Canada-UK AI 2019 : The self as agent-environment nexus - crossing disciplinary boundaries to help human selves and anticipate artificial selves

加拿大-英国人工智能 2019：自我作为主体与环境的联系 - 跨越学科界限帮助人类自我并预测人工自我

• 批准号: 548624-2019
• 负责人: Northoff, GeorgG
• 金额: $ 2.9万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757575
• 关键词: Canada; UK; AI; 2019; self

Both human and artificial agents learn from and potentially change their environments, but also align and adapt to them. Examples abound, from humans unconsciously tapping feet to the rhythm of music to a cleaning robot keeping a house dust-free. In humans, however, brain-world alignment is also closely linked with mental features like perception or self and mental health disorders lead to profound disruptions in how one considers 'the self'.As AI agents acquire human and superhuman skill levels and Artificial General Intelligence looms closer, ethical considerations about how an agent will interact with its environment become paramount. What is it about an AI agent that will ensure it can co-exist peacefully in human society and transfer skills seamlessly from one setting to another?Let us consider a real-world example of an adolescent who is watching television (on their laptop) via a streaming service. Suppose that individual has displayed anti-social behaviours at school or in their home and suppose that he has streamed several violent movies on his laptop over the past few days. Should the recommender system of the streaming service suggest a 'movie that you might like' that, again, contains strong scenes of violence? Or should the recommender system suggest, instead, a documentary on polar bears? This example illustrates three open questions and challenges in AI research. First, how would the recommender system infer the state of the young man? Second, how would a recommender system adapt its recommendations based upon this state, particularly given that the system itself has its own internal goals- i.e. to motivate people to consume more content? And third, is it ethical to probe the human at the human-computer interface in the first place?The current proposal aims to converge both "free energy" and "dynamic temporo-spatial" approaches to mental features to develop a mathematical and computational framework for environmental alignment and adaptation of intelligent agents.The primary, direct goal of this approach is to help human agents suffering from abnormal changes in their perception and self, as in psychiatric conditions like schizophrenia. This not only carries major ethical implications for understanding of ourselves, but will also inform the second, broader, aim of this project: paving the way for an understanding and mathematical theory of artificial selves.

人类和人工智能体都从环境中学习并可能改变它们的环境，但也会调整和适应它们。这样的例子比比皆是，从人类无意识地用脚敲击音乐的节奏到清洁机器人保持房间无尘。然而，在人类中，大脑世界的对齐也与感知或自我等心理特征密切相关，心理健康障碍会导致人们对“自我”的看法发生深刻的变化。随着人工智能智能主体获得人类和超人的技能水平，以及人工通用智能的逼近，关于主体如何与环境互动的伦理考虑变得至关重要。人工智能代理是如何确保它能够在人类社会中和平共处，并将技能从一个环境无缝转移到另一个环境？让我们考虑一个真实世界的例子，一个青少年正在通过流媒体服务（在笔记本电脑上）看电视。假设该人在学校或家中表现出反社会行为，并假设他在过去几天内在笔记本电脑上播放了几部暴力电影。流媒体服务的推荐系统是否应该推荐一部“你可能喜欢的电影”，再次包含强烈的暴力场景？或者，推荐系统应该推荐一部关于北极熊的纪录片吗？这个例子说明了人工智能研究中的三个开放性问题和挑战。首先，推荐系统如何推断年轻人的状态？其次，推荐系统如何根据这种状态调整其推荐，特别是考虑到系统本身有自己的内部目标-即激励人们消费更多内容？第三，首先在人机界面上探测人类是否道德？目前的建议旨在融合“自由能量”和“动态时空”的方法来研究心理特征，以开发一个数学和计算框架，用于智能代理的环境调整和适应。这种方法的主要直接目标是帮助人类代理在感知和自我方面发生异常变化，例如精神分裂症。这不仅对理解我们自己具有重大的伦理意义，而且还将为这个项目的第二个更广泛的目标提供信息：为理解人工自我和人工自我的数学理论铺平道路。