"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

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.

人工智能治理的政策和制度框架依赖于有关人工智能能力的最新信息。现在有可能训练人工智能在许多单独的任务上超过人类的表现，比如对图像进行分类或分析大型数据集。然而，这些系统不能在它们所训练的任务之外行动，并且经常在与其预期输入的微小偏差下失败（Shevlin et al., 2019）。基本的认知技能，如物体持久性，是灵活功能的核心，在人类婴儿期获得，但对人工智能来说是一个主要挑战（例如，Voudouris等人，2022），它们的发展将代表潜在应用的一个步骤变化。然而，目前的人工智能基准既没有足够的认知定义，也不够通用，无法衡量这些技能的表现。这个学生将成为一个项目的一部分，采用一种新的方法来评估人工智能——受认知科学的启发，在概念方面（这在人工智能研究中已经很常见），并应用一个全面而强大的概念和方法框架。由Lucy Cheke博士（心理学认知和动机行为实验室，Leverhulme智能未来中心智能项目主任）和Flavia Mancini博士（工程计算和生物学习研究小组）共同监督，她领导了发展/比较心理学和人工智能评估的研究，她领导了计算神经科学和人工智能的跨学科研究小组。学生将使用深度强化和贝叶斯学习技术创建和训练新的人工智能体，同时在Animal AI平台（http://animalai.org）内并行开发一系列认知任务，以评估这些智能体的能力。他们将把这种表现与儿童的表现作比较。最后，与两位导师一起，他们将学习如何使用贝叶斯和强化学习方法对行为数据进行计算建模，以提取对不同任务中使用不同架构生成的儿童和代理的“认知指纹”的细致而全面的理解。