Want to dig deeper into AI? Want to develop a better understanding of how it works? These 50 AI terms, phrases, and acronyms will help you build your knowledge base.
If any of the below AI terms are still unclear or if you want to chat about anything AI related, please send an email to firstname.lastname@example.org and we’ll be happy to go into more detail.
AI (artificial intelligence)
AI is an acronym that stands for artificial intelligence. It refers to machines being able to perform tasks that normally require human intelligence. This includes tasks like visual perception, speech recognition, decision-making, and translation between languages. AI systems are made possible through machine learning and training data that allows algorithms to improve over time.
AGI (artificial general intelligence)
AGI is an acronym that stands for artificial general intelligence. This refers to a hypothetical AI that has intelligence like a human across multiple domains. Current AI technology is considered narrow artificial intelligence because it focuses on specific tasks rather than general adaptability. AGI has not yet been achieved and may not be possible with current approaches.
Backward chaining is a reasoning method used in expert and rule-based systems. It starts with a goal and works logically backwards to determine what actions will achieve that goal by analyzing if the preconditions for achieving the goal are met.
In machine learning, bias refers to a model incorrectly favoring one outcome over others due to patterns in its training data. This can cause issues in areas like facial recognition where a model may perform better on lighter skin tones, showing bias against darker skin tones. Bias must be carefully monitored and corrected in AI systems.
Blind prompting is an AI technique where the user provides a textual prompt and the artificial intelligence responds without using any additional context like images or knowledge bases. Blind prompting relies entirely on the pattern recognition from the model's training data, which can limit its basic understanding and lead to incorrect assumptions.
An unsupervised machine learning technique where data is grouped together based on similarities. Similar data points are organized into clusters, with points within a cluster being more similar to each other than points in other clusters. Clustering requires the algorithm to define what "similar" means for the particular problem and data.
Computer vision is what enables machines to interpret and understand visual information from the world, similarly to the way the human visual system perceives and comprehends images or videos. The primary goal of computer vision in AI is to develop algorithms and systems that can automatically extract, analyze, and interpret visual data, enabling machines to make decisions or take actions based on the information they derive from images or videos.
A data lake is a system for storing vast amounts of raw data in its native format until it's needed. It allows data to be accessed and analyzed without loading it into a data warehouse. Data lakes provide scalability and flexibility but require more data governance processes due to the unstructured nature of the data.
Data mining refers to the application of automated techniques and algorithms to uncover patterns, insights, and knowledge from large datasets. Data mining enables machines to analyze data and uncover hidden relationships and trends. It involves the use of machine learning algorithms, statistical methods, and computational models to identify meaningful patterns that can contribute to the training and improvement of AI systems.
A decision tree is a model that predicts outcomes by learning if-then decision rules from data features. They have a tree structure, with leaves as labels and branches as rule conditions. They are interpretable but can overfit, need discrete features, and may struggle to generalize. Despite limitations, decision trees remain a popular machine learning model.
Deep learning is a branch of machine learning based on artificial neural networks. Deep learning neural networks have multiple layers that learn progressively more abstract representations of data. Deep learning models require massive amounts of labeled training data and computing power, but can perform extremely well for tasks involving imagery, text and speech.
Representing elements in a dataset as vectors of real numbers is called embedding. Embeds allow similar elements to have similar representations that machine learning models can detect. Embeddings reduce data sparsity and improve pattern recognition in AI algorithms.
Few-shot learning is an artificial intelligence technique that can learn from very small datasets. It relies on already-learned concepts to generalize from very few examples or "shots" through data augmentation and transfer learning. Few-shot learning is helpful for situations where large labeled datasets are not available.
Fine-tuning refers to the process of adjusting the parameters of a pre-trained AI model to adapt it to a specific task or domain. When a model is pre-trained on a large dataset for a general task, such as image recognition or natural language processing, fine-tuning allows the AI system to specialize and perform well on a more specific task or dataset. Instead of training the model from scratch, the process takes advantage of the knowledge already embedded in the pre-trained model, adjusting its parameters through additional training on the target task or dataset.
Forward chaining starts with known facts and uses inference rules to deduce new facts until reaching a goal. It simulates human reasoning from what is known to arrive at new conclusions. But forward chaining can examine all possible rules at each step, so it tends to be inefficient compared to backward chaining which works backwards from the goal.
