Emergent autonomous scientific research capabilities of large language models

Transformer-based large language models are rapidly advancing in the field of machine learning research, with applications spanning natural language, biology, chemistry, and computer programming. Extreme scaling and reinforcement learning from human feedback have significantly improved the quality of generated text, enabling these models to perform various tasks and reason about their choices. In this paper, we present an Intelligent Agent system that combines multiple large language models for autonomous design, planning, and execution of scientific experiments. We showcase the Agent's scientific research capabilities with three distinct examples, with the most complex being the successful performance of catalyzed cross-coupling reactions. Finally, we discuss the safety implications of such systems and propose measures to prevent their misuse.Comment: Version 1, April 11, 2023. 48 page

Driver Trust in Automated Driving Systems

Vehicle automation is a prominent example of safety-critical AI-based task automation. Recent digital innovations have led to the introduction of partial vehicle automation, which can already give vehicle drivers a sense of what fully automated driving would feel like. In the context of current imperfect vehicle automation, establishing an appropriate level of driver trust in automated driving systems (ADS) is seen as a key factor for their safe use and long-term acceptance. This paper thoroughly reviews and synthesizes the literature on driver trust in ADS, covering a wide range of academic disciplines. Pulling together knowledge on trustful user interaction with ADS, this paper offers a first classification of the main trust calibrators. Guided by this analysis, the paper identifies a lack of studies on adaptive, contextual trust calibration in contrast to numerous studies that focus on general trust calibration

Critical Scenario Identification for Testing of Autonomous Driving Systems

Background: Autonomous systems have received considerable attention from academia and are adopted by various industrial domains, such as automotive, avionics, etc. As many of them are considered safety-critical, testing is indispensable to verify their reliability and safety. However, there is no common standard for testing autonomous systems efficiently and effectively. Thus new approaches for testing such systems must be developed.Aim: The objective of this thesis is two-fold. First, we want to present an overview of software testing of autonomous systems, i.e., relevant concepts, challenges, and techniques available in academic research and industry practice. Second, we aim to establish a new approach for testing autonomous driving systems and demonstrate its effectiveness by using real autonomous driving systems from industry.Research Methodology: We conducted the research in three steps using the design science paradigm. First, we explored the existing literature and industry practices to understand the state of the art for testing of autonomous systems. Second, we focused on a particular sub-domain - autonomous driving - and proposed a systematic approach for critical test scenario identification. Lastly, we validated our approach and employed it for testing real autonomous driving systems by collaborating with Volvo Cars.Results: We present the results as four papers in this thesis. First, we conceptualized a definition of autonomous systems and classified challenges and approaches, techniques, and practices for testing autonomous systems in general. Second, we designed a systematic approach for critical test scenario identification. We employed the approach for testing two real autonomous driving systems from the industry and have effectively identified critical test scenarios. Lastly, we established a model for predicting the distribution of vehicle-pedestrian interactions for realistic test scenario generation for autonomous driving systems. Conclusion: Critical scenario identification is a favorable approach to generate test scenarios and facilitate the testing of autonomous driving systems in an efficient way. Future improvement of the approach includes (1) evaluating the effectiveness of the generated critical scenarios for testing; (2) extending the sub-components in this approach; (3) combining different testing approaches, and (4) exploring the application of the approach to test different autonomous systems

Using Artificial Intelligence to Circumvent the Teacher Shortage in Special Education: A Phenomenological Investigation

The purpose of this hermeneutic phenomenological research study was to understand district technology leaders’ receptivity to employing artificial co-teachers, based on their lived experiences with Artificial Intelligence (AI). Facing a problematic teacher shortage in special education, the Jade County School District was not readily employing available AI technologies such as IBM’s WATSON and MIT Media Lab’s TEGA, to aide in filling the instructional voids caused by special education teacher attrition. Veblen’s theory of technological determinism provided the necessary framework for this study, which focused on how district technology leaders described their willingness or apprehension to employ autonomous machines to independently instruct students with disabilities in the classroom. This research study was carried out in a large public-school district with a high number of special education teacher vacancies. Purposeful sampling was used to recruit 11 district-level technology leaders who were responsible for developing and sharing a vision for how new technology could be employed to support the needs of students. The principal researcher applied hermeneutic phenomenology to interpret data from photo-elicitations, audio-recorded focus groups, and individual interviews

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio