Exponential growth of datapoints used to train notable AI systems
What you should know about this indicator
- Training data size refers to the volume of data employed to train an artificial intelligence (AI) model effectively. It's a representation of the number of examples that the model learns from during its training process. It is a fundamental measure of the scope of the data used in the model's learning phase.
- To grasp the concept of training data size, imagine teaching a friend the art of distinguishing different types of birds. In this analogy, each bird picture presented to your friend corresponds to an individual piece of training data. If you showed them 100 unique bird photos, then the training data size in this scenario would be quantified as 100.
- Training data size is an essential indicator in AI and machine learning. First and foremost, it directly impacts the depth of learning achieved by the model. The more extensive the dataset, the more profound and comprehensive the model's understanding of the subject matter becomes. Additionally, a large training data size contributes significantly to improved recognition capabilities. By exposing the model to a diverse array of examples, it becomes adept at identifying subtle nuances, much like how it becomes skilled at distinguishing various bird species through exposure to a large variety of bird images.
Sources and processing
This data is based on the following sources
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Citations
How to cite this page
To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:
“Data Page: Exponential growth of datapoints used to train notable AI systems”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch. Retrieved from https://ourworldindata.org/grapher/exponential-growth-of-datapoints-used-to-train-notable-ai-systems [online resource]How to cite this data
In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:
Epoch (2024) – with major processing by Our World in DataFull citation
Epoch (2024) – with major processing by Our World in Data. “Exponential growth of datapoints used to train notable AI systems” [dataset]. Epoch, “Parameter, Compute and Data Trends in Machine Learning” [original data]. Retrieved December 15, 2024 from https://ourworldindata.org/grapher/exponential-growth-of-datapoints-used-to-train-notable-ai-systems