Olivia Guest · ÎŸÎ»ÎŻÎČÎčα ΓÎșΔστ

@olivia.science

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đŸ‘ïž https://olivia.science 🍃 https://metatheory.space associate professor of computational cognitive science · she/they · cypriot/kıbrıslı/ÎșÏ…Ï€ÏÎ±ÎŻÎ± · σáœșÎœ ገΞηΜ៷ Îșα᜶ Ï‡Î”áż–ÏÎ± ÎșÎŻÎœÎ”Îč

Basically identical drawings (one is black and the other orange, the very basic logo of Claude) Kurt Vonnegut's book says "To give an idea of the maturity of my illustrations for this book, here is my picture of an asshole:"
Turning our attention back to the specific type of induction relevant here, argument from analogy: what does this mean for our conditional “if our model correlates with neurocognitive data (P ), then our model can do everything else the human organism does (Q)”? Argument from analogy has the form (Salmon, 2013): X has properties α, ÎČ, Îł , etc. Y has properties α, ÎČ, Îł , etc., and also an additional property ω. Therefore, X has property ω as well. To demonstrate our P → Q is a false analogy, recall the example in Fig. 2 with the clocks. We can reformulate “if a clockwork clock behaves like a digital clock (P ), then the clockwork is the same as a digital clock (Q)” to the following false analogy — an induction that is not licensed by the facts: Digital clocks display the time. Clockwork clocks display the time, and require manual winding. Therefore, digital clocks require manual winding. This is an example of an argument from analogy, which must be licensed by material facts if we want it to locally obtain (Norton, 2003). In the case of CC
Chalmers (in video interview, Chalmers et al. 2024, n.p.): I think it’s possible for an AI system to be con- scious. I think it’s possible for a machine to be conscious. The brain itself is a big machine. Somehow that machine produces consciousness. We don’t know how, but it does it somehow. I think if biology can do it I don’t see why sil- icon can’t do it. I can’t. We don’t understand how silicon could give us consciousness. We also don’t understand how neurons could give us consciousness So I don’t see a difference in principle. H Hinton (quoted in Topol 2023, n.p.)
Looking a little goofy gonna be honest. Very big feet for this owl. Imagine the head barely offset from the body. Goofy goofy lil face and a pointy lil curved beak. Yellow orange eyes with black pupils. Goofy. I'm telling you. A plant with blue flowers behind this goober.
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My final plea We can't be anti AI if we don't make an effort to see these attempts at subversion. Entryism from industry is an extremely useful tactic, which still affects and harms science in general, as well as Science specifically as we see above. Regardless of how many times we try to root out the conflicts of interest caused by AI they keep popping up like satanic mushrooms, e.g. "Maples et al. find themselves part of a grotesque, predatory spectacle, laundering it with institutional credibility." And we live with the consequences of how industry forces have successfully done this to harm climate science, to protect the tobacco industry and keep inserting it into the scientific literature, and more. If we are not careful with our reaoning about these events and learning from the past, the same forces will act on us with respect to AI. Are petroleum companies good now because they also invest in renewables? Or do they still owe the whole planet reparations?
please do not the cat meme "Please Do Not The Cat refers to an image macro of an edited WikiHow drawing of a hand reaching out to an angry cat. The text reads "Please Do Not The Cat." The image has been used in several images parodying the verbless wording of the caption."
I want to underline, people can make mistakes. Not everybody is knowingly using cultish language from industry, some still think these terms refer. But notice who plows on regardless, who does not listen to feedback, who materially and not just ideologically aligns with the AI companies. And like with the case above from big P politics, once you see it, nothing the person can do or say means they can return back to the table, or that they can deserve an audience, or even that we owe them charitable readings of their work. This is not about everybody who slips up being irredeemable. These mistakes are normal. I am talking about those with immense power and those who consistently facilitate AI entryism being held to account. Nobody in the abstract is owed an audience nor do they deserve the extremely priviliged job of being an academic.
