iiinova — where your digital self keeps evolving

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

✨ Published version ✨ Iris van Rooij & Olivia Guest (2026). Combining Psychology with Artificial Intelligence: What Could Possibly Go Wrong? journals.sagepub.com/doi/10.1177/... @olivia.science Thread with highlights 🧵

Figure 1
Illustration of why AI systems cannot realistically scale to human cognition within the foreseeable future: (b) Human cognitive capacities (such as reasoning, communication, problem solving, learning, concept formation, planning etc.) can handle unbounded situations across many domains, ranging from simple to complex. (a) Engineers create AI systems using machine learning from human data. (d) In an attempt to approximate human cognition a lot of data is consumed. (c) Making AI systems that approximate human cognition is intractable (van Rooij, Guest, et al., 2024), i.e., the required resources (e.g. time, data) grows prohibitively fast as input domains get more complex, leading to diminishing returns. (a) Any existing AI system is
created in limited time (hours, months or years, not millennia or eons). Therefore, existing AI systems cannot realistically have the domain-general cognitive capacities that humans have. [Made with elements from freepik.com.]

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 13/09/2026

"(...) no computationally tractable account exists to date for substantive and domain-general cognitive capacities (...) Moreover, there is no good reason to believe that such tractable accounts will be forthcoming via machine learning or otherwise" journals.sagepub.com/doi/10.1177/...

journals.sagepub.com

Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong? - Iris van Rooij, Olivia Guest, 2026

The current AI hype cycle combined with psychology’s various crises make for a perfect storm. Psychology, on the one hand, has a history of weak theoretical fou...

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

We end with guidance on how Psych & AI can benefit Cognitive Science if these traps are avoided. This alternative approach disavows attempts to automate science—using machine learning or otherwise—and instead centers scientists’ own cognition. Download here: 📝 journals.sagepub.com/doi/epub/10....

Figure 2
A visual depiction of the connections between the Cognitive Sciences. Solid lines denote stronger interdisciplinary ties; and dashed lines denote weaker ones. This figure is derived from the original put forth by the Sloan Foundation in 1978 and reproduced from Figure 4 in Pléh and Gurova (2013). Different versions of it over time have used ‘Artificial intel- ligence’ (as above) instead of ‘Computer Science’ and vice versa (cf. Miller, 2003).

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david-s.bsky.social @david-s.bsky.social · 01/10/2026

Computational concepts remain valuable for carefully crafting theories in cognitive science (Guest & Martin, 2021; van Rooij & Baggio, 2021), but they can only flourish if we
(a) do not confuse AI systems for minds or theories,
(b) do not confuse machine learning for the scientific method, and
(c) understand that our computational models can track only the scope and limits of our understanding.

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

“Many of our contemporaries now want to combine the worst of these two worlds [i.e., Psychology and Artificial Intelligence]. What could possibly go wrong? Quite a lot.” 📝 journals.sagepub.com/doi/epub/10.... cc @olivia.science

Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong?

Abstract
The current AI hype cycle combined with psychology’s various crises make for a perfect storm. Psychology, on the one hand, has a history of weak theoretical foundations, a neglect for computational and formal skills, and a hyperempiricist privileging of experimental tasks and testing for effects. Artificial intelligence, on the other hand, has a history of conflating artifacts for theories of cognition, or even minds themselves, and its engineering offspring likes to move fast and break things. Many of our contemporaries now want to combine the worst of these two worlds. What could possibly go wrong? Quite a lot. Does this mean that psychology and artificial intelligence can best part ways? Not at all. There are very fruitful ways in which the two disciplines can interact and theoretically contribute to cognitive science, for instance, by studying the scope and limits of computational models of human cognition. But to reap the fruits, one needs to understand how to steer clear of potential traps.

Keywords
theoretical psychology, artificial intelligence, cognitive science, computationalism, epistemology

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

The paper is short and we’ve included a Glossary of terms to further improve accessibility. Download paper here: 📝 journals.sagepub.com/doi/epub/10.... If you find our paper useful in your teaching of students in Psych, AI, or otherwise, @olivia.science and I would love to know.

Table A1. Glossary of terms used in the article

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Instituto Universitario de Estudios Feministas y de Género @ifemgen.bsky.social · 01/10/2026

POSTERS: Have you considered NOT using AI? zenodo.org/records/1711... Thanks, @olivia.science 🫶 olivia.science/ai/#activism

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Andrew Clews @crackedwindscreen.bsky.social · 01/10/2026

Read this thread. Its really, REALLY good. I so wish that this sort of clear and intelligent analysis was being done on autonomous vehicles. But it isn't. The companies are being allowed to lie and con and push a false narrative.

