Using special tags embedded in the output, the model directly links every factual claim it makes to the specific source document or database row it pulled the information from.

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

Nota AI, a company specializing in AI model compression and optimization, announced that two of its papers on MoE-specific ...

A model of artificial intelligence, after partial training on a quantum computer, gave correct answers where it previously ...

Model quantization bridges the gap between the computational limitations of edge devices and the demands for highly accurate models and real-time intelligent applications. The convergence of ...

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...

A team of researchers from Q-CTRL and IBM says it has achieved a 3,000-fold wall-clock speedup over the best available ...

Quantum computers, systems that process information leveraging quantum mechanical effects, have the potential of ...