How can humans stay in control of advanced artificial intelligence systems?
One proposal is corrigibility, which requires the agent to follow the
instructions of a human overseer, without inappropriately influencing them. In
this paper, we formally define a variant of corrigibility called shutdown
instructability, and show that it implies appropriate shutdown behavior,
retention of human autonomy, and avoidance of user harm. We also analyse the
related concepts of non-obstruction and shutdown alignment, three previously
proposed algorithms for human control, and one new algorithm.

该论文提出了一种人类如何控制人工智能系统的方法 —— 可纠正性，即要求智能体遵循人类监督者的指示，而不会对其进行不适当的影响。它还定义了一种可纠正行为的变体称为关机教示性，并表明它意味着适当的关机行为，保留人类自主权和避免用户危害。研究了三个先前提出的人类控制算法和一个新算法的相关概念。

人类控制：定义与算法

Human Control: Definitions and Algorithms

Achieving machine autonomy and human control often represent divergent
objectives in the design of interactive AI systems. Visual generative
foundation models such as Stable Diffusion show promise in navigating these
goals, especially when prompted with arbitrary languages. However, they often
fall short in generating images with spatial, structural, or geometric
controls. The integration of such controls, which can accommodate various
visual conditions in a single unified model, remains an unaddressed challenge.
In response, we introduce UniControl, a new generative foundation model that
consolidates a wide array of controllable condition-to-image (C2I) tasks within
a singular framework, while still allowing for arbitrary language prompts.
UniControl enables pixel-level-precise image generation, where visual
conditions primarily influence the generated structures and language prompts
guide the style and context. To equip UniControl with the capacity to handle
diverse visual conditions, we augment pretrained text-to-image diffusion models
and introduce a task-aware HyperNet to modulate the diffusion models, enabling
the adaptation to different C2I tasks simultaneously. Trained on nine unique
C2I tasks, UniControl demonstrates impressive zero-shot generation abilities
with unseen visual conditions. Experimental results show that UniControl often
surpasses the performance of single-task-controlled methods of comparable model
sizes. This control versatility positions UniControl as a significant
advancement in the realm of controllable visual generation.

UniControl 是一种新的可控生成基础模型，集成了多种可控因素，实现了基于任意语言提示的像素级精确图像生成，并通过多任务学习，使其具有适应不同可控条件的能力，实验证明其性能优于其他同类型模型。

UniControl：一种用于自然环境中可控视觉生成的统一扩散模型

UniControl: A Unified Diffusion Model for Controllable Visual Generation  In the Wild

This paper contributes a preliminary report on the advantages and
disadvantages of incorporating simultaneous human control and feedback signals
in the training of a reinforcement learning robotic agent. While robotic
human-machine interfaces have become increasingly complex in both form and
function, control remains challenging for users. This has resulted in an
increasing gap between user control approaches and the number of robotic motors
which can be controlled. One way to address this gap is to shift some autonomy
to the robot. Semi-autonomous actions of the robotic agent can then be shaped
by human feedback, simplifying user control. Most prior work on agent shaping
by humans has incorporated training with feedback, or has included indirect
control signals. By contrast, in this paper we explore how a human can provide
concurrent feedback signals and real-time myoelectric control signals to train
a robot's actor-critic reinforcement learning control system. Using both a
physical and a simulated robotic system, we compare training performance on a
simple movement task when reward is derived from the environment, when reward
is provided by the human, and combinations of these two approaches. Our results
indicate that some benefit can be gained with the inclusion of human generated
feedback.

该研究探索一种使用人类提供的同时人类控制和反馈信号来训练一个强化学习机器人代理的方法，并旨在缩小用户控制方法和控制的机器人数量之间的差距。作者采用物理和模拟机器人系统的实验比较了在环境中获得奖励、由人类提供奖励以及这两种方法的组合下的训练表现，结果表明人类反馈可以提高代理的训练效果。