The three technologies behind chat bots are natural language processing (NLP), machine learning (ML), and artificial intelligence (AI).
Natural language processing:
According to IBM Natural language processing (NLP) refers to the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
It combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.
What is computational linguistics?
Syntax and grammar: Developing models of sentence structure and grammar that can be used to analyze and generate sentences.
Semantics and pragmatics: Studying the meaning of words and sentences, and developing models that can interpret the meaning of natural language expressions.
Discourse analysis: Analyzing longer texts such as conversations and articles, and developing models that can understand the structure and coherence of such texts.
Machine translation: Developing systems that can automatically translate text from one language to another.
Speech recognition and synthesis: Developing systems that can recognize and generate human speech.
What is machine learning?
According to MIT Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”
There are three sorts of Machine Learning:
Supervised machine learning:
Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today.
Unsupervised machine learning
In unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren’t explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases.
Reinforcement machine learning
Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.
All above forms of machine learning are being utilized in chatbots
Supervised learning: Training the chatbot on a set of labeled data, where the correct response for each input is provided. The chatbot then uses this data to learn how to respond to similar inputs in the future.
Unsupervised learning: The chatbot is not given labeled data, and instead must learn patterns and relationships in the data on its own. This can be useful for tasks like clustering similar messages or identifying topics.
Reinforcement learning: Training the chatbot through trial-and-error, where the bot receives feedback in the form of rewards or punishments for its actions. The chatbot then adjusts its responses to maximize its rewards.
Deep learning: Neural networks model complex relationships between inputs and outputs. It can be used in chatbots to improve natural language understanding and response generation.
According to MIT Deep learning networks are neural networks with many layers. The layered network can process extensive amounts of data and determine the “weight” of each link in the network — for example, in an image recognition system, some layers of the neural network might detect individual features of a face, like eyes, nose, or mouth, while another layer would be able to tell whether those features appear in a way that indicates a face.
Like neural networks, deep learning is modeled on the way the human brain works and powers many machine learning uses, like autonomous vehicles, chatbots, and medical diagnostics.
According to Hazel Raoult, in her article " Choosing the Best Language to Build Your Chatbot" Python is, by far, the most popular programming language amongst data scientists. Research suggests that more than 57% of data scientists used python for Machine Learning.
Python offers versatility. It is a simpler language to pick up, with its human-like language capabilities as a result of years of research and development and consistent syntax. The credit for this goes to the inclusion of AIML (Artificial Intelligence Markup Language) which makes it effortless for developers to write syntax.
Python is also used for building conversational components hosted by the web server and supports an extensive library for AI chatbots. It makes it possible for developers to build chatbots that have conversational nuances and empathetic tones on python.
We have asked Chat GPT:
As an AI language model, Chat GPT is designed to improve its performance through continuous learning from large volumes of text data. The outlook for Chat GPT is promising, as advancements in deep learning algorithms and natural language processing (NLP) technology will likely improve its abilities to understand and generate human-like responses.
In recent years, GPT-based models have been used in a variety of applications, including conversational agents, chatbots, question answering systems, and language translation services. As the field of AI continues to grow and more data becomes available, it is expected that Chat GPT and similar language models will continue to improve in accuracy and effectiveness.
However, as with any technology, there are potential challenges and limitations that need to be addressed. For example, there are concerns about the ethical implications of AI, including issues related to privacy, bias, and transparency. These challenges will need to be addressed to ensure that Chat GPT and other language models can be used in a responsible and ethical manner.
What is natural language processing (NLP)?
Retrieved from: https://www.ibm.com/topics/natural-language-processing
Sarah Brown: "Machine learning explained"
Retrieved from: https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
Hazel Raoult: " Choosing the Best Language to Build Your Chatbot"
Chat GPT inquiry:
"What is the outlook for Chat GPT"
"What are the computations and mathematical models behind chatbots"