A basic conceptual structure used to solve broad classes of problems is known as a framework. For example, machine learning frameworks provide tools and libraries that allow developers to build machine learning models faster and more easily. Frameworks standardize common steps to make AI development more efficient.
Fuzzy logic is an approach to reasoning that accounts for partial truths rather than pure true or false values. Fuzzy logic variables can have a truth value that ranges between 0 and 1. This degree of truth allows fuzzy logic to account for ambiguous or complex scenarios where clear binary logic breaks down. Fuzzy systems are useful when rules are imprecise or qualitative.
Generative AI is an approach to artificial intelligence that focuses on machines that can generate new content like images, text, audio, or videos. Generative AI learns patterns and rules from data and uses those to create new, similar content. While impressive, generative AI systems are still narrow and can only imitate specific domains found in their training data.
Hallucinations occur when generative AI models produce false or nonsensical output. Language models in particular may hallucinate information that is factually incorrect, illogical, or simply made up due to a lack of high-quality training data. Hallucinations highlight the limitations of current AI technology and the need for continued responsible development and oversight.
Hashes are a transformed text generated from an algorithm to create a unique identifier for a piece of data, like a password or digital file. The same input will always produce the same hash. Hashes make it easy to compare data for similarities or differences without revealing the actual contents, providing a level of security. Hashed data is one-way only, meaning the original input cannot be determined from the hash.
Human in the loop
An AI solution that involves human feedback and input as part of its operation is referred to as "human in the loop." This approach reduces risks from fully automated AI and leverages the strengths of both humans and machines. However, over-reliance on human input can introduce bias and limit the speed and scalability of an AI system.
Innferring is the act of deriving logical conclusions from facts and assumptions. Inference engines are software components that implement techniques for automated reasoning and inference. Inference helps the AI system reach conclusions that go beyond the training data it was explicitly given.
LLM (large language model)
LLM is an acronym that stands for large language model. It refers to a deep neural network trained on a massive amount of text data. LLMs can generate text or predict what comes next in a given sequence. However, LLM's still struggle with logical inconsistencies and can show bias when generating text about certain topics.
ML (machine learning)
ML is an acronym that stands for machine learning, the study of algorithms that improve automatically through experience and data. Machine learning algorithms build models from data to make predictions without being programmed explicitly. Though useful in many apps, machine learning systems struggle with bias, a lack of explainability, and not generalizing outside narrow use cases. Machine learning requires monitoring and corrective action to ensure proper functioning.
A model is a mathematical representation of a real-world process or concept. Machine learning models are trained using input data to make useful predictions. Models become more accurate as they are exposed to more training data and examples of the concept they are attempting to represent. For example, ChatGPT is a language model.
A neural network is a type of machine learning model inspired by the human brain. Neural networks contain layers of nodes that can uncover patterns through exposure to data. They are well suited for complex patterns and prediction problems but remain a "black box" that is difficult for humans to interpret.
NLG (natural language generation)
NLG stands for natural language generation. NLG is the process of automatically producing natural language text or speech from structured data or information. In other words, NLG involves the transformation of data into human-readable language. The goal of NLG is to enable machines to generate coherent and contextually relevant content that mimics human conversation.
NLP (natural language processing)
NLP is an acronym for natural language processing, a branch of AI focused on teaching machines to understand, process, and generate human language. Natural language processing techniques enable tasks like machine translation, conversational agents, automatic speech recognition, text or voice commands, and text analytics. These systems analyze text for parts of speech, semantics, syntax and context to comprehend its meaning beyond individual words. However, natural language remains complex and nuanced, posing challenges for current NLP models.
Output refers to the result that an AI model produces from a given input. Output depends on the type of task the model is trying to perform. Garbage in, garbage out still applies - if a model's training data is incomplete or biased, its output will reflect that.
In a neural network, parameters are the weights and settings that control how it processes data that’s fed into it. Think of these like knobs that let you control and change things on an electronic device.
A role that someone adopts as an effort at imitation. In AI, personas help simulate more natural human conversations by allowing models to adopt different speaking styles and personalities. However, AI personas are superficial and not true emotional intelligence.
A prompt is instructions you give an AI program to get it to do or say something specific. The prompt gives the AI context and details to help it make its response. Well-written prompts can help the AI's answer be useful, accurate, and not offensive. But even good prompts need a person to check the AI's work. The AI might get things wrong without somebody looking over its answer.
Prompt engineering is the process of developing prompts for generative AI models to improve the relevancy and quality of the responses. Prompts help define the task and condition the model, but still require human oversight to avoid toxicity and uneven performance.
