#Science Networks Of Science

[torpublishinggroup]

Murderbot is MurderBACK in the next installment of Martha Wells’s NYT bestselling Murderbot Diaries series System Collapse 🤖🚀

WHAT’S IT ABOUT

Following the events of Network Effect, our favorite lethally cybernetic television fiend has done the previously unthinkable: agreed to accompany the sentient spaceship Perihelion (dubbed ART by Murderbot, short for Asshole Research Transport) and crew on its next mission. 

Unfortunately, they’re not going to get too far. 

Having failed to harvest dangerous artifacts from their target planet by way of Murderbot misadventure, the Barish-Estranza corporation is much angered and determined to recoup their considerable losses. And when you’re a lethally opportunistic space corp, blood and muscle are valuable currency. 

Murderbot, ART’s crew, and the Preservation humans have planetside work to do as Barish-Estranza seeks to claim the planet’s beleaguered colony as a conscripted workforce. 

But for Murderbot, the challenge is as internal as it is external. Something is deeply, deeply wrong with it. Normal operational parameters are unmet, but with the corp’s SecUnit-heavy persuasion teams en route, Murderbot needs to resolve its issues, and fast!

[mouthtapedguy]

Wake me up when ChatGPT gets banned from a tapas bar for arguing with a pigeon

[driveintheaterofthemind]

The Venture Brothers

Art by Bill Sienkiewicz

[evandorkin]

FYI: We have new ELTINGVILLE CLUB t-shirt/sticker designs in our TeePublic shop, the four pinups of the club members from the hardcover collection. The "favorite shit" text is included in the designs (unlike these images, we'll have updated images asap). On sale now at Teepublic and discounted for the next two days. Wear your favorite or burn your hated enemy!

[scipunk]

Alien: Covenant (2017)

[biologist4ever]

In the neuron, a protective covering called myelin insulates the axon and increases the speed of electrical communication along the length of the neuron

[alpaca-clouds]

We need to talk about AI

Okay, several people asked me to post about this, so I guess I am going to post about this. Or to say it differently: Hey, for once I am posting about the stuff I am actually doing for university. Woohoo!

Because here is the issue. We are kinda suffering a death of nuance right now, when it comes to the topic of AI.

I understand why this happening (basically everyone wanting to market anything is calling it AI even though it is often a thousand different things) but it is a problem.

So, let's talk about "AI", that isn't actually intelligent, what the term means right now, what it is, what it isn't, and why it is not always bad. I am trying to be short, alright?

So, right now when anyone says they are using AI they mean, that they are using a program that functions based on what computer nerds call "a neural network" through a process called "deep learning" or "machine learning" (yes, those terms mean slightly different things, but frankly, you really do not need to know the details).

Now, the theory for this has been around since the 1940s! The idea had always been to create calculation nodes that mirror the way neurons in the human brain work. That looks kinda like this:

Basically, there are input nodes, in which you put some data, those do some transformations that kinda depend on the kind of thing you want to train it for and in the end a number comes out, that the program than "remembers". I could explain the details, but your eyes would glaze over the same way everyone's eyes glaze over in this class I have on this on every Friday afternoon.

All you need to know: You put in some sort of data (that can be text, math, pictures, audio, whatever), the computer does magic math, and then it gets a number that has a meaning to it.

And we actually have been using this sinde the 80s in some way. If any Digimon fans are here: there is a reason the digital world in Digimon Tamers was created in Stanford in the 80s. This was studied there.

But if it was around so long, why am I hearing so much about it now?

This is a good question hypothetical reader. The very short answer is: some super-nerds found a way to make this work way, way better in 2012, and from that work (which was then called Deep Learning in Artifical Neural Networks, short ANN) we got basically everything that TechBros will not shut up about for the last like ten years. Including "AI".

Now, most things you think about when you hear "AI" is some form of generative AI. Usually it will use some form of a LLM, a Large Language Model to process text, and a method called Stable Diffusion to create visuals. (Tbh, I have no clue what method audio generation uses, as the only audio AI I have so far looked into was based on wolf howls.)

LLMs were like this big, big break through, because they actually appear to comprehend natural language. They don't, of coruse, as to them words and phrases are just stastical variables. Scientists call them also "stochastic parrots". But of course our dumb human brains love to anthropogice shit. So they go: "It makes human words. It gotta be human!"

It is a whole thing.

It does not understand or grasp language. But the mathematics behind it will basically create a statistical analysis of all the words and then create a likely answer.

What you have to understand however is, that LLMs and Stable Diffusion are just a a tiny, minority type of use cases for ANNs. Because research right now is starting to use ANNs for EVERYTHING. Some also partially using Stable Diffusion and LLMs, but not to take away people'S jobs.

Which is probably the place where I will share what I have been doing recently with AI.

The stuff I am doing with Neural Networks

The neat thing: if a Neural Network is Open Source, it is surprisingly easy to work with it. Last year when I started with this I was so intimidated, but frankly, I will confidently say now: As someone who has been working with computers for like more than 10 years, this is easier programming than most shit I did to organize data bases. So, during this last year I did three things with AI. One for a university research project, one for my work, and one because I find it interesting.

