一般情报需要重新思考探索和探索
Minqi Jiang1, Tim Rocktäschel1, Edward Grefenstette1
1AI Centre, Department of Computer Science, University College London, London, UK.
Royal Society open science
|June 23, 2023
概括
人工智能 (AI) 研究正在将重点从从数据中学习到从哪些数据中学习. 这项研究引入了通用探索,以统一这一概念在AI,向更普遍的智能进步.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 认知科学 认知科学
背景情况:
- 目前的人工智能 (AI) 研究正在超越仅仅从现有数据中学习.
- 大型模型和像变压器这样的统一架构突出了新的瓶:有效地获取和利用相关数据.
- 这种数据采集挑战被定义为"探索",这是开放领域的普遍学习问题.
研究的目的:
- 提出"通用化探索"作为一个统一的概念,用于跨不同AI范式的探索驱动的学习.
- 突出探索在所有学习系统中的重要作用,包括监督学习,而不仅仅是强化学习.
- 通过监督学习和强化学习来识别探索中的关键相似之处和开放研究挑战.
主要方法:
- 开发概念框架,统一监督学习和强化学习中的探索.
- 分析探索作为一种适用于各种AI领域的通用学习问题.
- 确定通用探索作为开放式学习系统的关键目标.
主要成果:
- 探索是所有人工智能系统必不可少的学习的基本方面,不仅限于强化学习.
- 一般化探索为监督学习和强化学习的数据采集和利用提供了统一的视角.
- 拟议的框架揭示了探索驱动人工智能的共同点和研究差距.
结论:
- 一般化探索对于使人工智能系统能够不断发现和解决新问题至关重要.
- 这个概念是实现更普遍的人工智能的重要一步.
- 对一般化探索的进一步研究可以弥合当前的人工智能学习范式,并培养更强大的学习系统.
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