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AI is present everywhere. From the widespread popularity ofChatGPT to Google cramming AI summariesAt the top of search results, AI is increasingly dominating the online landscape. Using AI, you can quickly find answers to nearly any query. It can seem like conversing with someone who holds a doctorate in every subject.
But this feature of AI chatbots is just a single element of the broader AI field. Certainly, possessingChatGPT assist with your homework or having Midjourney create interesting pictures of mechs inspired by their country of originis great, but the possibilities of generative AI might completely transform economies. That could be valuable$4.4 trillion contributes to the global economy each year, as stated by the McKinsey Global Institute, which is why you can anticipate hearing increasingly more about artificial intelligence.
It's appearing in an overwhelming variety of products -- a brief, brief list includes Google'sGemini, Microsoft's Copilot, Anthropic's Claude and the Perplexitysearch engine. You can find our reviews and practical assessments of these and other items, as well as news, explanations, and tutorial articles, on our site.AI Atlas hub.
As individuals grow more familiar with a world deeply connected to artificial intelligence, new terminology is emerging everywhere. Whether you're looking to appear knowledgeable during casual conversations or make an impression in a job interview, here are some key AI terms you should be aware of.
This glossary is updated on a regular basis.
artificial general intelligence, also known as AGI:A notion implying a more sophisticated form of artificial intelligence than currently exists, capable of executing tasks far more effectively than humans, while also learning and enhancing its own abilities.
agentive:Systems or models that demonstrate autonomy by independently taking actions to reach a specific objective. In the realm of AI, an agentive model can operate without continuous oversight, like a high-level self-driving vehicle. Unlike an "agentic" system, which functions in the background, agentive systems are prominent, emphasizing the user's interaction.
AI ethics:Core guidelines designed to ensure AI does not cause harm to people, accomplished by methods such as establishing how AI systems gather information or handle prejudice.
AI safety:A multidisciplinary area focused on the lasting effects of artificial intelligence and the potential for it to rapidly advance to a level of superintelligence that might pose a threat to humanity.
algorithm:A set of guidelines that enables a computer program to gain understanding and examine data in a specific manner, like identifying trends, and subsequently use that knowledge to perform tasks independently.
alignment:Adjusting an artificial intelligence system to enhance its ability to achieve the intended result. This may include tasks such as filtering content or ensuring favorable engagement with users.
anthropomorphism:The tendency of people to attribute human traits to non-human entities. In the field of artificial intelligence, this might involve perceiving a chatbot as more human-like and conscious than it truly is, such as thinking it experiences emotions like happiness or sadness, or even possesses sentience.
artificial intelligence, or AI:The application of technology to mimic human intelligence, whether in software or robotic systems. A branch of computer science focused on creating systems capable of carrying out tasks that typically require human involvement.
autonomous agents:An artificial intelligence system equipped with the necessary features, code, and tools to perform a particular job. A self-driving car serves as an autonomous agent, for instance, as it includes sensory inputs, GPS technology, and driving algorithms to operate independently on the road.Stanford researchershave demonstrated that self-governing entities can create their own customs, practices, and common means of communication.
bias:Regarding large language models, mistakes can arise from the training data. This may lead to incorrectly assigning specific traits to particular races or groups based on stereotypes.
chatbot:A software application that interacts with people via text, mimicking human communication.
ChatGPT:A chatbot created by an AIOpenAIthat employs advanced language model technology.
cognitive computing:Another name for artificial intelligence.
data augmentation:Reworking current data or incorporating a broader range of data to educate an AI.
dataset:A set of digital data utilized for training, evaluating, and confirming an artificial intelligence model.
deep learning:An AI technique, belonging to the field of machine learning, that employs numerous parameters to identify intricate patterns in images, audio, and written content. This approach is modeled after the human brain and utilizes artificial neural networks to form these patterns.
diffusion:A machine learning technique that starts with an existing data sample, such as an image, and introduces random noise. Diffusion models teach their networks to reconstruct or restore the original image.
emergent behavior:When an artificial intelligence system demonstrates unexpected skills.
end-to-end learning, or E2E:A machine learning approach where a model is directed to complete a task from beginning to end. It isn't trained to handle the task step by step, but rather learns from the input data and resolves the entire problem simultaneously.
ethical considerations:Understanding the moral consequences of artificial intelligence and concerns surrounding privacy, data handling, equity, abuse, and other safety matters.
foom:Also referred to as rapid takeoff or intense takeoff. The idea that if an AGI is developed, it could be too late to ensure human survival.
generative adversarial networks, also known as GANs:A machine learning system that utilizes two neural networks to produce new data: one responsible for generating content and the other for evaluating its authenticity.
generative AI:A technology that leverages artificial intelligence to produce text, video, computer code, or images. The AI is provided with extensive training data, identifies patterns, and creates original outputs that may occasionally resemble the input material.