Reinforcement learning is a type of machine learning that focuses on training agents to make sequential decisions in an environment to maximize a cumulative reward. Unlike supervised learning, where the algorithm is trained on labeled examples, and unsupervised learning, which deals with unlabeled data, reinforcement learning involves an agent interacting with an environment and learning from the consequences of its actions.
Sand-bagging means holding something back on purpose to trick others. With artificial intelligence, programmers make the computer program seem less smart than it really is to fool competitors or under promise and over deliver to customers. Programmers can use it to hide an AI system's full abilities.
Self-supervised training is a machine learning technique where unlabeled data is used to train an AI model. Themodel generates its own supervisory signals for learning, without human-labeled data. This approach can utilize vast amounts of readily-available unlabeled data but still faces limitations when trying to fine-tune for narrow tasks that require labels.
Sentiment analysis refers to the process of identifying subjective information and attitudes in text data. Sentiment analysis determines whether the tone of a text is positive, negative or neutral. While useful, sentiment models still struggle with complex topics, sarcasm and subtle shifts in tone that humans pick up on.
Semi-supervised training is a machine learning technique that uses both labeled and unlabeled data for training. It can achieve high accuracy with a small amount of human-labeled data with the help of large amounts of unlabeled data. However, unlabeled data must still be screened for bias, outliers, and issues that impact model performance.
Structured data is organized information stored in a way a computer can easily understand. It uses things like tables, fields, and relationships. Examples are spreadsheets, databases, and forms. Structured data is neat and clean so machines can sort and analyze it easily.
Supervised learning is a training method that pairs a piece of data with an appropriate label. For example, if the model is supposed to identify pictures of food, it will be trained with a picture of an apple and a label that reads “apple.”
Tagging is the process of annotating data points with additional information, like keywords or categories. Tags help organize data and make it easier to search, filter and classify. However, tagging relies on human input that can introduce bias and errors into the data. Consistent tagging guidelines help improve quality.
Temperature represents a parameter in natural language models that controls how creative or generic the generated text is. Higher temperatures produce more creative but possibly less accurate outputs. However, even at lower temperatures language models still struggle with factual accuracy, appropriate content and logical consistency.
A token is the smallest bit of text an AI program works with. In natural language processing, tokens can be words, numbers, or symbols. The program breaks sentences into individual tokens before analyzing them to identify patterns. This helps the machine's ability to understand grammar and how words relate to each other. But tokens alone don't capture context or shades of meaning in the full text.
Training data refers to the information used in machine learning training. Artificial intelligence technology learns pattern recognition through the training data in order to make accurate predictions on new data. The quality, diversity and size of the data heavily impacts the effectiveness of the resulting AI model.
Adapting a machine learning model trained for one task to a different but related task is known as transferring. This often improves the performance of the new artificial intelligence model compared to training from scratch. However, transferring still requires fine-tuning the AI model to the new task and data to achieve the best results.
Unstructured data is information that does not have a clear format like numbers in a table. It includes things like text documents, images, audio files, and videos. Because unstructured data is messy and unorganized, it is hard for computers to sort and analyze. AI programs have to read through a lot of unstructured data to find patterns and make sense of it. Compared to structured data that is more organized and easier for computers to understand.
Unsupervised learning is training where the data does not have any labels associated with it. Instead, it processes such a large amount of data that it’s able to make associations. For example, ChatGPT went through unsupervised learning. It’s able to have a conversation about any topic because it wasn’t trained on a specific topic.
A vector database is a special kind of database used mainly in AI and machine learning. It's different from regular databases because it deals with vector data. Think of these as long lists of numbers that represent things like pictures, words, or sounds in a way that computers can understand and work with. These databases are really good at quickly finding and comparing these lists to find matches. This is super helpful for things like suggesting products, recognizing images, or understanding language. Essentially, vector databases help computers quickly sort through and make sense of complex information.
A workflow is a series of steps, or prompts, needed to finish a job. Workflow describes the process an AI program follows from start to the end result. Having a good workflow helps make sure the AI program works the right way and produces useful results. But human oversight is also important because AI systems can get stuck in bad workflows that don't actually help.
Zero-shot learning is an AI technique that allows models to perform tasks without being trained directly on those tasks. The model learns from other related tasks to generalize to the new task. However, zero-shot learning models still struggle with complex tasks that require a large and diverse dataset for direct training.