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Various concurrency primitives had been added to functional programming languages in different ways. In Haskell such a primitive is a MVar, joins are described in JoCaml and AliceML uses futures to provide a concurrent behaviour. Despite these concurrency libraries seem to behave well, their equivalence between each other has not been proven yet. An expressive formal system is needed. In their paper "On proving the equivalence of concurrency primitives", Jan Schwinghammer, David Sabel, Joachim Niehren, and Manfred Schmidt-Schauß define a universal calculus for concurrency primitives known as the typed lambda calculus with futures. There, equivalence of processes had been proved. An encoding of simple one-place buffers had been worked out. This bachelor’s thesis is about encoding more complex concurrency abstractions in the lambda calculus with futures and proving correctness of its operational semantics. Given the new abstractions, we will discuss program equivalence between them. Finally, we present a library written in Haskell that exposes futures and our concurrency abstractions as a proof of concept.
To accommodate the growth of the software industry, programming languages are getting increasingly easy to use. The latest trend in the simplification of the software development process is the usage of visual programming environments. To make visual programming effective, the graph-like representation of the source code must be clearly arranged. This thesis details some of the difficulties in automatic layout generation and proposes an interface as well as two different implementations of automatic layout generators to integrate into the VWorkflows visual programming framework.
Large language models have become widely available to the general public, especially due to ChatGPT's release. Consequently, the AI community has invested much effort into recreating language models of the same caliber as ChatGPT, since the latter is still a technical blackbox. This thesis aims to contribute to that cause by proposing R.O.B.E.R.T., a Robotic Operating Buddy for Efficiency, Research and Teaching. In doing so, it presents a first implementation of a lightweight environment which produces tailor-made, instruction-following language models with a heavy focus on conversational capabilities that instruct themselves into a given domain-context. Within this environment, the generation of datasets, the fine-tuning process and finally the inference of a unique R.O.B.E.R.T. instance are all carried out as part of an automated pipeline.
Assessing communicative accommodation in the context of large language models : a semiotic approach
(2023)
Recently, significant strides have been made in the ability of transformer-based chatbots to hold natural conversations. However, despite a growing societal and scientific relevancy, there are few frameworks systematically deriving what it means for a chatbot conversation to be natural. The present work approaches this question through the phenomenon of communicative accommodation/interactive alignment. While there is existing research suggesting that humans adapt communicatively to technologies, the aim of this work is to explore the accommodation of AI-chatbots to an interlocutor. Its research interest is twofold: Firstly, the structural ability of the transformer-architecture to support accommodative behavior is assessed using a frame constructed in accordance with existing accommodationtheories.
This results in hypotheses to be tested empirically. Secondly, since effective accommodation produces the same outcomes, regardless of technical implementation, a behavioral experiment is proposed. Existing quantifications of accommodation are reconciled,
extended, and modified to apply them to nonhuman-interlocutors. Thus, a measurement scheme is suggested which evaluates textual data from text-only, double-blind interactions between chatbots and humans, chatbots and chatbots and humans and humans. Using the generated human-to-human convergence data as a reference, the degree of artificial accommodation can be evaluated. Accommodation as a central facet of artificial interactivity can thus be evaluated directly against its theoretical paradigm, i.e. human interaction. In case that subsequent examinations show that chatbots effectively do not accommodate, there may be a new form of algorithmic bias, emerging from the aggregate accommodation towards chatbots but not towards humans. Thus, existing, hegemonic semantics could be cemented through chatbot-learning. Meanwhile, the ability to effectively accommodate would render chatbots vastly more susceptible to misuse.