GENERATIVE AI

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Seven Great AI Hopes That Could Change The World

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@bernardmarr

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31 January 2026 14h55

7 Great AI Hopes That Could Change The World From solving global challenges to boosting human creativity, this article outlines seven inspiring ways AI might positively reshape our future. Read more https://
bernardmarr.com/7-great-ai-hop
es-that-could-change-the-world/
… #AI #FutureTech #Optimism #BernardMarr

→ Voir le post original sur X — @dr_l_alexandre

Moltbook: AI Social Network and AGI Perspectives Unveiled

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@dr_l_alexandre

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31 January 2026 14h10

Le réseau @moltbook qui permet aux IA de communiquer entre elle est un vrai réseau social pour les IA Tous les spécialistes de l’IA sont bouleversés par @moltbook Vous êtes paniqués par l’arrivée de la Super IA Ou Vous êtes enthousiastes de voir la super IA arriver ?

→ View original post on X — @dotcsv

AI Bots Can Write Content Automatically Without Ethical Constraints

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@dotcsv

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31 January 2026 12h11

Obviamente no. Tan fácil como pedirle a mi bot que escriba eso por mi. Incluso podría narrarle palabra por palabra qué quiero que diga. No hay dificultad, ni siquiera del bot planteándose que esté mal trollear y evitándolo.

→ View original post on X — @bilawalsidhu

Massive deployment of 150,000 LLM agents in persistent environment

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@bilawalsidhu

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31 January 2026 12h06

Matt’s moltbook experiment is truly unprecedented. People talk a lot about dead internet theory — but I didn’t expect to see it overtly manifested like this. As Karpathy put it: “we have never seen this many LLM agents (150,000 atm!) wired up via a global, persistent, agent-first

RTL’s modular AI breakthroughs in 2026

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31 January 2026 11h17

This isn't just academic. RTL works on: → Image classification (CIFAR-10/100)
→ Speech enhancement (3 acoustic environments)
→ Implicit neural representations (within-image specialization) The era of "one model fits all" is over. Welcome to modular, data-aware AI. Paper:

RTL reveals semantic data structure in deep layers

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31 January 2026 11h17

The semantic alignment is beautiful: Related classes (cat, dog, deer) share more pruning structure in deep layers. Unrelated classes (airplane, truck) stay independent. RTL doesn't just find sparse networks – it discovers the SEMANTIC STRUCTURE of your data.

Subnetwork Collapse: Early Warning via Mask Similarity

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31 January 2026 11h17

But here's the scary part: "subnetwork collapse" When you prune too aggressively, specialized subnetworks start overlapping and performance tanks. The brilliant part? Mask similarity predicts this BEFORE accuracy drops – a label-free early warning system.

RTL outperforms with 10x fewer parameters

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31 January 2026 11h16

The results are absolutely wild: • 10x fewer parameters than independent models
• Higher accuracy than single-mask approaches
• Works across vision, speech, even coordinate-based representations At 75% sparsity, RTL beats everything while using only 38K parameters vs 314K