"Just because a model correlates with neural and behavioral data, it is not sufficient for us to infer that the model is performing cognition: correlation does not imply cognition." Again, the core logics of AI are the same as racism and sexism, which is why these issues pop up again and again in these systems: it involves taking the simple shallow features of things we see, like skin colour, or hair length, or whatever other superficial property, and moving to very flawed conclusions. There is something addictive to this form of sheer intellectual laziness — as well as structurally beneficial to those select few within capitalism, white supremacy and patriarchy — to this simplistic logic. It's easy to just point at people and call them names, I guess. Non-human animals can escape from this correlationist logic. Think of the animals who figure that out scarecrows, or other such fake predators, are not alive. So even though first they may fear them similarly to their real counterparts, animals realise that superficial resemblance is not identity: "the outward appearance and the essence of things [have not] directly coincided" — a fake owl is not an owl. Correlationism is false.
What can we do? I have a core take away I must repeat: just because somebody claims to be anti AI does not mean they are not doing entryism. Sadly, that is (in isolation) entirely consistent with entryism. Yes, these people lie about their beliefs. I repeat this quote from Marx that I put right at the top of our work critiquing this correlationist logic from 2023: "[A]ll science would be superfluous if the outward appearance and the essence of things directly coincided. (Marx, 1894, p. 592)" This is the logic of AI, of fascism, of sexism, of racism. That is, because things look a certain way, that therefore they are a certain way. Internal properties are wrongly derived from their superficial appearance. "I see that somebody has long hair, therefore I can conclude they are a woman." Or worse still: "I see the external features consistent with (what I think is) woman, and I conclude she must be stupid" — a fractal wrongness that starts with assuming that what you (naively, unquestioningly, think you) see is what you get when it comes to complex organisms. Further down in the same paper, we warn: "Just because a model correlates with neural and behavioral data, it is not sufficient for us to infer that the model is performing cognition: correlation does not imply cognition." Again, the core logics of AI are the same as racism and sexism, which is why these issues pop up again and again in these systems: it involves taking the simple shallow features of things we see, like skin colour, or hair length, or whatever other superficial property, and moving to very flawed conclusions. There is something addictive to this form of sheer intellectual laziness — as well as structurally beneficial to those select few within capitalism, white supremacy and patriarchy — to this simplistic logic. It's easy to just point at people and call them names, I guess.
As a man who has devoted his whole life to the most clear headed science, to the study of matter, I can tell you as a result of my research about atoms this much: There is no matter as such. All matter originates and exists only by virtue of a force which brings the particle of an atom to vibration and holds this most minute solar system of the atom together. We must assume behind this force the existence of a conscious and intelligent Mind. This Mind is the matrix of all matter. — Max Planck, Das Wesen der Materie (1944)
the very silly meme with the guy who is looking at another woman but her head here is a bitcoin and the guy is Nigel Farage, of course, his girlfriend is a pound coin
Table 1: The series of questions that comprise the Pygmalion lens. For a given technosocial relationship between AI and people, if one or more of the answers are “Yes”, then we can conclude that (an aspect of) Pygmalion displacement is occurring, which is damaging to women, and the feminised broadly construed. If not, then the lens does not apply, and we remain agnostic as to gendered or other harm within this framework. Our proverbial lens should be taken inter alia to be akin to an optical lens which allows for the eye to see more details than when naked, as depicted in the line drawing below. (Image is in the public domain.) Pygmalion lens 1) Feminised form: Is the AI, by its (default or exclusive) external characteristics, por- traying a hegemonically feminine character? Yes/No 2) Whitened form: Is the AI, by its (default or exclusive) external characteristics, por- traying a character that is inherently white (supremacist), Western, Eurocentric, etc.? Yes/No 3) Dislocation from work: Does the AI displace women from a role or occupation, or people in general from a role or occupation that tends to be (coded as) women’s work? Yes/No 4) Humanisation via feminisation: Are the AI’s claims to