AI has revolutionized the business landscape, making it accessible to everyone. However, not everyone possesses the technical know-how to comprehend its intricacies and functionalities fully. This AI glossary aims to provide essential terms and their definitions, enabling a comprehensive understanding of the technology at play in your daily interactions.
Why Artificial Intelligence Matters for Marketers
Artificial intelligence holds immense significance for marketers as it provides invaluable support to essential aspects of the marketing process, such as SEO research, personalized campaigns, data analysis, and content creation. One such example is the Campaign Assistant, which utilizes text inputs or prompts to aid marketers in efficiently generating copy for landing pages, emails, and ad campaigns.
By leveraging AI to handle crucial marketing tasks, valuable time is saved, allowing marketers to redirect their efforts towards optimizing and fine-tuning their campaigns. Explore the AI glossary provided below to gain deeper insights into the workings of the AI tools you employ.
Artificial Intelligence Terms Marketers Need to Know
Algorithm – An algorithm is a formula that establishes a connection between variables. In the context of machine learning, models rely on algorithms to make predictions based on the data they process. Social media networks utilize algorithms that consider users’ past behavior on the platform to present them with content that the algorithm predicts they will find most enjoyable.
Artificial intelligence – In the domain of computer science, artificial intelligence pertains to the capability of machines to accomplish tasks that would typically necessitate human intelligence. These tasks encompass various abilities such as learning, visual perception, speech communication, logical reasoning, and effective problem-solving.
Artificial General Intelligence (AGI) – The three stages of AI include AGI, which stands for Artificial General Intelligence. In this stage, AI systems possess intelligence that enables them to learn, adapt to novel situations, think abstractly, and solve problems at a level comparable to human intelligence. Presently, we are in the first stage of AI, and AGI remains largely theoretical. AGI is also referred to as general intelligence.
AI analytics – AI analytics represents a form of analysis that leverages machine learning to handle vast volumes of data and detect patterns, trends, and correlations. It operates autonomously without the need for human intervention, enabling businesses to derive valuable insights and make data-driven decisions, thereby maintaining a competitive edge.
AI assistant – An AI assistant, often in the form of a chatbot or virtual assistant, employs artificial intelligence to comprehend and address human inquiries. By using AI, it has the capability to schedule meetings, provide answers to questions, and automate repetitive tasks, leading to time savings and enhanced efficiency.
AI bias – AI bias refers to the concept that machine learning systems can exhibit bias due to biased training data, resulting in outputs that perpetuate and reinforce harmful stereotypes specific to certain communities. This phenomenon raises concerns about the fairness and ethical implications of AI algorithms.
AI chatbot – An AI chatbot is a software application that relies on machine learning (ML) and natural language processing (NLP) to engage in human-like conversations. These chatbots are widely used on websites, apps, and social media platforms to efficiently manage customer interactions and provide support.
AI ethics – AI ethics pertains to the responsibility of humans to carefully contemplate the ramifications of AI implementation and ensure its utilization in a manner that safeguards users and all those who interact with it from harm. As the field of AI continues to expand, AI ethics remains an ever-evolving domain, subject to ongoing research and examination.
Anthropomorphize – Anthropomorphization occurs when humans attribute human-like qualities to AI systems due to their ability to replicate certain human functions. This phenomenon often leads people to perceive AI as sentient, although experts and scientists emphasize that any appearance of human traits is merely a result of AI models executing the tasks they were programmed to perform.
Augmented reality (AR) – Augmented reality (AR) involves overlaying virtual elements onto real-world environments, allowing users to interact with enhanced elements while remaining in their current physical space. A popular illustration of AR can be seen through Snapchat filters, where users experience virtual augmentations in their real-time surroundings.
Autonomous machine – Autonomous machines are capable of learning, reasoning, and making decisions using the data available to them without the need for human intervention. A notable example of an autonomous machine is self-driving cars.
Auto-complete – Auto-complete is a functionality that examines input, whether in the form of text or voice, and proposes potential subsequent words or phrases by drawing from patterns it has learned through the analysis of historical data and an individual’s language usage and context.
Auto classification – Auto classification refers to the process of automatically categorizing and tagging data into distinct categories, simplifying the organization, management, and retrieval of information.
Bard – Bard, Google’s conversational AI powered by LaMDA (language model for dialogue applications), is akin to ChatGPT, but it comes with additional capabilities, including the ability to extract information from the internet.
Bayesian network – A Bayesian network is a probability model used to estimate the likelihood of an event happening. AI plays a vital role in constructing these networks by efficiently evaluating data at a considerable speed.
BERT – Google’s BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model exclusively tailored for natural language processing tasks such as question answering, sentiment analysis, and translation.
