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#ArtificialGeneralIntelligence

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>>> But inference engines are now out of fashion, because they rely (at least, for now) on human curated knowledge bases, and lack the facile fluency of #LLMs.

I do not believe that we can achieve #ArtificialGeneralIntelligence #AGI without a semantic layer comprising at least an explicit ontology, a semantic model, and an auditable, sourced, set of inference rules.

I think #LLMs are probably a dead end. And I think they are distracting from technologies which could be genuinely useful.

I get really sick of hearing about OpenAI and the rest of the LLM crowd making absurd claims about LLMs like ChatGPT being AGI. LLMs are not even close to AGI. If you want to see real research into AGI, watch this video on Chris Eliasmith's Spaun (semantic pointer architecture unified network). It may not look as snazzy and consumer-ready as all these hallucinating LLMs being marketed to us, but this is the real deal: A single system that can do lots of different things the brain does and that LLMs can never do.

youtu.be/I5h-xjddzlY?si=a-dS3y

#Spaun
#ArtificialGeneralIntelligence #AGI #AI #ML
#SemanticPointerArchitecture #SPA
#Nengo
#Hypervectors
#VectorSymbolicArchitecture #VSA
#HyperdimensionalComputing #HDC
#HolographicReducedRepresentation #HRR
#OpenAI #ChatGPT #LLM #LLMs

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@root The closest technologies we have to how the human brain works are not LLMs, but some less well-known ones: reinforcement learning algorithms and hyperdimensional computing. If you want to see what HDC is capable of, check out this video:

youtu.be/P_WRCyNQ9KY?si=JgAuOJ

#HDC #HyperdimensionalComputing
#VSA #VectorSymbolicArchitecture
#HRR #HolographicReducedRepresentation
#SpikingNeuralNetworks
#AGI #ArtificialGeneralIntelligence
#LLM