Add to ORKGWe analyze the growth of dataset sizes used in machine learning for natural language processing and computer vision, and extrapolate these using two methods; using the historical growth rate and estimating the compute-optimal dataset size for future predicted compute budgets. We investigate the growth in data usage by estimating the total stock of unlabeled data available on the internet over the coming decades. Our analysis indicates that the stock of high-quality language data will be exhausted soon; likely before 2026. By contrast, the stock of low-quality language data and image data will be exhausted only much later; between 2030 and 2050 (for low-quality language) and between 2030 and 2060 (for images). Our work suggests that the curr...
It took until the last decade to finally see a machine match human performance on essentially any ta...
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations...
As machine learning models grow much larger nowadays, recent research found thatadvances to improve ...
We study trends in model size of notable machine learning systems over time using a curated dataset....
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
Running faster will only get you so far — it is generally advisable to first understand where the ro...
We study the compute-optimal trade-off between model and training data set sizes for large neural ne...
Deep learning's recent history has been one of achievement: from triumphing over humans in the game ...
abstract: Recently, a well-designed and well-trained neural network can yield state-of-the-art resul...
In the real world, data used to build machine learning models always has different sizes and charact...
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navi...
The tremendous recent growth in the fields of artificial intelligence and machine learning has large...
Skyrocketing data volumes, growing hardware capabilities, and the revolution in machine learning (ML...
Determining the optimal amount of training data for machine learning algorithms is a critical task i...
Machine-learned components, particularly those trained using deep learning methods, are becoming int...
It took until the last decade to finally see a machine match human performance on essentially any ta...
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations...
As machine learning models grow much larger nowadays, recent research found thatadvances to improve ...
We study trends in model size of notable machine learning systems over time using a curated dataset....
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
Running faster will only get you so far — it is generally advisable to first understand where the ro...
We study the compute-optimal trade-off between model and training data set sizes for large neural ne...
Deep learning's recent history has been one of achievement: from triumphing over humans in the game ...
abstract: Recently, a well-designed and well-trained neural network can yield state-of-the-art resul...
In the real world, data used to build machine learning models always has different sizes and charact...
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navi...
The tremendous recent growth in the fields of artificial intelligence and machine learning has large...
Skyrocketing data volumes, growing hardware capabilities, and the revolution in machine learning (ML...
Determining the optimal amount of training data for machine learning algorithms is a critical task i...
Machine-learned components, particularly those trained using deep learning methods, are becoming int...
It took until the last decade to finally see a machine match human performance on essentially any ta...
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations...
As machine learning models grow much larger nowadays, recent research found thatadvances to improve ...