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 ...
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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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, ...
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 ...
The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over ...
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 ...
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 ...
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'The era of data-labeling companies is over,' says the CEO of a $2.2 billion AI training firm
Basic data-labeling work — the kind built on tagging images or sorting text — is becoming obsolete, said the CEO of a $2.2 billion AI training firm. Jonathan Siddharth, the CEO of Turing, said on an ...
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