标题： 神经科学能教人工智能在不断变化的环境中如何学习 显示英文标题

标题： What Neuroscience Can Teach AI About Learning in Continuously Changing Environments

摘要： 现代人工智能模型，例如大型语言模型，通常在庞大的数据语料库上进行一次训练，可能针对特定任务进行微调，然后以固定参数部署。 它们的训练成本高、速度慢且渐进，需要数十亿次重复。 与之形成鲜明对比的是，动物能够持续适应其环境中不断变化的情况。 这对社会性物种尤为重要，因为在与同伴互动中，行为策略和奖励结果可能会频繁变化。 这些底层计算过程通常表现为动物行为的快速变化以及神经元种群活动的突然转变。 这种计算能力对于在现实世界中运行的人工智能系统（如指导机器人或自动驾驶汽车的系统）或与人类在线互动的代理型人工智能来说，正变得越来越重要。 人工智能能从神经科学中学到什么？ 这一观点探讨了这个问题，将人工智能中的持续学习和上下文学习文献与行为任务中规则、奖励概率或结果变化的神经科学相结合。 我们将概述一个议程，具体说明神经科学的见解如何影响人工智能在此领域的当前发展，并反过来说，神经科学可以从中学习什么，从而为不断发展的神经人工智能领域做出贡献。 显示英文摘要

摘要： Modern AI models, such as large language models, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task, and then deployed with fixed parameters. Their training is costly, slow, and gradual, requiring billions of repetitions. In stark contrast, animals continuously adapt to the ever-changing contingencies in their environments. This is particularly important for social species, where behavioral policies and reward outcomes may frequently change in interaction with peers. The underlying computational processes are often marked by rapid shifts in an animal's behaviour and rather sudden transitions in neuronal population activity. Such computational capacities are of growing importance for AI systems operating in the real world, like those guiding robots or autonomous vehicles, or for agentic AI interacting with humans online. Can AI learn from neuroscience? This Perspective explores this question, integrating the literature on continual and in-context learning in AI with the neuroscience of learning on behavioral tasks with shifting rules, reward probabilities, or outcomes. We will outline an agenda for how specifically insights from neuroscience may inform current developments in AI in this area, and - vice versa - what neuroscience may learn from AI, contributing to the evolving field of NeuroAI.