"Common Sense" and flexible learning in AI agents: Do current AI agents possess the "basic skills" necessary for them to enter the workforce?

AI智能体的“常识”和灵活学习：当前的AI智能体是否具备进入劳动力市场所需的“基本技能”？

• 批准号: 2884814
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884814
• 关键词: Common; Sense; flexible; learning; AI; Common; Sense; flexible; learning; AI

Policy and institutional frameworks for AI governance rely on up-to-date information about AI capabilities. It is now possible to train AIs to exceed human performance on numerous, individual tasks such as classifying images or analysing large datasets. However, these systems cannot act outside of the task they have been trained for and often fail under even minor deviations from their expected inputs (Shevlin et al., 2019). Basic cognitive skills such as object permanence are central to flexible function, acquired in human infanthood, but are a major challenge for AI (e.g. Voudouris et al., 2022) and their development would represent a step-change in potential applications. However, current AI benchmarks are neither sufficiently cognitively defined nor sufficiently general to measure performance in these skills.This studentship will form part of a project taking a new approach to AI evaluation - inspired by cognitive science both in terms of concepts (which is already common across AI research) and in applying a conceptual and methodological framework that is comprehensive and robust. Jointly supervised by Dr Lucy Cheke (Cognition and Motivated Behaviour Lab, Psychology; Director of the kinds of Intelligence Program, Leverhulme Centre for the Future of Intelligence), who has led research in developmental/comparative psychology and AI evaluation, and Dr Flavia Mancini (Computational and Biological Learningresearch group, Engineering), who leads an interdisciplinary research group in computational neuroscience and AI. The student will create and train novel artificial agents using techniques in Deep Reinforcement and Bayesian learning while in parallel developing a series of cognitive tasks within the Animal AI platform (http://animalai.org) to assess the capabilities of these agents. They will benchmark this performance against that of children. Finally, together with both supervisors, they will learn how to computationally model behavioral data, using both Bayesian and RL approaches, to extract a nuanced andcomprehensive understanding of the "cognitive fingerprint" across different tasks, for both children and agents generated using different architectures.

AI治理的政策和机构框架依赖于有关AI功能的最新信息。现在可以训练AIS超越人类在许多单个任务（例如对图像进行分类或分析大型数据集进行分类）上的绩效。但是，这些系统不能超出他们接受过培训的任务，并且在与他们的预期投入的情况下往往会失败（Shevlin等，2019）。基本的认知技能（例如对象的持久性）对于人类信息中获得的灵活功能至关重要，但对于AI来说是一个主要的挑战（例如Voudouris等人，2022年），它们的发展将代表潜在应用中的逐步改变。但是，当前的AI基准测试既不是足够的认知定义，也不足以衡量这些技能的表现。本学生奖学将构成一个项目的一部分，以新的AI评估方法进行AI评估的项目 - 受认知科学的启发，既在AI研究中已经很常见），并且在应用概念和方法的框架方面都具有全面的概念和方法。 Jointly supervised by Dr Lucy Cheke (Cognition and Motivated Behaviour Lab, Psychology; Director of the kinds of Intelligence Program, Leverhulme Centre for the Future of Intelligence), who has led research in developmental/comparative psychology and AI evaluation, and Dr Flavia Mancini (Computational and Biological Learningresearch group, Engineering), who leads an interdisciplinary research group in computational neuroscience and AI.该学生将使用深度增强和贝叶斯学习的技术创建和训练新颖的人造代理，同时同时在动物AI平台（http://animalai.org）中同时制定一系列认知任务，以评估这些药物的能力。他们将对儿童的表现进行基准测试。最后，与两位主管一起，他们将学习如何使用贝叶斯和RL方法对行为数据进行计算对行为数据进行建模，以针对使用不同架构生成的儿童和代理人对不同任务的“认知指纹”提取细微差别且有意义的理解。