intelligence, human-likeness or personhood contingent on stereotypical feminine traits or behaviours? Yes/No 5) Competition with women: Is the AI pit (rhetorically or otherwise) against women in ways that favour it, and which are harmful to women? Yes/No 6) Diminishment via false equivalence: Does the AI facilitate a rhetoric that deems women as not having full intellectual abilities, or as otherwise less deserving of per- sonhood? Yes/No 7) Obfuscation of diversity: Does the AI, through displacement of specific groups of people, “neutralise” (i.e., whiten, masculinise) a role, vocation, or skill? Yes/No 8) Robot rights: Do the users and/or creators of the AI grant it (aspects of) legal per- sonhood or human(-like) rights? Yes/No 9) Social bonding: Do the users and/or creators
Second, the accommodation reflex and its institutional form: The accommodation reflex errs differently and more consequentially (see Table 1). Its institutional articulation is literacy-laundering, the repackaging of corporate product adoption as educational empowerment. When technology companies offer teachers and students ‘AI training’ or promote ‘AI literacy,’ they are engaged in brand marketing, framed as a progressive, philanthropically fundable intervention. The literacy label locates the problem in users’ understanding rather than in systems’ design, and in doing so, displaces accountability from the companies that built and profit from these systems toward the educators and students who are expected to become fluent in them, and forecloses the prior question of whether fluency is the goal at all. The accommodation reflex, in its institutional form, makes un-training structurally unthinkable by presupposing integration as inevitable; a critical account of AI literacy, by contrast, is not primarily about how to use systems but whether, when, and under what conditions their use is warranted at all (viz. Guest, Suarez, & van Rooij, 2025; Sano- Franchini, 2025).
Red poster with off white text and two kitsch cats BILLIONAIRES are not your friends [2 cats with bows] CATS ARE
Of note, this argument does not claim that LLMs are useless.‡ Dismissing systems that are already reshaping how knowledge is produced and labor is organized is neither analytically adequate nor strategically coherent. What it claims is narrower and perhaps more durable that the †Contemporary AI models inherit from connectionism both its successes and its discontents, including the characteristic temptation to treat pattern- matching as cognition and statistical regularity as understanding. Escaping it requires not better models but better meta-theory (Guest & Martin, 2025a). ‡Besides, usefulness is not a hallmark of ethical or desirable systems uniquely. Indentured laborers are ‘useful’ to those who steal their labor, indubitably so. The same for child laborers. Stealing is ‘useful’ to thieves.
Meme with 3 cats having their eyes covered by a character with 3 arms
Caliper The research assistant that tells you when to trust it. License Python Tests Status Providers An AI analyst that runs your data pipelines end-to-end, reports calibrated confidence on every result, and escalates to a human exactly when it shouldn't be trusted — getting sharper with each correction. Modern science runs on software that most scientists were never trained to use — and can't easily tell whether to believe. Caliper does the analysis for you. And it does the one thing no tool does today: it tells you, honestly, when the answer is solid and when you should ask a human.
me with the feral donkeys in Karpaz read more: https://www.cyprusparadise.com/blog/the-friendly-cypriot-donkey/
Peru joins Brazil, China, France, Germany, Japan, South Korea and other countries in actively moving toward the Linux operating system and other open-source alternatives that can mean millions of dollars in savings. But a leading advocate for such alternatives said it remains to be seen whether President Alejandro Toledo will sign the bill into law. "There are many associations on a national level that do not agree with this because they believe it will affect their interests," including revenues from the sale of proprietary systems, said Jose Cairo Gallardo, president of the Peruvian Free Software Association. The government palace did not respond to phone and fax requests seeking the president's position on the issue. In a move that went largely unnoticed, Congress voted 61-0 last Thursday to approve the legislation, which would prohibit any public institution from purchasing computer equipment that ties users to a particular type of software or "in any manner limits information autonomy." It also bars public institutions from having a predetermined preference for either proprietary software, like Microsoft Corp.'s, in which the source code is mostly secret and licensing fees are charged for upgrades, or for open-source software, in which the underlying code is available to anyone wanting to revise or customize it.
