Approved Yoruba tech terms in AI.
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Systems that perform tasks that typically require human intelligence.
Àwọn ètò tó ń ṣe iṣẹ́ tí ó jẹ́ mọ́ ọgbọ́n ènìyàn.
Systematic skew in data or models that unfairly favors some outcomes.
Ìyapa tó ń ṣeé fa àìṣòdodo nínú dátà tàbí àwòṣe.
Software that converses with users through text or voice.
Ètò tó ń bá olùmúlò sọ̀rọ̀ nípasẹ̀ ọ̀rọ̀ tàbí ohùn.
AI techniques that enable computers to interpret images and video.
Ọ̀nà Ọgbọ́n Àpinlẹ̀rọ tí ó jẹ́ kí ẹ̀rọ rí àti túmọ̀ àwòrán.
A model that splits data with if-then rules to reach a prediction.
Àwòṣe tó ń pín dátà pẹ̀lú àwọn òfin “bí… nígbà náà…”.
Machine learning using multi-layer neural networks.
Ìkẹ́ẹ̀kọ́ ẹ̀rọ pẹ̀lú àwọn nẹ́tíwọ́ọ̀kì ọpọlọ onípele púpọ̀.
A numeric vector that represents meaning for similarity search and ML.
Nọ́mbà tó ń sojú ìtumọ̀ ọ̀rọ̀ fún ìwádìí ìjọra.
AI that creates new text, images, audio, or code from prompts.
Ọgbọ́n àpinlẹ̀rọ tó ń ṣẹ̀dá ọ̀rọ̀, àwòrán, tàbí kóòdù tuntun.
Algorithms that improve from data instead of only following fixed rules.
Àwọn algọ́rídíìmù tó ń kọ́ láti inú dátà.
A learned or designed representation used to make predictions.
Àpéjúwe tí a kọ́ tàbí ṣe láti ṣe àsọtẹ́lẹ̀.
Computing techniques for understanding and generating human language.
Ọ̀nà kọ̀ǹpútà fún níníòye àti ṣíṣẹ̀dá èdè ènìyàn.
Instructions or context given to a generative AI system.
Ìtọ́sọ́nà tí a fún Ọgbọ́n àpinlẹ̀rọ aṣẹ̀dá.
How close two phrases are in meaning, not just spelling.
Bí ọ̀rọ̀ méjì ṣe jọra nínú ìtumọ̀.
Examples used to teach a machine learning model.
Àpẹẹrẹ tí a fi ń kọ́ àwòṣe Ìkẹ́ẹ̀kọ́ ẹ̀rọ.
