Exploring the Evolution of Computer Architecture for AI Processing

Software processes vast data to identify patterns using ones and zeroes. Deep learning models facilitate scaling to handle large data sets efficiently. Graphics processors were found suitable due to simulating the virtual world, similar mathematics, and adeptness at understanding the world. GPUs evolved into AI processors owing to superior processing capabilities over CPUs. Personal computing architecture wasn't ideal for AI, necessitating changes in IO, networking, and data processing. Utilizing GPU cores, in contrast to CPU cores, enables processing vast data for pattern recognition and learning representations across various subjects including music, languages, computer vision, proteins, and chemicals.