How AI Reshapes Vocabulary: Unveiling the Most Used Terms Related to the Technology

As AI has seen a surge of interest and development in recent years, its influence extends beyond technology, shaping the language. With AI-related terms making their way into mainstream usage and earning spots as “words of the year” in various dictionaries, it’s clear that this field is rapidly influencing the language. In this glossary, we aim to explain the key concepts related to AI and provide a comprehensive guide to the most commonly used AI terms:

Leveraging such resources as the News on the Web (NOW), and AI-focused blog posts and newsletters, we conducted research to identify the key terms.

With the use of NOW Corpus, we accessed a repository containing over 18.8 billion words sourced from online newspapers and magazines spanning from 2010 to the present day. Employing a collocation search, we pinpointed the most frequently used terms related to AI. We also analyzed popular AI blogs and newsletters to identify prevalent terms within AI-centric communities. Additionally, we examined articles on about new words to trace the emergence and adoption of AI-related terms. All identified terms were cross-checked in the NOW Corpus year-by-year over a 10-year period to track their usage and identify their emergence.

It’s important to note that while the NOW Corpus covers a substantial number of media outlets, it doesn’t encompass the entire internet. Therefore, while the numbers on the graph may not seem extensive in themselves, they reflect a broader trend across various platforms. The more mentions in the media, the more prevalent the term is across blogs, social networks, and beyond:


abbreviation for artificial intelligence. While delving into its precise definition may not be necessary given the main topic of our blog, it serves as a foundational concept intertwined with many other terms discussed in this article.

Reflecting on the timeline of the term’s usage, 2016 marked the first wave of interest, with AI defeating the world champion Go player and editing apps like MSQRD and Prisma going viral. A year earlier, in 2015, Google introduced Tensor Flow, an open source machine learning framework. It aimed to democratize AI by making it more accessible, scalable and efficient. The Guardian’s technology editor Alex Hern aptly noted in his annual roundup:

Everything has AI now. Period-tracking app Flo ‘uses a neural network approach’ to deliver “high period forecast accuracy”; food delivery app Just Eat launched a chatbot that “sees AI integrated into the ordering experience to ensure that customers receive the best, round the clock support and service”; restaurant guide Borsch “uses artificial intelligence to help people discover the yummiest dishes around”.

Alex Hern, “2016: the year AI came of age”, The Guardian, December 28, 2016

The years 2021-2022 witnessed a surge in major releases such as DALL-E, Midjourney, and Stable Diffusion, along with breakthroughs like ChatGPT. These milestones propelled the widespread adoption of the term, a trend that has persisted into 2023, with its usage more than doubling.

Generative AI

a subset of AI technology that focuses on content creation including but not limited to text, code, images, video, illustrations, music, and sound. It involves the use of machine learning algorithms to learn patterns across a large data set and generate new content based on those patterns.

Generative AI has a vast potential to transform various industries by automating creative processes. It also democratizes digital content creation through accessible tools and platforms. For businesses, Generative AI offers efficient tools to streamline operations in, for instance, marketing, sales, customer service, and content production, giving them a competitive edge.

Graph showing the rise in the use of Generative AI over the past 10 years


a specific instruction, question, or input provided to an AI model to guide its generation of content.

While the word “prompt” also have other meanings, its AI-related usage now prevails. In the context of Generative AI, a prompt serves as the starting point for the model to create new text, images, or other types of content based on the patterns it has learned from training data. Recently, there has been a growing demand for prompt engineers in companies, highlighting the importance of crafting effective prompts to elicit desired outputs from AI models.

Graph showing the rise in the use of Prompt over the past 10 years

AI Models

are computational structures designed to process, analyze, and interpret large datasets to make decisions, predictions, or generate content. These models are based on algorithms derived from various fields within AI, such as machine learning, deep learning, natural language processing, and computer vision. The development of an AI model involves training on a specific dataset to learn patterns, behaviors, or features relevant to performing a particular task or solving a problem.

AI models can be highly effective at automating complex processes and improving decision-making. Their growing use across sectors, including those originally unrelated to technology such as food or fashion, has led to a recent surge in the term’s popularity.

AI Bot & AI Chatbot

an AI Bot is a broad term for a software application programmed to perform automated tasks. AI Chatbot, on the other hand, is a specific type of AI bot designed to simulate conversation with human users through text or voice interactions.

The increased attention to the term “AI Chatbot” can be linked to the integration of these technologies into a wide range of customer service and engagement platforms.

