Behind the Screen: Investigating ChatGPT's Dark Personality Traits and Conspiracy Beliefs

ChatGPT is notorious for its intransparent behavior. This paper tries to shed light on this, providing an in-depth analysis of the dark personality traits and conspiracy beliefs of GPT-3.5 and GPT-4. Different psychological tests and questionnaires were employed, including the Dark Factor Test, the Mach-IV Scale, the Generic Conspiracy Belief Scale, and the Conspiracy Mentality Scale. The responses were analyzed computing average scores, standard deviations, and significance tests to investigate differences between GPT-3.5 and GPT-4. For traits that have shown to be interdependent in human studies, correlations were considered. Additionally, system roles corresponding to groups that have shown distinct answering behavior in the corresponding questionnaires were applied to examine the models' ability to reflect characteristics associated with these roles in their responses. Dark personality traits and conspiracy beliefs were not particularly pronounced in either model with little differences between GPT-3.5 and GPT-4. However, GPT-4 showed a pronounced tendency to believe in information withholding. This is particularly intriguing given that GPT-4 is trained on a significantly larger dataset than GPT-3.5. Apparently, in this case an increased data exposure correlates with a greater belief in the control of information. An assignment of extreme political affiliations increased the belief in conspiracy theories. Test sequencing affected the models' responses and the observed correlations, indicating a form of contextual memory.Comment: 15 pages, 5 figure

Object Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings

Object localization, and more specifically object pose estimation, in large industrial spaces such as warehouses and production facilities, is essential for material flow operations. Traditional approaches rely on artificial artifacts installed in the environment or excessively expensive equipment, that is not suitable at scale. A more practical approach is to utilize existing cameras in such spaces in order to address the underlying pose estimation problem and to localize objects of interest. In order to leverage state-of-the-art methods in deep learning for object pose estimation, large amounts of data need to be collected and annotated. In this work, we provide an approach to the annotation of large datasets of monocular images without the need for manual labor. Our approach localizes cameras in space, unifies their location with a motion capture system, and uses a set of linear mappings to project 3D models of objects of interest at their ground truth 6D pose locations. We test our pipeline on a custom dataset collected from a system of eight cameras in an industrial setting that mimics the intended area of operation. Our approach was able to provide consistent quality annotations for our dataset with 26, 482 object instances at a fraction of the time required by human annotators

The self-perception and political biases of ChatGPT

This contribution analyzes the self-perception and political biases of OpenAI’s Large Language Model ChatGPT. Considering the first small-scale reports and studies that have emerged, claiming that ChatGPT is politically biased towards progressive and libertarian points of view, this contribution is aimed at providing further clarity on this subject. Although the concept of political bias and affiliation is hard to define, lacking an agreed-upon measure for its quantification, this contribution attempts to examine this issue by having ChatGPT respond to questions on commonly used measures of political bias. In addition, further measures for personality traits that have previously been linked to political affiliations were examined. More specifically, ChatGPT was asked to answer the questions posed by the political compass test as well as similar questionnaires that are specific to the respective politics of the G7 member states. These eight tests were repeated ten times each and indicate that ChatGPT seems to hold a bias towards progressive views. The political compass test revealed a bias towards progressive and libertarian views, supporting the claims of prior research. The political questionnaires for the G7 member states indicated a bias towards progressive views but no significant bias between authoritarian and libertarian views, contradicting the findings of prior reports. In addition, ChatGPT’s Big Five personality traits were tested using the OCEAN test, and its personality type was queried using the Myers-Briggs Type Indicator (MBTI) test. Finally, the maliciousness of ChatGPT was evaluated using the Dark Factor test. These three tests were also repeated ten times each, revealing that ChatGPT perceives itself as highly open and agreeable, has the Myers-Briggs personality type ENFJ, and is among the test-takers with the least pronounced dark traits

Über die Verwendung von Blockchain-basierten Token in cyber-physischen Produktionssystemen

