模块化设计是未来大型空间设施的On On On构造技术的基础。标准界面是未来空间机器人系统和空间设施模块化设计的关键技术。本文介绍了Petlock的设计和测试，标准和测试无性别界面可以在未来的模块化空间机器人操纵器和航天器之间传递机械载荷，功率和数据。Petlock采用完全无性别的设计，包括连接面，锁定机制，数据和功率接口。连接表面提供了较大的翻译和旋转错位耐受性，由于其120度对称和3D形状的设计。锁定机制具有三个锁定引脚撤回结构设计，这是简单可靠的。高锁定力，高容忍度，高可靠性和低成本的优势，Petloc K在未来的轨道施工任务中具有很大的应用潜力。

向前和向后触及逆运动学（FABRIK）是一种启发式逆运动求解器，逐渐应用于具有快速收敛和生成更真实配置的优势的操纵器。但是，在高误差限制下，Fabrik表现出不稳定的收敛行为，这对于操纵器的实时运动计划是不满意的。在本文中，提出了一种结合Fabrik和顺序二次编程（SQP）算法的新型逆运动学算法，其中Fabrik推迟的关节角度将被视为SQP算法的初始种子，以避免粘在局部最小值中。通过实验评估合并的算法，在高误差约束下，我们的算法比FabRik获得更高的成功率和更快的解决方案时间。此外，联合算法可以在路径跟踪中为UR5和KUKA LBR IIWA 14 R820操纵器生成连续轨迹，而无姿势误差和最终效应器的允许位置误差。

深度学习（DL）模型的功能可以通过模型提取被盗，其中攻击者通过利用原始模型的预测API来获得替代模型。在这项工作中，我们提出了一种称为Dynamarks的新型水印技术，以保护DL模型的知识产权（IP）免受黑箱设置中的模型提取攻击。与现有方法不同，Dynamarks不会改变原始模型的训练过程，而是通过基于推理运行时的某些秘密参数从原始模型预测API中动态更改输出响应来将水印嵌入替代模型中。时尚MNIST，CIFAR-10和Imagenet数据集的实验结果证明了Dynamarks方案对水印替代模型的功效，同时保留了部署在边缘设备中的原始模型的准确性。此外，我们还执行实验，以评估Dynamarks对各种水印策略的鲁棒性，从而使DL模型所有者可以可靠地证明模型所有权。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

基于相机的非接触式光电子溶血性描绘是指一组流行的非接触生理测量技术。目前的最先进的神经模型通常以伴随金标准生理测量的视频以监督方式培训。但是，它们通常概括域名差别示例（即，与培训集中的视频不同）。个性化模型可以帮助提高型号的概括性，但许多个性化技术仍然需要一些金标准数据。为了帮助缓解这一依赖性，在本文中，我们展示了一种名为Mobilememon的新型移动感应系统，该系统是第一个移动个性化远程生理传感系统，它利用智能手机上的前后相机，为培训产生高质量的自我监督标签个性化非接触式相机的PPG模型。为了评估MobilemeLephys的稳健性，我们使用39名参与者进行了一个用户学习，他们在不同的移动设备下完成了一组任务，照明条件/强度，运动任务和皮肤类型。我们的研究结果表明，Mobilephys显着优于最先进的设备监督培训和几次拍摄适应方法。通过广泛的用户研究，我们进一步检查了Mobilephys如何在复杂的真实环境中执行。我们设想，从我们所提出的双摄像机移动传感系统产生的校准或基于相机的非接触式PPG模型将为智能镜，健身和移动健康应用等许多未来应用打开门。

分层多粒度分类（HMC）将分层多粒度标签分配给每个对象，专注于对标签层次结构进行编码，例如[“Albatross”，“Laysan Albatross”]从粗略级别进行。然而，细粒度的定义是主观的，并且图像质量可能会影响识别。因此，可以在层次结构的任何水平处观察样本，例如，例如，[“信天翁”]或[“白金贸易”，“Laysan Albatross”，并且在致动类别中辨别的示例在HMC的传统设置中通常被忽略。在本文中，我们研究了HMC问题，其中对象在层次结构的任何级别上标记。所提出的方法的基本设计源自两个动机：（1）学习在各个级别标记的物体应该转移级别之间的分层知识; （2）较低级别的类应继承与上级超类相关的属性。所提出的组合损失通过从树层次结构中定义的相关标签聚合信息来最大化观察到的地面真实标签的边际概率。如果观察到的标签处于叶片水平，则组合损失进一步施加了多级跨熵损失，以增加细粒度分类损失的重量。考虑到分层特征交互，我们提出了一个分层剩余网络（HRN），其中来自父级的粒度特定特征作为残留连接的特定特征被添加到儿童级别的特征。与最先进的HMC方法和精细的视觉分类（FGVC）方法相比，三种常用数据集的实验证明了我们的方法的有效性和利用标签层次结构的方法。

A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. We unfold the design of SCFL in three steps. \emph{i) Stable social cluster formation. Considering users' heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping}. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants' model updates depending on social trust degrees. iii) Distributed convergence}. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on how to turn it into one that can be productively studied empirically. We first present an experimental design centered on choosing tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment following meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat -- a trivial baseline strategy for scalable oversight -- substantially outperform both the model alone and their own unaided performance. These results are an encouraging sign that scalable oversight will be tractable to study with present models and bolster recent findings that large language models can productively assist humans with difficult tasks.