ChatGPT Glossary, 56 AI Terms perhaps Everyone Should Know, received 2025 09 06

[Colin Howard]

CNET - Friday, September 5, 2025

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AI is rapidly changing the world around us. It's eliminating jobs and
flooding the internet with slop. Thanks to the massive popularity of ChatGPT
to Google cramming AI summaries at the top of its search results, AI is
completely taking over the internet. With AI, you can get instant answers to
pretty much any question. It can feel like talking to someone who has a
doctoral degree in everything.

But that aspect of AI chatbots is only one part of the AI landscape. Sure,
having ChatGPT help do your homework or having Midjourney create fascinating
images of mechs based on the country of origin is cool, but the potential of
generative AI could completely reshape economies. That could be worth $4.4
trillion to the global economy annually, according to McKinsey Global
Institute, which is why you should expect to hear more and more about
artificial intelligence.

It's showing up in a dizzying array of products -- a short, short list
includes Google's Gemini, Microsoft's Copilot, Anthropic's Claude and the
Perplexity search engine. You can read our reviews and hands-on evaluations
of those and other products, along with news, explainers and how-to posts,
at our AI Atlas hub.

As people become more accustomed to a world intertwined with AI, new terms
are popping up everywhere. So whether you're trying to sound smart over
drinks or impress in a job interview, here are some important AI terms you
should know.

This glossary is regularly updated.

artificial general intelligence, or AGI: A concept that suggests a more
advanced version of AI than we know today, one that can perform tasks much
better than humans while also teaching and advancing its own capabilities.

agentive: Systems or models that exhibit agency with the ability to
autonomously pursue actions to achieve a goal. In the context of AI, an
agentive model can act without constant supervision, such as an high-level
autonomous car. Unlike an "agentic" framework, which is in the background,
agentive frameworks are out front, focusing on the user experience.

AI ethics: Principles aimed at preventing AI from harming humans, achieved
through means like determining how AI systems should collect data or deal
with bias.

AI safety: An interdisciplinary field that's concerned with the long-term
impacts of AI and how it could progress suddenly to a super intelligence
that could be hostile to humans.

algorithm: A series of instructions that allows a computer program to learn
and analyze data in a particular way, such as recognizing patterns, to then
learn from it and accomplish tasks on its own.

alignment: Tweaking an AI to better produce the desired outcome. This can
refer to anything from moderating content to maintaining positive
interactions with humans.

anthropomorphism: When humans tend to give nonhuman objects humanlike
characteristics. In AI, this can include believing a chatbot is more
humanlike and aware than it actually is, like believing it's happy, sad or
even sentient altogether.

artificial intelligence, or AI: The use of technology to simulate human
intelligence, either in computer programs or robotics. A field in computer
science that aims to build systems that can perform human tasks.

autonomous agents: An AI model that have the capabilities, programming and
other tools to accomplish a specific task. A self-driving car is an
autonomous agent, for example, because it has sensory inputs, GPS and
driving algorithms to navigate the road on its own. Stanford researchers
have shown that autonomous agents can develop their own cultures, traditions
and shared language.

bias: In regards to large language models, errors resulting from the
training data. This can result in falsely attributing certain
characteristics to certain races or groups based on stereotypes.

chatbot: A program that communicates with humans through text that simulates
human language.

ChatGPT: An AI chatbot developed by OpenAI that uses large language model
technology.

cognitive computing: Another term for artificial intelligence.

data augmentation: Remixing existing data or adding a more diverse set of
data to train an AI.

dataset: A collection of digital information used to train, test and
validate an AI model.

deep learning: A method of AI, and a subfield of machine learning, that uses
multiple parameters to recognize complex patterns in pictures, sound and
text. The process is inspired by the human brain and uses artificial neural
networks to create patterns.

diffusion: A method of machine learning that takes an existing piece of
data, like a photo, and adds random noise. Diffusion models train their
networks to re-engineer or recover that photo.

emergent behavior: When an AI model exhibits unintended abilities.

end-to-end learning, or E2E: A deep learning process in which a model is
instructed to perform a task from start to finish. It's not trained to
accomplish a task sequentially but instead learns from the inputs and solves
it all at once.

ethical considerations: An awareness of the ethical implications of AI and
issues related to privacy, data usage, fairness, misuse and other safety
issues.

foom: Also known as fast takeoff or hard takeoff. The concept that if
someone builds an AGI that it might already be too late to save humanity.

generative adversarial networks, or GANs: A generative AI model composed of
two neural networks to generate new data: a generator and a discriminator.
The generator creates new content, and the discriminator checks to see if
it's authentic.

generative AI: A content-generating technology that uses AI to create text,
video, computer code or images. The AI is fed large amounts of training
data, finds patterns to generate its own novel responses, which can
sometimes be similar to the source material.

