Deep Nostalgia: Remediated memory, algorithmic nostalgia and technological ambivalence

Abstract

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.

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

“…We characterise the resultant creations as 'remediations' (Bolter and Grusin 1999) given that the videos producedwith or without voiceoverrefashion other media including the photo and the blueprint video. In this context, remediation might be done out of curiosity, but is also clearly done for mnemonic and nostalgic purposes, often with deeply affective consequences (Kidd and Nieto McAvoy 2023).…”

“…MyHeritage notes for example that 'Depending on the video and the angle, the technology sometimes needs to simulate parts that do not appear in the original photo, such as teeth or ears' (MyHeritage 2023b). As we have noted elsewhere (Kidd and Nieto McAvoy 2023), algorithmic remediation works best at a degree of abstraction, and tends to lead to uniformity and conformity in how an image is 'brought to life', as programmers tend to exclude more atypical or 'chaotic' data entries to ensure more predictable and persuasive outputs (Markham 2020: 10). We found a level of homogeneity in the looped videos (the tilting of the head, the movement of the eyes and a smile) that contrasts with the claimed uniqueness of the remediation.…”

“…Claiming the dead as our own, and for our own purposes, is of course what has driven genealogical research since its earliest days (Laqueur 2023), but it takes on new significance within these emergent formats where 'algorithmic afterlife' (Lambert et al 2018) or 'synthetic resurrection' (Ajder et al 2019) work across the technological and the material, resulting in forms of 'biomediation' (Garde-Hansen et al 2009) which can be particularly affective, but unsettling also. We have argued elsewhere (Kidd and Nieto McAvoy 2023) that these applications can be seen as generating and exploiting what we have termed 'algorithmic nostalgia'; eliciting distinctly nostalgic effects and affects that we understand as persistent rather than adaptive, and quite literally perpetual in that they play on a loop. The animated human bodies produced by Deep Nostalgia and DeepStory are recognisable but flimsy, seeming to channel the past, present and (lost) future simultaneously.…”

“…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. Machine learning processes are inserted into the very fabric of personal archival assets such as photographs, 'remediating' (Bolter and Grusin 1999) memories through recursive and mechanised algorithmic logics (Kidd and Nieto McAvoy 2023). As the promotion of deepfake and voice-synthesizing technologies intensifies within genealogical contexts aggrandised as mechanisms for 'bringing people back to life'it is crucial that we critically examine these processes and the socio-technical infrastructures that underpin them, as well as their mnemonic impacts.…”

Communicative remembering: Revisiting a basic mnemonic concept

“…Today's Big Five (Alphabet-Google, Meta-Facebook, Apple, Amazon, and Microsoft) and their Chinese counterparts Tencent, Alibaba, Baidu, and JD.com provide a comprehensive infrastructure through which many communications and transactions take place. Yet their mnemonic potential lies in the permanent recording, accumulation, and perpetual evaluation and reorganisation of data (Annabell 2022; Corry 2023; Kang et al 2023; Kidd and McAvoy 2023). Invoking a distinction made by Garde-Hansen (2011, 72), we can say that digital media first create archives of past activities.…”