This paper presents a tentative outline for the construction of an artificial, generally intelligent system (AGI). It is argued that building a general data compression algorithm solving all problems up to a complexity threshold should be the main thrust of research. A measure for partial progress in AGI is suggested. Although the details are far from being clear, some general properties for a general compression algorithm are fleshed out. Its inductive bias should be flexible and adapt to the input data while constantly searching for a simple, orthogonal and complete set of hypotheses explaining the data. It should recursively reduce the size of its representations thereby compressing the data increasingly at every iteration.
Abstract Based on that fundamental ability, a grounded reasoning system is proposed. It is argued how grounding and flexible feature bases made of hypotheses allow for resourceful thinking. While the simulation of representation contents on the mental stage accounts for much of the power of propositional logic, compression leads to simple sets of hypotheses that allow the detection and verification of universally quantified statements.
Abstract Together, it is highlighted how general compression and grounded reasoning could account for the birth and growth of first concepts about the world and the commonsense reasoning about them.
MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
School of Information, Computer；Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand 1
Can Complexity and Uncomputability Explain Intelligence? SuperARC: A Test for Artificial Super Intelligence Based on Recursive Compression
Oxford Immune Algorithmics（牛津免疫算法公司）；Oxford University Innovation（牛津大学创新中心）；London Institute for Healthcare Engineering（伦敦医疗工程研究所）；Algorithmic Dynamics Lab（算法动力实验室）；Center of Molecular Medicine, Karolinska Institute（卡罗林斯卡研究所分子医学中心）；King’s College London（伦敦大学国王学院）；Centre for Logic, Epistemology and the History of Science, University of Campinas (UNICAMP)（逻辑、知识论和科学史研究中心，坎皮纳斯大学）；DEXL, National Laboratory for Scientific Computing (LNCC)（DEXL，国家科学计算实验室）；King’s Institute for Artificial Intelligence, King’s College London（伦敦大学国王学院人工智能研究所）
Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）
The $\mathbf{Y}$-Combinator for LLMs: Solving Long-Context Rot with $λ$-Calculus
The School of Interdisciplinary Research（跨学科研究学院）；Indian Institute of Technology (IIT) Delhi（印度理工学院德里分校）；Robert Bosch GmbH, India（德国罗伯特·博世有限公司，印度）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Huawei Noah’s Ark UCL Centre for Artificial Intelligence（华为诺亚方舟大学伦敦人工智能中心）
