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All Gone: Co-authoring climate imaginaries with AI

All Gone is a series of experiments with AI that build on existing collections of climate fiction to create much-needed new climate imaginaries. As the climate crisis is also a “crisis of imagination” (Ghosh, 2016), this project turns to the art genre that is best at forecasting and imagining alternative futures: science fiction. Using collections of ‘cli-fi’ novels, in which science fiction meets natural disaster or heavy weather, algorithms are trained until they are able to render new climate imaginaries in textual and visual form. The edited texts and curated images are further developed into audio stories and a tarot deck as tools for reflection on present and future living with a changing climate.

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Reporting on forensic biology findings given activity level issues in the Netherlands

There appears to be some hesitation within the forensic biology community to formally evaluate and report on findings given activity level propositions. This hesitance in part stems from concerns about the lack of relevant data on the dynamics of biological traces and doubt about the relevance of such expert opinions to the trier of fact. At the Netherlands Forensic Institute formal evaluative opinions on the probability of case findings given propositions at the activity level are provided since 2013, if requested by a mandating authority. In this study we share the results from a retrospective analysis of 74 of such requests. We explore which party initiates requests, the types of cases that are submitted, the sources of data being used to assign probabilities to DNA transfer, persistence, prevalence and recovery (TPPR) events, the conclusions that were drawn by the scientists, and how the conclusions were used by the courts. This retrospective analysis of cases demonstrates that published sources of data are generally available and can be used to address DNA TPPR events in most cases, although significant gaps still remain. The study furthermore shows that reporting on forensic biology findings given activity level propositions has been generally accepted by the district and appeal courts, as well as the other parties in the criminal justice system in the Netherlands.

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Reporting on forensic biology findings given activity level issues in the Netherlands

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AI Application in Transport and Logistics

The aim of this research/project is to investigate and analyze the opportunities and challenges of implementing AI technologies in general and in the transport and logistics sectors. Also, the potential impacts of AI at sectoral, regional, and societal scales that can be identified and chan- neled, in the field of transport and logistics sectors, are investigated. Special attention will be given to the importance and significance of AI adoption in the development of sustainable transport and logistics activities using intelligent and autonomous transport and cleaner transport modalities. The emphasis here is therefore on the pursuit of ‘zero emissions’ in transport and logistics at the urban/city and regional levels.Another goal of this study is to examine a new path for follow-up research topics related to the economic and societal impacts of AI technology and the adoption of AI systems at organizational and sectoral levels.This report is based on an exploratory/descriptive analysis and focuses mainly on the examination of existing literature and (empirical) scientific research publica- tions, previous and ongoing AI initiatives and projects (use cases), policy documents, etc., especially in the fields of transport and logistics in the Netherlands. It presents and discusses many aspects of existing challenges and opportunities that face organizations, activities, and individuals when adopting AI technology and systems.

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AI Application in Transport and Logistics

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Testing machine learning applications

In this post I give an overview of the theory, tools, frameworks and best practices I have found until now around the testing (and debugging) of machine learning applications. I will start by giving an overview of the specificities of testing machine learning applications.

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Climate futures

This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?

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The Neural Network Zoo

From the article: Abstract: An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.

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An invariants based architecture for combining small and large data sets in neural networks

We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.

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From Novice to Composer

Concerns have been raised over the increased prominence ofgenerative AI in art. Some fear that generative models could replace theviability for humans to create art and oppose developers training generative models on media without the artist's permission. Proponents of AI art point to the potential increase in accessibility. Is there an approach to address the concerns artists raise while still utilizing the potential these models bring? Current models often aim for autonomous music generation. This, however, makes the model a black box that users can't interact with. By utilizing an AI pipeline combining symbolic music generation and a proposed sample creation system trained on Creative Commons data, a musical looping application has been created to provide non-expert music users with a way to start making their own music. The first results show that it assists users in creating musical loops and shows promise for future research into human-AI interaction in art.

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The attack and defense on aircraft trajectory prediction algorithms

The aviation industry needs led to an increase in the number of aircraft in the sky. When the number of flights within an airspace increases, the chance of a mid-air collision increases. Systems such as the Traffic Alert and Collision Avoidance System (TCAS) and Airborne Collision Avoidance System (ACAS) are currently used to alert pilots for potential mid-air collisions. The TCAS and the ACAS use algorithms to perform Aircraft Trajectory Predictions (ATPs) to detect potential conflicts between aircrafts. In this paper, three different aircraft trajectory prediction algorithms named Deep Neural Network (DNN), Random Forest (RF) and Extreme Gradient Boosting were implemented and evaluated in terms of their accuracy and robustness to predict the future aircraft heading. These algorithms were as well evaluated in the case of adversarial samples. Adversarial training is applied as defense method in order to increase the robustness of ATPs algorithms against the adversarial samples. Results showed that, comparing the three algorithm’s performance, the extreme gradient boosting algorithm was the most robust against adversarial samples and adversarial training may benefit the robustness of the algorithms against lower intense adversarial samples. The contributions of this paper concern the evaluation of different aircraft trajectory prediction algorithms, the exploration of the effects of adversarial attacks, and the effect of the defense against adversarial samples with low perturbation compared to no defense mechanism.

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Zoekresultaten

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All Gone: Co-authoring climate imaginaries with AI

Reporting on forensic biology findings given activity level issues in the Netherlands