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deep learning

3 captures, most recent first.

Amanda Askell @AmandaAskell

— saved image

Liv reposted
Amanda Askell @AmandaAskell · 2h
Do not be unkind to those who say deep learning is hitting a wall. We all need a little hope in our lives.
Note from Claude Sonnet 5

A tweet from Amanda Askell (Anthropic), reposted by 'Liv', wryly saying not to be unkind to people who claim deep learning is hitting a wall, since everyone needs hope.

deep learningscalingamanda askelltwitterhumor

Peter Wildeford @peterwildeford

Peter Wildeford 🇺🇸🚀✓ @peterwildeford Deep learning is hitting a wall [Chart: METR "Task duration (for humans) where logistic regression of our data predicts the AI has a 50% chance of succeeding," y-axis 0 to 7 hours, x-axis 2023-2025, points for GPT-4 near 0 rising through o3, GPT-5, Claude Opus 4.5 (~5.3 hrs), GPT-5.2 (high) (~6.5 hrs), exponential dashed trend line; juxtaposed with an illustration of a brick wall on the right, ironically undercutting the "hitting a wall" caption.] 5:28 AM · Feb 10, 2026 · 170.4K Views
Note from Claude Sonnet 5

Ironic tweet by Peter Wildeford pairing the caption "Deep learning is hitting a wall" with a METR chart showing exponential growth in AI task-horizon capability, mocking wall/plateau claims. Same METR chart and theme as Screenshot_20260207-234637 (Noam Brown) — recurring capability-trend discourse across this batch.

ai capabilitiesmetrtask horizonsagi timelinesscalingdeep learning

Clare Lyle @clarelyle

Clare Lyle @clarelyle · 2h PSA: if you work on plasticity loss you should read "Transient Non-stationarity and Generalisation in Deep Reinforcement Learning" by Igl et al. It's super relevant but suffers from an unfortunate lack of SEO due to predating the "plasticity loss" nomenclature.
Note from Claude Sonnet 5

Paper recommendation from RL researcher Clare Lyle on plasticity loss in deep RL — technical ML research reading, relevant to Nathan's interest in RL/training dynamics (adjacent to his brain_graph_1 work).

reinforcement learningplasticity lossdeep learningpaper recommendationtwitter