Of note, this argument does not claim that LLMs are useless.‡ Dismissing systems that are already reshaping how knowledge is produced and labor is organized is neither analytically adequate nor strategically coherent. What it claims is narrower and perhaps more durable that the †Contemporary AI models inherit from connectionism both its successes and its discontents, including the characteristic temptation to treat pattern- matching as cognition and statistical regularity as understanding. Escaping it requires not better models but better meta-theory (Guest & Martin, 2025a). ‡Besides, usefulness is not a hallmark of ethical or desirable systems uniquely. Indentured laborers are ‘useful’ to those who steal their labor, indubitably so. The same for child laborers. Stealing is ‘useful’ to thieves.
Human communication involves not only internal coherence but also reference to shared reality, and communicated words, thus, carry extensions, relations to the actual nature of things in the world, which calculated words do not (Bunz, 2026). Two terms may share the adjacent intension in a high-dimensional vector space while differing radically in extension, a phenomenon impossible in communicated language, but routine in calculation. The result is writing that is relevant without being reliable; contextually proximate, but not anchored to the things those words ostensibly name. “Hallucination,” then, is not a bug awaiting a patch but a symptomatic expression of this structural condition. When users treat LLM outputs as communications of “meaning” rather than calculations of form, they engage in “human hallucination”, anthropomorphizing generated writing by conceptualizing it in the image of human expression (Bunz, 2026), producing a systematic over-attribution of understanding to systems where none exists. Training critical thinkers to recognize the mode of production underlying a text, rather than reasoning solely from its surface fluency, thus becomes a pedagogical obligation. Of note, this argument does not claim that LLMs are useless.‡
As Chatoo notes, “when men use AI, we question their effort; when women use AI, we question their integrity” a double bind that rationally depresses adoption among women even as institutions nudge them toward fluency (Pringle, 2026). What is framed as a literacy deficit is, then, better understood as a form of rational refusal — epistemically grounded skepticism that the literacy frame is structurally unable to recognize, because within that frame, the only direction of travel is toward more adoption.
Screenshot of email attempting to be composed and it says Type a message or Draft with Copilot where I'm trying to have some white space to type and plan an email
Critical discourse on large language models (LLMs) has bifurcated between epistemic dismissal that invokes some form of the stochastic parrot metaphor to puncture hype, and pragmatic accommodation that treats LLM capability improvements as grounds for updating the critique. We argue that both misdiagnose the problem as the issue is not whether or not LLMs work, but what kind of working is happening and at whose cost. Drawing on meta-theoretical frameworks of cognitive science, feminist labor analysis and critical pedagogy, we propose a conceptual reorientation. We develop this claim through registers of (i) the cognitive, examining what is forfeited when statistical pattern-matching substitutes for the iterative, grounded processes that constitute thinking; (ii) the pedagogical, examining how “AI literacy” as currently deployed is itself a symptom of the confusion it purports to address; and (iii) the political, examining how the infrastructure framing of AI naturalizes asymmetric labor displacement, particularly of feminized cognitive and reproductive work. The stochastic parrot, deployed with mechanistic precision rather than mere rhetorical convenience, specifies what is forfeited when cognitive labor is delegated, who bears the cost, and why a literacy adequate to this moment must begin from the epistemology of those most harmed by the systems it describes. We conclude with underlining that critical AI literacy, which this paper embodies an instance of, is the only sensible way forward.
