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Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Lazy evaluation and the list and LogicT monads make it easy to express non-deterministic algorithms, which is often the case. Infinite data structures are useful for search trees. The language's features enable a compositional way to express algorithms. Working with graphs is however a bit harder at first because of functional purity.
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Generative artificial intelligence (generative AI, GenAI, [167] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 168 ] [ 169 ] [ 170 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 171 ...
Using procedural generation in games had origins in the tabletop role playing game (RPG) venue. [4] The leading tabletop system, Advanced Dungeons & Dragons, provided ways for the "dungeon master" to generate dungeons and terrain using random die rolls, expanded in later editions with complex branching procedural tables.
AI and machine learning (ML) further improve computation efficiency in complex climate-responsive sustainable design. one study employed reinforcement learning to identify the relationship between design parameters and energy use for a sustainable campus, [22] while some other studies tried hybrid algorithms, such as using the genetic algorithm ...
Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model. [ 1 ] A prompt is natural language text describing the task that an AI should perform. [ 2 ]
The Sidewinder algorithm is trivial to solve from the bottom up because it has no upward dead ends. [5] Given a starting width, both algorithms create perfect mazes of unlimited height. Most maze generation algorithms require maintaining relationships between cells within it, to ensure the result will be solvable.