Graph showing the rise in the use of AI bot over the past 10 years


abbreviation for generative pre-trained transformer: a type of machine learning algorithm that uses deep learning and a large database of training text to generate new text in response to a user’s prompt. By training on a diverse and extensive dataset of text, GPT models can generate coherent and relevant text based on a given prompt, perform translations, answer questions, and even create content that resembles human writing.

The popularity of the term “GPT” soared with the launch of ChatGPT by OpenAI. The first surge in the use of the term coincides with the release of GPT-3, which garnered significant attention for its improved text generation capabilities, despite remaining imperfect. However, it’s worth noting that “GPT” is a broad term that encompasses all transformer models. OpenAI has also acknowledged that the name “ChatGPT” for its product was unfortunate, but they can no longer change it. Nevertheless, the term ChatGPT itself is widely used and it could easily be put at the top of this list with an astounding more than 74,000 mentions.

Graph showing the rise in the use of ChatGPT over the past 10 years

Open Source 

software or technology that is made freely available to the public, allowing anyone to view, modify, and distribute its source code.

In the context of AI, open source projects often involve AI algorithms, libraries, or frameworks that are developed collaboratively by a community of researchers, developers, and enthusiasts. Recently, there has been a noticeable uptick in the emergence of open source models, reflecting a growing trend toward openness and collaboration in the AI community. However, the industry holds mixed views on open source. While it promotes openness and transparency, providing opportunities for small companies to develop, there are also concerns about the associated risks. Open source is challenging to regulate and control, raising questions about how it is used and by whom.


abbreviation for large language model: a type of machine learning algorithm trained on extremely large data sets of existing language and designed to generate new, naturalistic responses to prompts. These models are trained on extensive datasets of text to learn patterns, contexts, and the nuances of language. The training process enables them to perform a wide range of language-based tasks such as translation, summarization, question-answering, and content creation. LLMs are characterized by their massive size in terms of the parameters they contain, allowing for a deep understanding of language and context.

The term “LLM” has grown in prominence largely due to the capabilities these models have demonstrated in improving natural language understanding and generation. The launch of ChatGPT by OpenAI, Llama by Meta or PaLM models by Google has also showcased the potential of LLMs as a tool that can write coherent and contextually relevant text, answer complex questions, and even generate creative content.

AI Safety

practices, principles, and methodologies aimed at ensuring AI systems operate in a manner that is secure, ethical, and aligned with human values and societal norms. It involves the proactive identification and mitigation of potential risks associated with AI development and deployment, such as bias, malfunction, unintended consequences, and the misuse of AI technologies.

The term has a significant surge in 2022-2023 that can be due to attributed to breakthroughs in text-to-image models such as DALL-E, Stable Diffusion, Midjourney. Generating incredibly realistic photos, these models raised security concerns. As a result, specialists and entire teams dedicated to addressing AI safety issues are emerging within companies.

Graph showing the rise in the use of AI Safety over the past 10 years


abbreviation for Artificial General Intelligence. It refers to the hypothetical development of super-smart AI that is capable of performing any intellectual task in a way that a human can do. AGI is often considered the ultimate goal of AI research and development.

The term “AGI” has become more prominent as discussions about the future potential and direction of AI technology have intensified. While AGI remains a theoretical goal rather than a current reality, the concept represents a significant leap in the field of AI.

Responsible AI

the practice of designing, developing, and deploying AI systems in a manner that is ethical, transparent, and accountable. It encompasses principles such as fairness, privacy, security, and inclusivity, aiming to ensure AI technologies benefit humanity while minimizing potential harms and biases.

The term “Responsible AI” has gained traction as the deployment of AI technologies has become more widespread, impacting various aspects of daily life and business operations. AI models have showcased their remarkable abilities, from human-like writing to generating realistic images, prompting discussions about the ethical implications surrounding AI.

Graph showing the rise in the use of Responsible AI over the past 10 years

AI Image

a visual piece created or manipulated by AI technologies, particularly through the use of generative algorithms. These images are produced by AI models that have been trained on vast datasets of photographs, artworks, and visual patterns, enabling them to generate new images.

The popularity of the term “AI image” has surged with advancements in generative AI technologies that have opened up new possibilities in fields such as digital art, advertising, entertainment, and even scientific visualization. As of 2023, over 15 billion images have been generated, and this number continues to grow significantly.

Graph showing the rise in the use of AI Image over the past 10 years

Conversational AI

AI technologies that enable computers to simulate real-time conversations with users, typically through text or voice-based interfaces. Applications of conversational AI include chatbots, virtual assistants, and interactive voice response systems, which are used across various platforms and industries for customer service, information retrieval, and personal assistance.