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

We present a typology of traps to avoid: 1. Believing that AI systems are minds 2. Believing that AI systems are theories 3. Believing that cognitive science can be automated. Learn to recognise and avoid these traps. Failure to avoid leads to numerous problems.

Table 1: Typology of traps, what goes wrong if not avoided, and how the traps can be avoided. Note that all traps in a sense constitute category errors (Ryle & Tanney, 2009) and the success-to-truth inference (Guest & Martin, 2023) is an important driver in most, if not all, of the traps.

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dj vulcan barbie @spacedaddy.bsky.social · 30/09/2026

i salute @timnitgebru.blacksky.app and all of you who fight for humanity.

A graphic that says USA, Timnit Gebru. "For
“For challenging concentrated power in artificial intelligence and pioneering digital technology rooted in justice, lived experience and human agency.” Background is a light blue. Next to the graphic with words there's a picture of a woman in glasses, a red scarf, and short curly hair.

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Grant Jacobs BioinfoTools/NotJustDNA 🔬🧬🖥️✍️📚🇳🇿 @notjustdna.bsky.social · 01/10/2026

Sharing for scientists grappling with use of 'AI'; some useful thoughts here – – Jumping in the middle to highlight key take-home points, but best to track back up, and read the thread from the top 🧵⬇️

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 19/06/2026

“if someone claims their AI system is a “theory” of human cognition, they are more likely than not overstating the scope & capacities of the system (van Rooij et al., 2019) and obfuscating the system’s limits, including the human in the loop needed to make such systems “work” (Guest & Martin, 2025)”

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 30/09/2026

Our work is here with even more references: Erscoi, L., Kleinherenbrink, A., & Guest, O. (2023). Pygmalion Displacement: When Humanising AI Dehumanises Women. SocArXiv. doi.org/10.31235/osf...

doi.org

OSF

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 28/09/2026

ultimately: what is a scientific theory? olivia.science/theory/#what

what's a
theory?

Each person might have their own idea of what a theory is, but for this context and to help understand where I am coming from, here's a definition from Guest and Martin (2021, p. 794):

A theory is a scientific proposition — described by a collection of natural‐language sentences, mathematics, logic, and figures — that introduces causal relations with the aim of describing, explaining, and/or predicting a set of phenomena.

You will have to look at Guest and Martin (2021) to fully grasp how I separate theory from other scientific concepts, especially if you cannot yet disentangle it from hypothesis, a different beast altogether.

Guest, O. & Martin, A. E. (2021). How Computational Modeling Can Force Theory Building in Psychological Science. Perspectives on Psychological Science. https://doi.org/10.1177/1745691620970585

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 28/09/2026

So proud of this and fond memories of Olivia and me cooking these 'function' ideas 👩‍🍳✨

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

Additionally, be aware of responsibility washing. See e.g. 📝 Amnesty International (2026). Unlawful by design: Exposing the human rights costs of generative AI. www.amnesty.org/en/documents...

Cover image of Amnesty International (2026). Unlawful by design: Exposing the human rights costs of generative AI. https://www.amnesty.org/en/documents/pol40/0996/2026/en/

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 29/09/2026

Dear @erc.europa.eu , we DO already have common standards & frameworks. 'AI' likes to pose itself as 'special' but we do not need to reinvent the wheel. We already have established codes of conduct for research integrity. We just need to apply them. See below + more resources in thread🧵

Protecting the Ecosystem of Human Knowledge: Five Principles
We must protect and cultivate the ecosystem of human knowledge. AI models can mimic
the appearance of scholarly work, but they are (by construction) unconcerned with truth
— the result is a torrential outpouring of unchecked but convincing-sounding “informa-
tion”. At best, such output is accidentally true, but generally citationless, divorced from
human reasoning and the web of scholarship that it steals from. At worst, it is confidently
wrong. Both outcomes are dangerous to the ecosystem.
Olivia Guest, Iris van Rooij, Marcela Suarez, et al. (2025, n.p.)
Knowledge production is supposed to be safeguarded by (inter)national codes of conduct for research
integrity (ALLEA 2023; KNAW et al. 2018). Such codes forbid, for instance, fabrication of data,
falsification of results, plagiarism, and, generally, distortion of the scientific record. Many argue that
new rules are required to regulate academic AI use, but pre-existing guidelines fit the bill (cf. Tafani

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 30/09/2026

"we show that contemporary AI can comprise research misconduct and demonstrate how critical literacy can protect us from this." -- @olivia.science & @irisvanrooij.bsky.social 📝 Guest, O. & van Rooij, I. (2025). Critical Artificial Intelligence Literacy for Psychologists. doi.org/10.31234/osf...

