Tag: disruptive technology

Slow AI

Jeremy Huggett
Slow Down road sign
Image by Tristan Schmurr (CC 2.0)

None of us can be unaware of the hype surrounding artificial intelligence at the moment. We see multi-billion-dollar companies being built around generative AI while older more established companies are placing multi-billion-dollar bets on the development of generative AI to ensure they don’t miss out. Many of the chief executives of those same companies have warned of the imminent danger of artificial general intelligence when human cognition is surpassed by machines, arguing for greater regulation while at the same time ensuring that they invest in attempts to develop precisely what they warn about. Others are more alarmist still: Yuval Harari, the historian and writer, dramatically claimed last year that “What nukes are to the physical world…AI is to the virtual and symbolic world”, and that artificial intelligence is an alien threat potentially resulting in the end of human history. We’re told we have to take the welfare rights of artificial intelligence seriously because it will become conscious or at least robustly agentic within the next ten years (Long et al. 2024), even if a Google engineer was fired in 2022 for claiming its AI technology had become sentient and had a soul. Even the notorious failures – the tendencies of ChatGTP and its ilk to hallucinate and simply make things up, for instance – do little more than introduce a brief pause while such predispositions are inevitably programmed around.

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Is Now the Winter of AI Discontent?

Jeremy Huggett
Snow-covered road at Kanangra-Boyd National Park, NSW, Australia. Image by Toby Hudson CC BY-SA 3.0 via Wikimedia Commons

With Google’s introduction of ‘AI Overviews’ beginning to replace its traditional search engine, Apple launching its ‘Apple Intelligence’ system embedded in its latest variants of iOS, Adobe incorporating an AI Photo Editor in Photoshop, and so on, it’s fair to say that artificial intelligence – in the form of generative AI, at least – is infiltrating many of the digital tools and resources we are accustomed to rely upon. While many uncritically embrace such developments, others are asking whether such developments are desirable or even useful. Indeed, John Naughton (2023) suggests that we are currently in the euphoric stage of AI development and adoption which he predicts will soon be followed by a period of profit-taking before the AI bubble bursts.

In many ways, we’ve been here before. Haigh (2023, 35) describes AI as “… born in hype, and its story is usually told as a series of cycles of fervent enthusiasm followed by bitter disappointment”, and similarly,

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Discovery Machines

Jeremy Huggett
A model robot reading a kindle
Adapted from the original by Brian J. Matis (CC BY-NC-SA 2.0)

Michael Brian Schiffer is perhaps best-known (amongst archaeologists of a certain age in the UK at least), for his development of behavioural archaeology, which looked at the changing relationships between people and things as a response to the processual archaeology of Binford et al. (Schiffer 1976; 2010), and for his work on the formation processes of the archaeological record (Schiffer 1987). But Schiffer also has an extensive track record of work on archaeological (and behavioural) approaches to modern technologies and technological change (e.g., Schiffer 1992; 2011) which receives little attention in the digital archaeology arena, in part because despite his interest in a host of other electrical devices involved in knowledge creation (e.g., Schiffer 2013, 81ff) he has little to say about computers beyond observing their use in modelling and simulation or as an example of an aggregate technology constructed from multiple technologies and having a generalised functionality (Schiffer 2011, 167-171).

In his book The Archaeology of Science, Schiffer introduces the idea of the ‘discovery machine’. In applying such an apparatus,

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Productive Friction

Jeremy Huggett
Mastodon vs Twitter meme
Mastodon vs Twitter meme (via https://mastodon.nz/@TheAtheistAlien/109331847144353101)

Right now, the great #TwitterMigration to Mastodon is in full flood. The initial trickle of migrants when Elon Musk first indicated he was going to acquire Twitter surged when he finally followed through, sacked a large proportion of staff and contract workers, turned off various microservices including SMS two-factor authentication (accidentally or otherwise), and announced that Twitter might go bankrupt. Growing numbers of archaeologists opened accounts on Mastodon, and even a specific archaeology-focussed instance (server) was created at archaeo.social by Joe Roe.

