[D] Are there unconventional cognition architectures that learn without SGD, weights between neurons, or can only be done on the CPU?

Let's say in the year 2002 you are tasked with making a CPU-based system that can recognize if a human is in picture or not and show where on the image. How do you do it with plain old programming and your smarts? I suspect the ML rush in recent years with GPUs getting faster has created a blindspot where people are always thinking through the GPU lens, thinking that weights and synapses and big GPU processing is as good …

cognition cpu machinelearning neurons