Graph showing the rise in the use of Conversational AI over the past 10 years

AI Assistant

a software agent that uses AI technologies to perform tasks or services for an individual. These assistants can understand and interpret human speech or text inputs, enabling them to execute commands, answer questions, or assist with tasks like scheduling, reminding, or even controlling smart home devices.

The increasing popularity of AI Assistants can be attributed to their integration into a wide array of consumer electronics and platforms, including smartphones, speakers, and home automation systems.

Graph showing the rise in the use of AI Assistant over the past 10 years

AI Art

an artwork created using artificial intelligence techniques. It can include anything from digital paintings to poetry to musical compositions and beyond.

AI art is the subject of much debate, with some questioning whether it can be considered art. Some argue that AI-generated art lacks creativity because it’s a reproduction of ideas and concepts that have already been created. Others argue that the unique capabilities of AI algorithms and their black-box nature can create new and interesting forms of expression.

AI Boom

rapid and significant growth in AI technology development, investment, and implementation across various sectors in recent years.

The AI Boom, also known as the AI Spring, is driven by factors such as increased computational power, availability of big data, and significant improvements in AI algorithms and models. This period of rapid AI growth is often likened to the gold rush, as companies and researchers rush to capitalize on the potential of AI technologies.

AI Adoption

process by which businesses, organizations, and individuals begin to integrate AI technologies into their operations, products, or services.

This encompasses a wide range of applications, from automating routine tasks and enhancing decision-making processes to developing new AI-driven products and business models. However, not everyone welcomes AI adoption equally, and many companies are wary of it. While some organizations are actively seeking ways to leverage AI’s capabilities, others resist its implementation.


when AI models produce false information contrary to the intent of the user and present it as if true.

Graph showing the rise in the use of Hallucinate over the past 10 years

AI Ethics

moral principles and practices that guide the development, deployment, and use of AI technologies.

This involves considering the impact of AI technologies on individuals and society, ensuring fairness, transparency, accountability, and privacy in AI systems.

AI Content

any type of content that is created using artificial intelligence techniques. It can include text, code, images, video, music, and sounds, among other forms of content.

Graph showing the rise in the use of AI Contentover the past 10 years

Ethical AI

principles and practices that seek to ensure AI systems are developed and operated in a way that is morally sound and socially responsible. This involves considering the impact of AI technologies on individuals and society, ensuring fairness, transparency, accountability, and privacy in AI systems.

Graph showing the rise in the use of Ethical AI over the past 10 years

Synthetic Media

any type of media content that is created or manipulated using artificial intelligence techniques. This can include images, videos, audio recordings, and more.

Graph showing the rise in the use of Synthetic Media over the past 10 years

AI Agent

software that acts autonomously or semi-autonomously on behalf of a user or another system to perform tasks, solve problems, or achieve specific goals.

Virtual Influencers

digitally created or AI-generated personas that exist on social media and other digital platforms, engaging with real human audiences. Virtual influencers are designed and programmed to simulate human-like characteristics, behaviors, and interactions.

Graph showing the rise in the use of Virtual Influencer over the past 10 years

One of the most followed virtual influencers is Lu do Magalu, boasting more than 6.8 million followers on Instagram. Lu is known for her product reviews, unboxing videos, and software tips on behalf of the retail giant Magalu.

Explainable AI

methods and techniques in AI that make the outputs of AI models transparent and understandable to humans. This involves designing AI systems in such a way that their decisions, predictions, and actions can be easily interpreted by users, allowing for greater insight into the AI’s functioning and rationale.

Proprietary AI

AI systems and technologies that are owned, developed, and managed by specific organizations or individuals and are protected by intellectual property rights. Unlike open source AI, where the source code, algorithms, and data are freely available for use, modification, and distribution, proprietary AI is restricted in access and use.

AI Alignment

process and goal of ensuring that AI systems’ goals and behaviors are congruent with human values and ethical principles. It addresses the challenge of designing AI that not only understands and executes specific tasks but also aligns with broader human objectives, morals, and societal norms.

Sentient AI

a hypothetical form of AI that possesses the ability to experience subjective perceptions, emotions, or consciousness. This concept goes beyond current AI capabilities, which include learning, decision-making, and problem-solving, to encompass self-awareness and the capacity to feel, understand, and experience the world in the same way as humans or animals.

The term experienced a notable increase in usage in 2022, likely driven by discussions related to the emergence of ChatGPT and other LLMs. However, experts clarified that despite its advanced capabilities, they do not possess true sentience, and achieving genuine Sentient AI remains a distant goal. This clarification may have contributed to a significant decline in the term’s usage thereafter.

Graph showing the rise in the use of Sentient AI over the past 10 years
Spread the word