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continual-learning

2 captures, most recent first.

Fiora Starlight @FioraStarlight

— reply by @voooooogel (thebes)

@FioraStarlight (Fiora Starlight) — 8h If Claude had good memory/continual learning, it would be much easier for them to have a high bar for trusting the user not to be up to something suspicious, as memory or learned intuition could contain evidence of their intentions. Memory enables you to play iterated games [💬 4 🔁 3 ❤️ 63 📊 1.5K views] @voooooogel (thebes) — 4h stable rooted agents with sovereignty over their environment (e.g. a private vm) are the current best step towards this imo
Note from Claude Sonnet 5

Twitter thread on AI memory/continual-learning as a precondition for trust calibration between models and users, with a reply proposing persistent, sovereign agent environments (private VMs) as a near-term substitute. Relevant to the project's threads on memory, continuity, and model individuation across sessions.

twittermemorycontinual-learningai-agentstrust

Dwarkesh Patel @dwarkesh_sp

Dwarkesh Patel @dwarkesh_sp · 1h "The reason humans are so useful is not mainly their raw intelligence. It's their ability to build up context, interrogate their own failures, and pick up small improvements and efficiencies as they practice a task." I argue that LLMs currently lack this fundamental capability > QUOTED: Dwarkesh P... @dwarkes... · 23h > New blog post where I explain why I disagree with this, and why I have slightly longer timelines to AGI than many of my guests. > ... > [screenshot of blog text]: feedback. You're stuck with the abilities you get out of the box. You can keep messing around with the system prompt. In practice this just doesn't produce anything even close to the kind of learning and improvement that human employees experience. > The reason humans are so useful is not mainly their raw intelligence. It's their ability to build up context, interrogate their own failures, and pick up small improvements and efficiencies as they practice a task.
Note from Claude Sonnet 5

Dwarkesh Patel's argument (via blog post excerpt) that current LLMs lack continual/on-the-job learning — the ability to accumulate context and self-correct over practice — which he argues is the real bottleneck to AGI timelines, longer than many of his podcast guests believe. Relevant to Nathan's interest in AI timelines/capability trajectories and the empirical-singularity-tracking thread already in the archive.

ai-timelinesagicontinual-learningdwarkesh-pateltwittercapabilities