Table 1
Core reasoning issues (first column), which we name after the relevant numbered section, are characterised using a plausible quote. In the
second column are responses per row; also see the named section for further reading, context, and explanations.

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Andrea Reyes E (she/her) @altibel.bsky.social · 27/09/2026

When will universities & scientists take a stance against genAI? Where is the promised democratisation of knowledge, the innovation in research? We have been warning about the destruction of knowledge & the deskilling of humans. #resistAI irisvanrooijcogsci.com/2025/08/12/a...

irisvanrooijcogsci.com

AI slop and the destruction of knowledge

Cite as: van Rooij, I. (2025) AI slop and the destruction of knowledge. This week I was looking for info on what cognitive scientists mean when they speak of ‘domain-general’ cognition. I was curio…

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 27/09/2026

thank you! full thread here on that paper doi.org/10.1177/0963...

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 14/12/2025

Some examples of decolonial automata/computations, apropos of giving a mini talk on this tomorrow: Towards Critical Artificial Intelligence Literacies doi.org/10.5281/zeno... 🐯 Tipu's tiger is amazing; sadly stolen & now in the V&A, created in India & depicting a tiger mauling an English man. 1/n

On the flip side, we can include positive examples in our teaching on the history of AI: Tipu’s
tiger from late 18th century India, featuring the eponymous robotic cat mauling a European man
(Karp et al. 2013); the Antikythera Mechanism from ancient Greece, which was a clockwork astro-
logical calender; and the Mayans’ calendrical system that was highly precise and directly translat-
able to a system of gears. We can also explain the origin of ‘algorithm’ and ‘algebra’, conceptually
developed during the Islamic Golden Age. These serve not only to properly contextualise the his-
tory of AI (Mayor 2018), but to reimagine with students what could be done differently.
Regardless of AI, science has always been intertwined with domination, extraction, binary
thinking, and the imposition of hierarchies (Tacheva et al. 2023). For example, the Cartesian dualist
idea that the mind can be separated from the body is at the core of AI hype. Corporate discourses
around AI feature preposterous claims, such that ‘ChatGPT has PhD level cognition capacities’,
and they will be able to surpass human intelligence in the near future (Welt 2025), implying that
technologies are human-like. However, far from LLMs having cognitive capacities, companies ob-
fuscate the labour that is extracted from data workers (Miceli et al. 2020). In the classroom, we
should question the mind and body binary and render visible its eugenical and colonial legacies.
While we refute corporate claims, we also take them seriously as dehumanising workers, women,

https://en.wikipedia.org/wiki/Tipu%27s_Tiger Tipu's Tiger, Tippu's Tiger or Tipoo’s Tiger is an 18th-century automaton created for Tipu Sultan, the ruler of the Kingdom of Mysore (present day Karnataka) in India. The carved and painted wood casing represents a tiger mauling a near life-size European man. Mechanisms inside the tiger and the man's body make one hand of the man move, emit a wailing sound from his mouth and grunts from the tiger. In addition a flap on the side of the tiger folds down to reveal the keyboard of a small pipe organ with 18 notes.[1]

zoomed in to the tiger biting the man's neck

Tipu's Tiger with the organ keyboard visible

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 14/12/2025

Another cool one is the Mayan calendrical system: "highly precise and directly translatable to a system of gears" doi.org/10.5281/zeno... — much like gears in clockwork & the Antikythera Mechanism of course. See more: maya.nmai.si.edu/sites/defaul... www.mayaarchaeologist.co.uk/public-resou... 2/n

