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

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许多单独的任务上超越人类的表现，例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已经被训练的任务之外行动，并且经常在与它们的预期输入的甚至微小的偏差下失败（舍夫林等人，2019年）。基本的认知技能，如物体永久性，是人类婴儿时期获得的灵活功能的核心，但对AI来说是一个重大挑战（例如，2022年），它们的发展将代表潜在应用的一个阶段性变化。然而，目前的人工智能基准既没有足够的认知定义，也没有足够的通用性来衡量这些技能的表现。这个学生将成为一个项目的一部分，该项目采用一种新的方法来评估人工智能-在概念方面受到认知科学的启发（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（认知和动机行为实验室，心理学; Leverhulme智能未来中心各种智能计划主任）共同监督，他领导了发展/比较心理学和AI评估的研究，以及Flavia Mancini博士（计算和生物学习研究小组，工程），他领导了计算神经科学和AI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Animal AI平台（http：//www.example.com）中开发一系列认知任务，以评估这些智能体的能力。animalai.org他们将以儿童的表现作为衡量标准。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建模，以提取对不同任务的“认知指纹”的细致入微和全面的理解，为使用不同架构生成的儿童和代理。

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.