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On progressing human understanding in the shadow of superhuman deep learning artificial intelligence entities

在超人类深度学习人工智能实体的阴影下推进人类理解

基本信息

批准号：
22K00015
负责人：
EGRINAGY Attila
金额：
$ 2.08万
依托单位：
Akita International University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2022
资助国家：
日本
起止时间：
2022-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22K00015/
关键词：
artificial intelligence machine learning scientific method philosophy of science data mining game of Go deep learning epistemology

项目摘要

For our testbed application, the game of Go, we developed the `cost of passing' measure to assess the evolution of human understanding. It allows us to have a fine-grained context-dependent evaluation of playing performance and will be the primary tool in the historical game analysis. The cost of passing is also suitable for the visual 'fingerprinting' of a large number of games, which will help the data mining efforts greatly.We also researched and evaluated several hardware options for computationally intensive bulk game analysis. We purchased, set up the equipment, and stress-tested it with machine learning algorithms for training better AIs for the analysis.We also considered mathematically and philosophically what the existence of a perfect player would mean for this application domain. Philosophically it gives an interesting perspective on scientific realism, as the ultimate reality of a board game is the complete game tree, and it has a not-efficiently computable but seemingly straightforward existence. Mathematically, considering the space of possibilities for AI and human knowledge growth led to a side project for constructing transducers for representing perfect game strategies using logic programming.

对于我们的测试平台应用程序，围棋游戏，我们开发了“通过成本”措施来评估人类理解的演变。它使我们能够对游戏表现进行细粒度的上下文相关评估，并将成为历史游戏分析的主要工具。通过的成本也适用于大量游戏的视觉“指纹”，这将极大地帮助数据挖掘工作。我们还研究和评估了几个硬件选项，用于计算密集型批量游戏分析。我们购买并安装了设备，并使用机器学习算法对其进行了压力测试，以训练更好的AI进行分析。我们还从数学和数学角度考虑了完美玩家的存在对该应用领域的意义。从哲学上讲，它提供了一个有趣的科学实在论视角，因为棋盘游戏的最终现实是完整的博弈树，它有一个效率不高但看似简单的存在。在数学上，考虑到人工智能和人类知识增长的可能性空间，导致了一个副项目，即使用逻辑编程构建用于表示完美游戏策略的传感器。