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Reposted by Maxime Adam

@olivia.science

Finally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia:
doi.org/10.5281/zeno...

We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical
thinking, expertise, academic freedom, & scientific integrity.
1/n

Abstract: Under the banner of progress, products have been uncritically adopted or
even imposed on users — in past centuries with tobacco and combustion engines, and in
the 21st with social media. For these collective blunders, we now regret our involvement or
apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we
are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not
considered a valid position to reject AI technologies in our teaching and research. This
is why in June 2025, we co-authored an Open Letter calling on our employers to reverse
and rethink their stance on uncritically adopting AI technologies. In this position piece,
we expound on why universities must take their role seriously toa) counter the technology
industry’s marketing, hype, and harm; and to b) safeguard higher education, critical
thinking, expertise, academic freedom, and scientific integrity. We include pointers to
relevant work to further inform our colleagues.

Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI
(black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are
in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative adversarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are
both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and
Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf.
Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al.
2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA).

Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms
are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe.

Protecting the Ecosystem of Human Knowledge: Five Principles

September 6, 2025 at 8:13 AM

Reposted by Maxime Adam

Eugene Vinitsky 🍒

@eugenevinitsky.bsky.social

Reminded again of Kevin Murphy's excellent RL overview: arxiv.org/abs/2412.05265
A lot of the stuff covered here really is at the cutting edge and not compiled so nicely anywhere else

Reinforcement Learning: An Overview

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based methods, policy-based methods, model-based m...

arxiv.org

September 3, 2025 at 1:47 AM

Reposted by Maxime Adam

Earl K. Miller

@earlkmiller.bsky.social

Inside the search for a universal signature of unconsciousness
bigthink.com/neuropsych/u...
"Neuroscientists like Earl K. Miller argue that the brain’s coordinated waves do the real work"
#neuroscience @picowerinstitute.bsky.social @mitbcs.bsky.social

Inside the search for a universal signature of unconsciousness

A universal marker of unconsciousness could transform both medicine and our understanding of the mind.

bigthink.com

August 20, 2025 at 5:28 PM

Reposted by Maxime Adam

Luke Sjulson

@lukesjulson.bsky.social

Neuroscience students asked us to teach a PRACTICAL course on experimental methods, and it is now on YouTube!

Please like and repost to help us get the word out!

www.youtube.com/playlist?lis...

Lecture 1: Signals and data acquisition
Focusing on hardware, digital/analog I/O, synchronization
🧵

Neuroscience methods - YouTube

Nanocourse: Approaches to Study Neural Circuits This course was taught by Anita Autry, Tiago Gonçalves, and Luke Sjulson at Albert Einstein College of Medici...

www.youtube.com

December 13, 2024 at 11:33 AM

Reposted by Maxime Adam

Mari Sosa

@marisosa.bsky.social

Great piece from @thetransmitter.bsky.social about the field’s current understanding of the hippocampus! Honored for the nod to our recent paper among these many other excellent studies www.thetransmitter.org/memory/expan...