Something most Twitter migrants experienced on first encounter with Mastodon was that it worked in a manner that was just different enough from Twitter to be somewhat disconcerting. This was nothing to do with tweets being called ‘toots’ (recently changed to posts following the influx of new users), or retweets being called ‘boosts’, or the absence of a direct equivalent to quote tweets. It had a lot to do with the federated model with its host of different instances serving different communities which meant that the first decision for any new user was which server to sign up with, and many struggled with this after the centralised models of Twitter (and Facebook, Instagram etc.) though older hands welcomed it as a reminder of how the internet used to be. It also had a lot to do with the feeds (be they Home, Local, or Federated) no longer being determined by algorithms that automatically promoted tweets but simply presenting posts in reverse chronological order. And it had to do with anti-harassment features that meant you could only find people on Mastodon if you knew their username and server, and the inability to search text other than hashtags. These were deliberately built into Mastodon, together with other, perhaps more obviously, useful features like Content Warnings on text and Sensitive Content on images, and simple alt-text handling for images.

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The Digital Derangement of Archives

Jeremy Huggett
Modified from original by Michael Schwarzenberger via Pixabay

Bill Caraher has recently been considering the nature of ‘legacy data’ in archaeology (Caraher 2019) (with a commentary by Andrew Reinhard). Amongst other things, he suggests there has been a shift from paper-based archives designed with an emphasis on the future to digital archives which often seem more concerned with present utility. Coincidentally, Bill’s post landed just as I was pondering the nature of the relationship between digital archives and our use of data.

So do digital archives represent a paradigm shift from traditional archives and archival practice, or are they simply a technological development of them? Digital archives are commonly understood to be a means of storing, organising, maintaining, and making data accessible in digital format. Relative to traditional archives they are therefore not limited by physical space or its associated costs and so can make much more information available more easily, cheaply, and widely. But a consequence of this can be a kind of ‘storage mania’, in which data become easier to accumulate than to delete because of digitalisation, and where data are released from the limitations of time and space through their dematerialisation (Sluis 2017, 28). This is akin to David Berry’s “infinite archives” (2017, 107), who suggests that “One way of thinking about computational archives and new forms of abstraction they produce is the specific ways in which they manage the ‘derangement’ of knowledge through distance.” (Berry 2017, 119). At the same time, digital archives represent new technological material structures built on the performativity of the software which delivers large-scale processing of these apparently dematerialised data (Sluis 2017, 28).

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On Digital Scholarship

Jeremy Huggett

I recently published a paper, ‘Resilient Scholarship in the Digital Age’, which looked at the tensions between digital practice and academic labour (Huggett 2019). My focus was on the nature of academic experience within the modern university and the way in which the professional and personal life of the university academic is influenced by the digital technologies which enable and support the neoliberal commodification and commercialisation of universities (at least in the UK, North America and Australasia). It was a difficult paper to write, not least because of a strong personal interest and involvement, but also because of the way it ranged across digital sociology, the sociality of labour, resilience theory, management theory, feminist and Marxist theory, and so on, most of which was entirely new to me.

The referees were very positive in their comments (thankfully!), but one particular observation they made was that in focussing on university academia, I overlooked the implications for archaeological scholarship more widely, given that much of it occurs within the realms of Cultural Resource Management and related contract work, within governmental departments and non-governmental agencies, as well as within community initiatives. This is certainly true, as is underlined in the periodic surveys of archaeological employment in the UK (e.g. Aitchison 2019). However, in my response to the editors I argued that this was too broad a definition of scholarship for the scope of this particular paper, and, perhaps more importantly, would require a level of knowledge about the scholarly experience outside the university environment that I simply didn’t have – it’s some 30 years since I worked in contract archaeology, for example. Other people are better qualified than I to discuss scholarship in these working contexts.

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Towards a digital ethics of agential devices

Jeremy Huggett
Image by Rawpixel CC0 1.0 via Creative Commons

Discussion of digital ethics is very much on trend: for example, the Proceedings of the IEEE special issue on ‘Ethical Considerations in the Design of Autonomous Systems’ has just been published (Volume 107 Issue 3), and the Philosophical Transactions of the Royal Society A published a special issue on ‘Governing Artificial Intelligence – ethical, legal and technical opportunities and challenges’ late in 2018. In that issue, Corinne Cath (2018, 3) draws attention to the growing body of literature surrounding AI and ethical frameworks, debates over laws governing AI and robotics across the world and points to an explosion of activity in 2018 with a dozen national strategies published and billions in government grants allocated. She also notes the way that many of the leaders in both debates and the technologies are based in the USA which itself presents an ethical issue in terms of the extent to which AI systems mirror the US culture rather than socio-cultural systems elsewhere around the world (Cath 2018, 4).

