人们如何积极学习学习？也就是说，人们如何以及何时选择促进长期学习和选择更有益的行动的行动？我们在积极的因果学习领域中探索这些问题。我们提出了一个层次的贝叶斯模型，该模型通过预测人们不仅追求有关因果关系的信息，而且还涉及因果关系的信息，$ \ unicode {x2014} $摘要信念关于因果关系的抽象信念，这些关系跨越了多种情况，并约束了我们如何约束我们如何限制了我们如何限制我们的因果关系。在每种情况下学习细节。在具有14个受试者间操作的两个主动“泡沫检测器”实验中，我们的模型受到参与者行为的定性趋势和基于个体差异的模型比较的支持。我们的结果表明，当在积极的因果学习问题之间存在抽象相似之处时，人们很容易就这些相似性学习和转移过度的疏忽。此外，人们利用这些夸张的人来促进长期的积极学习。

难以理解的AI系统很难信任，尤其是当它们在自动驾驶（例如自动驾驶）等安全环境中运行时。因此，有必要建立透明且可查询的系统以提高信任水平。我们提出了一种基于现有的称为IGP2的现有白盒系统的自动驾驶汽车运动计划和预测的透明，以人为中心的解释生成方法。我们的方法将贝叶斯网络与无上下文生成规则相结合，并可以为自动驾驶汽车的高级驾驶行为提供因果自然语言解释。对模拟方案的初步测试表明，我们的方法捕获了自动驾驶汽车行动背后的原因，并产生了具有不同复杂性的可理解解释。

我们提出了一种小型任务，可以衡量人们如何基于观察单个（实验1）或几个（实验2）对象对之间的因果相互作用来概括物体的因果动力。我们提出了一种计算建模框架，可以在我们的任务环境中综合人类的泛化模式，并阐明人们如何有效地浏览可能的因果函数和类别的组成空间。我们的建模框架结合了使用代理和收件人对象的特征和关系的因果函数发生器，以及贝叶斯非参数推断过程，以控制基于相似性的概念。我们的模型具有自然的“资源合理的”变体，可以在描述参与者时优于一个天真的贝叶斯账户，特别是在我们的行为实验中再现透明阶效应和因果不对称。我们认为，该建模框架为真实世界因果概念提供了计算上的合理机制。

空气污染是世界上死亡率最重要的原因之一。监测空气污染对于了解健康与污染物之间的联系并确定干预区域很有用。这种监视很昂贵，因此重要的是要尽可能有效地放置传感器。事实证明，贝叶斯优化对选择传感器位置有用，但通常依赖于忽略空气污染统计结构的内核功能，例如污染趋势沿盛行的风向传播。我们描述了两个新的风向内核，并研究了它们在使用贝叶斯优化的最大污染位置主动学习位置的任务中的优势。

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.

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.

A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i.e., to individual clients) and generalization (i.e., to unseen data) properties concurrently. Existing techniques in federated learning (FL) have encountered a steep tradeoff between these objectives and impose large computational requirements on edge devices during training and inference. In this paper, we propose SplitGP, a new split learning solution that can simultaneously capture generalization and personalization capabilities for efficient inference across resource-constrained clients (e.g., mobile/IoT devices). Our key idea is to split the full ML model into client-side and server-side components, and impose different roles to them: the client-side model is trained to have strong personalization capability optimized to each client's main task, while the server-side model is trained to have strong generalization capability for handling all clients' out-of-distribution tasks. We analytically characterize the convergence behavior of SplitGP, revealing that all client models approach stationary points asymptotically. Further, we analyze the inference time in SplitGP and provide bounds for determining model split ratios. Experimental results show that SplitGP outperforms existing baselines by wide margins in inference time and test accuracy for varying amounts of out-of-distribution samples.

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants$\unicode{x2014}$what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world$\unicode{x2014}$also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing$\unicode{x2014}$leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first$\unicode{x2014}$and key$\unicode{x2014}$step towards such an ecology.

本文考虑通过模型量化提高联邦学习（FL）的无线通信和计算效率。在提出的Bitwidth FL方案中，Edge设备将其本地FL模型参数的量化版本训练并传输到协调服务器，从而将它们汇总为量化的全局模型并同步设备。目的是共同确定用于本地FL模型量化的位宽度以及每次迭代中参与FL训练的设备集。该问题被视为一个优化问题，其目标是在每卷工具采样预算和延迟要求下最大程度地减少量化FL的训练损失。为了得出解决方案，进行分析表征，以显示有限的无线资源和诱导的量化误差如何影响所提出的FL方法的性能。分析结果表明，两个连续迭代之间的FL训练损失的改善取决于设备的选择和量化方案以及所学模型固有的几个参数。给定基于线性回归的这些模型属性的估计值，可以证明FL训练过程可以描述为马尔可夫决策过程（MDP），然后提出了基于模型的增强学习（RL）方法来优化动作的方法选择迭代。与无模型RL相比，这种基于模型的RL方法利用FL训练过程的派生数学表征来发现有效的设备选择和量化方案，而无需强加其他设备通信开销。仿真结果表明，与模型无RL方法和标准FL方法相比，提出的FL算法可以减少29％和63％的收敛时间。

逆运动学（IK）系统通常相对于其输入特征很僵硬，因此需要将用户干预适应新骨架。在本文中，我们旨在创建一个适用于各种人类形态的灵活的，学到的IK求解器。我们扩展了最先进的机器学习IK求解器，以在众所周知的皮肤多人线性模型（SMPL）上运行。我们称我们的模型SMPL-IK，并表明当集成到实时3D软件中时，该扩展系统为定义新型AI-Asissist Animation Workfrows提供了机会。例如，通过允许用户在摆姿势的同时修改性别和身体形状，可以使姿势创作更加灵活。此外，当使用现有姿势估计算法链接时，SMPL-IK通过允许用户从2D图像引导3D场景来加速摆姿势，同时允许进一步编辑。最后，我们提出了一种新颖的SMPL形状反转机制（SMPL-SI），将任意类人形特征映射到SMPL空间，使艺术家能够在自定义字符上利用SMPL-IK。除了显示拟议工具的定性演示外，我们还介绍了H36M和Amass数据集上的定量SMPL-IK基准。