This is a list of 42 AI terms that everyone should know.

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The first time you probably heard of AI was probably in ChatGPT. The OpenAI robot that can answer any question and help you write songs, resumes, and fusion recipes can be very helpful. People have said that ChatGPT is like autocomplete but better. 

But apps aren’t the only thing that AI does. It’s cool to have ChatGPT do your homework or Midjourney make interesting pictures of mechs based on their place of origin, but what it could do is change economies. The McKinsey Global Institute says that this area of promise could be worth $4.4 trillion a year to the world economy. This is why you will be hearing a lot more about AI. 

New words are being used all over the place as people get used to a world where AI is a part of it. Here are some important AI terms you should know, whether you want to sound smart over drinks or in a job interview. 

This glossary will always be improved

AGI stands for “artificial general intelligence.” This is an idea for a more advanced form of AI than the one we have now, one that can do things much better than humans and also learn and improve itself. 

AI ethics are rules that try to keep AI from hurting people. This is done by figuring out things like how AI systems should gather data or handle bias. 

AI safety is a multidisciplinary field that looks at the long-term effects of AI and how it could quickly become a superintelligence that is unfriendly to people. 

An algorithm is a set of steps that tells a computer program how to learn and look at data in a certain way, like finding patterns. It can then use what it has learned to do things on its own.

Alignment: Changing an AI so that it does what you want it to do better. This can mean a lot of different things, from controlling material to keeping interactions with people pleasant. 

Anthropomorphism is when people tend to give alien things humanlike traits. In AI, this can mean thinking that a robot is smarter and more like a person than it is, like thinking it’s happy, sad, or even alive. 

Artificial intelligence, or AI, is the use of technology to make computer programs or robots act smart like people. A branch of computer science that tries to make machines that can do things that people can do.

Bias: When it comes to big language models, mistakes happen because of the training data. Based on assumptions, this can lead to wrongly linking certain traits to certain races or groups.

A chatbot is a computer program that talks to people through text messages that sound like real conversations. 

An AI robot called ChatGPT was made by OpenAI and uses big language model technology.

Artificial intelligence is also understood as cognitive computing.

Data enrichment means adding new data or changing old data in different ways to train an AI. 

Deep learning is an AI method and a branch of machine learning that uses many factors to find complicated patterns in text, images, and sounds. The method is based on the way the brain works and makes patterns using artificial neural networks.

Diffusion is a way for computers to learn by adding random noise to a piece of data that already exists, like a picture. Diffusion models teach their networks how to fix or get back that picture.

Emergent behavior is when an AI model does things that weren’t meant to be done

E2E stands for “end-to-end learning.” This is a deep learning method in which a model is taught to complete a job from beginning to end. It’s not taught to do things in a certain order; instead, it learns from what it is given and answers the problem all at once. 

Ethical considerations: Being aware of the moral effects of AI, including problems of privacy, data use, fairness, abuse, and other safety concerns. 

If you want to call it by another name, you can call it from. This idea says that if someone makes an AGI, it might be too late to save people.

GANs, or generative adversarial networks, are a type of generative AI model made up of two neural networks that work together to create new data: a producer and a discriminator. The discriminator checks to see if the content is real, while the creator makes new content.

Generative AI is a system that uses AI to make writing, video, computer code, or pictures. A lot of training data is fed to the AI, which then looks for patterns to come up with its answers, which are sometimes close to the original data.

Google Bard is an AI robot made by Google that works like ChatGPT but gets its information from the web right now, while ChatGPT can only use data from now until 2021 and isn’t tied to the internet.

There are rules and policies that AI models must follow to make sure that data is treated properly and that the model doesn’t make upsetting content. 

Hallucination: An AI reaction that isn’t right. Can include creative AI that gives answers that are wrong but are given with confidence as if they were right. We don’t fully comprehend why this is happening. If you ask an AI robot, “When did Leonardo da Vinci paint the Mona Lisa?” it might give you the wrong answer and say, “Leonardo da Vinci painted the Mona Lisa in 1815,” which is 300 years after it was painted

A large language model, or LLM, is an AI model that has been taught on a huge amount of text data to understand language and write new material in language that voices like it was written by a person.

Machine learning, or ML, is an aspect of AI that lets computers learn and make better predictions without being explicitly programmed to do soIt can be used with training sets to make new material. 

Google’s Bing is a search engine that can now use the same technology that powers ChatGPT to give you AI-powered search results. Being linked to the internet makes it like Google Bard. 

Multimodal AI is a type of AI that can handle different kinds of data, like words, writing, pictures, and videos. 

A part of AI called natural language processing uses machine learning and deep learning to help computers understand human language. It does this by using learning algorithms, statistical models, and rules of language.

Neural network: A computer model that looks like the organization of the human brain and is used to find trends in data. is made up of neurons, which are linked nodes that can learn and spot patterns over time. 

Overfitting is a mistake in machine learning that happens when the model works too closely with the training data and might only be able to find specific examples in that data but not in new data. 

Paperclips: Nick Boström, a physicist at the University of Oxford, came up with the idea of the “Paperclip Maximizer theory,” which is a made-up situation in which an AI system will make as many real paperclips as it can. An AI system might use up or change all resources to make as many paper clips as possible to reach its goal. This could mean taking apart other machines to make more paperclips, machines that could be useful to people. This AI system may kill humanity in its quest to make paperclips, which wasn’t what it was meant to do.

Parameters are numbers that tell LLM how to behave and give it structure so it can make predictions.

Prompt chaining is AI’s ability to use what it learned from previous contacts to shape how it responds in the future. 

There is a comparison between LLMs and a stochastic parrot that shows the software doesn’t understand language or the world around it, no matter how believable the result sounds. This saying refers to how a bird can copy human speech without knowing what it means

Style transfer is the skill of changing the look of one image to fit the content of another. This lets an AI understand the visual features of one image and use them on another. For example, making a self-portrait in the style of Picasso out of a Rembrandt painting. 

Temperature: The settings that determine how random the output of a language model is. When the temperature goes up, the computer takes on more danger. 

Text-to-image generation: making pictures from written words.

Training data are sets of text, pictures, code, or data that are used to help AI models learn.

The transformer model is a type of neural network design and deep learning model that learns context by following links in data, like parts of sentences or pictures. It doesn’t have to look at each word in a phrase one at a time; it can look at the whole thing and figure out what it means.

The Turing test, named after the famous philosopher and computer scientist Alan Turing, checks how well a machine can act like a person. If a person can’t tell the difference between the machine’s answer and that of another person, the machine passes. 

Weak AI, also called narrow AI, is AI that is only good at one job and can’t learn new things. A lot of AI today isn’t very good

Zero-shot learning is a test in which a model has to finish a job without being given the training data it needs. One example is being able to spot a lion even though you’ve only been taught to spot tigers.

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