I’ve been reading about recent research on how the human brain processes and stores memories, and it’s fascinating! It seems that our brains compress and store memories in a simplified, low-resolution format rather than as detailed, high-resolution recordings. When we recall these memories, we reconstruct them based on these compressed representations. This process has several advantages, such as efficiency, flexibility, and prioritization of important information.
Given this understanding of human cognition, I can’t help but wonder why AI isn’t being trained in a similar way. Instead of processing and storing vast amounts of data in high detail, why not develop AI systems that can compress and decompress input like the human brain? This could potentially lead to more efficient learning and memory management in AI, similar to how our brains handle information.
Are there any ongoing efforts in the AI community to explore this approach? What are the challenges and benefits of training AI to mimic this aspect of human memory? I’d love to hear your thoughts!
Oh, so it’s mostly a side effect, but they are still primarily being trained to predict the next word.
Not necessarily, sometimes dimensionality reduction (the more common terminology used, for what is basically compression) is the explicit goal.
Can be used for outlier detection, similarity search, etc.
During training, you find a projection of the input, for example an image, to a smaller space, and then back to the original image. This is referred to as encoding and decoding. The error fuction would be a measure of how similar the in- and output images are.