Google Gemini: An AI chatbot by Google that functions similarly to ChatGPT
but also pulls information from Google's other services, like Search and
Maps.

guardrails: Policies and restrictions placed on AI models to ensure data is
handled responsibly and that the model doesn't create disturbing content.

hallucination: An incorrect response from AI. Can include generative AI
producing answers that are incorrect but stated with confidence as if
correct. The reasons for this aren't entirely known. For example, when
asking an AI chatbot, "When did Leonardo da Vinci paint the Mona Lisa?" it
may respond with an incorrect statement saying, "Leonardo da Vinci painted
the Mona Lisa in 1815," which is 300 years after it was actually painted.

inference: The process AI models use to generate text, images and other
content about new data, by inferring from their training data.

large language model, or LLM: An AI model trained on mass amounts of text
data to understand language and generate novel content in human-like
language.

latency: The time delay from when an AI system receives an input or prompt
and produces an output.

machine learning, or ML: A component in AI that allows computers to learn
and make better predictive outcomes without explicit programming. Can be
coupled with training sets to generate new content.

Microsoft Bing: A search engine by Microsoft that can now use the technology
powering ChatGPT to give AI-powered search results. It's similar to Google
Gemini in being connected to the internet.

multimodal AI: A type of AI that can process multiple types of inputs,
including text, images, videos and speech.

natural language processing: A branch of AI that uses machine learning and
deep learning to give computers the ability to understand human language,
often using learning algorithms, statistical models and linguistic rules.

neural network: A computational model that resembles the human brain's
structure and is meant to recognize patterns in data. Consists of
interconnected nodes, or neurons, that can recognize patterns and learn over
time.

open weights: When a company releases an open weights model, the final
weights of the model -- how it interprets information from its training
data, including biases -- are made publicly available. Open weights models
are typically available for download to be run locally on your device.

overfitting: Error in machine learning where it functions too closely to the
training data and may only be able to identify specific examples in said
data, but not new data.

paperclips: The Paperclip Maximiser theory, coined by philosopher Nick
Boström of the University of Oxford, is a hypothetical scenario where an AI
system will create as many literal paperclips as possible. In its goal to
produce the maximum amount of paperclips, an AI system would hypothetically
consume or convert all materials to achieve its goal. This could include
dismantling other machinery to produce more paperclips, machinery that could
be beneficial to humans. The unintended consequence of this AI system is
that it may destroy humanity in its goal to make paperclips.

parameters: Numerical values that give LLMs structure and behavior, enabling
it to make predictions.

Perplexity: The name of an AI-powered chatbot and search engine owned by
Perplexity AI. It uses a large language model, like those found in other AI
chatbots, but has a connection to the open internet for up-to-date results.

prompt: The suggestion or question you enter into an AI chatbot to get a
response.

prompt chaining: The ability of AI to use information from previous
interactions to color future responses.

prompt engineering: the process of writing prompts for AIs to achieve a
desired outcome. It requires detailed instructions, combining
chain-of-thought prompting and other techniques, including highly specific
text. Prompt engineering can also be used maliciously to force models to
behave in ways they weren't originally intended for.

quantization: The process by which an AI large learning model is made
smaller and more efficient (albeit, slightly less accurate) by lowering its
precision from a higher format to a lower format. A good way to think about
this is to compare a 16-megapixel image to an 8-megapixel image. Both are
still clear and visible, but the higher resolution image will have more
detail when you're zoomed in.

slop: low-quality online content made at high volume by AI to garner views
with little labor or effort. The goal with AI slop, in the realm of Google
Search and social media, is to flood feeds with so much content that it
captures as much ad revenue as possible, usually at the detriment of actual
publishers and creators. While some social media sites embrace the influx of
AI slop, others are pushing back.

stochastic parrot: An analogy of LLMs that illustrates that the software
doesn't have a larger understanding of meaning behind language or the world
around it, regardless of how convincing the output sounds. The phrase refers
to how a parrot can mimic human words without understanding the meaning
behind them.

style transfer: The ability to adapt the style of one image to the content
of another, allowing an AI to interpret the visual attributes of one image
and use it on another. For example, taking the self-portrait of Rembrandt
and re-creating it in the style of Picasso.

synthetic data: Data created by generative AI that isn't from the actual
world but is trained on real data. It's used to train mathematical, ML and
deep learning models.

temperature: Parameters set to control how random a language model's output
is. A higher temperature means the model takes more risks.

text-to-image generation: Creating images based on textual descriptions.

tokens: Small bits of written text that AI language models process to
formulate their responses to your prompts. A token is equivalent to four
characters in English, or about three-quarters of a word.

training data: The datasets used to help AI models learn, including text,
images, code or data.

transformer model: A neural network architecture and deep learning model
that learns context by tracking relationships in data, like in sentences or
parts of images. So, instead of analyzing a sentence one word at a time, it
can look at the whole sentence and understand the context.

Turing test: Named after famed mathematician and computer scientist Alan
Turing, it tests a machine's ability to behave like a human. The machine
passes if a human can't distinguish the machine's response from another
human.

unsupervised learning: A form of machine learning where labeled training
data isn't provided to the model and instead the model must identify
patterns in data by itself.

weak AI, aka narrow AI: AI that's focused on a particular task and can't
learn beyond its skill set. Most of today's AI is weak AI.

zero-shot learning: A test in which a model must complete a task without
being given the requisite training data. An example would be recognizing a
lion while only being trained on tigers.

https://www.cnet.com/tech/services-and-software/chatgpt-glossary-56-ai-terms-everyone-should-know/#ftag=CADf328eec


Colin Howard, Southern England.