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Generative artificial intelligence ( generative AI, GenAI, [1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models, [2] often in response to prompts. [3] [4] Generative AI models learn the patterns and structure of their input training data and then generate new data that has ...
Jupyter Notebooks can execute cells of Python code, retaining the context between the execution of cells, which usually facilitates interactive data exploration. [5] R is widely used in new-style artificial intelligence, involving statistical computations, numerical analysis, the use of Bayesian inference , neural networks and in general ...
Several code generation DSLs (attribute grammars, tree patterns, source-to-source rewrites) Active. DSLs represented as abstract syntax trees. DSL instance. Well-formed output language code fragments. Any programming language (proven for C, C++, Java, C#, PHP, COBOL) DRAKON.
GPT-3, specifically the Codex model, is the basis for GitHub Copilot, a code completion and generation software that can be used in various code editors and IDEs. GPT-3 is used in certain Microsoft products to translate conventional language into formal computer code.
LangChain.com. LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.
Keras. Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [can be used] as a low-level cross-framework language to develop custom components such as layers ...
GitHub reports that Copilot’s autocomplete feature is accurate roughly half of the time; with some Python function header code, for example, Copilot correctly autocompleted the rest of the function body code 43% of the time on the first try and 57% of the time after ten attempts.
They said that GPT-4 could also read, analyze or generate up to 25,000 words of text, and write code in all major programming languages. [196] Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier revisions. [197]