The Maya Calendar System
Using their knowledge of astronomy and mathematics, the Maya developed one of the most accurate cal-
endar systems in human history. The Maya calendar system has its roots in older, Mesoamerican1 indig-
enous civilizations, particularly the
Olmec. The Maya calendar is complex
and serves both practical and ceremoni-
al purposes. The Maya calendar system
includes several calendars that mea-
sure time periods of varying lengths.
These calendars are based on solar,
lunar, planetary, and human cycles.
There are three most commonly known
cyclical calendars used by the Maya.
These include the Haab which is a 365-
day solar calendar, the Tzolk’in which
is a 260-day sacred calendar, and the
Calendar Round of 52 years. In
addition, the Maya developed the Long
Count calendar to date mythical and
historical events chronologically.
1 Definitions for words in red-colored font can be found in the Glossary page in the Resources section of the “Living
Maya Time” website.
A contemporary representation of the Tzolk’in (inner green circle)
and Haab (outer brown circle) calendars.
The Haab
The Maya solar calendar, called Haab, is a count of 365 days and thus approximates the solar year. The word
“haab” means “year” in the Yucatec Mayan language. The Haab is composed of 18 months made of 20 days
each, plus one month made of 5 days. A month made of 20 days is called a uinal. Each uinal has its own
name. These 18 months together equal 360 days. The last month made of 5 days is called Wayeb. The 19
months together total 365 days.
18 x 20 + 5 = 3652
Table 1: Haab Months and Days
Pop
Wo’
Sip
Sotz’
Sek
Xul
Yaxk’in
Mol
Ch’en
Yax
Sak’
Keh
Mak
K’ank’in
Muwan
Pax
K’ayab
Kumk’u
Wayeb
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7…

the calendar also shown as gears! from https://www.mayaarchaeologist.co.uk/public-resources/maya-world/maya-calendar-system/

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 28/09/2026

yay this little paper is finally out with @blokpoel.bsky.social & @irisvanrooij.bsky.social “What the Func? Multiple Realizability Need Not Be Vague.” doi.org/10.1017/S014... pdf: zenodo.org/records/1938...

doi.org

What the func? Multiple realizability need not be vague | Behavioral and Brain Sciences | Cambridge Core

What the func? Multiple realizability need not be vague - Volume 49

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Ajuna Soerjadi 🌸 @ethicsofai.bsky.social · 28/09/2026

Will share more about the wonderful festival organized by @samiraibnelkaid.bsky.social 🩷 but for now, just sharing my drawing from the amazing workshop by @marentierra.bsky.social and @joostvossers.bsky.social to create situated and social depictions of AI to decolonize public representations ✨️✨️🙏

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Esther Mondragón @e-mondragon.bsky.social · 23/09/2026

Perfect! 🤣

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kajak.bsky.social @kajak.bsky.social · 24/09/2026

il dibattito filosofico su AI

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 25/09/2026

For the AI specific case, see the Pygmalion paper I linked to two posts higher, but also this for a synopsis of how gender plays a significant but obfuscated role...

olivia.science

Turing test

Critically analysing the Turing test.

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 25/09/2026

Add to the above this general trend I'm sure you're familiar with: in many situations under patriarchy credit to women is avoided even when they clearly did all the work.

olivia.science

Cryptogyny

On the systematic obfuscation of women's contributions.

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 25/09/2026

And see the gendered history here too — perhaps can help to see why you're also being mostly given men to read:

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 25/09/2026

For a historical perspective that goes back all the way, see:

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REwadent Evil @ewacat.bsky.social · 24/09/2026

lmao almost straight away help is provided, you lot are brilliant bsky.app/profile/iris...

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 25/09/2026

Thank you for this thread, amazing! 🤩 Also @ethicsofai.bsky.social will be there with the board game "AI Utopia", developed during the Summer School Critical AI Literacies For Resisting And Reclaiming -- Session info: reclaimingourfutures.org/descriptions... Sneak peek 👇

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 25/09/2026

Thanks so much to my wonderful colleagues Marieke & @marentierra.bsky.social for setting up at @samiraibnelkaid.bsky.social's amazing festival: reject AI instead reclaim the future! 🫚 💞 💥 Posters are here: zenodo.org/records/1736... & you can read more here: olivia.science/ai about our work.

a photo of this poster: https://zenodo.org/records/17111928

photo of some cool orange lighting, a fluorescent green frame with this poster in it: https://zenodo.org/records/17367323

a very blue frame with a poster from here in it: https://zenodo.org/records/17367323 which says Have you considered not using AI?

a room with a black table and a blue frame with a poster that says AI is evil: https://zenodo.org/records/17367323

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Mark Dingemanse @markdingemanse.net · 25/09/2026

This weekend! Reclaiming our Futures, De Lindenberg, Nijmegen reclaimingourfutures.org A two-day festival of arts and sciences convened and curated by @samiraibnelkaid.bsky.social with funding from KNAW & our Futures of Language project Almost full — some last minute registrations still possible!