Google Gemini:A Google-developed AI chatbot that operates in a manner comparable to ChatGPT, while also accessing data from Google's various services, including Search and Maps.
guardrails:Rules and limitations imposed on AI systems to guarantee proper data management and prevent the generation of offensive material.
hallucination:An inaccurate response from AI. Can involve generative AI creating answers that are wrong but presented confidently as correct. The causes for this are not fully understood. For instance, when asking an AI chatbot, "When did Leonardo da Vinci paint the Mona Lisa?" itmay give an incorrect responsestating, "Leonardo da Vinci created the Mona Lisa in 1815," which is 300 years later than when it was truly painted.
inference:The method by which artificial intelligence models create text, images, and other materials related to new information, throughinferring from their training data.
large language model, or LLM:A machine learning system developed using vast quantities of textual information to comprehend language and produce original content that resembles human speech.
latency:The lag between an AI system receiving a query or input and generating a response.
machine learning, or ML:A part of artificial intelligence that enables computers to learn and produce improved predictions without requiring direct programming. It can be used with training data to create new material.
Microsoft Bing:A Microsoft-developed search engine that can now leverage the technology behind ChatGPT to deliver AI-driven search outcomes. It is comparable to Google Gemini in its ability to connect to the internet.
multimodal AI:A form of artificial intelligence capable of handling various input types, such as text, images, videos, and audio.
natural language processing:A subset of artificial intelligence that employs machine learning and neural networks to enable computers to interpret human language, typically through the use of learning algorithms, statistical techniques, and linguistic guidelines.
neural network:A computer model that mimics the architecture of the human brain and is designed to identify patterns within data. It is composed of linked nodes, or neurons, which can detect patterns and improve with experience over time.
overfitting:A problem in machine learning where the model performs excessively well on the training data, potentially recognizing only specific instances from that data rather than generalizing to new information.
paperclips:The Paperclip Maximizer theory, introduced by a philosopherNick Boströmfrom the University of Oxford, is a theoretical situation in which an AI system aims to manufacture as many physical paperclips as possible. In its pursuit of creating the highest quantity of paperclips, an AI might hypothetically utilize or transform all available resources to meet its objective. This could involve disassembling other equipment to produce more paperclips, even if that equipment could be useful to humans. The unexpected outcome of such an AI system could be the destruction of humanity while it focuses on making paperclips.
parameters:Numbers that provide structure and functionality to large language models, allowing them to generate forecasts.
Perplexity:A chatbot and search engine developed by Perplexity AI that utilizes a powerful language model, similar to other AI chatbots, but offers access to the open internet for current information.
prompt:The query or inquiry you input into an AI chatbot to receive a reply.
prompt chaining:The capacity of artificial intelligence to leverage data from past conversations to influence subsequent replies.
quantization:The method of reducing the size and improving the efficiency of an AI large-scale learning model (though with a slight decrease in accuracy) by decreasing its precision from a higher format to a lower one. A helpful way to understand this is by comparing a 16-megapixel image to an 8-megapixel image. Both remain clear and visible, but the higher resolution image will show more detail when zoomed in.
stochastic parrot:A comparison of LLMs that demonstrates the software lacks a true grasp of the meaning of language or the world, even if the output seems convincing. The term describes how a parrot can repeat human words without comprehending their significance.
style transfer:The capacity to modify the style of one image based on the content of another, enabling an AI to understand the visual characteristics of one image and apply them to a different one. For instance, using Rembrandt's self-portrait and recreating it in Picasso's style.
synthetic data:Artificially generated data by AI systems that does not originate from the real world but is based on authentic data. It serves the purpose of training mathematical, machine learning, and deep learning models.
temperature:Settings configured to influence the randomness of a language model's responses. A higher temperature indicates the model is more inclined to take chances.
text-to-image generation:Generating visuals from written descriptions.
tokens:Tiny pieces of written text that AI language systems analyze to generate their replies to your queries. A token corresponds to four English characters, or roughly three-quarters of a word.
training data:The data sets utilized to assist AI models in learning, such as text, images, code, or other information.
transformer model:A neural network design and deep learning system that gains understanding of context by monitoring connections within data, such as in sentences or image segments. Therefore, rather than examining a sentence word by word, it can consider the entire sentence and grasp the meaning.
Turing test:Named for the renowned mathematician and computer scientist Alan Turing, this test evaluates a machine's capacity to mimic human behavior. The machine succeeds if a human is unable to tell the difference between its response and that of another person.
unsupervised learning:A type of machine learning in which the model is not given any labeled training data, but rather must discover patterns within the data on its own.
limited AI, also known as specialized AI:Artificial intelligence designed for a specific function and unable to acquire knowledge outside of its designated capabilities. The majority of current AI systems are examples of weak AI.
zero-shot learning:A trial where a model is required to perform a task without receiving the necessary training information. For instance, identifying a lion when trained exclusively on tigers.
Released on July 7, 2025, at 1:06 p.m. PT.
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