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, ...

So-called “unlearning” techniques are used to make a generative AI model forget specific and undesirable info it picked up from training data, like sensitive private data or copyrighted material. But ...

At Bloomberg’s Technology and Innovation Forum in Singapore, the most useful conversation about quant research did not start ...

When AI-driven detection underperforms, the instinct is to tune the algorithm, retrain the model or push the vendor for a ...

Once, the world’s richest men competed over yachts, jets and private islands. Now, the size-measuring contest of choice is clusters. Just 18 months ago, OpenAI trained GPT-4, its then state-of-the-art ...

LFM2.5-230M proves that while 3-billion-parameter models like VibeThinker are solving advanced calculus, a 230-million-parameter model is the superior, highly optimized choice for executing structured ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

As institutions become more data-rich and data-reliant, their capacity for analytics is not necessarily keeping up. In an Educause QuickPoll of higher education IT leadership focused on data strategy ...