Curated knowledge graphs encode domain expertise and improve the performance of recommendation, segmentation, ad targeting, and other machine learning systems in several domains. As new concepts emerge in a domain, knowledge graphs must be expanded to preserve machine learning performance. Manually expanding knowledge graphs, however, is infeasible at scale. In this work, we propose a method for knowledge graph expansion with humans-in-the-loop. Concretely, given a knowledge graph, our method predicts the "parents" of new concepts to be added to this graph for further verification by human experts. We show that our method is both accurate and provably "human-friendly". Specifically, we prove that our method predicts parents that are "near" concepts' true parents in the knowledge graph, even when the predictions are incorrect. We then show, with a controlled experiment, that satisfying this property increases both the speed and the accuracy of the human-algorithm collaboration. We further evaluate our method on a knowledge graph from Pinterest and show that it outperforms competing methods on both accuracy and human-friendliness. Upon deployment in production at Pinterest, our method reduced the time needed for knowledge graph expansion by ~400% (compared to manual expansion), and contributed to a subsequent increase in ad revenue of 20%.

本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片（Sota）。它填补了空白，因为现有的调查文章在其范围内或被约会。我们包括两个重要方面，目前正在挖掘和建模社交媒体的重要性：动态和网络。社会动态对于了解影响影响或疾病的传播，友谊的形成，友谊的形成等，另一方面，可以捕获各种复杂关系，提供额外的洞察力和识别否则将不会被注意的重要模式。

由于算法预测对人类的影响增加，模型解释性已成为机器学习（ML）的重要问题。解释不仅可以帮助用户了解为什么ML模型做出某些预测，还可以帮助用户了解这些预测如何更改。在本论文中，我们研究了从三个有利位置的ML模型的解释性：算法，用户和教学法，并为解释性问题贡献了一些新颖的解决方案。

即使机器学习算法已经在数据科学中发挥了重要作用，但许多当前方法对输入数据提出了不现实的假设。由于不兼容的数据格式，或数据集中的异质，分层或完全缺少的数据片段，因此很难应用此类方法。作为解决方案，我们提出了一个用于样本表示，模型定义和培训的多功能，统一的框架，称为“ Hmill”。我们深入审查框架构建和扩展的机器学习的多个范围范式。从理论上讲，为HMILL的关键组件的设计合理，我们将通用近似定理的扩展显示到框架中实现的模型所实现的所有功能的集合。本文还包含有关我们实施中技术和绩效改进的详细讨论，该讨论将在MIT许可下发布供下载。该框架的主要资产是其灵活性，它可以通过相同的工具对不同的现实世界数据源进行建模。除了单独观察到每个对象的一组属性的标准设置外，我们解释了如何在框架中实现表示整个对象系统的图表中的消息推断。为了支持我们的主张，我们使用框架解决了网络安全域的三个不同问题。第一种用例涉及来自原始网络观察结果的IoT设备识别。在第二个问题中，我们研究了如何使用以有向图表示的操作系统的快照可以对恶意二进制文件进行分类。最后提供的示例是通过网络中实体之间建模域黑名单扩展的任务。在所有三个问题中，基于建议的框架的解决方案可实现与专业方法相当的性能。

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.

在本文中，我们试图通过引入深度学习模型的句法归纳偏见来建立两所学校之间的联系。我们提出了两个归纳偏见的家族，一个家庭用于选区结构，另一个用于依赖性结构。选区归纳偏见鼓励深度学习模型使用不同的单位（或神经元）分别处理长期和短期信息。这种分离为深度学习模型提供了一种方法，可以从顺序输入中构建潜在的层次表示形式，即更高级别的表示由高级表示形式组成，并且可以分解为一系列低级表示。例如，在不了解地面实际结构的情况下，我们提出的模型学会通过根据其句法结构组成变量和运算符的表示来处理逻辑表达。另一方面，依赖归纳偏置鼓励模型在输入序列中找到实体之间的潜在关系。对于自然语言，潜在关系通常被建模为一个定向依赖图，其中一个单词恰好具有一个父节点和零或几个孩子的节点。将此约束应用于类似变压器的模型之后，我们发现该模型能够诱导接近人类专家注释的有向图，并且在不同任务上也优于标准变压器模型。我们认为，这些实验结果为深度学习模型的未来发展展示了一个有趣的选择。

组合优化是运营研究和计算机科学领域的一个公认领域。直到最近，它的方法一直集中在孤立地解决问题实例，而忽略了它们通常源于实践中的相关数据分布。但是，近年来，人们对使用机器学习，尤其是图形神经网络（GNN）的兴趣激增，作为组合任务的关键构件，直接作为求解器或通过增强确切的求解器。GNN的电感偏差有效地编码了组合和关系输入，因为它们对排列和对输入稀疏性的意识的不变性。本文介绍了对这个新兴领域的最新主要进步的概念回顾，旨在优化和机器学习研究人员。

Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

行业分配根据预定义的行业分类系统（ICS）将公司分配给行业，这对于大量关键业务实践至关重要，从公司的运营和战略决策到政府机构的经济分析。三种专家知识对于有效行业分配至关重要：基于定义的知识（即每个行业的专家定义），基于结构的知识（即ICS中指定的行业之间的结构关系）和基于任务的知识（即，域专家执行的事先公司行业任务）。现有的行业分配方法仅利用基于任务的知识来学习将未分配的公司分类为行业的模型，并忽略基于定义和基于结构的知识。此外，这些方法仅考虑已分配了公司的哪个行业，但忽略了基于分配的知识的时间特异性，即在任务发生时。为了解决现有方法的局限性，我们提出了一种新颖的基于深度学习的方法，该方法不仅无缝整合了三种类型的行业分配知识，而且还考虑了基于分配的知识的特定时间。从方法上讲，我们的方法具有两种创新：动态行业表示和分层分配。前者通过通过我们提出的时间和空间聚集机制整合了三种类型的知识，将行业代表为一系列特定时间的向量。后者将行业和公司的表现作为投入，计算将公司分配给不同行业的可能性，并将公司分配给具有最高概率的行业。

Multiple choice questions (MCQs) are widely used in digital learning systems, as they allow for automating the assessment process. However, due to the increased digital literacy of students and the advent of social media platforms, MCQ tests are widely shared online, and teachers are continuously challenged to create new questions, which is an expensive and time-consuming task. A particularly sensitive aspect of MCQ creation is to devise relevant distractors, i.e., wrong answers that are not easily identifiable as being wrong. This paper studies how a large existing set of manually created answers and distractors for questions over a variety of domains, subjects, and languages can be leveraged to help teachers in creating new MCQs, by the smart reuse of existing distractors. We built several data-driven models based on context-aware question and distractor representations, and compared them with static feature-based models. The proposed models are evaluated with automated metrics and in a realistic user test with teachers. Both automatic and human evaluations indicate that context-aware models consistently outperform a static feature-based approach. For our best-performing context-aware model, on average 3 distractors out of the 10 shown to teachers were rated as high-quality distractors. We create a performance benchmark, and make it public, to enable comparison between different approaches and to introduce a more standardized evaluation of the task. The benchmark contains a test of 298 educational questions covering multiple subjects & languages and a 77k multilingual pool of distractor vocabulary for future research.

在过去的十年中，计算机愿景，旨在了解视觉世界的人工智能分支，从简单地识别图像中的物体来描述图片，回答有关图像的问题，以及围绕物理空间的机器人操纵甚至产生新的视觉内容。随着这些任务和应用程序的现代化，因此依赖更多数据，用于模型培训或评估。在本章中，我们展示了新颖的互动策略可以为计算机愿景提供新的数据收集和评估。首先，我们提出了一种众群界面，以通过数量级加速付费数据收集，喂养现代视觉模型的数据饥饿性质。其次，我们探索使用自动社交干预措施增加志愿者贡献的方法。第三，我们开发一个系统，以确保人类对生成视觉模型的评估是可靠的，实惠和接地在心理物理学理论中。我们结束了人机互动的未来机会，以帮助计算机愿景。

基于变压器的语言模型最近在许多自然语言任务中取得了显着的结果。但是，通常通过利用大量培训数据来实现排行榜的性能，并且很少通过将明确的语言知识编码为神经模型。这使许多人质疑语言学对现代自然语言处理的相关性。在本文中，我介绍了几个案例研究，以说明理论语言学和神经语言模型仍然相互关联。首先，语言模型通过提供一个客观的工具来测量语义距离，这对语言学家很有用，语义距离很难使用传统方法。另一方面，语言理论通过提供框架和数据源来探究我们的语言模型，以了解语言理解的特定方面，从而有助于语言建模研究。本论文贡献了三项研究，探讨了语言模型中语法 - 听觉界面的不同方面。在论文的第一部分中，我将语言模型应用于单词类灵活性的问题。我将Mbert作为语义距离测量的来源，我提供了有利于将单词类灵活性分析为方向过程的证据。在论文的第二部分中，我提出了一种方法来测量语言模型中间层的惊奇方法。我的实验表明，包含形态句法异常的句子触发了语言模型早期的惊喜，而不是语义和常识异常。最后，在论文的第三部分中，我适应了一些心理语言学研究，以表明语言模型包含了论证结构结构的知识。总而言之，我的论文在自然语言处理，语言理论和心理语言学之间建立了新的联系，以为语言模型的解释提供新的观点。

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.

We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda capturing complex computational legal processes, and embedding them in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.

