A new generation of increasingly autonomous and self-learning embodied
systems is about to be developed. When deploying embodied systems into a
real-life context we face various engineering challenges, as it is crucial to
coordinate the behavior of embodied systems in a beneficial manner, ensure
their compatibility with our human-centered social values, and design
verifiably safe and reliable human-machine interaction. We are arguing that
traditional systems engineering is coming to a climacteric from embedded to
embodied systems, and with assuring the trustworthiness of dynamic federations
of situationally aware, intent-driven, explorative, ever-evolving, largely
non-predictable, and increasingly autonomous embodied systems in uncertain,
complex, and unpredictable real-world contexts. We are therefore identifying a
number of urgent systems challenges for trustworthy embodied systems, including
robust and human-centric AI, cognitive architectures, uncertainty
quantification, trustworthy self-integration, and continual analysis and
assurance.

研究了面对真实环境中的各种工程挑战，为保证具有人类中心社会价值的协调行为，设计可靠和可验证安全的人机交互，我们正在为值得信赖的具有不断发展、自主学习、意图驱动、探索性的、大规模非可预测的、增强的具体化系统识别一些紧迫的系统挑战，包括具有鲁棒性和人类中心的 AI，认知架构、不确定性量化、可信自我集成和持续分析和保证。

用于可信体系结构的系统挑战

Systems Challenges for Trustworthy Embodied Systems

A key challenge for AI is to build embodied systems that operate in
dynamically changing environments. Such systems must adapt to changing task
contexts and learn continuously. Although standard deep learning systems
achieve state of the art results on static benchmarks, they often struggle in
dynamic scenarios. In these settings, error signals from multiple contexts can
interfere with one another, ultimately leading to a phenomenon known as
catastrophic forgetting. In this article we investigate biologically inspired
architectures as solutions to these problems. Specifically, we show that the
biophysical properties of dendrites and local inhibitory systems enable
networks to dynamically restrict and route information in a context-specific
manner. Our key contributions are as follows. First, we propose a novel
artificial neural network architecture that incorporates active dendrites and
sparse representations into the standard deep learning framework. Next, we
study the performance of this architecture on two separate benchmarks requiring
task-based adaptation: Meta-World, a multi-task reinforcement learning
environment where a robotic agent must learn to solve a variety of manipulation
tasks simultaneously; and a continual learning benchmark in which the model's
prediction task changes throughout training. Analysis on both benchmarks
demonstrates the emergence of overlapping but distinct and sparse subnetworks,
allowing the system to fluidly learn multiple tasks with minimal forgetting.
Our neural implementation marks the first time a single architecture has
achieved competitive results on both multi-task and continual learning
settings. Our research sheds light on how biological properties of neurons can
inform deep learning systems to address dynamic scenarios that are typically
impossible for traditional ANNs to solve.

通过生物学对神经元的性质进行深度学习网络的架构设计和实现，成功地解决了动态情境下的任务适应及多任务学习问题，进一步说明了神经元的生物学特征可以启示深度学习系统解决传统神经网络无法解决的动态场景中的问题.