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.
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prmph
> The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony.
Is every tech, including database search "AI" now?
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.
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egormakarov
Can't wait to be killed by my toaster because some sexy mossad agent seduced it.
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bix6
Has anyone used the Rx Scanner mentioned in the opening?
Where is a good place to start with training SLM these days if you don't have the compute locally?
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HelloUsername
What about small (offline) AI Models in places with weak hardware?
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.
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
enoint
Fascinating to wonder whether the bigger model finds fewer or more counterfeits than the on-device one.
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.
> The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony.
Is every tech, including database search "AI" now?
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.
Can't wait to be killed by my toaster because some sexy mossad agent seduced it.
Has anyone used the Rx Scanner mentioned in the opening?
https://rxall.net/rxscanner/
Where is a good place to start with training SLM these days if you don't have the compute locally?
What about small (offline) AI Models in places with weak hardware?
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.
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
Fascinating to wonder whether the bigger model finds fewer or more counterfeits than the on-device one.
100% agree on this.
I've been working on small local models for years with txtai (https://github.com/neuml/txtai). I've published close to 100 models that can run local for RAG, Agents, Vector Search and more (https://huggingface.co/NeuML/collections).
I looked into this a bit but unfortunately because of starlink most of this won’t be needed
SLMs for the rescue!