随着深度神经网络的兴起，解释这些网络预测的挑战已经越来越识别。虽然存在许多用于解释深度神经网络的决策的方法，但目前没有关于如何评估它们的共识。另一方面，鲁棒性是深度学习研究的热门话题;但是，在最近，几乎没有谈论解释性。在本教程中，我们首先呈现基于梯度的可解释性方法。这些技术使用梯度信号来分配对输入特征的决定的负担。后来，我们讨论如何为其鲁棒性和对抗性的鲁棒性在具有有意义的解释中扮演的作用来评估基于梯度的方法。我们还讨论了基于梯度的方法的局限性。最后，我们提出了在选择解释性方法之前应检查的最佳实践和属性。我们结束了未来在稳健性和解释性融合的地区研究的研究。

科学数据的一套简洁且可衡量的公平（可访问，可互操作和可重复使用的）原则正在转变用于数据管理和管理的最新实践，以支持和支持发现和创新。从这项计划中学习，并承认人工智能（AI）在科学和工程实践中的影响，我们为AI模型引入了一套实用，简洁和可衡量的公平原则。我们展示了如何在统一的计算框架内创建和共享公平的数据和AI模型，结合了以下要素：Argonne国家实验室的高级光子源，材料数据设施，科学数据和学习中心，Funcx和Argonne Leadersition的数据和学习中心计算设施（ALCF），尤其是ALCF AI测试台的Thetagpu SuperCuputer和Sambanova Datascale系统。我们描述了如何利用这种域 - 不足的计算框架来实现自主AI驱动的发现。

通过使用低成本，远程，无维护的无线传感器进行增强，数十亿个日常对象可能会成为物联网（IoT）的一部分。射频识别（RFID）是一种低成本的无线技术，可以实现这一愿景，但是它受到短暂的通信范围和缺乏足够的能量来限制辅助电子和传感器。在这里，我们探讨了柔性钙钛矿光伏电池的使用，以提供半邮用RFID标签的外部功率，以增加外部电子设备（例如微控制器和数字传感器）的范围和能量可用性。钙钛矿是有趣的材料，具有开发高性能，低成本，可调节性（吸收不同的光谱）和柔性轻能量收割机的可能性。在标准测试条件下，我们的塑料底物上的原型钙钛矿光伏细胞的效率为13％，电压为0.88 V。我们构建了由这些柔性光伏电池供电的RFID传感器的原型原型，以展示现实世界的应用。我们对原型的评估表明：i）柔性PV细胞耐用至5 mm的弯曲半径，相对效率仅下降20％； ii）RFID通信范围增加了5倍，并满足能源需求（10-350 microwatt）以实现自动无线传感器； iii）钙钛矿动力无线传感器启用许多无电池传感应用程序（例如，易腐烂的良好监控，仓库自动化）

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。

我们介绍了AAM-GYM，这是一种高级空气流动性（AAM）的研发测试。 AAM有可能通过利用新型飞机（例如电动垂直起飞和降落（EVTOL）飞机）和新的高级人工智能（AI）算法来减少地面交通和排放来彻底改变旅行。 AI算法的验证需要代表性的AAM场景，以及快速的仿真测试以评估其性能。到目前为止，AAM还没有这样的测试床可以为政府，工业或学术界的个人提供一个共同的研究平台。麻省理工学院林肯实验室已经开发了AAM-GYM来解决这一差距，通过提供一个生态系统来开发，训练和验证各种AAM用例的新型AI算法。在本文中，我们使用AAM-GYM来研究AAM用例，AAM走廊中的分离保证的两种增强学习算法的性能。根据AAM-GYM提供的一系列指标，证明了两种算法的性能，显示了测试床对AAM研究的实用性。

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.