Evolution of the chatbot
Chatbots have been around long enough now and especially in the last few years have gained a lot of popularity. If you’re interested in building a chatbot or AI assistant and want to know where it all began this is for you. We’ll be looking at the history of conversational artificial intelligence (AI), chatbots or chatterbots from the 1950s to now. From ELIZA, the first chatbot, to Siri, Alexa, and ChatGPT, AI technology has seen many interesting changes over time.
1950s Turing
Thoughts about human and technological interaction began in the 1950s with the Turing Test. Could a computer program convince a group of people that it was human? This question would become a common goal of chatbot developers.
1966 ELIZA
1966 marked the development of the pioneer ELIZA. Created by Joseph Weizenbaum to explore communication between humans and machines. It used pattern matching to simulate human conversation, and responses were based on keywords and script instructions.
The most famous script for ELIZA was DOCTOR, which simulated a psychotherapist who responded with questions using the patient’s words. ELIZA could give the impression of understanding but couldn’t truly understand. Although limited, ELIZA was among the first considered for the Turing test. Overall, ELIZA sparked curiosity, leading to further advancements.
1972 PARRY
Parry was created by psychiatrist Kenneth Colby and was considered like ELIZA but with a personality. Colby was one of few psychiatrists who thought computers could contribute to our understanding of mental illness.
Mental health chatbots are being used today to increase the number of people who can access mental health services. PARRY portrayed a patient with schizophrenia and interacted with ELIZA (specifically the DOCTOR script) at the ICCC (International Conference on Computer Communications) in 1972. I wonder how that conversation went.
1988 Jabberwacky
Jabberwacky marked the dawn of artificial intelligence in chatbots. Created by developer Rollo Carpenter, it was one of the first chatbots to use artificial intelligence to mimic human conversation. Jabberwacky was designed to learn from human interaction to improve its responses. It became capable of carrying complex discussions and learning new words and phrases.
1995 ALICE
ALICE is a pioneering chatbot that made its debut in 1995 and was inspired by ELIZA. Created by Richard Wallace, using pattern matching, which involved using trial and error or loosely defined rules to converse. This approach enabled ALICE to hold more engaging and natural-sounding conversations.
ALICE was a significant improvement over ELIZA, with 41,000 templates and patterns, allowing it to respond to various queries. ALICE used artificial intelligence markup language (AIML), later modified to work with Java in 1998. Its remarkable performance earned it the prestigious Lebner Prize, cementing its place in the history of chatbots. Chatbots by the end of the 20th century used rule based systems as opposed to keyword based response systems like ELIZA.
2001 SmarterChild
SmarterChild is an AI chatbot made by ActiveBuddy in 2001. It made AI interaction popular. Users could talk to it in natural language, not computer code.
At first, you could only use it on AOL Instant Messenger. It provided users with weather updates, game scores and more. It one of the first ‘virtual assistants’ before the term became popular.
SmarterChild made people more comfortable with AI, and its influence is evident in modern virtual assistants and chatbots. SmarterChild drove natural language processing and understanding advancements into the new century.
2010 Siri
The 2010s ushered in the first of the voice assistants Siri. Apple created Siri as an AI personal assistant to help with daily tasks. As the first AI assistant to gain mainstream attention, its development marked a significant breakthrough in artificial intelligence. Launched in 2011 with the iPhone 4S, Siri introduced millions to interacting with devices through natural language.
Its origins trace back to Stanford University’s Center for Computation and Natural Language. Where early research laid the groundwork for its natural language processing and machine learning capabilities. After Apple acquired Siri in 2010, the assistant became a central feature. Showcasing AI’s potential to streamline daily tasks like scheduling, messaging, and online searches.
Despite mixed reviews at launch, Apple invested in Siri’s development, adding new features and improving its accuracy. Today, Siri has over 500 million active users worldwide, has become ingrained in Apple’s ecosystem. Siri can now do everything from setting reminders to controlling smart home devices. This AI virtual assistant’s development marked a significant milestone in the history of conversational AI.
