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1 / Deep Nostalgia: Remediated memory, algorithmic nostalgia and technological ambivalence. 

Kidd, J., & Nieto McAvoy, E. (2023) in Convergence, 29(3), 620-640.


Digital recreations of the past, and of the deceased, are part of the Internet’s present. They circulate within social networks where logics of connection and connectivity underpin increasingly performative memory work. In this article we explore these developments through a case study of the MyHeritage deep learning feature, Deep Nostalgia. Our analysis is informed by a close critical study of Deep Nostalgia creations, and discourses circulating around them, shared on Twitter during the two-week period following its launch, February 2021 (n.6935). We examine how memory is evoked, framed, re-worked and distorted through algorithmic processes, and within social networks in particular, and explore what this tells us about peoples' need to connect with their pasts. First, we analyse how the shift from photo to video ‘revives’ the dead via a process that we have termed ‘remediated memory’. Second, we explore the affective dimensions and resonances of Deep Nostalgia creations. In doing so, we introduce the concept of ‘algorithmic nostalgia’ to describe the ways nostalgia is generated, organised and exploited through Deep Nostalgia’s automated and recursive algorithmic mechanisms. Third, we interrogate the ways social media logics shape the use and influence of these outputs. Our study’s scholarly contribution is at the intersection of memory, automation, and algorithms. We highlight the importance of studying the ambivalence of emerging media at their nexus with memory studies and, critically, of attending to the ways corporate interests increasingly shape – and assimilate – these activities.

2 / Synthetic Heritage: Online platforms, deceptive genealogy and the ethics of algorithmically generated memory

Nieto McAvoy, E. & Kidd, J. (2024) in Memory, Mind and Media 3, doi:10.1017/mem.2024.10 


Services offered by genealogy companies are increasingly underpinned by computational remediation and algorithmic power. Users are encouraged to employ a variety of mobile web and app plug-ins to create progressively more sophisticated forms of synthetic media featuring their (often deceased) ancestors. As the promotion of deepfake and voice-synthesizing technologies intensifies within genealogical contexts – aggrandised as mechanisms for ‘bringing people back to life’ – we argue it is crucial that we critically examine these processes and the socio-technical infrastructures that underpin them, as well as their mnemonic impacts. In this article, we present a study of two AI-enabled services released by the genealogy company MyHeritage: Deep Nostalgia (launched 2020), and DeepStory (2022). We carry out a close critical reading of these services and the outputs they produce which we understand as examples of ‘remediated memory’ (Kidd and Nieto McAvoy 2023) shaped by corporate interests. We examine the distribution of agency where the promotion by these platforms of unique and personalised experiences comes into tension with the propensity of algorithms to homogenise. The analysis intersects with nascent ethical debates about the exploitative and extractive qualities machine learning. Our research unpacks the social and (techno-)material implications of these technologies, demonstrating an enduring individual and collective need to connect with our past(s), and to test and extend our memories and recollections through increasingly intense and proximate new media formats.

3 / Synthetic Pasts, Digital Memory and Algorithmic Culture

Kidd, J., & Nieto McAvoy, E. (forthcoming). Bloomsbury. [Part of the Bloomsbury Studies in Digital Cultures series].


1 / Digital story overview

Eva Nieto McAvoy introduces the research context and key project concerns in a 5 minute digital story.

2 / Website

This website is a reflection of our critical-creative approach to synthetic pasts. It is inspired by the work of scholars in adjacent and overlapping fields:

Image outputs

One of the concerns of Synthetic Pasts is how AI systems interact with images; in their creation, manipulation and circulation. In the project we have created many hundreds of images as researcher-users of platforms and devices, and as a way of visualising our findings and concerns in relation to synthetic pasts. Across this website you will see some of these creations, prompted using AI image generators like Duet AI, Magic Media, Dall-E4 and Stable Diffusion. Creating these images prompts us in return; to (re-)consider what kinds of representations or ‘predictions’ (Manovich 2023) AI conjure, and where their blind spots are.

Image credits: [1] Google Duet AI, 30 Nov 2023, prompt by JK. [2] Wix, 9 Jan 2024, prompt by JK. [3] Wix, 9 Jan 2024, prompt by JK. [4] Google Duet AI, 30 Nov 2023, prompt by JK. [5] DALL-E 3, 9 Jan 2024, prompt by JK. [6] Wix, 9 Jan 2024 prompt by JK. [7] Stable Diffusion, 30 Nov 2023, prompt by JK. [8] DALL-E , 9 Jan 2024, prompt by JK. [9] Google Duet AI, 30 Nov 2023, prompt by JK. [10] Google Duet AI, 30 Nov 2023, prompt by JK. [11] Google Duet AI, 30 Nov 2023, prompt by JK.

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