Ask HN: Is neuromorphic computing going to replace traditional AI?
I’ve recently learned about neuromorphic computing while thinking about the core inefficiencies of current deep learning.Today AI relies heavily on super-dense layers, continuous computation of all layers and neurons at all times, and global backpropagation to find optimal weight updates. So this scaling will naturally hit a wall at some point as electricity is not unlimited. That’s why I believe that long term progress can not come from just scaling forever.
- ▪I’ve recently learned about neuromorphic computing while thinking about the core inefficiencies of current deep learning.Today AI relies heavily on super-dense layers, continuous computation of all layers and neurons at all times, and globa
- ▪So this scaling will naturally hit a wall at some point as electricity is not unlimited.
- ▪That’s why I believe that long term progress can not come from just scaling forever.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,243 of its stories.
Opening excerpt (first ~120 words) tap to expand
I’ve recently learned about neuromorphic computing while thinking about the core inefficiencies of current deep learning.Today AI relies heavily on super-dense layers, continuous computation of all layers and neurons at all times, and global backpropagation to find optimal weight updates. But when you look at the human brain, none of that happens.The brain operates on principles that stand in contrast to modern LLMs:- Local Evolution: Neurons are largely independent, evolving based on their local neighborhood and simple feedback loops like neurotransmitters (e.g., dopamine) rather than a global error signal. - Extreme Sparsity: The system is massively sparse (neurons only evolve and get updated when they have been involved in a firing-chain).
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
Discussion
0 commentsMore from Hacker News (AI / LLM)
