Merging, in the context of generative AI, refers to the combination or fusion of different AI models or their characteristics. Similar to creating a collage, the best or desired features of multiple models are united into a new model. A practical example is the merging of different Stable Diffusion models, where one model’s ability to … Read more
Multimodal (from Latin: multi = many, multiple and modus = way, manner) refers, in the context of Artificial Intelligence, to an AI system’s ability to process and understand different types of input or “modalities” simultaneously. While earlier AI systems typically focused on a single form of communication – such as text or images – multimodal … Read more
Temperature in Large Language Models (LLMs) and other generative AI systems is a crucial control parameter that influences the randomness and creativity of the outputs. The value typically ranges from 0 to 1, where 0 represents highly deterministic (predictable) and 1 represents very random responses. With a low temperature setting, the system consistently repeats the … Read more
Fine tuning refers to the subsequent optimization of a pre-trained AI model for a specific task or vocabulary. For the original training of these models, large amounts of data from the Internet were first processed to gain a general understanding. Fine tuning then adapts this general knowledge to a specific use case. For example, an … Read more
RAG (Retrieval-Augmented Generation) is a technique where AI language models are enhanced with additional, external sources of information. In this process, the AI system’s responses are not only generated from its original training but are also supplemented (augmented) with specifically retrieved current or specialized data. It can be compared to an expert who doesn’t just … Read more