Zero-Input AI (ZIA) introduces a novel framework for human-computer interaction by enabling proactive intent prediction without explicit user commands. It integrates gaze tracking, bio-signals (EEG, heart rate), and contextual data (time, location, usage history) into a multi-modal model for real-time inference, targeting <100 ms latency. The proposed architecture employs a transformer-based model with cross-modal attention, variational Bayesian inference for uncertainty estimation, and reinforcement learning for adaptive optimization. To support deployment on edge devices (CPUs, TPUs, NPUs), ZIA utilizes quantization, weight pruning, and linear attention to reduce complexity from quadratic to linear with sequence length. Theoretical analysis establishes an information-theoretic bound on prediction error and demonstrates how multi-modal fusion improves accuracy over single-modal approaches. Expected performance suggests 85-90% accuracy with EEG integration and 60-100 ms inference latency. ZIA provides a scalable, privacy-preserving framework for accessibility, healthcare, and consumer applications, advancing AI toward anticipatory intelligence.

翻译：零输入人工智能（ZIA）提出了一种新颖的人机交互框架，其核心在于无需显式用户指令即可主动预测用户意图。该框架将眼动追踪、生物信号（脑电图、心率）和上下文数据（时间、位置、使用历史）集成到一个多模态模型中，以实现实时推断，目标延迟低于100毫秒。所提出的架构采用基于Transformer的模型，具备跨模态注意力机制，利用变分贝叶斯推断进行不确定性估计，并采用强化学习进行自适应优化。为支持在边缘设备（CPU、TPU、NPU）上部署，ZIA采用了量化、权重剪枝和线性注意力技术，将计算复杂度从随序列长度的平方级降低至线性级。理论分析建立了预测误差的信息论界，并论证了多模态融合相较于单模态方法如何提升准确性。预期性能表明，在集成脑电图信号时，准确率可达85-90%，推断延迟为60-100毫秒。ZIA为无障碍辅助、医疗健康和消费级应用提供了一个可扩展且保护隐私的框架，推动了人工智能向预见性智能的发展。