虽然通过简单的因素问题回答，文本理解的大量进展，但更加全面理解话语仍然存在重大挑战。批判性地反映出文本的人将造成好奇心驱动，通常是开放的问题，这反映了对内容的深刻理解，并要求复杂的推理来回答。建立和评估这种类型的话语理解模型的关键挑战是缺乏注释数据，特别是因为找到了这些问题的答案（可能根本不回答），需要高度的注释载荷的高认知负荷。本文提出了一种新的范式，使可扩展的数据收集能够针对新闻文件的理解，通过话语镜头查看这些问题。由此产生的语料库DCQA（疑问回答的话语理解）包括在607名英语文件中的22,430个问题答案对组成。 DCQA以自由形式，开放式问题的形式捕获句子之间的话语和语义链接。在评估集中，我们向问题上的问题提交了来自好奇数据集的问题，我们表明DCQA提供了有价值的监督，以回答开放式问题。我们还在使用现有的问答资源设计预训练方法，并使用合成数据来适应不可批售的问题。

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.

Current SQL generators based on pre-trained language models struggle to answer complex questions requiring domain context or understanding fine-grained table structure. Humans would deal with these unknowns by reasoning over the documentation of the tables. Based on this hypothesis, we propose DocuT5, which uses off-the-shelf language model architecture and injects knowledge from external `documentation' to improve domain generalization. We perform experiments on the Spider family of datasets that contain complex questions that are cross-domain and multi-table. Specifically, we develop a new text-to-SQL failure taxonomy and find that 19.6% of errors are due to foreign key mistakes, and 49.2% are due to a lack of domain knowledge. We proposed DocuT5, a method that captures knowledge from (1) table structure context of foreign keys and (2) domain knowledge through contextualizing tables and columns. Both types of knowledge improve over state-of-the-art T5 with constrained decoding on Spider, and domain knowledge produces state-of-the-art comparable effectiveness on Spider-DK and Spider-SYN datasets.

Across the financial domain, researchers answer complex questions by extensively "searching" for relevant information to generate long-form reports. This workshop paper discusses automating the construction of query-specific document and entity knowledge graphs (KGs) for complex research topics. We focus on the CODEC dataset, where domain experts (1) create challenging questions, (2) construct long natural language narratives, and (3) iteratively search and assess the relevance of documents and entities. For the construction of query-specific KGs, we show that state-of-the-art ranking systems have headroom for improvement, with specific failings due to a lack of context or explicit knowledge representation. We demonstrate that entity and document relevance are positively correlated, and that entity-based query feedback improves document ranking effectiveness. Furthermore, we construct query-specific KGs using retrieval and evaluate using CODEC's "ground-truth graphs", showing the precision and recall trade-offs. Lastly, we point to future work, including adaptive KG retrieval algorithms and GNN-based weighting methods, while highlighting key challenges such as high-quality data, information extraction recall, and the size and sparsity of complex topic graphs.

开发有效的自动分类器将真实来源与工件分开，对于宽场光学调查的瞬时随访至关重要。在图像差异过程之后，从减法伪像的瞬态检测鉴定是此类分类器的关键步骤，称为真实 - 博格斯分类问题。我们将自我监督的机器学习模型，深入的自组织地图（DESOM）应用于这个“真实的模拟”分类问题。 DESOM结合了自动编码器和一个自组织图以执行聚类，以根据其维度降低的表示形式来区分真实和虚假的检测。我们使用32x32归一化检测缩略图作为底部的输入。我们展示了不同的模型训练方法，并发现我们的最佳DESOM分类器显示出6.6％的检测率，假阳性率为1.5％。 Desom提供了一种更细微的方法来微调决策边界，以确定与其他类型的分类器（例如在神经网络或决策树上构建的）结合使用时可能进行的实际检测。我们还讨论了DESOM及其局限性的其他潜在用法。

Grillbot是2022年Alexa奖Taskbot挑战赛中的获胜系统，朝着下一代的多模式任务助手迈进。它是一位语音助手，可以指导用户完成烹饪和家庭装修领域中复杂的现实世界任务。这些是长期且复杂的任务，需要灵活的调整和适应。该演示突出了核心方面，包括一个新的神经决策解析器，用于上下文化语义解析，一种支持条件执行的新“任务图”状态表示，知识接地的Chit-Chat以及使用图像和视频自动丰富任务。