Bing Search – Microsoft’s Bing Search is a machine learning search tool that employs neural networks to comprehend prompt inputs, display the most pertinent results, and provide answers. Additionally, it has the capability to generate fresh text-based content and images.
Bots – Bots, commonly referred to as chatbots, are text-based applications utilized by humans to automate tasks or obtain information. They can either be rule-based, limited to predefined tasks, or possess more advanced functionalities.
Chatbot -A chatbot mimics human conversations in online settings, responding to frequently asked questions or directing individuals to relevant resources for addressing their requirements.
ChatGPT – ChatGPT is an AI-driven conversational tool powered by GPT, a language model that utilizes natural language processing to comprehend text prompts, provide answers to inquiries, and create content.
Cognitive Science – Cognitive science explores the functioning of the human mind and its processes. Artificial intelligence, as an application of cognitive science, utilizes mind-like systems such as neural networks to model and operate machines.
Composite AI – Composite AI integrates various AI technologies and methodologies, synergistically employing them to address challenges and manage intricate tasks collaboratively.
Computer vision – Computer vision involves deep learning models examining, deciphering, and comprehending visual data, specifically images and videos. An illustration of computer vision is exemplified by reverse image search.
Conversational AI – Conversational AI refers to technology that emulates human conversational patterns and is capable of engaging in coherent and precise discussions. It relies on natural language processing (NLP) and natural language generation (NLG) to grasp context and deliver relevant responses.
Data mining – Data mining involves uncovering patterns, relationships, and trends in extensive datasets to derive valuable insights. Machine learning algorithms accelerate this process, enabling faster analysis. A recommendation algorithm is a form of data mining, as it analyzes substantial user data to provide personalized recommendations.
Deep learning – Deep learning is an AI technique in which computers acquire knowledge from data and employ this understanding to construct neural networks that imitate the functioning of the human brain.
DALL-E – DALL-E, an innovative system developed by OpenAI, utilizes elaborate natural language prompts to generate images and artwork.
Emergent behavior – Emergent behavior in AI refers to the phenomenon where the system accomplishes tasks or acquires skills that were not explicitly built or programmed into it.
Entity annotation – Entity annotation, as a technique in natural language processing, involves categorizing data into predefined classes (e.g., identifying individuals’ names) to facilitate the analysis, comprehension, and organization of information.
Expert systems – An expert system emulates the decision-making capabilities of human experts in a particular domain.
Explainable AI (XAI) – Explainable AI aids in enhancing human comprehension of its functioning, decision-making, and predictions. This increased understanding fosters trust, accountability, and ethical usage of AI outputs in our daily lives.
Feature engineering – Feature engineering involves handpicking particular attributes from raw data to guide the system’s learning during training.
Feature extraction – Feature extraction entails breaking down input into distinct features and using them for classification and understanding. For instance, in image recognition, a specific element of an image can be identified as a feature, which is then utilized to predict the likelihood of the entire image’s content.
Generative AI – Generative AI utilizes patterns from prompts to create outputs that align with its initial learning. These outputs can encompass answers to questions, text, images, audio, video, code, and even synthetic data.
General Intelligence – General Intelligence, the second stage of AI, refers to Artificial General Intelligence (AGI).
GPT – GPT (Generative Pre-trained Transformer) is OpenAI’s language model trained on vast amounts of data. From its training, GPT can comprehend natural language inputs, engage in human-like conversations, answer questions, and generate content. GPT-4 represents the latest and most advanced version of GPT.
Hallucination – Hallucination in AI occurs when the system generates outputs and information that are factually incorrect.
Image recognition – Image recognition is a machine learning technique that employs algorithms to recognize objects, people, or locations within images and videos. An illustration of image recognition can be observed in Google Lens.
Large Language Model (LLM) – An LLM is trained on extensive historical data and utilizes its knowledge to accomplish specific tasks. Language models such as GPT and LamDA fall into this category.
Language Model for Dialogue Applications (LamDA) – LamDA, developed by Google, is a sizable language model with the capacity to engage in lifelike conversations with humans, provide accurate responses, and generate fresh content.
Limited memory AI – Limited memory AI is the second type among four classifications of AI. These systems are restricted to executing tasks based on a limited amount of stored data and cannot extend their capabilities beyond that. Furthermore, they lack the ability to retain any previous memories for learning while completing future tasks.
Machine Learning – Machine learning is a form of artificial intelligence in which machines utilize data and algorithms to make decisions, predictions, and accomplish tasks. As machine learning systems gain new experiences and access to additional data, they improve and become more accurate over time.
Midjourney – Midjourney is an AI generative model capable of generating new images based on natural language prompts.
Narrow AI – Narrow AI, also known as weak AI and narrow intelligence, is a specialized system designed to perform specific tasks without the ability to adapt to other functions. It represents the initial stage of AI, and most current systems fall under this category.
