Dictionary of AI Words Everyone Should Know
This post is part of Lifehacker’s “Living with AI” series. We explore the current state of AI, what it can do (and what it can’t do), and assess where this revolutionary technology will go next. Read more here .
Artificial Intelligence (AI) is the latest technological revolution. Just as the cryptocurrency boom brought a whole new set of jargon to the world, the hype around artificial intelligence has brought with it a set of terms that are often used but not always explained. If you’re wondering the difference between a chatbot and an LLM, or between deep learning and machine learning, you’ve come to the right place: here’s a glossary of 20 AI-related terms, along with beginner-friendly explanations of what AI is. it all means.
Artificial Intelligence (AI)
Simply put, AI is the intelligence of computers or machines, especially one that mimics human intelligence. AI is a broad term that covers many different types of machine intelligence, but right now the discussion around AI largely centers around tools that create art, content, and summarize or decipher content. Calling these tools “intelligent” is controversial, but the term “AI” has caught on.
Algorithm
An algorithm is a set of instructions that a program follows to give you a result. Common examples of algorithms include search engines that show you a set of results based on your queries, or social media apps that show content based on your interests. Algorithms allow artificial intelligence tools to create predictive models or create content or images based on your data.
Bias
In the context of AI, bias refers to erroneous results obtained because the algorithm makes incorrect assumptions or lacks sufficient data. For example, speech recognition tools may not be able to correctly understand some English accents because the tools were only trained on American accents.
Conversational AI
AI tools that you can communicate with, such as chatbots or voice assistants, are called conversational AI. If you yourself ask the assistant about something, then this is conversational AI.
Data collection
The process of analyzing large data sets to identify patterns or trends. Some AI tools use data mining to help you understand what makes people buy more items in a store or website, or how to optimize your business to meet increased demand during peak times.
Deep learning
Deep learning attempts to recreate the way the human brain learns by using three or more “layers” of a neural network to process large amounts of data and learn from examples. Each of these layers processes the data differently and combines to arrive at the final conclusion.
Self-driving car software uses deep learning to recognize stop signs, lane markings and traffic lights through object recognition: this is achieved by showing the AI tool many examples of what a certain object (such as a stop sign) looks like, and by retraining the AI tool intelligence will eventually be able to identify this object with the greatest possible accuracy.
Large Language Model (LLM)
The Large Language Model (LLM) is a deep learning algorithm that trains on a huge dataset to generate, translate, and process text. LLMs ( like OpenAI’s GPT-4 ) allow AI tools to understand your queries and generate textual inputs based on them. LLMs are also used in artificial intelligence tools that can identify important parts of text or video and summarize them for you .
Generative AI
Generative AI can generate drawings, images, text, or other results based on your input data, which is often based on LLM. It has become an all-encompassing term for the current artificial intelligence technologies that many companies are now adding to their products. For example, a generative AI model can generate an image using a few text prompts or turn a vertical photo into a widescreen wallpaper.
Hallucination
When AI presents fiction as fact, we call it hallucinations. Hallucinations can occur when the AI’s data set is inaccurate or its training is flawed, so it produces an answer that it is confident of based on the knowledge it has. However, because AI is based on a complex network of networks, we don’t necessarily understand every example of hallucination. Lifehacker author Steven Johnson has great advice on how to spot AI hallucinations .
Image recognition
Ability to identify specific objects in an image. Computer programs can use image recognition to find and name flowers in an image, and to identify different species of birds in a photograph.
Machine learning
When algorithms can improve by learning from experience or data, it is called machine learning. Machine learning is a general practice from which the other AI terms we discuss arise: deep learning is a form of machine learning, and large language models are trained using machine learning.
Natural Language Processing
When a program can understand input written in human languages, it falls under natural language processing. This is how your calendar app knows what to do when you text, “I have a meeting tomorrow at 8 p.m. at the café on Fifth Avenue,” or when you ask Siri, “What’s the weather today?”
Neural networks
The human brain is made up of layers of neurons that constantly process information and learn from it. An artificial intelligence neural network imitates this structure of neurons to learn from data sets. A neural network is a system that enables machine learning and deep learning and ultimately enables machines to perform complex tasks such as image recognition and text generation.
Optical Character Recognition (OCR)
The process of extracting text from images is done using OCR. Programs that support OCR can recognize handwritten or typed text and allow you to copy and paste it.
Operational Design
A hint is any sequence of words that you use to get a response from a program, such as a generative AI. In the context of artificial intelligence, prompt engineering is the art of writing prompts to allow chatbots to give you the most helpful answers. This is also an area where people are hired to provide creative input to test AI tools and identify its limitations and weaknesses.
Reinforcement learning from human feedback (RLHF)
RLHF is the process of training AI using feedback from humans. When the AI produces incorrect results, a human shows it what the correct answer should be. This allows AI to provide accurate and useful results much faster than it would otherwise.
Speech recognition
The program’s ability to understand human speech. Speech recognition can be used for conversational AI to understand your queries and provide answers, or for speech-to-text tools to understand spoken words and convert them into text.
Token
When you feed a text query into an AI tool, it breaks that text into tokens, common sequences of characters in the text, which are then processed by the AI program. For example, if you use the GPT model, the price depends on the number of tokens processed: you can calculate this number using the company’s tokenizer tool , which also shows how words are broken down into tokens. OpenAI states that one token represents approximately four characters of text.
Training data
The training set or training data is the information that an algorithm or machine learning tool uses to learn and perform its function. For example, large language models can use training data by scraping some of the world’s most popular websites to collect text, queries, and human expressions.
Turing test
Alan Turing was a British mathematician known as the “father of theoretical computer science and artificial intelligence.” His Turing Test (or “Imitation Game”) is designed to determine whether a computer’s intelligence is identical to that of a human. A computer is said to have passed the Turing test when a person is tricked into thinking that the machine’s answers were written by a human.