Moving Consumer Goods (FMCG) landscape by 2030, driven by mega consumer trends like digitalization, health consciousness, and ...

Old game boxes sitting in a closet can feel like junk until you see what some people have paid for them. Sealed copies of… ...

Data collected under the Death in Custody Reporting Act has some serious problems. Here’s how we fixed some of them.

Historically, medical imaging datasets have supported only image‑level classification. For example, an X‑ray might be labeled as “showing cardiomegaly” or “no abnormalities detected.” While functional ...

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their ...

Abstract: Semi-Supervised Partial Label Learning (SSPLL) is an important branch of weakly supervised learning, where the data consists of both partial label examples and unlabeled ones. In SSPLL, the ...

Abstract: This survey reviews how large language models (LLMs) are transforming synthetic training data generation in both natural language and code domains. By producing artificial but task-relevant ...

This article was originally published on ARPU. View the original post here. Meta Platforms, the parent company of Facebook and Instagram, is reportedly in talks for a massive investment in artificial ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

Time-series data—measurements collected over time like stock prices or heart rates—plays a vital role in AI forecasting systems across industries. As these systems advance, the need for time-series ...

It’s no secret that the success of today’s large language models wouldn’t be possible without the armies of humans creating the data, like solved math or coding problems, for the AI to be trained on.