AI Glossar

Natural language processing (NLP) is a subfield of AI and linguistics which enables computers to understand, interpret and manipulate human language. 

https://research.aimultiple.com/nlp-use-cases/

Natural Language Understanding (NLU) is a field that focuses on understanding the meaning of text or speech to respond better. It searches for what is the meaning and the purpose of that speech. 

https://research.aimultiple.com/nlu/

Natural language generation (NLG) is the AI technology behind text content automation with its capability to convert data into words, sentences, articles and even film scripts.

https://research.aimultiple.com/nlg/

A Generative Adversarial Network (GAN) is a type of generative AI model that utilizes two neural networks in a unique and adversarial way to generate new data that resembles the training data. 

https://research.aimultiple.com/gan-use-cases/

Sentiment analysis is a Natural Language Processing (NLP) technique that categorizes texts, audio, images, or videos as positive, negative, or neutral based on their emotional tone. It helps businesses understand customer sentiment and improve their services and products based on customer needs. 

Large Language Models (LLMs) are advanced artificial intelligence models that can process and generate human-like text on various topics using training data.

AI hallucinations are incorrect or misleading results that AI models generate. These errors can be caused by a variety of factors, including insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model. AI hallucinations can be a problem for AI systems that are used to make important decisions, such as medical diagnoses or financial trading.

A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data. These deep learning algorithms are commonly used for ordinal or temporal problems, such as language translation, natural language processing (nlp), speech recognition, and image captioning; they are incorporated into popular applications such as Siri, voice search, and Google Translate.

Fine-tuning represents a technique to supplement the training of models like GPT-3, which rely on extensive data sets sourced from diverse origins with more specialized or enterprise-specific data. When the model subsequently generates text, it will produce more focused and accurate output, thereby mitigating the likelihood of spurious or nonmeaningful text. This process facilitates the model's capacity to tailor its output in alignment with the newly incorporated training data

The essence of prompt engineering lies in the deliberate configuration of input structure and content, which is orchestrated to guide and shape the model's output qualitatively. The merits of prompt engineering are manifold—notably, enhancing the precision of the generated text, exercising some degree of control over the output, and, crucially, mitigating inherent bias.