Alphazero，Leela Chess Zero和Stockfish Nnue革新了计算机国际象棋。本书对此类引擎的技术内部工作进行了完整的介绍。该书分为四个主要章节 - 不包括第1章（简介）和第6章（结论）：第2章引入神经网络，涵盖了所有用于构建深层网络的基本构建块，例如Alphazero使用的网络。内容包括感知器，后传播和梯度下降，分类，回归，多层感知器，矢量化技术，卷积网络，挤压网络，挤压和激发网络，完全连接的网络，批处理归一化和横向归一化和跨性线性单位，残留层，剩余层，过度效果和底漆。第3章介绍了用于国际象棋发动机以及Alphazero使用的经典搜索技术。内容包括minimax，alpha-beta搜索和蒙特卡洛树搜索。第4章展示了现代国际象棋发动机的设计。除了开创性的Alphago，Alphago Zero和Alphazero我们涵盖Leela Chess Zero，Fat Fritz，Fat Fritz 2以及有效更新的神经网络（NNUE）以及MAIA。第5章是关于实施微型α。 Shexapawn是国际象棋的简约版本，被用作为此的示例。 Minimax搜索可以解决六ap峰，并产生了监督学习的培训位置。然后，作为比较，实施了类似Alphazero的训练回路，其中通过自我游戏进行训练与强化学习结合在一起。最后，比较了类似α的培训和监督培训。

机器学习（ML）越来越多地用于支持高风险的决策，这是由于其相对于人类评估的优势预测能力的承诺而欠的趋势。但是，决策目标与观察到的作为训练ML模型的标签的结果中捕获的内容之间经常存在差距。结果，机器学习模型可能无法捕获决策标准的重要维度，从而阻碍了他们的决策支持。在这项工作中，我们探讨了历史专家决策作为组织信息系统中通常可用的丰富（但不完美）的信息来源，并表明它可以利用它来弥合决策目标与算法目标之间的差距。当数据中的每个案例都由单个专家评估并提出基于影响函数的方法作为解决此问题的解决方案时，我们会间接考虑估计专家一致性的问题。然后，我们将估计的专家一致性通过培训时间标签合并方法纳入预测模型。这种方法使ML模型可以在有推断的专家一致性和观察标签的情况下向专家学习。我们还提出了通过混合和延期模型来利用推断一致性的替代方法。在我们的经验评估中，专注于儿童虐待热线筛查的背景下，我们表明（1）有一些高风险案例，其风险是专家考虑的，但在目标标签中没有完全捕获用于培训已部署模型和培训的目标标签（2）提出的方法可显着提高这些情况的精度。

Generating novel and useful concepts is essential during the early design stage to explore a large variety of design opportunities, which usually requires advanced design thinking ability and a wide range of knowledge from designers. Growing works on computer-aided tools have explored the retrieval of knowledge and heuristics from design data. However, they only provide stimuli to inspire designers from limited aspects. This study explores the recent advance of the natural language generation (NLG) technique in the artificial intelligence (AI) field to automate the early-stage design concept generation. Specifically, a novel approach utilizing the generative pre-trained transformer (GPT) is proposed to leverage the knowledge and reasoning from textual data and transform them into new concepts in understandable language. Three concept generation tasks are defined to leverage different knowledge and reasoning: domain knowledge synthesis, problem-driven synthesis, and analogy-driven synthesis. The experiments with both human and data-driven evaluation show good performance in generating novel and useful concepts.

大型知识图（KGS）提供人类知识的结构化表示。然而，由于不可能包含所有知识，KGs通常不完整。基于现有事实的推理铺平了一种发现缺失事实的方法。在本文中，我们研究了了解完成缺失事实三胞胎的知识图表的推理的学习逻辑规则问题。学习逻辑规则将具有很强的解释性的模型以及概括到类似任务的能力。我们提出了一种称为MPLR的模型，可以改进现有模型以完全使用培训数据，并且考虑多目标方案。此外，考虑到缺乏评估模型表现和开采规则的质量，我们进一步提出了两名新颖的指标来帮助解决问题。实验结果证明我们的MPLR模型在五个基准数据集中优于最先进的方法。结果还证明了指标的有效性。

We present the OPEN GRAPH BENCHMARK (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at https://ogb.stanford.edu.

自动问题应答（QA）系统的目的是以时间有效的方式向用户查询提供答案。通常在数据库（或知识库）或通常被称为语料库的文件集合中找到答案。在过去的几十年里，收购知识的扩散，因此生物医学领域的新科学文章一直是指数增长。因此，即使对于领域专家，也难以跟踪域中的所有信息。随着商业搜索引擎的改进，用户可以在某些情况下键入其查询并获得最相关的一小组文档，以及在某些情况下从文档中的相关片段。但是，手动查找所需信息或答案可能仍然令人疑惑和耗时。这需要开发高效的QA系统，该系统旨在为用户提供精确和精确的答案提供了生物医学领域的自然语言问题。在本文中，我们介绍了用于开发普通域QA系统的基本方法，然后彻底调查生物医学QA系统的不同方面，包括使用结构化数据库和文本集合的基准数据集和几种提出的方​​法。我们还探讨了当前系统的局限性，并探索潜在的途径以获得进一步的进步。