Data
Exponential growth of parameters in notable AI systems
What you should know about this indicator
- Parameters are internal variables that machine learning models adjust during their training process to improve their ability to make accurate predictions. They act as the model's "knobs" that are fine-tuned based on the provided data. In deep learning, a subset of artificial intelligence (AI), parameters primarily consist of the weights assigned to the connections between the small processing units called neurons. Picture a vast network of interconnected neurons where the strength of each connection represents a parameter.
- The total number of parameters in a model is influenced by various factors. The model's structure and the number of “layers” of neurons play a significant role. Generally, more complex models with additional layers tend to have a higher number of parameters. Special components of specific deep learning architectures can further contribute to the overall parameter count.
- Understanding the number of parameters in a model is crucial to design effective models. More parameters can help the model understand complex data patterns, potentially leading to higher accuracy. However, there's a fine balance to strike. If a model has too many parameters, it risks memorizing the specific examples in its training data rather than learning their underlying patterns. Consequently, it may perform poorly when presented with new, unseen data. Achieving the right balance of parameters is a critical consideration in model development.
- In recent times, the AI community has witnessed the emergence of what are often referred to as "giant models." These models boast an astounding number of parameters, reaching into the billions or even trillions. While these huge models have achieved remarkable performance, they have a significant computational cost. Effectively managing and training such large-scale models has become a prominent and active area of research and discussion within the AI field.
Exponential growth of parameters in notable AI systems
Total number of learnable variables or weights that the model contains. Parameters are adjusted during the training process to optimize the model's performance.
Source
Epoch (2024) – with major processing by Our World in Data
Last updated
November 3, 2024
Next expected update
December 2024
Sources and processing
This data is based on the following sources
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Citations
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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 parameters in 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-parameters-in-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 parameters in notable AI systems” [dataset]. Epoch, “Parameter, Compute and Data Trends in Machine Learning” [original data]. Retrieved November 21, 2024 from https://ourworldindata.org/grapher/exponential-growth-of-parameters-in-notable-ai-systems