Health researchers need to fully understand the underlying assumptions to uncover cause and effect. Timothy Feeney and Paul Zivich explain Physicians ask, answer, and interpret myriad causal questions ...
Jim Fan is one of Nvidia’s senior AI researchers. The shift could be about many orders of magnitude more compute and energy needed for inference that can handle the improved reasoning in the OpenAI ...
Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future ...
Artificial intelligence (AI) is a powerful force for innovation, transforming the way we interact with digital information. At the core of this change is AI inference. This is the stage when a trained ...
In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
Hosted on MSN
What is inferencing and training in AI?
That’s where training and inferencing come in - the dynamic duo transforming AI from a clueless apprentice to a master predictor. You can think of training as the intense cram session where AI models ...
Designing AI/ML inferencing chips is emerging as a huge challenge due to the variety of applications and the highly specific power and performance needs for each of them. Put simply, one size does not ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results