Iridiscently coloured ginger with slogan "The Future if Rhizomatic"

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Ajuna Soerjadi 🌸 @ethicsofai.bsky.social · 23/09/2026

My representation of some of the discourse about @irisvanrooij.bsky.social her work 🤣🤣🤣

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 09/09/2026

"Hard limits are in view and true believers, with no vision for the future beyond whatever they think ‘AI’ or ‘AGI’ are (usually whatever they’re told by the corporates), dimly aware of these limits but unable to accept what it means, are lashing out." -- @cloudquistador.eurosky.social

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 20/09/2026

Honoured to be mentioned by @felienne.bsky.social in her AI newsletter! Riffing off of her vitamin-C phrasing, I may start to refer to this type of work as vitamin-CC, or vitamin-C^2

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Benjamin @benjami.no · 20/09/2026

This should be required reading for ALL tech journalists before you credulously report on AGI and ASI claims by the AI companies. Yes, ALL. If you don't understand it, ask a friend to read it with you and discuss it. It is not beyond you, even if you have to chew on it a bit to really 'get' it.

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 01/03/2026

that quote reminds me of the opening quote we used @andreaeyleen.bsky.social here: [A]ll science would be superfluous if the outward appearance and the essence of things directly coincided. (Marx, 1894, p. 592) doi.org/10.1007/s421... so even a baby knows "correlation does not imply cognition" 🙃

doi.org

On Logical Inference over Brains, Behaviour, and Artificial Neural Networks - Computational Brain & Behavior

In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such...

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Olivia Guest · Ολίβια Γκεστ @olivia.science · 20/09/2026

LLMs, as presented by these companies, aren't algorithms because they aren't completely formal since they have humans more than just as users

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 19/09/2026

All we can expect when trying to scale up AI systems is diminishing returns and devastating environmental damage. Calls for `slowing down’ can conveniently hide the diminishing returns, while still convincing people that the tech is “all powerful”.

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 19/09/2026

Niet AGI gaat ons vernietigen, maar wel technofascism, als we in de hype en misleidende AI industry frames blijven trappen

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 19/09/2026

It's GENUINELY like this: You cannot "solve" intractability with heuristics. Trust me. I devoted 20 years to this, and really, if you squeeze out intractability in one spot it'll pop up in another. It may look different on the surface, but in essence you're still facing the same epistemic problem.

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 19/09/2026

Thanks for sharing. Reminds me of our work showing that "good enough" selection of "good enough" heuristics is also intractable. mu.social/profile/iris...

Box 2. Proof intuition
NP-hard models are considered to be intractable, because there do not exist any tractable (formally, polynomial-time) algorithms for computing them (unless P = NP, see footnote 1). How can one prove that a computational model is NP-hard (and thus intractable)? A model is NP-hard if it is at least as hard as another NP-hard model, that is, one can use the former to compute the latter with minimal (only polynomial) overhead. We can prove such a relationship using polynomial-time reduction, a technique from computational complexity (Garey & Johnson, 1979). In the supplementary materials, we use this technique to prove that Adaptive Toolbox is NP-hard. Specifically, we reduce from the known NP-hard graph problem Dominating Set (which takes as input any graph and asks as output a so-called dominating set, that is, a subset of vertices with the property that for each vertex v in the graph, either v or one of its neighbors is in the subset). This is done as follows:
Provide a tractable (polynomial-time) algorithm that transforms any input iDS for Dominating Set into input iAT for Toolbox Adaptation (proof step 1 in Supplementary Materials).
Show that after the transformed input is processed as specified by Toolbox Adaptation, the resulting output oAT can be transformed back into a solution oDS for Dominating Set (proof steps 2 and 3).
Both transformations only take polynomial time to compute. This means we can use Toolbox Adaptation to solve Dominating Set with minimal (only polynomial) overhead.
Toolbox Adaptation is NP-hard, because it is at least as hard as the NP-hard problem Dominating Set.
This is, in a nutshell, the strategy we used to prove that Toolbox Adaptation is NP-hard. Fig. 2 illustrates this strategy.

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 19/09/2026

"Mensen onderschatten vaak wat de menselijke cognitie allemaal kan, en overschatten wat AI systemen kunnen. Velen geloven dat hedendaagse AI systemen zich binnenkort tot echte AGI zullen ontpoppen (of zelfs al AGI zijn). Mijn collega’s en ik hebben onlangs wiskundig bewezen dat dit onzin is."

beste-id.nl

Denken is meer dan we denken: AI en cognitiewetenschap - Beste-ID

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Iris van Rooij 💭 @irisvanrooij.bsky.social · 19/09/2026

Gezien de opnieuw oplaaiend angst voor "artificial general intelligence" (AGI), aangewakkerd door de AI industrie en de media, gooi ik even deze post in de herhaling: www.beste-id.nl/salon/denken...

beste-id.nl

Denken is meer dan we denken: AI en cognitiewetenschap - Beste-ID

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