深度卷积神经网络（DCNNS）在面部识别方面已经达到了人类水平的准确性（Phillips等，2018），尽管目前尚不清楚它们如何准确地区分高度相似的面孔。在这里，人类和DCNN执行了包括相同双胞胎在内的具有挑战性的面貌匹配任务。参与者（n = 87）查看了三种类型的面孔图像：同一身份，普通冒名顶替对（来自相似人口组的不同身份）和双胞胎冒名顶替对（相同的双胞胎兄弟姐妹）。任务是确定对是同一个人还是不同的人。身份比较在三个观点区分条件下进行了测试：额叶至额叶，额叶至45度，额叶为90度。在每个观点 - 差异条件下评估了从双胞胎突变器和一般冒险者区分匹配的身份对的准确性。人类对于一般撞击对比双重射手对更准确，准确性下降，一对图像之间的观点差异增加。通过介绍给人类的同一图像对测试了经过训练的面部识别的DCNN（Ranjan等，2018）。机器性能反映了人类准确性的模式，但除了一种条件以外，所有人的性能都处于或尤其是所有人的表现。在所有图像对类型中，比较了人与机器的相似性得分。该项目级别的分析表明，在九种图像对类型中的六种中，人类和机器的相似性等级显着相关[范围r = 0.38至r = 0.63]，这表明人类对面部相似性的感知和DCNN之间的一般协议。这些发现还有助于我们理解DCNN的表现，以区分高度介绍面孔，表明DCNN在人类或以上的水平上表现出色，并暗示了人类和DCNN使用的特征之间的均等程度。

面部合成的进步已经提出了关于合成面的欺骗性使用的警报。合成综合性可以有效地用于欺骗人类观察者吗？在本文中，我们介绍了使用不同策略产生的合成面的人类感知的研究，包括基于最先进的深学的GaN模型。这是第一次严格研究从心理学的实验技术接地的合成面代发电技术的有效性研究。我们回答了重要的问题，如GaN的频率和更传统的图像处理的技术混淆人类观察者，并且在综合性脸部图像中有细微的线索，导致人类将其视为假冒，而无需寻找明显的线索还为了回答这些问题，我们进行了一系列大规模众群行为实验，具有不同的面膜。结果表明，人类无法在几个不同的情况下区分真实面的合成面。这一发现对面部图像呈现给人类用户的许多不同应用具有严重影响。

面部识别水平的度量对于确保专业法医面部考官和其他在应用方案中执行面部识别任务的其他人的准确和一致的表现至关重要。当前的熟练度测试依赖于静态刺激项目的集合，因此不能多次有效地对同一个人进行有效管理。要创建熟练度测试，必须组装大量“已知”难度的项目。可以构建多个相等难度的测试，然后使用项目子集。我们介绍了三合会身份匹配（TIM）测试，并使用项目响应理论（IRT）对其进行评估。参与者查看面部图像“三合会”（n = 225）（一个身份的两个图像，一个不同身份的一个图像），然后选择不同的身份。在实验1中，大学生（n = 197）在TIM测试中显示出广泛的准确性，IRT建模表明TIM项目涵盖了各种难度水平。在实验2中，我们使用基于IRT的项目指标将测试分配为特定困难的子集。模拟显示，TIM项目的子集产生了对受试者能力的可靠估计。在实验3A和3B中，我们发现学生衍生的IRT模型可靠地评估了非学生参与者的能力以及在不同的测试课程中推广的能力。在实验3C中，我们显示TIM测试性能与其他常见的面部识别测试相关。总而言之，TIM测试为开发一个灵活和校准的框架提供了一个起点，以衡量各种能力水平（例如，具有面部处理缺陷的专业人员或人群）的能力。

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

现实世界的行为通常是由多种代理之间复杂的相互作用来塑造的。为了可靠地研究多代理行为，无监督和自我监督的学习的进步使从轨迹数据中学到了各种不同的行为表示。迄今为止，还没有一组统一的基准测试，可以在广泛的行为分析设置中进行定量和系统地比较方法。我们的目的是通过引入来自现实世界行为神经科学实验的大规模，多代理轨迹数据集来解决这一问题，该数据集涵盖了一系列行为分析任务。我们的数据集由来自通用模型生物的轨迹数据组成，其中有960万帧的小鼠数据和440万帧的飞行数据，在各种实验环境中，例如不同的菌株，相互作用的长度和光遗传学刺激。框架的子集还包括专家注销的行为标签。我们数据集的改进对应于跨多种生物的行为表示，并能够捕获常见行为分析任务的差异。

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。

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. We use an autoregressive large language model (OpenAI's text-davinci-003) to determine if proposed U.S. Congressional bills are relevant to specific public companies and provide explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model, which outperforms the baseline of predicting the most common outcome of irrelevance. However, we test the ability to determine the relevance of a bill with the previous OpenAI GPT-3 model (text-davinci-002), which was state-of-the-art on many language tasks until text-davinci-003 was released on November 28, 2022. The performance of text-davinci-002 is worse than simply always predicting that a bill is irrelevant to a company. These results suggest that, as large language models continue to improve core natural language understanding capabilities, performance on corporate lobbying related tasks will continue to improve. We then discuss why this could be problematic for societal-AI alignment.

In the past years, deep learning has seen an increase of usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their own uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole-Slide-Images under domain shift using the H\&E stained Camelyon17 breast cancer dataset. Although it is known that histopathological data can be subject to strong domain shift and label noise, to our knowledge this is the first work that compares the most common methods for uncertainty estimation under these aspects. In our experiments, we compare Stochastic Variational Inference, Monte-Carlo Dropout, Deep Ensembles, Test-Time Data Augmentation as well as combinations thereof. We observe that ensembles of methods generally lead to higher accuracies and better calibration and that Test-Time Data Augmentation can be a promising alternative when choosing an appropriate set of augmentations. Across methods, a rejection of the most uncertain tiles leads to a significant increase in classification accuracy on both in-distribution as well as out-of-distribution data. Furthermore, we conduct experiments comparing these methods under varying conditions of label noise. We observe that the border regions of the Camelyon17 dataset are subject to label noise and evaluate the robustness of the included methods against different noise levels. Lastly, we publish our code framework to facilitate further research on uncertainty estimation on histopathological data.

In large-scale machine learning, recent works have studied the effects of compressing gradients in stochastic optimization in order to alleviate the communication bottleneck. These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays. Perhaps surprisingly, despite the surge of interest in large-scale, multi-agent reinforcement learning, almost nothing is known about the analogous question: Are common reinforcement learning (RL) algorithms also robust to similar perturbations? In this paper, we investigate this question by studying a variant of the classical temporal difference (TD) learning algorithm with a perturbed update direction, where a general compression operator is used to model the perturbation. Our main technical contribution is to show that compressed TD algorithms, coupled with an error-feedback mechanism used widely in optimization, exhibit the same non-asymptotic theoretical guarantees as their SGD counterparts. We then extend our results significantly to nonlinear stochastic approximation algorithms and multi-agent settings. In particular, we prove that for multi-agent TD learning, one can achieve linear convergence speedups in the number of agents while communicating just $\tilde{O}(1)$ bits per agent at each time step. Our work is the first to provide finite-time results in RL that account for general compression operators and error-feedback in tandem with linear function approximation and Markovian sampling. Our analysis hinges on studying the drift of a novel Lyapunov function that captures the dynamics of a memory variable introduced by error feedback.