Introduction

According to Matt Davies Stockton, chatbots are catching a lot of attention lately. Most of the credit goes to ChatGPT from OpenAI and other such chatbots for getting better at conversations, finding errors in programming codes, and creating stories or even marketing material from scratch. Let’s check out how chatbots work.

The Details

1. NLP – While most people know that rules-based chatbots learn match patterns and deliver output accordingly, they aren’t as sophisticated as AI-driven chatbots. Most AI-driven chatbots use a sophisticated machine-learning algorithm. For ChatGPT, it is Proximal Policy Optimization and Reinforced Learning algorithm with tech like NLP (Natural Language Processing) at the heart of it.

NLP is the cross-section between computer science and linguistics, and you come across rudimentary forms of it all the time with auto-correct and auto-suggest on virtual keyboards of smartphones. It enables computers to interpret and generate human language.

2. Stages of NLP – NLP works in steps. First, a sentence or a phrase is segmented to create tokens and then common words that don’t contribute to the meaning of the sentence are removed. For instance, with this first step, a sentence like “I am learning ChatGPT right now” would be converted to “i am learning chatgpt now”. Here, words are tokenized into lower cases, segmented into individual words, and redundant words are removed.

Next, the tokenized words are speech tagged for nouns, verbs, pronouns, and more for the chatbot to understand the structure of the sentence. Finally, named entities are recognized as places, people, organizations, or something else that’s found in the chatbot’s database or on the internet.  

3. Understanding – After the above-mentioned phase, the tokenized words with their speech tags and named identities are converted into numerical or vector-based data structures. AI-driven chatbots are fed thousands of such data and trained on a specified model so that they can understand human language and output intelligible human-like responses.

4. Transformers – Finally there are transformers that make these AI-driven chatbots a breakthrough technology. Transformers have encoders and decoders with the above-mentioned phases happening in the encoders. Encoders spit out a numerical or vector-based representation of the input sentence that captures the meaning of the sentence in the most efficient form.

That is used by the decoder in a sequence-to-sequence form where one sentence is fed to get another sentence or answer to a question. Transformers are a new machine learning technology that uses a self-attention mechanism that allows the model to focus on the most relevant parts while generating an output. Transformers allow chatbots to understand the importance of one vector based on the others. It allows chatbots to understand the context and deliver incredible human-like responses.

Conclusion

Matt Davies Stockton suggests that you also research chatbots and figure out how they may help your workflow in the future. With massive amounts of data being fed every day and processed by their neural networks, chatbots are getting more powerful each day. Adopting chatbots in your own work can help save you cost and cut down on time.