<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cognitive Science on Koby Bibas</title><link>https://kobybibas.github.io/tags/cognitive-science/</link><description>Recent content in Cognitive Science on Koby Bibas</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Fri, 31 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://kobybibas.github.io/tags/cognitive-science/index.xml" rel="self" type="application/rss+xml"/><item><title>[Summary] Why AI Systems Don't Learn (and What to Do About It)</title><link>https://kobybibas.github.io/posts/20260731_why_ai_systems_dont_learn/summary/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0000</pubDate><guid>https://kobybibas.github.io/posts/20260731_why_ai_systems_dont_learn/summary/</guid><description>TL;DR Current AI systems do not learn autonomously after deployment. The usual process includes fine-tuning, in which a human carefully selects data for a specific task and tunes the model on it. This paper proposes combining learning from observation (System A), learning from action (System B), and a meta-controller (System M) that decides what and how to learn.
Why Current AI Systems Do Not Learn The current AI paradigm focuses on scaling LLMs.</description></item></channel></rss>