Can morality be computed? An exploration of whether machines can be moral agents

A Research Report submitted to the Faculty of Humanities, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Arts, Applied Ethics for Professionals 31 May, 2015, JohannesburgTechnology is an integral part of our daily lives and continues to advance rapidly, impacting our physical and social worlds. We increasingly rely on advanced machines to act in more autonomous and sophisticated ways. What would happen if artificial forms of intelligence developed to the point where machines behaved more like us and we started treating them as people? Ethics should anticipate and account for such possibilities so that science does not move faster than our moral understanding. My thesis states that when we are able to feel gratitude or resentment towards the actions of artificially intelligent machines we can be said to see them as morally responsible agents. I argue that standard ethics frames morality developmentally – only when we reach adulthood are we deemed able to enter into the type of relationships where we can hold one another morally responsible for our actions. I apply a more abstract notion of moral development to future versions of technology and couple this with a definition of morality as a relational or social construct. This allows me to argue that machines could develop to a point in the future where we react to them morally as we would to humans. Questions on whether we ought to react in this way are muted as relationally we quite simply would be unable to feel otherwise. Objections from definitions of moral agency based on innate qualities, specifically those associated with the concept of intelligence, are dispelled in favour of a relational definition

Context-aware Captions from Context-agnostic Supervision

We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of "siamese cat" and "tiger cat", we generate language that describes the "siamese cat" in a way that distinguishes it from "tiger cat". Our key novelty is that we show how to do joint inference over a language model that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB-200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination.Comment: Accepted to CVPR 2017 (Spotlight

Dreaming of atmospheres

Here we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrievals of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep belief neural (DBN) networks trained to accurately recognise molecular signatures for a wide range of planets, atmospheric thermal profiles and compositions. Reconstructions of the learned features, also referred to as `dreams' of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work towards retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.Comment: ApJ accepte

Sons of Disobedience and their Machines: How Sin and Anthropology Can Inform Evangelical Thought About AI

The purpose of this paper is to further discussion about artificial intelligence by examining AI from the perspective of the doctrine of sin. As such, philosophy of mind and theological anthropology, specifically, what it means to be human, the effects of sin, and the consequent social ramifications of AI drive the analysis of this paper. Accordingly, the conclusions of the analysis are that the depravity of fallen humanity is cause for concern in the very programming of AI and serves as a corrupted foundation for artificial machine cognition. Given the fallen nature of human thought, and therefore, fallen AI thought, this paper then examines how this “fallen” AI is already impacting imago Dei in the work and in social governance of the technological society

Playing Smart - Artificial Intelligence in Computer Games

Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games