This appendix contains two sections. The first provides a list of quotes that contain (critically or otherwise) the claim to match Statement 2 (and đ›œ in Figure 1): ‘ANNs models are a black box or unknowable even if opened’; and the second the claim to match Statement 3: ‘We understand neither human nor artificial cognition, and therefore they are similar.’
the bicycle meme with the person who puts a stick in their own spokes and falls over in pain
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Blue and beige artificial marble kind of figure of 8 like dish I use to store my glasses when I'm sleeping
Greek Cypriot definition for misharo
Misharo means lizard in both Cypriot Greek and Cypriot Turkish
thin normal style
Book cover of the dictionary
After 52 years abroad, a Turkish Cypriot returns home and opens a café in his old village school Mehmet Kamil turns the former school in Kato Arodes into the Old School Café, reconnecting with childhood memories and village life in the hills of Paphos.
In a sane and free society such an arbitrary domination of [humanity’s] functions by either clock or machine would obviously be out of the question. The domina- tion of [humanity] by the creation of [humanity] is even more ridiculous than the domination of [humanity] by [humanity]. (Woodcock, 1944, p. 8) But in the present, and as Marx (1867) notes above, there is the domination of humans by other humans via religious-, conspiratorial-, or cult-like logic (DuprĂ©, 2025; Guest & Martin, 2023; Hao, 2025; Heffernan, 2025; Montell, 2021; Reader, 2024; Samuel, 2023; Wendling, 2009) and through these machines, algorithms, and models. Humans’ expressions and social relations are not even mediated through technology, like when using a texting application to communicate with another person, but constitute technology as such. Technology, in this scheme, controlled by a private company (as all examples in Figure 4), produces our so- called self-expressions. These relations are no longer captured by already flawed metaphors like the echo chamber—the echo has gone, the chamber is devoid of people: we neither shout nor are heard. We abandon our voice, forget how to use it, and forfeit what makes humans special in all columns of Figure 4. In centring cognition as the analytical tool in Figure 1 ensures, we are forced to look the human-in-the-loop in the eyes and recognise that these relations are harmful to people.
go to settings
emma.xyz domain being sold for 233,704.43 dollars!!!
screenshot of the menu with the "edit post" option!!!!!!
Anthropic accuses Chinese rival Alibaba of illicitly extracting AI capabilities 16 hours ago Osmond ChiaBusiness reporter Bloomberg via Getty Images Dario Amodei, co-founder and chief executive officer of Anthropic, speaking during an interview in a room decorated with plantsBloomberg via Getty Images Anthropic CEO Dario Amodei
Woman in traditional Alsatian costume huge dark bow, big shawl, big dress, she looks like she's spinning wool too https://commons.wikimedia.org/wiki/File:Adolphe_Braun_Alsace_costume.jpg
the always have been meme with the astronauts wait, we're all Cypriots? (TRNC and RoC Flags) Always have been (USA, UK, TR, GR flags)
Logitech keyboard zoomed in on the cmd key
An orthodox priest and a smiling young guy in a checkered shirt with the caption Remember us
collage of text with green and red Fragments of Rebellion a feminist iSSUE
Chaos in the Brickyard Once upon a time, among the activi- ties and occupations of man there was an activity called scientific research and the performers of this activity were called scientists. In reality, however, these men were builders who con- structed edifices, called explanations or laws, by assembling bricks, called facts. When the bricks were sound and were assembled properly, the edifice was useful and durable and brought pleas- ure, and sometimes reward, to the builder. If the bricks were faulty or if they were assembled badly, the edifice would crumble, and this kind of dis- aster could be very dangerous to in- nocent users of the edifice as well as to the builder who sometimes was de- stroyed by the collapse. Because the quality of the bricks was so -important to the success of the edifice, and be- cause bricks were so scarce, in those days the builders made their own bricks. The making of bricks was a difficult and expensive undertaking and the wise builder avoided waste by making only bricks of the shape and size necessary for the enterprise at hand. The builder was guided in this manufacture by a blueprint, called a theory or hypothesis. It came to pass that builders realized that they were sorely hampered in their efforts by delays in obtaining bricks
Wereldwijd groeit de weerzin tegen AI en haar gulzige datacenters: ‘Het gros van wat er in de cloud staat, is onzin’ Aktiegroep Tegengas bezet een terrein waar Microsoft in Amsterdam-West een nieuw datacentrum aan het bouwen is.Bron Guus Dubbelman / de Volkskrant Door de bouw van het enorme Microsoft-datacenter bij Sloterdijk te verstoren, gaven demonstranten afgelopen week een nieuwe slinger aan het verzet tegen big tech en AI. Die beweging blijft zich uitbreiden, zowel in Nederland als het buitenland.
