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 ...

The reason why large language models are called ‘large’ is not because of how smart they are, but as a factor of their sheer size in bytes. At billions of parameters at four bytes each, they pose a ...

Industry 4.0 depends on continuous data exchange between sensors, machines, production lines, and enterprise systems, but much of this data cannot be centralized due to privacy, security, and ...

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 ...

What the firm found challenges some basic assumptions about how this technology really works. The AI firm Anthropic has developed a way to peer inside a large language model and watch what it does as ...

The proliferation of edge AI will require fundamental changes in language models and chip architectures to make inferencing and learning outside of AI data centers a viable option. The initial goal ...

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models. Two years ago, Yuri Burda and Harri ...