The history of artificial intelligence assistants represents a remarkableevolution-from simple rule-based systems to sophisticated conversational partners capable of supporting complex strategic decisions. As more and more organizations use these assistants to improve productivity and decision making, understanding this evolution provides valuable context for effectively leveraging these technologies.
According to the research of Al-Amin et al. (2023), the first theoretical basis for future chatbots dates as far back as 1906, when Russian mathematician Andrey Markov developed the"Markov Chain," a fundamental statistical model for predicting random sequences. This method, although rudimentary compared to today's technologies, represented a first step in teaching machines to generate new text probabilistically.
A pivotal moment in the history of conversational artificial intelligence was the publication ofAlan Turing's article"Computing Machinery and Intelligence" in 1950, where he proposed what we know today as the "Turing Test." This test assesses the ability of a machine to exhibit intelligent behavior indistinguishable from human behavior through natural language conversations.
The first widely recognized chatbot was ELIZA, developed by Joseph Weizenbaum at MIT in 1966. As pointed out by Al-Amin et al. (2023), ELIZA simulated a therapist using simple pattern matching techniques, reflecting the user's responses to simulate a conversation. Despite the simplicity, many users attributed human-like understanding to the system.
Unlike ELIZA, PARRY (developed in 1972 by psychiatrist Kenneth Colby at Stanford) simulated a patient with paranoid schizophrenia. It was the first chatbot subjected to a version of the Turing Test, marking the beginning of the use of these tests to assess the conversational intelligence of chatbots.
The 1980s saw the emergence of Racter (1983), capable of generating creative texts using grammatical rules and randomization, followed by JABBERWACKY (1988) and TINYMUD (1989), which represented further advances in the simulation of natural conversations.
A significant advance came with ALICE (Artificial Linguistic Internet Computer Entity), developed by Richard Wallace in 1995. ALICE used the Artificial Intelligence Markup Language (AIML), created specifically to model natural language in human-chatbot interactions.
The period between 2000 and 2015 saw the application of more advanced Natural Language Processing statistical techniques that significantly improved language understanding:
SmarterChild, developed by ActiveBuddy in 2001, was one of the first chatbots integrated into instant messaging platforms, reaching more than 30 million users.
The CALO' (Cognitive Assistant that Learns and Organizes) project, launched by DARPA in 2003, laid the groundwork for Siri, which was acquired by Apple and launched in 2011 as the virtual assistant in the iPhone 4S. As Al-Amin et al. (2023) notes, Siri represented a major breakthrough in integrating voice assistants into consumer devices, using deep neural networks to process and understand voice commands.
The evolution of Siri* has reached a new milestone with the integration of advanced artificial intelligence models that have revolutionized its capabilities. According to Al-Amin et al. (2023), this new enhanced version of Siri leverages more sophisticated neural architectures to understand the context of the conversation in a deeper way, maintaining memory of previous interactions and adapting to the user's individual preferences. The assistant can now understand complex, multi-turn requests with much richer contextual understanding that enables more natural and less fragmented interactions. This integration represents a significant step toward virtual assistants capable of supporting truly two-way conversations.
Alexa+ marks a radical evolution of the Amazon ecosystem, transforming the voice assistant into a comprehensive home AI platform. Al-Amin et al. (2023) highlight how Alexa+ is no longer limited to responding to specific commands, but is now able to anticipate user needs through the integration of advanced predictive models. The system can autonomously coordinate smart home devices, suggest personalized automations based on detected behavioral patterns, and facilitate more natural interactions through enhanced contextual understanding. Among the most significant innovations, Alexa+ can now perform complex multi-step tasks without the need for repeated activations, maintaining context through long sequences of interactions.
Microsoft's Cortana (now Copilot), launched in 2014, offered speech recognition capabilities for tasks such as setting reminders, while IBM's Watson Assistant demonstrated advanced language comprehension and analysis capabilities, winning at Jeopardy! in 2011 and subsequently finding applications in various industries.
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Research by Al-Amin et al. (2023) highlights how OpenAI's introduction of ChatGPT marked a major breakthrough. Starting from GPT-1 (2018) with 117 million parameters, to GPT-3 (2020) with 175 billion parameters, these models use the Transformer architecture to understand and generate text with unprecedented capabilities. The public release of ChatGPT in November 2022 marked a defining moment in the accessibility of conversational AI.
As a response to ChatGPT, Google launched Bard (now Gemini) in 2023, based on its Language Model for Dialogue Applications (LaMDA) model. Al-Amin et al. (2023) highlight how Bard used an incremental approach, progressively adding features such as multilingual capability and professional skills in programming and mathematics.
Looking ahead, AI assistants are evolving toward more advanced forms of collaborative intelligence. Research by Al-Amin et al. (2023) identifies several promising areas of development:
In addition, the research highlights the expansion of AI assistants in specific areas:
The evolution from simple chatbots to strategic AI partners represents one of the most significant technological transformations of our time. This progression has been driven by interdisciplinary scientific forces, commercial applications, and user needs. The integration of advanced foundational models into assistants such as Siri and Alexa+ is accelerating this transformation, leading to increasingly personalized and contextualized experiences. As these systems become more influential, responsible and transparent development that can balance innovation and ethical considerations becomes crucial.
Updated Note (November 2025): The advanced version of Siri with Apple Intelligence described in the article has not yet been released. Apple has postponed the release from spring 2025 to spring 2026 (iOS 26.4) and announced a partnership with Google to use Gemini as the underlying model for key parts of the new Siri . Advanced features-personal context, on-screen understanding, and app integration-remain under development, with only incremental improvements available with iOS 26.
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