Why are GPUs better than CPUs at performaing machine learning tasks?
Unlike CPUs, GPUs are designed to be able to perform operations with very high degree of parallelism, thanks to hundreds of processing units (at the cost of lesser capabilities of each unit). If a task is about manipulating large arrays of numbers, which is often the case in both 3D graphics and neural or networks, a GPU may complete it much faster than a CPU.
Ia holographic memory useful for AI and AGI?
Yes, holographic memory is a promising technology for AI and AGI. Holographic memory is a type of non-volatile memory that can store large amounts of data in a small space. It is also very fast and energy-efficient. These properties make it ideal for AI and AGI applications, which require large amounts of data to be processed quickly and efficiently.

Holographic memory works by storing data in a three-dimensional pattern. This allows it to store much more data than traditional two-dimensional memory technologies. Holographic memory is also very fast because it can access data in parallel. This means that it can read and write data from multiple locations at the same time.

Holographic memory is still under development, but it has the potential to revolutionize AI and AGI. It could enable AI systems to learn and process information much faster than they can today. This could lead to major breakthroughs in areas such as natural language processing, computer vision, and robotics.