Certain wealthy countries with reputations for widespread cycling and few cars, such as the Netherlands, still have many more cars per person than most countries do; there are more cars in the Netherlands than in Nigeria despite Nigeria's population being 12 times greater. Automobility in the wealthiest countries harms residents within those wealthy countries, but the consequences of automobility also spread across borders and harm populations with the fewest cars—those who benefit least from automobility. For example, depleted lead batteries from cars are dismantled mainly in places with few cars resulting in automotive lead exposure without car ownership (Ericson et al., 2017).
Extract from section 3.2 of Guest, O., Suarez, M., MĂŒller, B., van Meerkerk, E., Oude Groote Beverborg, A., de Haan, R., Reyes Elizondo, A., Blokpoel, M., Scharfenberg, N., Kleinherenbrink, A., Camerino, I., Woensdregt, M., Monett, D., Brown, J., Avraamidou, L., Alenda-Demoutiez, J., Hermans, F., & van Rooij, I. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education, 16(2), 85–118. https://doi.org/10.5281/zenodo.20082828
Me wearing my Birkenstocks right now with socks ...it's soooo embarrassing and cringe that I'm so enthralled by this Germanic infection but I don't wanna hide either 😆
Extract from dialogue 3 and 4 in the pdf
Table 1: The series of questions that comprise the Pygmalion lens. For a given technosocial relationship between AI and people, if one or more of the answers are “Yes”, then we can conclude that (an aspect of) Pygmalion displacement is occurring, which is damaging to women, and the feminised broadly construed. If not, then the lens does not apply, and we remain agnostic as to gendered or other harm within this framework. Our proverbial lens should be taken inter alia to be akin to an optical lens which allows for the eye to see more details than when naked, as depicted in the line drawing below. (Image is in the public domain.) Pygmalion lens 1) Feminised form: Is the AI, by its (default or exclusive) external characteristics, por- traying a hegemonically feminine character? Yes/No 2) Whitened form: Is the AI, by its (default or exclusive) external characteristics, por- traying a character that is inherently white (supremacist), Western, Eurocentric, etc.? Yes/No 3) Dislocation from work: Does the AI displace women from a role or occupation, or people in general from a role or occupation that tends to be (coded as) women’s work? Yes/No 4) Humanisation via feminisation: Are the AI’s claims to intelligence, human-likeness or personhood contingent on stereotypical feminine traits or behaviours? Yes/No 5) Competition with women: Is the AI pit (rhetorically or otherwise) against women in ways that favour it, and which are harmful to women? Yes/No 6) Diminishment via false equivalence: Does the AI facilitate a rhetoric that deems women as not having full intellectual abilities, or as otherwise less deserving of per- sonhood? Yes/No 7) Obfuscation of diversity: Does the AI, through displacement of specific groups of people, “neutralise” (i.e., whiten, masculinise) a role, vocation, or skill? Yes/No 8) Robot rights: Do the users and/or creators of the AI grant it (aspects of) legal per- sonhood or human(-like) rights? Yes/No 9) Social bonding: Do the users and/or creators o

My desk with a lot of stuff, especially pink and plants and random reading lights and a teeny sprinkling of trash
A slide on a big screen that says: The AI inevitability narrative and then next to it a photo of a mural that says Fuck this imperialist bullshit with Lea Morgana On the right is Lucy presenting what I can only imagine is the coolest keynote ever
An important form of high-level rejection is to push back against correlationism, which I use to mean the idea that because things can appear similar that therefore their unobservable properties must also match.17 My reflection in a mirror or a video of me may directly coincide with many of my outward appearances, but are neither me nor a valuable model of me — and relatedly, objects may act identically, but we may not be able to infer anything about the similarities of their internal components, think of a clockwork versus a digital timepiece.18 This becomes utterly damaging pedagogically when we assume the ‘output’ of an assignment, the essay or horrifically the grade, is the essence. The point is for students to engage with the subject matter and sharpen their writing skills such that they produce an argument the teacher can read. Just