We are interested in neurosymbolic systems consisting of a high-level symbolic layer for explainable prediction in terms of human-intelligible concepts; and a low-level neural layer for extracting symbols required to generate the symbolic explanation. Real data is often imperfect meaning that even if the symbolic theory remains unchanged, we may still need to address the problem of mapping raw data to high-level symbols, each time there is a change in the data acquisition environment or equipment. Manual (re-)annotation of the raw data each time this happens is laborious and expensive; and automated labelling methods are often imperfect, especially for complex problems. NEUROLOG proposed the use of a semantic loss function that allows an existing feature-based symbolic model to guide the extraction of feature-values from raw data, using `abduction'. However, the experiments demonstrating the use of semantic loss through abduction appear to rely heavily on a domain-specific pre-processing step that enables a prior delineation of feature locations in the raw data. We examine the use of semantic loss in domains where such pre-processing is not possible, or is not obvious. We show that without any prior information about the features, the NEUROLOG approach can continue to predict accurately even with substantially incorrect feature predictions. We show also that prior information about the features in the form of even imperfect pre-training can help correct this situation. These findings are replicated on the original problem considered by NEUROLOG, without the use of feature-delineation. This suggests that symbolic explanations constructed for data in a domain could be re-used in a related domain, by `feature-adaptation' of pre-trained neural extractors using the semantic loss function constrained by abductive feedback.

类比推理问题挑战了连接主义者和符号AI系统，因为这些系统需要将背景知识，推理和模式识别的结合。符号系统摄入显式域知识并执行演绎推理，但它们对噪声敏感，并且需要输入以预设符号特征。另一方面，Connectionist系统可以直接摄入丰富的输入空间，例如图像，文本或语音，即使使用嘈杂的输入也可以识别模式。但是，Connectionist模型努力将明确的领域知识用于演绎推理。在本文中，我们提出了一个框架，将神经网络的模式识别能力与象征性推理和背景知识结合在一起，以解决一类类似推理问题，其中一组属性和可能的​​关系是已知的。我们从“神经算法推理”方法[DeepMind 2020]中汲取灵感，并通过（i）基于问题的象征模型学习分布式表示（ii）培训神经网络转化反映了关系的分布式表示形式。参与问题，最后（iii）培训神经网络编码器，从图像到（i）中的分布式表示。这三个要素使我们能够使用神经网络作为操纵分布式表示的基本功能执行基于搜索的推理。我们在乌鸦渐进式矩阵中的视觉类比问题上进行了测试，并在人类绩效中实现准确性竞争，在某些情况下，优于初始端到端神经网络方法的方法。尽管最近接受大规模训练的神经模型产生了SOTA，但我们的新型神经符号推理方法是该问题的有希望的方向，可以说是更笼统的，尤其是对于可用的域知识的问题。

我们考虑一类视觉模拟推理问题，涉及发现输入/输出图像对相关的转换序列，以类似地改变未来输入。该程序综合任务可以通过符号搜索轻松解决。使用（Velickovic和Blundell 2021）的“神经模拟推理”方法的变化，Edw，例如，搜索一系列基本神经网络变换，其操纵从符号空间导出的分布式表示，输入图像直接编码。我们评估了我们的“神经原理”方法对具有看不见形状和位置的图像的程度。

我们提出了一种调查，其中在构建具有神经网络的模型时包括现有科学知识的方式。纳入领域知识不仅仅是构建科学助理，而且还有许多其他领域，涉及使用人机协作了解数据的其他领域。在许多这样的情况下，基于机器的模型结构可以显着地利用具有以足够精确的形式编码的域的人人类知识。本文审查了通过更改的域名知识：输入，丢失功能和深网络的架构。分类是为了便于阐述：在实践中，我们预计将采用这种变化的组合。在每个类别中，我们描述了所显示的技术，以产生深度神经网络性能的显着变化。

The ability to monitor the evolution of topics over time is extremely valuable for businesses. Currently, all existing topic tracking methods use lexical information by matching word usage. However, no studies has ever experimented with the use of semantic information for tracking topics. Hence, we explore a novel semantic-based method using word embeddings. Our results show that a semantic-based approach to topic tracking is on par with the lexical approach but makes different mistakes. This suggest that both methods may complement each other.

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

Legal contracts, such as employment or lease agreements, are important documents as they govern the obligations and entitlements of the various contracting parties. However, these documents are typically long and written in legalese resulting in lots of manual hours spent in understanding them. In this paper, we address the task of summarizing legal contracts for each of the contracting parties, to enable faster reviewing and improved understanding of them. Specifically, we collect a dataset consisting of pairwise importance comparison annotations by legal experts for ~293K sentence pairs from lease agreements. We propose a novel extractive summarization system to automatically produce a summary consisting of the most important obligations, entitlements, and prohibitions in a contract. It consists of two modules: (1) a content categorize to identify sentences containing each of the categories (i.e., obligation, entitlement, and prohibition) for a party, and (2) an importance ranker to compare the importance among sentences of each category for a party to obtain a ranked list. The final summary is produced by selecting the most important sentences of a category for each of the parties. We demonstrate the effectiveness of our proposed system by comparing it against several text ranking baselines via automatic and human evaluation.

Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.

Project Loon is a Google initiated research project from the Google X Lab. The project focuses on providing remote internet access and network connectivity. The connectivity is established in vertical and horizontal space; vertical connectivity between Google Access Point (GAP) and the balloons, and between balloons and antennas installed at land; horizontal connectivity is between the balloons. This research focuses on the connectivity between the balloons in a mesh network. The proposal focuses on implementing graphical methods like convex hull with adhoc communication protocols. The proposed protocol includes content-based multicasting using angular sector division rather than grids, along with dynamic core-based mesh protocol defining certain core active nodes and passive nodes forming the convex hull. The transmission (multicasting and broadcasting) between the nodes will be evaluated using the link probability defining the probability of the link between two nodes failing. Based on the link probability and node features, best path between transmitting and receiver nodes will be evaluated.

Climate change, population growth, and water scarcity present unprecedented challenges for agriculture. This project aims to forecast soil moisture using domain knowledge and machine learning for crop management decisions that enable sustainable farming. Traditional methods for predicting hydrological response features require significant computational time and expertise. Recent work has implemented machine learning models as a tool for forecasting hydrological response features, but these models neglect a crucial component of traditional hydrological modeling that spatially close units can have vastly different hydrological responses. In traditional hydrological modeling, units with similar hydrological properties are grouped together and share model parameters regardless of their spatial proximity. Inspired by this domain knowledge, we have constructed a novel domain-inspired temporal graph convolution neural network. Our approach involves clustering units based on time-varying hydrological properties, constructing graph topologies for each cluster, and forecasting soil moisture using graph convolutions and a gated recurrent neural network. We have trained, validated, and tested our method on field-scale time series data consisting of approximately 99,000 hydrological response units spanning 40 years in a case study in northeastern United States. Comparison with existing models illustrates the effectiveness of using domain-inspired clustering with time series graph neural networks. The framework is being deployed as part of a pro bono social impact program. The trained models are being deployed on small-holding farms in central Texas.