Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation

Consumer protection agencies are charged with safeguarding the public from hazardous products, but the thousands of products under their jurisdiction make it challenging to identify and respond to consumer complaints quickly. From the consumer's perspective, online reviews can provide evidence of product defects, but manually sifting through hundreds of reviews is not always feasible. In this paper, we propose a system to mine Amazon.com reviews to identify products that may pose safety or health hazards. Since labeled data for this task are scarce, our approach combines positive unlabeled learning with domain adaptation to train a classifier from consumer complaints submitted to the U.S. Consumer Product Safety Commission. On a validation set of manually annotated Amazon product reviews, we find that our approach results in an absolute F1 score improvement of 8% over the best competing baseline. Furthermore, we apply the classifier to Amazon reviews of known recalled products; the classifier identifies reviews reporting safety hazards prior to the recall date for 45% of the products. This suggests that the system may be able to provide an early warning system to alert consumers to hazardous products before an official recall is announced

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Modeling the severity of complaints in social media

The speech act of complaining is used by humans to communicate a negative mismatch between reality and expectations as a reaction to an unfavorable situation. Linguistic theory of pragmatics categorizes complaints into various severity levels based on the face-threat that the complainer is willing to undertake. This is particularly useful for understanding the intent of complainers and how humans develop suitable apology strategies. In this paper, we study the severity level of complaints for the first time in computational linguistics. To facilitate this, we enrich a publicly available data set of complaints with four severity categories and train different transformer-based networks combined with linguistic information achieving 55.7 macro F1. We also jointly model binary complaint classification and complaint severity in a multi-task setting achieving new state-of-the-art results on binary complaint detection reaching up to 88.2 macro F1. Finally, we present a qualitative analysis of the behavior of our models in predicting complaint severity levels

Representation Engineering: A Top-Down Approach to AI Transparency

In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.Comment: Code is available at https://github.com/andyzoujm/representation-engineerin

Efficient Learning Machines

Computer scienc

The terminator : an AI-based framework to handle dependability threats in large-scale distributed systems

With the advent of resource-hungry applications such as scientific simulations and artificial intelligence (AI), the need for high-performance computing (HPC) infrastructure is becoming more pressing. HPC systems are typically characterised by the scale of the resources they possess, containing a large number of sophisticated HW components that are tightly integrated. This scale and design complexity inherently contribute to sources of uncertainties, i.e., there are dependability threats that perturb the system during application execution. During system execution, these HPC systems generate a massive amount of log messages that capture the health status of the various components. Several previous works have leveraged those systems’ logs for dependability purposes, such as failure prediction, with varying results. In this work, three novel AI-based techniques are proposed to address two major dependability problems, those of (i) error detection and (ii) failure prediction. The proposed error detection technique leverages the sentiments embedded in log messages in a novel way, making the approach HPC system-independent, i.e., the technique can be used to detect errors in any HPC system. On the other hand, two novel self-supervised transformer neural networks are developed for failure prediction, thereby obviating the need for labels, which are notoriously difficult to obtain in HPC systems. The first transformer technique, called Clairvoyant, accurately predicts the location of the failure, while the second technique, called Time Machine, extends Clairvoyant by also accurately predicting the lead time to failure (LTTF). Time Machine addresses the typical regression problem of LTTF as a novel multi-class classification problem, using a novel oversampling method for online time-based task training. Results from six real-world HPC clusters’ datasets show that our approaches significantly outperform the state-of-the-art methods on various metrics

Methods for constructing an opinion network for politically controversial topics

The US presidential race, the re-election of President Hugo Chavez, and the economic crisis in Greece and other European countries are some of the controversial topics being played on the news everyday. To understand the landscape of opinions on political controversies, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on specific aspects of these topics. The work described in this thesis aims to automatically derive a map of the opinions-people network from news and other Web docu- ments. The focus is on acquiring opinions held by various stakeholders on politi- cally controversial topics. This opinions-people network serves as a knowledge- base of opinions in the form of (opinion holder) (opinion) (topic) triples. Our system to build this knowledge-base makes use of online news sources in order to extract opinions from text snippets. These sources come with a set of unique challenges. For example, processing text snippets involves not just iden- tifying the topic and the opinion, but also attributing that opinion to a specific opinion holder. This requires making use of deep parsing and analyzing the parse tree. Moreover, in order to ensure uniformity, both the topic as well the opinion holder should be mapped to canonical strings, and the topics should also be organized into a hierarchy. Our system relies on two main components: i) acquiring opinions which uses a combination of techniques to extract opinions from online news sources, and ii) organizing topics which crawls and extracts de- bates from online sources, and organizes these debates in a hierarchy of political controversial topics. We present systematic evaluations of the different compo- nents of our system, and show their high accuracies. We also present some of the different kinds of applications that require political analysis. We present some application requires political analysis such as identifying flip-floppers, political bias, and dissenters. Such applications can make use of the knowledge-base of opinions.Kontroverse Themen wie das US-Präsidentschaftsrennen, die Wiederwahl von Präsident Hugo Chavez, die Wirtschaftskrise in Griechenland sowie in anderen europäischen Ländern werden täglich in den Nachrichten diskutiert. Um die Bandbreite verschiedener Meinungen zu politischen Kontroversen zu verstehen, ist es hilfreich herauszufinden, welcher Politiker bzw. Interessenvertreter welchen Standpunkt (Pro oder Contra) bezüglich spezifischer Aspekte dieser Themen einnimmt. Diese Dissertation beschreibt ein Verfahren, welches automatisch eine Übersicht des Meinung-Mensch-Netzwerks aus aktuellen Nachrichten und anderen Web-Dokumenten ableitet. Der Fokus liegt hierbei auf dem Erfassen von Meinungen verschiedener Interessenvertreter bezüglich politisch kontroverser Themen. Dieses Meinung-Mensch-Netzwerk dient als Wissensbasis von Meinungen in Form von Tripeln: (Meinungsvertreter) (Meinung) (Thema). Um diese Wissensbasis aufzubauen, nutzt unser System Online-Nachrichten und extrahiert Meinungen aus Textausschnitten. Quellen von Online-Nachrichten stellen eine Reihe von besonderen Anforderungen an unser System. Zum Beispiel umfasst die Verarbeitung von Textausschnitten nicht nur die Identifikation des Themas und der geschilderten Meinung, sondern auch die Zuordnung der Stellungnahme zu einem spezifischen Meinungsvertreter.Dies erfordert eine tiefgründige Analyse sowie eine genaue Untersuchung des Syntaxbaumes. Um die Einheitlichkeit zu gewährleisten, müssen darüber hinaus Thema sowie Meinungsvertreter auf ein kanonisches Format abgebildet und die Themen hierarchisch angeordnet werden. Unser System beruht im Wesentlichen auf zwei Komponenten: i) Erkennen von Meinungen, welches verschiedene Techniken zur Extraktion von Meinungen aus Online-Nachrichten beinhaltet, und ii) Erkennen von Beziehungen zwischen Themen, welches das Crawling und Extrahieren von Debatten aus Online-Quellen sowie das Organisieren dieser Debatten in einer Hierarchie von politisch kontroversen Themen umfasst. Wir präsentieren eine systematische Evaluierung der verschiedenen Systemkomponenten, welche die hohe Genauigkeit der von uns entwickelten Techniken zeigt. Wir diskutieren außerdem verschiedene Arten von Anwendungen, die eine politische Analyse erfordern, wie zum Beispiel die Erkennung von Opportunisten, politische Voreingenommenheit und Dissidenten. All diese Anwendungen können durch die Wissensbasis von Meinungen umfangreich profitieren