Machine learning’s impact on technology is significant, but it’s crucial to acknowledge the common issues of insufficient training and testing data.

Machine learning models are trained with huge amounts of data and must be tested before practical use. For this, the data must first be divided into a larger training set and a smaller test set ...

Data for model training and testing were generated from over 13,500 DNA and RNA contrived samples, with variants spiked in at a variant allele frequency (VAF) of 0.1%-82% for DNA and 6-5,000 copies ...

OpenAI is launching a new program to encourage organizations to contribute data -- including text and images -- to train future AI models.

Where real data is unethical, unavailable, or doesn’t exist, synthetic data sets can provide the needed quantity and variety.

As the discipline advances, Ether0’s synergy of Q&A-guided training, chain-of-thought clarity, and data frugality represents a new standard for what is possible in scientific reasoning models.

Our understanding of progress in machine learning has been colored by flawed testing data. The 10 most cited AI data sets are riddled with label errors, according to a new study out of MIT, and it ...