Artificial Intelligence Glossary: Neural Networks and Other Terms Explained

We have compiled a list containing phrases and concepts that can be used to understand artificial intelligence, especially the new chatbots with A.I. enabled like ChatGPT and Bing.

You might consider asking chatbots if you don't get the explanations or want to know more. Answering these questions is one of their most valuable skills and one of the best methods to understand A.I. It is important to use it. Keep in mind, however, that sometimes they get it wrong.

Bing and Bard chatbots will be slowly rolled out. You may have to wait on their waiting lists in order to gain access. ChatGPT does not have a waiting list at the moment, but you will need to create a free account.

Check out this article for more information about A.I.

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You may think it is cruel or kind based on its answers. Or you might believe it to be intelligent. It is able to mimic human language and is therefore sentient.

Bias is an error that can be caused by large-scale language models if the model's output is biased by its training data. A model might associate certain traits or professions to a particular race or gender, which can lead to incorrect predictions and offensive responses.

Emergent behavior: Unexpected and unintended capabilities in large language models, which are enabled by its learning patterns and rules derived from its training data. Models that have been trained in programming and coding can create new code. Creative abilities include the ability to write poetry, music, and create fictional stories.

Generative A.I. Generative A.I. : Technology that creates content, including images and video, by identifying patterns in large amounts of training data and creating original material with similar characteristics. ChatGPT is a text-only program, while DALL-E and Midjourney are images.

Hallucination is a well-known phenomenon in large language model, where the system gives an answer that is nonsensical, incorrect or irrelevant due to limitations in its training data.

Large language model: A type if neural network that can learn skills, such as writing code, conducting conversations and generating prose. It is able to analyze large amounts of text across the internet. These models are able to predict the next word in any sequence. However, experts have been amazed by their ability to learn new skills.

Natural language processing: A technique used by large language models for understanding and generating human language. It includes text classification and sentiment analysis. These methods use a combination machine learning algorithms, statistical models, and linguistic rules.

Neural network: A mathematical model of the human brain that learns by looking for statistical patterns in data. It is made up of layers of artificial neurons. The first layer receives input data and the final layer outputs the results. Even experts in neural networks don’t always know what happens between them.

Parameters are numerical values that describe a language model's structure or behavior. They can be clues that tell it what words to use next. GPT-4 systems are thought to contain hundreds of billions in parameters.

Reinforcement learning is a technique that teaches A.I. A technique that teaches an algorithm how to learn from its mistakes and rewards them or punish them. Humans can give feedback to improve the system's performance by giving it ratings, corrections, and suggestions.

Transformer model: A neural network architecture that is useful in understanding language. It does not need to analyse words one by one but can view a whole sentence at once. This was A.I. This was an A.I. breakthrough because it allowed models to understand context and long-term dependencies of language. Self-attention is a technique that allows a model to concentrate on specific words in order to understand the meaning of a sentence.