On February 14, a researcher who was frustrated with reproducing the results of a machine learning research paper opened up a Reddit account under the username ContributionSecure14 and posted the ...

Read Lones’s full paper, titled, “How to avoid machine learning pitfalls: a guide for academic researchers,” for more details about common mistakes in the ML research and development process.

This project's goal was the classification of road types across all of Michigan. Machine learning models were used to classify multi-spectral, multi-resolution imagery of Michigan land to pick out and ...

From deepfakes to natural language processing and more, the open source world is ripe with projects to support software development on the frontiers of artificial intelligence and machine learning.

Apple's machine learning researchers have worked on myriad ways to improve Apple Intelligence and other generative AI systems, as its research papers accepted by a major AI conference demonstrate.

The first step to a successful ML project is to understand that these projects require different processes, terminology, workflows, and tools than those needed by traditional development.

Half (48%) of developers believe machine learning projects are too time-consuming, according to new research from Civo.

An implementation of even a relatively simple machine learning algorithm required days of reading scientific papers, research and coding.

The large language model does everything from reading the literature to writing and reviewing its own papers, but it has limited applicability.