Agential devices, whether software or hardware, essentially extend the human mind by scaffolding or supporting our cognition. This broad definition therefore runs the gamut of digital tools and technologies, from digital cameras to survey devices (e.g. Huggett 2017), through software supporting data-driven meta-analyses and their incorporation in machine-learning tools, to remotely controlled terrestrial and aerial drones, remotely operated vehicles, autonomous surface and underwater vehicles, and lab-based robotic devices and semi-autonomous bio-mimetic or anthropomorphic robots. Many of these devices augment archaeological practice, reducing routinised and repetitive work in the office environment and in the field. Others augment work by developing data-driven methods which represent, store, and manipulate information in order to undertake tasks previously thought to be uncomputable or incapable of being automated. In the process, each raises ethical issues of various kinds. Whether agency can be associated with such devices can be questioned on the basis that they have no intent, responsibility or liability, but I would simply suggest that anything we ascribe agency to acquires agency, especially bearing in mind the human tendency to anthropomorphize our tools and devices. What I am not suggesting, however, is that these systems have a mind or consciousness themselves, which represents a whole different ethical set of questions.

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Artificial Archaeologies

Jeremy Huggett
Adapted from original by Adam Purves CC BY 2.0

In his book Homo Deus, Yuval Noah Harari rather randomly chooses archaeology as an example of a job area that is ‘safe’ from Artificial Intelligence:

The likelihood that computer algorithms will displace archaeologists by 2033 is only 0.7 per cent, because their job requires highly sophisticated types of pattern recognition, and doesn’t produce huge profits. Hence it is improbable that corporations or government will make the necessary investment to automate archaeology within the next twenty years (Harari 2015, 380; citing Frey and Osborne 2013).

It’s an intriguing proposition, but is he right? Certainly, archaeology is far from a profit-generating machine, but he rather assumes that it’s down to governments or corporations to invest in archaeological automation: a very limited perspective on the origins of much archaeological innovation. However, the idea that archaeology is resistant to artificial intelligence is something that is worth unpicking.

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Pleasing algorithms

Jeremy Huggett
datacenter
CC0 – by Kewl via Pixabay

Social media have been the focus of much attention in recent weeks over their unwillingness/tardiness in applying their own rules. Whether it’s Twitter refusing to consider Trump’s aggressive verbal threats against North Korea to be in violation of their harassment policy, or YouTube belatedly removing a video by Logan Paul showing a suicide victim (Matsakis 2018, Meyer 2018), or US and UK government attempts to hold Facebook and Twitter to account over ‘fake news’ (e.g. Hern 2017a, 2017b), there is a growing recognition that not only are ‘we’ the data for these social media behemoths, but that these platforms are optimised for this kind of abuse (Wachter-Boettcher 2017, Bridle 2017).

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The Digital Deficit

Jeremy Huggett
Mind the Gap
(by Christopher Brown, CC-BY 2.0)

Nicholas Carr has just pointed to some recently published research which suggests that the presence of smartphones divert our attention, using up cognitive resources which would otherwise be available for other activities, and consequently our performance on those non-phone-related activities suffers. In certain respects, this might not seem to be ‘news’ – we’re becoming increasingly accustomed to the problem of technological interruptions to our physical and cognitive activities: the way that visual and aural triggers signal new messages, new emails, new tweets arriving to distract us from the task in hand. However, this particular study was rather different.

In this case, the phones were put into silent mode so that participants would be unaware of any incoming messages, calls etc. (and if the phone was on the desk, rather than in their pocket or bag or in another room altogether, it was placed face-down to avoid any visible indicators) (Ward et al. 2017, 144). Despite this, they found that

“… the mere presence of one’s smartphone may reduce available cognitive capacity and impair cognitive functioning, even when consumers are successful at remaining focused on the task at hand” (Ward et al, 2017, 146).

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