"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。现在可以训练人工智能在众多单独的任务上超越人类的表现，例如图像分类或分析大型数据集。然而，这些系统无法在其接受训练的任务之外执行操作，并且即使在与预期输入有微小偏差的情况下也常常会失败（Shevlin 等人，2019）。物体持久性等基本认知技能是人类婴儿时期获得的灵活功能的核心，但对人工智能来说是一个重大挑战（例如 Voudouris 等人，2022），它们的发展将代表潜在应用的阶跃变化。然而，当前的人工智能基准既没有足够的认知定义，也没有足够普遍来衡量这些技能的表现。这项学生资助将成为一个项目的一部分，该项目采用新的人工智能评估方法——在概念方面受到认知科学的启发（这已经很常见）跨人工智能研究）并应用全面而稳健的概念和方法框架。由曾领导发展/比较心理学和人工智能评估研究的 Lucy Cheke 博士（心理学认知与动机行为实验室；Leverhulme 未来智能中心各类智能项目主任）和 Flavia Mancini 博士共同指导。计算和生物学习研究小组（工程），领导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型人工智能体，同时在动物人工智能平台 (http://animalai.org) 内开发一系列认知任务以评估这些智能体的能力。他们会将这一表现与儿童的表现进行比较。最后，他们将与两位主管一起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，以便为使用不同架构生成的儿童和代理提取不同任务中“认知指纹”的细致而全面的理解。