对于当前深度学习模型而言，推断出针对序列的预测的能力，即对训练示例的序列进行预测，这是一个具有挑战性的问题。最近的工作表明，这种限制仍然存在于最新的基于变压器的模型中。该问题的大多数解决方案都使用特定的体系结构或培训方法，这些方法不会推广到其他任务。我们证明，大型语言模型可以在不修改其体系结构或培训程序的情况下成功推断。实验结果表明，生成逐步的理由和引入标记令牌都是有效推断所必需的。首先，我们诱使它产生逐步的理由，然后再输出答案以有效地将任务传达给模型。但是，随着序列的更长，我们发现当前的模型难以跟踪令牌位置。为了解决这个问题，我们将输出令牌与标记令牌交织在一起，这些标记是显式位置和计数符号。我们的发现表明，这两种互补方法如何实现明显的序列外推，并突出显示当前体系结构的局限性，可以有效地推广而无需明确的表面形式指导。代码可在https://github.com/mirelleb/s.-rations-rationals-markup-tokens中获得

心血管疾病是一个大的全球医疗保健问题;症状通常突然存在，最小的警告。心电图（ECG）是一种快速，简单可靠，通过测量通过皮肤上的电极记录的电极来评估心脏健康的方法。 ECG经常需要通过心脏病专家分析，花时间可以花在改善患者护理和结果上。因此，已经提出了使用机器学习的自动ECG分类系统，可以学习ECG功能之间的复杂交互，并使用它来检测异常。然而，为此目的构建的算法经常无法概括到解开数据，报告最初令人印象深刻的结果，在应用于新环境时急剧下降。此外，机器学习算法遭受“黑匣子”问题，其中难以确定如何做出决定。这对医疗保健的应用至关重要，因为临床医生需要能够验证评估过程以信任算法。本文提出了一种用于在MIT-BIH心律失常数据集中的每个类中可视化模型决策的方法，使用完整类的平均调整显着图来确定正在学习的模式。我们通过基于最先进的模型构建两种算法来实现这一点。本文突出了这些地图如何用于在模型中找到可能影响概括性和模型性能的模型中的问题。比较完整类的显着性图给出了模型中混淆变量或其他偏差的总体印象，而不同于在ECG-By-ECG基础上比较显着图时会突出显示的内容。

本文通过实时主轴振动的表征，提出了一种白色盒子支持向量机（SVM）框架及其群体的优化。通过加速度和统计特征的时域响应，通过了过程失败（即侧面，侧面，侧面，鼻磨损，火山口和凹槽磨损，边缘骨折）而演化的异常时刻。使用作为估计器的横跨验证（RFECV）的递归特征消除，因为估计器已经用于特征选择。此外，已经检查了标准SVM的能力，用于刀具健康监测，然后通过应用群基于群的算法进行优化。已经进行了五个元启发式算法性能的比较分析（大象放牧优化，Monarch蝶优化，Harris Hawks优化，粘液模算法和飞蛾搜索算法）。考虑到全局和本地表示，已经介绍了白盒方法，这些代表可以深入了解工具状况监控中机器学习模型的性能。

We investigate how the activation function can be used to describe neural firing in an abstract way, and in turn, why it works well in artificial neural networks. We discuss how a spike in a biological neurone belongs to a particular universality class of phase transitions in statistical physics. We then show that the artificial neurone is, mathematically, a mean field model of biological neural membrane dynamics, which arises from modelling spiking as a phase transition. This allows us to treat selective neural firing in an abstract way, and formalise the role of the activation function in perceptron learning. The resultant statistical physical model allows us to recover the expressions for some known activation functions as various special cases. Along with deriving this model and specifying the analogous neural case, we analyse the phase transition to understand the physics of neural network learning. Together, it is shown that there is not only a biological meaning, but a physical justification, for the emergence and performance of typical activation functions; implications for neural learning and inference are also discussed.