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Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]
Free artificial intelligence applications (8 P) Pages in category "Open-source artificial intelligence" The following 39 pages are in this category, out of 39 total.
Mycroft, a free and open-source intelligent personal assistant that uses a natural language user interface. [62] PARRY, another early chatterbot, written in 1972 by Kenneth Colby, attempting to simulate a paranoid schizophrenic. [63] SHRDLU, an early natural language processing computer program developed by Terry Winograd at MIT from 1968 to ...
Free and open-source software (FOSS) or free/libre and open-source software (FLOSS) is openly shared source code that is licensed without any restrictions on usage, modification, or distribution. [ citation needed ] Confusion persists about this definition because the "free", also known as "libre", refers to the freedom of the product, not the ...
This is a list of free and open-source software packages, computer software licensed under free software licenses and open-source licenses.Software that fits the Free Software Definition may be more appropriately called free software; the GNU project in particular objects to their works being referred to as open-source. [1]
This page was last edited on 2 September 2020, at 12:13 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
Artificial intelligence engineering (or AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making published research more easily reproducible [24] [137] while providing users with a simple interface for interacting with these ...