ChatGPT Glossary

Question 1

artificial general intelligence, or AGI

Answer 1

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.

Question 2

AI ethics

Answer 2

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

Question 3

AI safety

Answer 3

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.

Question 4

algorithm

Answer 4

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.

Question 5

alignment

Answer 5

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

Question 6

anthropomorphism

Answer 6

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.

Question 7

artificial intelligence, or AI

Answer 7

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.

Question 8

bias

Answer 8

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.

Question 9

chatbot

Answer 9

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

Question 10

ChatGPT

Answer 10

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

Question 11

cognitive computing

Answer 11

Another term for artificial intelligence.

Question 12

data augmentation

Answer 12

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

Question 13

deep learning

Answer 13

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.

Question 14

diffusion

Answer 14

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.

Question 15

emergent behavior

Answer 15

When an AI model exhibits unintended abilities.

Question 16

end-to-end learning, or E2E

Answer 16

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.

Question 17

ethical considerations

Answer 17

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

Question 18

foom

Answer 18

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.

Question 19

generative adversarial networks, or GANs

Answer 19

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.

Question 20

generative AI

Answer 20

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.

Question 21

Google Gemini

Answer 21

An AI chatbot by Google that functions similarly to ChatGPT but pulls information from the current web, whereas ChatGPT is limited to data until 2021 and isn’t connected to the internet.

Question 22

guardrails

Answer 22

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

Question 23

hallucination

Answer 23

An incorrect response from AI. Can include generative AI producing answers that are incorrect but stated with confidence as if correct.

Question 24

large language model, or LLM

Answer 24

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

Question 25

machine learning, or ML

Answer 25

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.

Question 26

Microsoft Bing

Answer 26

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.

Question 27

multimodal AI

Answer 27

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

Question 28

natural language processing

Answer 28

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.

Question 29

neural network

Answer 29

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.

Question 30

overfitting

Answer 30

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.

Question 31

paperclips

Answer 31

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.

Question 32

parameters

Answer 32

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

Question 33

prompt

Answer 33

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

Question 34

prompt chaining

Answer 34

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

Question 35

stochastic parrot

Answer 35

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.

Question 36

style transfer

Answer 36

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.

Question 37

temperature

Answer 37

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

Question 38

text-to-image generation

Answer 38

Creating images based on textual descriptions.

Question 39

tokens

Answer 39

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.

Question 40

training data

Answer 40

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

Question 41

transformer model

Answer 41

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.

Question 42

Turing test

Answer 42

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.

Question 43

weak AI, aka narrow AI

Answer 43

AI that’s focused on a particular task and can’t learn beyond its skill set. Most of today’s AI is weak AI.

Question 44

zero-shot learning

Answer 44

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.