because an AI product can create an essay-shaped piece of text, indicates nothing about the ability of the learner, now reduced to the user of a product, to write. Worse, this frictionless technology blocks the user from learning opportunities, such as mentally modelling what the teacher may be asking, honing their theory of mind, dealing with feedback, as well as reading materials and digesting them. And worse still, the design of these technologies is addictive through weaponisation of the Eliza effect,19 people’s tendency to project human-likeness onto computational systems, and the Barnum-Forer effect,20 the mistaken inference “that generic personality descriptions that could apply to anybody, applies uniquely to them.”21 The second way to push back is to make space for, by fortifying our and our mentees’ time for, by designing tools and environments that promote, the slow and steady praxis of modelling to explore our understandings and to
Models in cognitive and related sciences are part of an overarching methodology, computational or broadly formal modelling, that aims to test, refine, and showcase our understandings of brain, cognition, and behaviour.1 In their foundational book Models as Mediators, Mary S. Morgan and Margaret Morrison (1999)2 make the case that models play a mediatory role in science between what we observe and what we want to build to house our understandings, theories. Under this conception of models, known as the pragmatic view,3 we can carve out not only their unique role, but also why and how they must be protected from forces that do not prize understanding. Taking a broad perspective that sees all human activity that strives towards understanding as scientific or as under our purview as modellers, allows for this characterisation of models as mediators to be more generally fruitful.4 In the context of human-centred design, for example, our theories may be anything from our understanding of users’ behaviours in a specific context to our understanding of what a user expects from a product or system a priori. Importantly, scientific modelling is apart from other scientific praxis, such as concocting a verbal or formal theory or designing and running experiments.5 Models, as described by Morrison and Morgan,6 have the following characteristics: a. construction — models are neither directly derived from theory nor from data, but through an effortful friction between the modeller and the medium (e.g. their favourite programming language, mathematics, natural language) through which they are building their model;7
Artificial intelligence (AI) companies and their rhetoric infringe on academia in harmful ways, mirroring past uncritical acceptance of industry logics, such as those of tobacco and petroleum. In this position piece, we tease apart and explain why phrases like 'generative AI' impede scholarly discussion because by design these expressions are used to dazzle and sidestep scrutiny. Furthermore, we contend with the AI industry's logics to enable rejecting frames such as: that we must embrace the future, that this hype cycle is unique, that anthropomorphism and circular reasoning hold water when discussing AI systems, and that students are now all cheating or all need to use AI. To these ends, we expound on why universities must take their role seriously to a) counter the AI industry's marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. For each point we raise, we include pointers to relevant work to further inform and convince our colleagues.
Students Block Logistics Center of the Radboud Science Faculty in Protest against its complicity with the Gaza Genocide, and the violence inflicted on the Palestinian people.
You all rock. Remind them that when a building is failing all bricks and rebar need to play their role. The mortar cannot worry what the windows are doing — we all need to stay put to keep it standing. Our role at work is to also "stay put" and speak out in the literature and in outreach. We form the protective knowledge core that others will build on. That is why academics paid by the state / with legal protections like tenure etc, exist. To be collectively held to account to produce knowledge to protect from, and inform wider society on, fascism. 😊 Stay strong! Solidarity. Optimism of the will. Pessimism of the intellect.
Plants in a flowerbed in bloom with small pink and purple flowers and some with orange leaves creating aesthetic effects. The ones at the front are flowering catnip.
photo of nareg school in nicosia