AI & IA

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Richard Sutton @RichardSSutton · 08/11 16:18
Brute force computation is not enough for intelligence. Clever new methods are needed. The science of AI is far from finished. IMHO
7 relayeurs à 7 · il y a 22 h Voir sur X
Qui l'a repris
Tommy” elvis” ℏεsam” Gary Marcus” cocktail peanut” Srini Pagidyala”
Ce qu'ils en ont dit
Tommy @Shaughnessy119 a cité · 08/11 16:18
I’ve been actively on the hunt for non LLM based AI models and I’ve invested into one already Looking for totally new designs
elvis @omarsar0 a cité · 08/11 01:26
1000% agree with Sutton here. Having worked in AI for over a decade (pre-LLMs), it's clear we need smarter methods for intelligence to truly compound in the way some of us envision it. To me, Transformers just scratched the surface. This arc is coming to an end; a new
ℏεsam @Hesamation a cité · 07/11 23:55
> “what happened to the bitter lesson?” people are genuinely trying to explain the bitter lesson to the guy who wrote it. it’s called the bitter lesson not the dumb lesson. the bitter lesson says general methods that scale with compute win in the long run. you still have to
Gary Marcus @GaryMarcus a cité · 07/11 23:32
what i have been saying for years maybe now people will listen?
cocktail peanut @cocktailpeanut a cité · 07/11 21:29
Srini Pagidyala @Srini_Pa a cité · 07/11 21:16
Human like learning does it. Implies understanding, concept formation, metacognition, self-directed, goal-oriented, persistent memory, contextual awareness, belief revision, dynamic world models are all part of it. That is Integrated Neuro Symbolic Architecture- INSA.
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Anthropic @AnthropicAI · 08/11 06:17
Memory is what allows Claude to learn from experience. In a new Engineering Blog post, we share how “dreaming” helps Claude organize what it’s learned, correct its mistakes, and find what it needs more easily the next time.
anthropic.com The components of agent memory How we think about agent memory, and how “dreaming” helps Claude correct mistakes, organize what it’s learned, and find it in future sessions.
7 relayeurs à 7 · il y a 20 h Voir sur X
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A Alvin-GenAI” AI Rahul” Somi” Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)” elvis”
Ce qu'ils en ont dit
A
Alvin-GenAI @AlvinWeb3 a cité · 08/11 06:17
AI can also DREAM now? Anthropic inspired from how our brains organize memories during sleep to build a memory system (similar to wikis) for Claude. agents can now “dream” about past experiences: revisit its transcripts, correct mistakes, and evolve its memory wiki when not in
Rahul @sairahul1 a cité · 07/11 03:31
POV: you're Claude playing Pokémon. You've fallen off the same ledge three times. You write "I'm going to stop rather than keep looping." The test setup auto-replies: continue. So you keep walking, all the way back to the last town, and never make it. Most relatable thing an
Somi @somi_ai a cité · 07/11 02:01
Claude wrote "Mt Moon DONE" in its own Pokémon notes. It wasn't done. One wrong note, and it fell off the same ledge three times. Anthropic's "dreaming" pass checked the note against the old transcript and fixed it. That run reached Cerulean City. I'll take memory that checks
Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) @teortaxesTex a cité · 07/11 00:54
Anthropic proposes an answer to Wenfeng's question about continual learning: …have a smarter model write more .md files. It's impressive how far this goes, but I still believe that it's a crutch with a ceiling set by the pretrain's ability, just like RLMs are a context crutch.
elvis @omarsar0 a cité · 06/11 23:49
Highly recommended read. Learn how one of the most advanced agent memory systems works. Your memory system is one of the biggest levers you have in the harness. I also don't think a one-size-fits-all memory system exists today. Memory is something you constantly have to