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.

According to a number of research initiatives (e.g., Hidden Debt in Machine Learning Systems) technical debt resides in areas common to many machine learning projects: Data Quality, Model Quality ...

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.

One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule's properties, such as ...

What began as a Ph.D. project has grown into a website with 120,000 unique visitors each year. With the platform OpenML, ...

This article looks at 13 open source projects that are remaking the world of AI and machine learning. Some are elaborate software packages that support new algorithms. Others are more subtly ...

The researchers identified a clear trade-off between environmental cost and predictive performance. Fully DFT-based workflows ...

How many Google AI researchers does it take to screw in a lightbulb? A recent research paper detailing the technical core ...

A team of machine-learning researchers has now tried to make this possible. ... The project involves (i) ... AI tools are spotting errors in research papers: inside a growing movement.

It analyzes research papers, extracts crucial information, and generates recommendations that are pertinent to the context using machine learning and NLP techniques.