The history of artificial intelligence assistants: from origins to the present day

The history of artificial intelligence assistants represents a remarkable evolution-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.

Origins: the first statistical models (1906)

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.

The Turing Test (1950)

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 rule-based chatbots (1960-2000)

ELIZA (1966)

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.

PARRY (1972)

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.

Racter and other developments (1980-1990)

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.

ALICE and AIML (1995)

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 NLP revolution and the era of voice services (2000-2015)

The period between 2000 and 2015 saw the application of more advanced Natural Language Processing statistical techniques that significantly improved language understanding:

SmarterChild (2001)

SmarterChild, developed by ActiveBuddy in 2001, was one of the first chatbots integrated into instant messaging platforms, reaching more than 30 million users.

CALO and Siri (2003-2011)

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 era of advanced voice assistants and foundational models

Siri with advanced AI integration

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+ and the future of home care

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.

Cortana and Watson Assistant

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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Today's strategic assistants: the era of transformers (2018-present)

ChatGPT and the LLM revolution (2018-2022)

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.

Google Bard (2023)

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.

The future: collaborative intelligence (2025 and beyond)

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:

1. Personalized assistants: Chatbots that can adapt to the individual user based on their implicit profile.
2. Collaborative chatbots: Systems that can cooperate with both other chatbots and humans to achieve common goals.
3. Creative chatbots: Assistants capable of generating artistic content and supporting creative processes.

In addition, the research highlights the expansion of AI assistants in specific areas:

• Healthcare: For appointment management, symptom assessment, and personalized patient support.
• Education: As open educational resources with adaptive and personalized content.
• Human resource management: To automate HR processes and improve business communication.
• Social media: For sentiment analysis and content generation.
• Industry 4.0: For predictive maintenance and supply chain optimization.

Conclusion

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.

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*Nb. as of the current date (March 2025), it is important to point out that the advanced version of Siri described in the passage has not actually been released to the public by Apple. Possible reasons for this non-release, with reference to Apple's usual policies, could involve user privacy considerations and a desire to present a product up to the company's own standards.