SNN
Smith & Nephew plc
stock NYSE ADR

Yea, it’s an interesting problem. I think it’s important to not hide a model’s limitations when they all suffer from mathematically imperfect non deterministic floating point additions.
You find solutions in building complementary structures around them. There are interesting hybrid systems being built right now. And you can still get 100% deterministic results from your system if built properly. Even inside of inherently non deterministic floating point additions. SNN has made a lot of progress and it’s getting competitive.
Spiking doesn’t always mean efficient though. You need neuromorphic hardware to get maximum energy efficiency and precision out of spike computing resulting in it sometimes being slower and energy inefficiency. I think the solution is using them for specific tasks like relatively rare meaningful events that should involve continuous temporal observation. Unless you’ve spent $10,000 on a multi chip asynchronous system. And a lot of them still rely on research clouds.
I think the meaningful measurement is not measuring energy per token but measuring energy per useful outcome.
Larger LLMs have tons of information I don’t need for training my model for it’s job so my systems energy to functionality cost massively outweighs what a traditional LLM costs. And it’s significantly better at its job because I can train it on my exact workflow and know that I’m not training someone else’s software or giving someone else my information.

Yea, it’s an interesting problem. I think it’s important to not hide a model’s limitations when they all suffer from mathematically imperfect non deterministic floating point additions.
You find solutions in building complementary structures around them. There are interesting hybrid systems being built right now. And you can still get 100% deterministic results from your system if built properly. Even inside of inherently non deterministic floating point additions. SNN has made a lot of progress and it’s getting competitive.
Spiking doesn’t always mean efficient though. You need neuromorphic hardware to get maximum energy efficiency and precision out of spike computing resulting in it sometimes being slower and energy inefficiency. I think the solution is using them for specific tasks like relatively rare meaningful events that should involve continuous temporal observation. Unless you’ve spent $10,000 on a multi chip asynchronous system. And a lot of them still rely on research clouds.
I think the meaningful measurement is not measuring energy per token but measuring energy per useful outcome.
Larger LLMs have tons of information I don’t need for training my model for it’s job so my systems energy to functionality cost massively outweighs what a traditional LLM costs. And it’s significantly better at its job because I can train it on my exact workflow and know that I’m not training someone else’s software or giving someone else my information.