Changing AI math could reduce the hardware burden, researchers show

Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that footprint involves a process called quantization, which changes how model weights are represented and stored. But quantization has its drawbacks. Andrés Mac Allister, CEO and founder of The SEMQ Group, believes there's another way to make machine learning more efficient and less resource intensive. Instead of compressing model weights (specifically embeddings), he con