ActiveAI - active learning and selective attention for robust, transparent and efficient AI

ActiveAI - 主动学习和选择性关注，实现稳健、透明和高效的人工智能

基本信息

批准号：
EP/S030964/1
负责人：
Andrew Philippides
金额：
$ 121.51万
依托单位：
University of Sussex
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FS030964%2F1
关键词：
ActiveAI active learning selective attention

项目摘要

We will bring together world leaders in insect biology and neuroscience with world leaders in biorobotic modelling and computational neuroscience to create a partnership that will be transformative in understanding active learning and selective attention in insects, robots and autonomous systems in artificial intelligence (AI). By considering how brains, behaviours and the environment interact during natural animal behaviour, we will develop new algorithms and methods for rapid, robust and efficient learning for autonomous robotics and AI for dynamic real world applications.Recent advances in AI and notably in deep learning, have proven incredibly successful in creating solutions to specific complex problems (e.g. beating the best human players at Go, and driving cars through cities). But as we learn more about these approaches, their limitations are becoming more apparent. For instance, deep learning solutions typically need a great deal of computing power, extremely long training times and very large amounts of labeled training data which are simply not available for many tasks. While they are very good at solving specific tasks, they can be quite poor (and unpredictably so) at transferring this knowledge to other, closely related tasks. Finally, scientists and engineers are struggling to understand what their deep learning systems have learned and how well they have learned it. These limitations are particularly apparent when contrasted to the naturally evolved intelligence of insects. Insects certainly cannot play Go or drive cars, but they are incredibly good at doing what they have evolved to do. For instance, unlike any current AI system, ants learn how to forage effectively with limited computing power provided by their tiny brains and minimal exploration of their world. We argue this difference comes about because natural intelligence is a property of closed loop brain-body-environment interactions. Evolved innate behaviours in concert with specialised sensors and neural circuits extract and encode task-relevant information with maximal efficiency, aided by mechanisms of selective attention that focus learning on task-relevant features. This focus on behaving embodied agents is under-represented in present AI technology but offers solutions to the issues raised above, which can be realised by pursuing research in AI in its original definition: a description and emulation of biological learning and intelligence that both replicates animals' capabilities and sheds light on the biological basis of intelligence.This endeavour entails studying the workings of the brain in behaving animals as it is crucial to know how neural activity interacts with, and is shaped by, environment, body and behaviour and the interplay with selective attention. These experiments are now possible by combining recent advances in neural recordings of flies and hoverflies which can identify neural markers of selective attention, in combination with virtual reality experiments for ants; techniques pioneered by the Australian team. In combination with verification of emerging hypotheses on large-scale neural models on-board robotic platforms in the real world, an approach pioneered by the UK team, this project represents a unique and timely opportunity to transform our understanding of learning in animals and through this, learning in robots and AI systems. We will create an interdisciplinary collaborative research environment with a "virtuous cycle" of experiments, analysis and computational and robotic modelling. New findings feed forward and back around this virtuous cycle, each discipline informing the others to yield a functional understanding of how active learning and selective attention enable small-brained insects to learn a complex world. Through this understanding, we will develop ActiveAI algorithms which are efficient in learning and final network configuration, robust to real-world conditions and learn rapidly.

我们将汇集昆虫生物学和神经科学领域的世界领导者与世界领导者的生物生物学建模和计算神经科学领域，以建立一种伙伴关系，该伙伴关系将在理解人工智能（AI）中的昆虫，机器人和自主系统中的主动学习和选择性关注方面具有变革性。通过考虑在自然动物行为期间的大脑，行为和环境如何相互作用，我们将开发新的算法和方法，用于快速，健壮，有效地学习自主机器人技术和动态现实世界应用的AI。在AI中的进步，尤其是深度学习，在深度学习中，在为特定的复杂问题上创造了对特定的复杂问题的成功（例如，通过驾驶人类和驾驶人的驾驶和驾驶，都可以在驾驶中，并在驾驶中驾驶。但是，随着我们更多地了解这些方法，它们的局限性变得越来越明显。例如，深度学习解决方案通常需要大量的计算能力，非常长的培训时间和大量标记的培训数据，这些数据根本无法用于许多任务。尽管他们非常擅长解决特定的任务，但在将这些知识转移到其他密切相关的任务中，它们可能非常贫穷（并且不可预测）。最后，科学家和工程师正在努力了解他们的深度学习系统学到了什么以及他们学到的知识。当与昆虫的自然发展智能形成对比时，这些局限性尤其明显。昆虫当然不能玩或开车，但是它们擅长做自己进化的事情。例如，与当前的任何AI系统不同，Ants学习了如何通过小脑提供的有限计算能力和对世界的最小探索来有效地觅食。我们认为这种差异之所以出现，是因为自然智力是闭环脑体环境相互作用的属性。与专门的传感器和神经回路提取并以最大效率编码与任务相关的信息的一致的先天行为，并在选择性关注的机制的帮助下，将学习与任务相关的功能的重点关注。在当前的AI技术中，这种对行为体现的代理的关注不足，但可以解决上述问题的解决方案，可以通过在AI中进行AI的研究来实现，这是对生物学学习和智力的描述和仿真的描述和仿真，既可以重复动物的能力，又阐明了在智能的生物学基础上阐明的动物，这是在努力研究的行为，这是在努力的行为，即在工作中，这是在研究中的工作。神经活动与环境，身体和行为以及相互作用的相互作用，并与选择性注意力相互作用。现在可以通过结合果蝇和气管神经记录的最新进展，这些实验可以鉴定有选择性注意的神经标记，并结合蚂蚁的虚拟现实实验。澳大利亚团队开创的技术。结合对现实世界中大规模神经模型的新兴假设的验证，这是英国团队开创的一种方法，该项目代表了一个独特而及时的机会，可以改变我们对动物学习的理解，并通过此过程，在机器人和AI系统中学习。我们将通过实验，分析，计算和机器人建模的“良性周期”创建一个跨学科的协作研究环境。新发现在这个良性周期中向前和返回，每个学科都告知其他人对积极的学习和选择性关注如何使小脑的昆虫能够学习复杂的世界。通过这种理解，我们将开发有效的学习和最终网络配置，对现实世界条件的强大并迅速学习的ActiveAi算法。