该报告概述了建模变革性AI风险（MTAIR）项目的工作，试图在有关高级AI的灾难性风险及其之间的关系中绘制关键的假设，不确定性和分歧。这是基于Ben Cottier和Rohin Shah的较早图，它们以视觉上列出了一些关键分歧（“ Cruxes”），并进行了一些解释。根据广泛的文献综述和与专家的参与，该报告解释了涉及的问题的模型以及最初的基于软件的实施，该实施可以纳入概率估计或其他定量因素，以实现探索，计划和/或决策支持。通过将各种辩论和讨论中的信息收集到一个更连贯的演讲中，我们希望能够更好地讨论和辩论有关的问题。该模型从通过类比的推理和对人工智能的一般性信念进行讨论开始。此后，它提出了一个不同的路径模型，并为高级机器智能提供了技术，以及这些系统能力的进步如何进行的模型，包括有关自我支持，不连续改进的辩论以及的可能性以及分布式，非代理高级智能或较慢的改进。该模型还专门研究了学习优化的问题，以及机器学习系统是否会创建MESA-OPTIMIZES。然后检查了不同的安全研究对先前问题集的影响，以了解研究以及如何在实现更安全的系统中有用。最后，我们讨论了一个不同的故障模式的模型以及控制或接管场景的丧失。

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.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Federated learning has become a popular machine learning paradigm with many potential real-life applications, including recommendation systems, the Internet of Things (IoT), healthcare, and self-driving cars. Though most current applications focus on classification-based tasks, learning personalized generative models remains largely unexplored, and their benefits in the heterogeneous setting still need to be better understood. This work proposes a novel architecture combining global client-agnostic and local client-specific generative models. We show that using standard techniques for training federated models, our proposed model achieves privacy and personalization that is achieved by implicitly disentangling the globally-consistent representation (i.e. content) from the client-dependent variations (i.e. style). Using such decomposition, personalized models can generate locally unseen labels while preserving the given style of the client and can predict the labels for all clients with high accuracy by training a simple linear classifier on the global content features. Furthermore, disentanglement enables other essential applications, such as data anonymization, by sharing only content. Extensive experimental evaluation corroborates our findings, and we also provide partial theoretical justifications for the proposed approach.

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.

脑病理通常表现为组织的部分或完全丧失。许多神经影像学研究的目的是捕获有关感兴趣的临床变量（例如疾病进展）的组织变化的位置和数量。形态分析方法捕获了与临床变量有关的组织分布或其他含量的兴趣分布的局部差异。我们建议通过基于不平衡的最佳传输的附加特征提取步骤来增强形态分析。最佳运输特征提取步骤增加了导致空间分散组织损失的病理学的统计能力，从而最大程度地减少了由于空间未对准或大脑拓扑差异而对变化的敏感性，并将由于组织位置而导致的变化而分离。我们证明了在阿尔茨海默氏病的OASIS-1研究的体积形态学分析的背景下，提出的最佳运输特征提取步骤。结果表明，所提出的方法可以识别组织的变化和差异，而这些差异是无法测量的。

培训和评估语言模型越来越多地要求构建元数据 - 多样化的策划数据收集，并具有清晰的出处。自然语言提示最近通过将现有的，有监督的数据集转换为多种新颖的预处理任务，突出了元数据策划的好处，从而改善了零击的概括。尽管将这些以数据为中心的方法转化为生物医学语言建模的通用域文本成功，但由于标记的生物医学数据集在流行的数据中心中的代表性大大不足，因此仍然具有挑战性。为了应对这一挑战，我们介绍了BigBio一个由126个以上的生物医学NLP数据集的社区库，目前涵盖12个任务类别和10多种语言。 BigBio通过对数据集及其元数据进行程序化访问来促进可再现的元数据策划，并与当前的平台兼容，以及时工程和端到端的几个/零射击语言模型评估。我们讨论了我们的任务架构协调，数据审核，贡献指南的过程，并概述了两个说明性用例：生物医学提示和大规模，多任务学习的零射门评估。 BigBio是一项持续的社区努力，可在https://github.com/bigscience-workshop/biomedical上获得。

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。

语义图像分割是手术中的背景知识和自治机器人的重要前提。本领域的状态专注于在微创手术期间获得的传统RGB视频数据，但基于光谱成像数据的全景语义分割并在开放手术期间获得几乎没有注意到日期。为了解决文献中的这种差距，我们正在研究基于在开放手术环境中获得的猪的高光谱成像（HSI）数据的以下研究问题：（1）基于神经网络的HSI数据的充分表示是完全自动化的器官分割，尤其是关于数据的空间粒度（像素与Superpixels与Patches与完整图像）的空间粒度？ （2）在执行语义器官分割时，是否有利用HSI数据使用HSI数据，即RGB数据和处理的HSI数据（例如氧合等组织参数）？根据基于20猪的506个HSI图像的全面验证研究，共注释了19个类，基于深度的学习的分割性能 - 贯穿模态 - 与输入数据的空间上下文一致。未处理的HSI数据提供优于RGB数据或来自摄像机提供商的处理数据，其中优势随着输入到神经网络的输入的尺寸而增加。最大性能（应用于整个图像的HSI）产生了0.89（标准偏差（SD）0.04）的平均骰子相似度系数（DSC），其在帧间间变异性（DSC为0.89（SD 0.07）的范围内。我们得出结论，HSI可以成为全自动手术场景理解的强大的图像模型，其具有传统成像的许多优点，包括恢复额外功能组织信息的能力。

我们考虑通过马尔可夫决策过程轨迹传达外源信息的问题。我们称之为马尔可夫编码游戏（MCG）的设置概括了源编码和大量的参考游戏。 MCG还隔离了一个在不可用的分散控制环境中很重要的问题，即不可用的问题 - 即，他们需要平衡沟通与相关的交流成本。我们基于最大的熵增强学习和我们称为模因的最小熵耦合，为MCGS提供理论上的基础方法。由于最近在最小熵耦合的近似算法中突破，模因不仅是理论算法，而且可以应用于实际设置。从经验上讲，我们表明模因能够在小MCG上胜过强大的基线，并且该模因能够在极大的MCG上实现强大的性能。到后点，我们证明了Meme能够通过Cartpole和Pong的轨迹无误地传达二进制图像，同时同时获得最大或接近最大的预期回报，并且甚至在执行器噪声的情况下甚至能够表现良好。