MACHINE LEARNING

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Recursive Self-Improvement: Path to Technological Singularity

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@ceobillionaire

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31 January 2026 15h20

From Seed AI to Technological Singularity via Recursively Self-Improving Software Roman V. Yampolskiy: https://
arxiv.org/abs/1502.06512 #ArtificialIntelligence #RSI #Singularity

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.

CIFAR-10 Results Break Performance Barriers

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31 January 2026 11h16

The CIFAR-10 results broke my brain: Balanced accuracy: 0.781 vs 0.711 (baselines)
Recall: 0.821 vs 0.480 And this is with 10× parameter efficiency. The model literally learned to allocate capacity where it matters most.

RTL learns multiple specialized masks

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31 January 2026 11h16

Here's what makes RTL different: Instead of pruning once globally, it learns MULTIPLE masks from the same initialization. Each mask specializes to a data subset (class, cluster, or environment). Then joint retraining refines them WITHOUT letting them interfere.

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Share AI and Machine Learning Insights with Your Network

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@akshay_pachaar

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31 January 2026 11h16

If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

Samsung’s RTL: Tailored Subnetworks for Data Classes

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31 January 2026 11h16

The problem with current pruning methods: They assume ONE mask works for all data. Like forcing every student to learn math the same way. Samsung's RTL (Routing the Lottery) discovers specialized subnetworks – each tailored to specific classes, clusters, or conditions.

Samsung disproves Lottery Ticket Hypothesis

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31 January 2026 11h16

Samsung just broke the Lottery Ticket Hypothesis Everyone's been searching for ONE winning subnetwork in neural networks. Turns out we should've been finding MULTIPLE specialized ones. This changes everything about neural network pruning