Small AI Models Gain Traction In places with unreliable networks

bombcar

99% of the model "work" (meaning the connection to your computer) is just spinning a spinner - something that makes me want to wrap it with a mosh shell so I can just keep moving from network to network.

enoint

Fascinating to wonder whether the bigger model finds fewer or more counterfeits than the on-device one.

tim-fan

Is anyone making LLM-in-a-box for emergency supply kits yet? I feel that would be handy in all sorts of situations when networks are down.

jdonaldson

I think neuro-symbolic AI has a lot of potential here, since small models can handle a lot of conversational inputs, while relying on wired-in solvers for more complex symbolic math/computation needs. https://jjd.io/posts/swollm-bbh-leaderboard.html

bix6

Has anyone used the Rx Scanner mentioned in the opening? https://rxall.net/rxscanner/

mountainriver

I looked into this a bit but unfortunately because of starlink most of this won’t be needed

N_Lens

I strongly believe this premise in the article is correct - we will see a lot of tiny, hyper specialized models for individual tasks, and perhaps that will converge with an orchestration layer for a generalized intelligence that controls these specialized tiny models, that will be quite capable. I don't foresee AGI arising out training bigger LLMs (Though investors won't realise that for a while yet). It's actually how organic brains work - specialized tasks are offloaded to local cortical columns. The overall coordination between these sub-brains creates emergent skills/abilities.