Towards Deeper Learning: Hierarchical Temporal Memory
a more ‘biological’ unsupervised AI model
I’m an AI optimist. One of those lads without any neuroscience background who assuredly claims that “the brain is just a carbon-based computer that can rewire itself”. I also believe that the answer to difficult questions usually begins with what feels right in your gut — complexity is often solved with surprising simplicity.
This sometimes leads to me writing sentences like “Neural Networks Allow AI to Learn Like Your Brain Does”. This is the tech journalism equivalent of “Doctors Hate This One Easy Trick”. Modern deep learning models are stupendous and complex, but they’re far from being truly biological.
Yet we remain fixated on the idea of mechanically recreating the human brain, because from a computational standpoint, it’s so damn impressive.
A Fleshy Pattern Recognition Machine
Your brain is really good at doing more with less work. Neurons fire anywhere from .1–2 times per second, but 90% rarely fire at all. Only a small percentage of your neurons are firing at any given moment; 2% is thrown around a lot.
The brain’s core rule: Context matters.