Natural Language Processing (NLP) – NLP refers to a machine’s capability to comprehend and interpret both spoken and written language, enabling conversational experiences. Examples range from basic spell check to more advanced forms such as language models. It is also referred to as natural language understanding (NLU).
Natural Language Generation (NLG) – NLG involves a model processing language and utilizing its understanding to accurately complete tasks, like answering questions or generating outlines for essays.
Natural Language Query (NLQ) – A natural language query is written input presented as if it were spoken, without any special characters or syntax.
Neutral networks – In AI, neural networks are computerized imitations of human brain neural networks, enabling systems to acquire knowledge and make predictions in a manner similar to human cognition.
OpenAI – OpenAI is an AI research laboratory that developed various AI tools, including GPT, DALL-E, and more.
Pattern recognition – Pattern recognition is the capability of a machine to identify patterns in data, relying on the algorithms it learned during training.
Predictive analytics – Predictive analytics in AI employs algorithms to forecast the likelihood of future events by leveraging patterns found in historical data.
Prompts – Prompts are natural language inputs given to a model, such as questions, tasks, or descriptions of the desired content users want the AI to generate. In simple terms, prompts are instructions that prompt the model to perform specific tasks.
Prompt engineering – Prompt engineering involves determining the most suitable words and phrases to guide generative systems in precisely aligning with the input’s intent. For instance, it involves identifying the optimal wording to ensure that AI produces the desired output accurately.
Reactive machines – Reactive machines, also known as reactive AI, represent the second category among four types of AI. These systems lack memory or context understanding and can only execute specific tasks. Rule-based chatbots fall under the category of reactive machines.
Reinforcement learning – Reinforcement learning is a machine learning approach wherein systems learn and improve through trial and error.
Responsible AI – Responsible AI entails deploying AI ethically and with good intentions to avoid perpetuating biases and harmful stereotypes.
Robotics – Robotics involves designing robots capable of performing tasks and taking actions based on programmed AI, without the need for human guidance.
Self-aware AI – Self-aware AI belongs to the fourth category among the four types of AI. It represents a more evolved form of theory of mind AI, where machines can understand human emotions and possess their own emotions, needs, and beliefs. Sentient AI is a self-aware AI, although, as of now, it remains a theoretical concept.
Semantic analysis – Semantic analysis involves machines extracting meaning from information inputs, similar to natural language processing but with the capacity to account for more intricate factors like cultural context.
Sentiment analysis – Sentiment analysis is the process of identifying emotional signals and cues from text to predict the overall sentiment of a statement.
Sentient AI – Sentient AI experiences emotions and sensations at a level akin to humans. This emotionally intelligent AI can perceive the world and translate those perceptions into feelings.
Structured data – Structured data refers to well-organized data that machine learning algorithms can easily comprehend.
Supervised learning – Supervised learning entails humans overseeing the machine learning process and providing specific instructions for learning and expected outcomes.
Artificial superintelligence (ASI) – Artificial superintelligence (ASI) represents the most advanced stage of AI, where systems can tackle complex problems and make decisions beyond the capabilities of human intelligence. It remains a subject of heated debate, as its potential and risks remain speculative. ASI is also known as Super AI, Strong AI, and superintelligence.
Stable diffusion – Stable diffusion is a generative model that creates images based on detailed text descriptions (prompts).
Singularity – Singularity refers to a hypothetical future in artificial intelligence where systems undergo uncontrolled growth and take actions that significantly impact human life. It is closely linked to superintelligence and sentient AI.
Theory of mind AI – Theory of mind AI represents the third stage of the four types of AI evolution, encompassing an advanced class of technology capable of understanding human mental states and employing that knowledge for genuine interactions. For instance, a system with theory of mind AI can comprehend the emotions of a dissatisfied customer and respond accordingly.
Token – In language models, a token refers to a singular unit of data used to interpret input and make predictions. It can be a single word, characters within a string of words, subwords, or individual features in visual information.
Training data – Training data is the information provided to a machine for learning purposes, which enables it to complete future tasks.
Transfer learning – Transfer learning is a machine learning technique wherein a pre-trained model serves as the starting point for a new task.
Turing Test – Invented by Alan Turing in 1950, the Turing Test gauges whether a machine’s level of intelligence allows it to perform in a manner indistinguishable from human performance. It was originally called the imitation game.
Unsupervised learning – Unsupervised learning is a process where a system autonomously discovers patterns, draws conclusions, and derives insights from data without human intervention. This stands in contrast to supervised learning, where human input guides the learning process.
Virtual reality (VR) – VR refers to software that engrosses users in an interactive, three-dimensional virtual environment through the use of sensory devices.