Ray: A Distributed Execution Engine for the Machine Learning Ecosystem

In recent years, growing data volumes and more sophisticated computational procedures have greatly increased the demand for computational power. Machine learning and artificial intelligence applications, for example, are notorious for their computational requirements. At the same time, Moores law is ending and processor speeds are stalling. As a result, distributed computing has become ubiquitous. While the cloud makes distributed hardware infrastructure widely accessible and therefore offers the potential of horizontal scale, developing these distributed algorithms and applications remains surprisingly hard. This is due to the inherent complexity of concurrent algorithms, the engineering challenges that arise when communicating between many machines, the requirements like fault tolerance and straggler mitigation that arise at large scale and the lack of a general-purpose distributed execution engine that can support a wide variety of applications.In this thesis, we study the requirements for a general-purpose distributed computation model and present a solution that is easy to use yet expressive and resilient to faults. At its core our model takes familiar concepts from serial programming, namely functions and classes, and generalizes them to the distributed world, therefore unifying stateless and stateful distributed computation. This model not only supports many machine learning workloads like training or serving, but is also a good t for cross-cutting machine learning applications like reinforcement learning and data processing applications like streaming or graph processing. We implement this computational model as an open-source system called Ray, which matches or exceeds the performance of specialized systems in many application domains, while also offering horizontally scalability and strong fault tolerance properties

What actually happened? Novel econometric methods to improve estimates of climate impacts and policies

Climate change is one of the most crucial societal challenges of the 21st century, affecting a wide range of social, economic, and environmental aspects of modern society. To design and implement policies that can deal with the climate challenge requires an accurate and robust understanding of the physical impacts of climate change as well as understanding the potential impacts of different policy instruments, their limitations, as well as their successes and failures in the past. Despite this necessity, there remains substantial empirical uncertainty around the effectiveness of policy approaches both in the context of mitigation and adaptation. In this doctoral thesis, I present a total of five papers that advance the field of impact estimation in the context of climate change. By using and developing novel econometric methods, I show how existing gaps in this literature can be addressed. In Paper 1, I develop a novel statistical test to illustrate the impact that outlying observations have on regression coefficients of econometric climate impact estimates. In Paper 2, I use these methods and advance climate impact estimates further by presenting a first set of economic climate damage projections that incorporate the effects of extreme weather events and adaptation. While current climate and weather impact data collection approaches focus on manual bottom-up database records, Paper 3 uses machine learning algorithms to predict the occurrence of weather impact events reliably without manual input or on-the-ground knowledge. In Paper 4, I operationalise an alternative way to empirically evaluate policy, which is used in Paper 5 to identify the effects of 10 distinct road transport mitigation policies in the EU15. Overall, I argue that when econometric methods are specified correctly, are applied to the most pressing research questions, and make use of appropriate data then using these methods can allows us to direct adaptation funding more efficiently, track Loss and Damage events around the world, and allow policy-makers to focus on those policy packages that have the largest chance of making a difference

What drives Consumers\u27 Trust in Proactive Services: A Best-Worst scaling approach

Increasing advancements in digital technologies, especially in artificial intelligence, are changing the nature of services. Services no longer rely on the consumers making the first move, but instead, service providers can anticipate consumers’ needs and address them proactively by so-called proactive services (PAS). Within this new service type, consumers may enable the service provider to decide upon the consideration, decision, and enactment of the service. In PAS, consumers assign these previously “owned” phases to the service provider and thereby, also devolve power to the provider. Thus, trust is an indisputable prerequisite for consumer acceptance. However, it is unclear how individual characteristics of PAS impact consumers’ trust. Addressing this research gap, this research-in-progress paper proposes a Best-Worst scaling survey in which potential consumers of two exemplary PAS state their trust with respect to different PAS characteristics. Thereby, this paper will extend the knowledge in understanding PAS