Artificial intelligence (AI) algorithms based on neural networks have been designed for decades with the goal of maximising some measure of accuracy. This has led to two undesired effects. First, model complexity has risen exponentially when measured in terms of computation and memory requirements. Second, state-of-the-art AI models are largely incapable of providing trustworthy measures of their uncertainty, possibly `hallucinating' their answers and discouraging their adoption for decision-making in sensitive applications. With the goal of realising efficient and trustworthy AI, in this paper we highlight research directions at the intersection of hardware and software design that integrate physical insights into computational substrates, neuroscientific principles concerning efficient information processing, information-theoretic results on optimal uncertainty quantification, and communication-theoretic guidelines for distributed processing. Overall, the paper advocates for novel design methodologies that target not only accuracy but also uncertainty quantification, while leveraging emerging computing hardware architectures that move beyond the traditional von Neumann digital computing paradigm to embrace in-memory, neuromorphic, and quantum computing technologies. An important overarching principle of the proposed approach is to view the stochasticity inherent in the computational substrate and in the communication channels between processors as a resource to be leveraged for the purpose of representing and processing classical and quantum uncertainty.

翻译：摘要：基于神经网络的人工智能算法数十年来一直以最大化某种准确率指标为设计目标，这导致了两个不良后果。首先，以计算和存储需求衡量，模型复杂度呈指数级增长。其次，现有顶尖AI模型在提供可信的不确定性度量方面存在严重缺陷，可能出现"幻觉"式答案，阻碍其在敏感应用决策中的采用。为实现高效可信的人工智能，本文聚焦硬件与软件设计的交叉领域，提出将物理洞察融入计算基板、借鉴神经科学高效信息处理原理、运用信息论最优不确定性量化成果、以及遵循分布式处理通信理论准则的研究方向。总体而言，本文倡导新型设计方法论——不仅瞄准准确率，更关注不确定性量化，同时突破传统冯·诺依曼数字计算范式，拥抱存算一体、神经形态计算和量子计算等新兴计算硬件架构。该方法的重要统摄原则是将计算基板与处理器间通信信道固有的随机性视为可资利用的资源，用以表征和处理经典及量子不确定性。