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Undetectable AI (or Undetectable.ai) is an artificial intelligence content detection and modification software designed to identify and alter artificially generated text, such as that produced by large language models.
Prompt engineering may involve phrasing a query, specifying a style, choice of words and grammar, [3] providing relevant context, or describing a character for the AI to mimic. [1] When communicating with a text-to-image or a text-to-audio model, a typical prompt is a description of a desired output such as "a high-quality photo of an astronaut ...
Jupyter Notebooks can execute cells of Python code, retaining the context between the execution of cells, which usually facilitates interactive data exploration. [5] Elixir is a high-level functional programming language based on the Erlang VM. Its machine-learning ecosystem includes Nx for computing on CPUs and GPUs, Bumblebee and Axon for ...
In the field of artificial intelligence (AI), a hallucination or artificial hallucination (also called bullshitting, [1] [2] confabulation [3] or delusion [4]) is a response generated by AI that contains false or misleading information presented as fact.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
Generative AI can also be trained on the motions of a robotic system to generate new trajectories for motion planning or navigation. For example, UniPi from Google Research uses prompts like "pick up blue bowl" or "wipe plate with yellow sponge" to control movements of a robot arm. [78]
GitHub Copilot was initially powered by the OpenAI Codex, [13] which is a modified, production version of the Generative Pre-trained Transformer 3 (GPT-3), a language model using deep-learning to produce human-like text. [14] The Codex model is additionally trained on gigabytes of source code in a dozen programming languages.
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.