not even realise they were speaking with a machine. Through machine learning and a wealth of conversational data, this programme tries to understand the subtleties of human language. The bot benefits from NLP by being able to read syntax, sentiment, and intent in text data. The extensive range of features provided by NLP, including text summarizations, word vectorization, topic modelling, PoS tagging, n-gram and sentiment polarity analysis, are principally responsible for this.
- Recurrent Neural Networks:
Recurrent Neural Networks are the type of Neural networks that allow sequential data in order to capture context of the words in a given input of text. RNN processes the text input just the way biological intelligence processes the information, it processes sequences by iterating through the sequence elements and maintaining a state containing information relative to what it has seen so far. Thus, allowing us to interpret and capture the context of the input.
- Long short-term memory:
LSTM is a type of recurrent neural network which is better than simple RNN as LSTMs are designed to capture not only state of previous inputs but also it carries memories of previous inputs of the sequence, which is not the case with RNN. LSTM are used in processing large sequences. It is applied in conversational AI to predict the next word.
- Markov models for text generation
Markov chains are often used in chatbots and text production. They operate by calculating the likelihood of moving from one state to another. Because it may be conveniently stored as matrices, this model is easy to use and summarise. These chains rely on the prior state to identify the present state rather than considering the route taken to get there.
- Grammar and Parsing Algorithms
Algorithms for grammar and parsing can effectively identify and resolve ambiguities in sentences. A formal definition of a language's structure is provided by the grammar algorithm to guarantee that the chatbot interacts without grammatical mistakes. The grammar is used by the parsing algorithm to examine the sentence's grammatical structure.
Programming Languages for an AI chatbots
So far, we saw what algorithms are used in building and teaching AI chatbots. Now let’s look at the some of the popular programming languages used to build chatbots in real-world use-cases:
- Python: It is one of most popular programming languages for AI chatbots owing to libraries like NLTK and Spacy.
- Java: Algorithms are the foundation of AI programming, and Java is a strong option for chatbot development.
- Ruby: Ruby is regarded as a good option for creating a chatbot due to the abundance of reliable libraries.
Apart from these languages, CSML, Lisp, and Clojure can also be used to create chatbots. Originally developed as a language for AI projects, Lisp has improved in efficiency.
Creating effective chatbots
Natural language processing is moving incredibly fast and trained models such as BERT, GPT-3 have good representations of text data. Chatbots are very useful and effective for conversation with users visiting websites because of the availability of good algorithms.
In order to create effective chatbots, one should focus on providing Huge training data while creating chatbot algorithms, including out-of-vocabulary words into training data and writing effective code to manage similar intents of users. These are some of the points one should take while creating an AI chatbots
In this article, we saw how AI chatbots work and what are different algorithms like Naïve Bayes, RNNs, LSTMs, etc. used in creating AI chatbots. We also saw programming languages that can be used along with points to keep in mind while creating AI chatbots.
 Data analytics using python by Bharti Motwani
 Deep learning with python by Francoise Chollet