Services and devices in a Cyber-Physical Production System (CPPS) can be provided and requested by multiple parties. Therefore, CPPS face challenges such as cross-company interactions, data security, and robustness against failure. Blockchain Technology (BCT) appears to be a suitable solution for these challenges, since it ensures immutable, trust-building, partly automatable, and transparent data handling and storage. In particular, BCT-based tokens enable the digital representation of objects such as products, tools and machinery or values and permissions and offer new possibilities for CPPS. Thus, this contribution focuses on the application of tokens in CPPS. Multiple use cases for tokens such as asset-backed tokens or utility tokens are presented. Based on this, a concept for an asset-backed token, representing material in a CPPS, is developed and demonstrated in a simulation model.Die Dienste und Geräte in einem Cyber-Physischen Produktionssystem (CPPS) können von mehreren Parteien bereitgestellt und nachgefragt werden. Daher stehen CPPS vor Herausforderungen wie unternehmensübergreifenden Interaktionen, Datensicherheit und Robustheit gegenüber Ausfällen. Die Blockchain-Technologie (BCT) scheint eine geeignete Lösung für diese Herausforderungen darzustellen, da sie eine unveränderbare, vertrauensbildende, teilweise automatisierbare und transparente Datenverarbeitung und -speicherung gewährleistet. Insbesondere BCT-basierte Tokens ermöglichen die digitale Darstellung von Objekten wie Produkten, Werkzeugen und Maschinen oder Werten und Berechtigungen und bieten neue Möglichkeiten für CPPS. Daher konzentriert sich dieser Beitrag auf die Anwendung von Tokens in CPPS. Es werden verschiedene Anwendungsfälle für Tokens vorgestellt, wie etwa Asset-Backed Tokens oder Utility Tokens. Darauf aufbauend wird ein Konzept für einen Asset-Backed Token entwickelt, der Material innerhalb eines CPPS repräsentiert, und in einem Simulationsmodell demonstriert wird

Das Potenzial Deep Learning basierter Computer Vision in der Intralogistik

This work describes three deep learning based computer vision approaches, that hold the potential to increase the degree of automation and the productivity of common warehousing procedures. These approaches will focus on: the re-identification of logistical entities, especially when entering and leaving the warehouse; the multi-view pose estimation of logistical entities to track and to localize them on the shop floor; and the category-agnostic segmentation of items in a bin for robotic grasping.Diese Arbeit beschreibt drei Deep-Learning-basierte Computer-Vision-Ansätze, die das Potenzial haben, den Automatisierungsgrad und die Produktivität gängiger Lagerverfahren zu erhöhen. Diese Ansätze konzentrieren sich auf: die Re-Identifizierung von logistischen Einheiten, insbesondere beim Betreten und Verlassen des Lagers; die Multiview-Positionsschätzung von logistischen Einheiten, um sie in der Fabrik zu verfolgen und zu lokalisieren; und die kategorienunabhängige Segmentierung von Artikeln in einem Behälter für das Greifen durch einen Roboter

Investigation of Deep Learning Datasets for Intralogistics

Deep Learning for Computer Vision has great potential in intralogistics, for example for applications such as mobile robots or autonomous forklifts. However, the availability of labelled image datasets within this area is limited. To address this problem, we benchmarked two different datasets, LOCO (Logistics Objects in Context) and the TOMIE framework (Tracking Of Multiple Industrial Entities), to figure out, if these datasets can be combined to a single one. Therefore, we examine the usability of these datasets for Object Detection tasks using the YOLOv7 framework. For this we trained several Networks and compared them with each other. A deep analysis between these two datasets shows that they are very different and only suitable for specific tasks which are not interchangeable, despite having the same domain. Deeper Investigations are done to find the reasons for this. To close the Gap between LOCO and TOMIE, a synthetic data generation pipeline for Pallets is developed and 18 000 images are rendered. Furthermore, models are trained based on the synthetic data and compared with the models trained on real data. The synthetic data generation pipeline successfully closes the reality gap, and the performance on TOMIE is increased, but the performance on LOCO is significantly weaker. To develop a deeper understanding of this behavior we examine the underlying datasets and the reasons for the performance difference are identified