Recent advancements in large generative models have resulted in widespread interest in their ability to act on complex instructions. These so-called foundational large language models (LLMs), e.g., ...

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Amid the boom of AI in application building, companies face a significant data-labeling problem, especially when it comes to labeling images or other media content they want to train deep learning ...

Before you can even think about building an algorithm to read an X-ray or interpret a blood smear, the machine has to know what’s what in an image. All of the promise of AI in healthcare — an area ...

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, ...

Is it possible for an AI to be trained just on data generated by another AI? It might sound like a harebrained idea. But it’s one that’s been around for quite some time — and as new, real data is ...

Two years ago, the entire world spent an estimated $800 million on data labeling: the painstaking process of annotating images and other information to train machine-learning and AI models. Now, the ...

Simple data labeling is becoming obsolete as AI models require more complex training data, says Turing's CEO. AI training companies need to be a "proactive research partner" for major labs, Jonathan ...