2012 Google Now
As an intelligent personal assistant, Google Now utilised predictive technology to anticipate users’ needs based on their behaviour, location, and search history. This set Google Now apart, allowing it to provide relevant information based on the user’s location, such as weather updates and traffic conditions.
Google Now was also integrated with Google’s other services, such as Gmail, Google Calendar, and Google Maps. Allowing it to provide even more tailored recommendations. Google Now later evolved into the Google Assistant in 2017.
2014 Cortana & Alexa
Launched by Microsoft in 2014, Cortana emerged as a virtual assistant designed to integrate with Windows devices seamlessly. Over the years, Cortana evolved to understand natural language queries, manage schedules, and offer proactive suggestions. Cortana combines rule-based programming, machine learning, and natural language processing.
Amazon introduced Alexa in 2014 as the voice-controlled virtual assistant powering the Echo smart speaker. This conversational AI uses natural language processing (NLP) and machine learning (ML) to understand and respond to user queries. Alexa quickly gained widespread popularity for its ability to perform tasks, answer questions, and control smart home devices. The release of the Alexa Skills Kit in 2015 allowed third-party developers to enhance its capabilities.
2020 ChatGPT
ChatGPT is an AI-powered assistant developed by OpenAI, introduced in 2020 as a successor to GPT-3. Trained on internet data, ChatGPT uses deep learning techniques to process and generate text. A general-purpose language model compared to specialised assistant models like ALEXA or Cortana. However responses can be fine-tuned by giving ChatGPT custom instructions.
Unlike assistants that focus on specific tasks, ChatGPT can handle a wide range of topics and conversations. ChatGPT is being used in customer support, education, and content creation. ChatGPT’s development highlights significant progress in natural language processing and its potential to transform how humans interact with AI systems.
2023 Gemini formerly known as Bard
Like ChatGPT, Gemini uses generative AI, allowing this conversational AI to create content autonomously. While functionally they are very similar their language models were a little different. The data source for GPT-3.5 is capped at January 2022 while Gemini is trained on real time data.
However as of 2024 both GPT-4 (paid version) and Gemini have access to real time data. Both are very similar but very generally the slightly better chatbot for text is ChatGPT and Gemini is better for multimedia content.
The technology that made chatbots almost human
Now that we’ve covered some products of chatbot technology, let’s have a look at the individual developments.
There are various methods for response generation, from rule-based and retrieval-based to generative-based methods. The approaches range from pattern matching and AIML to machine learning models like NLP and ANNs. This diversity in methods reflects the ongoing quest to improve the efficacy of chatbot responses. While rule-based systems and retrieval methods rely on predefined patterns, generative approaches, driven by machine learning models, allow for more dynamic and contextually relevant conversation.
Rule based and retrieval systems allowed chatbots to operate on logic but struggled with more complicated user inputs. When machine learning was developed, chatbots could learn based on user input over time and their conversation ability improved.
This was further improved by natural language learning. Natural Language Processing (NLP) techniques allowed AI to understand users input and intention. Essentially allowing chatbots to dissect a sentence by word and phrase and analyse the text for meaning and emotional tone.
The advancements in machine learning algorithms, including supervised, unsupervised, and reinforcement learning, have also played a critical role. Supervised learning models, for instance, help chatbots predict appropriate responses based on labeled data, while unsupervised learning identifies patterns in conversation to improve interaction. Reinforcement learning further refines chatbot behaviour by allowing systems to learn from trial and error, improving responses over time.
As we reflect on the evolution of chatbots, it’s evident that chatbots have become ingrained in people’s everyday tech-driven lives. We have seen the evolution of chatbots, chatterbots, virtual assistants and artificial intelligence. With advancements in machine learning and natural language processing accelerating at an unprecedented pace. One question remains: what’s next for this rapidly growing technology?
*Updated: 09/01/25*