The university research project trained an AI to watch video live streams of our biology department's fish tanks, analyse the behavior of the fish and notify someone if a fish showed signs of being sick. We used an AI named "YOLO" for this, that is very good at analyzing pictures, though the base framework did not know anything about stuff that lived not on land. So we needed to teach it what a fish was, how to analyze videos (as the base framework only can look at single pictures) and then we needed to teach it how fish were supposed to behave. We still managed to get that whole thing working in about 5 months. So... Yeah. But nobody can watch hundreds of fish all the time, so without this, those fish will just die if something is wrong.

The second is for my work. For this I used a really old Neural Network Framework called tesseract. This was developed by Google ages ago. And I mean ages. This is one of those neural network based on 1980s research, simply doing OCR. OCR being "optical character recognition". Aka: if you give it a picture of writing, it can read that writing. My work has the issue, that we have tons and tons of old paper work that has been scanned and needs to be digitized into a database. But everyone who was hired to do this manually found this mindnumbing. Just imagine doing this all day: take a contract, look up certain data, fill it into a table, put the contract away, take the next contract and do the same. Thousands of contracts, 8 hours a day. Nobody wants to do that. Our company has been using another OCR software for this. But that one was super expensive. So I was asked if I could built something to do that. So I did. And this was so ridiculously easy, it took me three weeks. And it actually has a higher successrate than the expensive software before.

Lastly there is the one I am doing right now, and this one is a bit more complex. See: we have tons and tons of historical shit, that never has been translated. Be it papyri, stone tablets, letters, manuscripts, whatever. And right now I used tesseract which by now is open source to develop it further to allow it to read handwritten stuff and completely different letters than what it knows so far. I plan to hook it up, once it can reliably do the OCR, to a LLM to then translate those texts. Because here is the thing: these things have not been translated because there is just not enough people speaking those old languages. Which leads to people going like: "GASP! We found this super important document that actually shows things from the anceint world we wanted to know forever, and it was lying in our collection collecting dust for 90 years!" I am not the only person who has this idea, and yeah, I just hope maybe we can in the next few years get something going to help historians and archeologists to do their work.

Make no mistake: ANNs are saving lives right now

Here is the thing: ANNs are Deep Learning are saving lives right now. I really cannot stress enough how quickly this technology has become incredibly important in fields like biology and medicine to analyze data and predict outcomes in a way that a human just never would be capable of.

I saw a post yesterday saying "AI" can never be a part of Solarpunk. I heavily will disagree on that. Solarpunk for example would need the help of AI for a lot of stuff, as it can help us deal with ecological things, might be able to predict weather in ways we are not capable of, will help with medicine, with plants and so many other things.

ANNs are a good thing in general. And yes, they might also be used for some just fun things in general.

And for things that we may not need to know, but that would be fun to know. Like, I mentioned above: the only audio research I read through was based on wolf howls. Basically there is a group of researchers trying to understand wolves and they are using AI to analyze the howling and grunting and find patterns in there which humans are not capable of due ot human bias. So maybe AI will hlep us understand some animals at some point.

Heck, we saw so far, that some LLMs have been capable of on their on extrapolating from being taught one version of a language to just automatically understand another version of it. Like going from modern English to old English and such. Which is why some researchers wonder, if it might actually be able to understand languages that were never deciphered.

All of that is interesting and fascinating.

Again, the generative stuff is a very, very minute part of what AI is being used for.

Yeah, but WHAT ABOUT the generative stuff?

So, let's talk about the generative stuff. Because I kinda hate it, but I also understand that there is a big issue.

If you know me, you know how much I freaking love the creative industry. If I had more money, I would just throw it all at all those amazing creative people online. I mean, fuck! I adore y'all!

And I do think that basically art fully created by AI is lacking the human "heart" - or to phrase it more artistically: it is lacking the chemical inbalances that make a human human lol. Same goes for writing. After all, an AI is actually incapable of actually creating a complex plot and all of that. And even if we managed to train it to do it, I don't think it should.

AI saving lives = good.

AI doing the shit humans actually evolved to do = bad.

And I also think that people who just do the "AI Art/Writing" shit are lazy and need to just put in work to learn the skill. Meh.

However...

I do think that these forms of AI can have a place in the creative process. There are people creating works of art that use some assets created with genAI but still putting in hours and hours of work on their own. And given that collages are legal to create - I do not see how this is meaningfully different. If you can take someone else's artwork as part of a collage legally, you can also take some art created by AI trained on someone else's art legally for the collage.

And then there is also the thing... Look, right now there is a lot of crunch in a lot of creative industries, and a lot of the work is not the fun creative kind, but the annoying creative kind that nobody actually enjoys and still eats hours and hours before deadlines. Swen the Man (the Larian boss) spoke about that recently: how mocapping often created some artifacts where the computer stuff used to record it (which already is done partially by an algorithm) gets janky. So far this was cleaned up by humans, and it is shitty brain numbing work most people hate. You can train AI to do this.

And I am going to assume that in normal 2D animation there is also more than enough clean up steps and such that nobody actually likes to do and that can just help to prevent crunch. Same goes for like those overworked souls doing movie VFX, who have worked 80 hour weeks for the last 5 years. In movie VFX we just do not have enough workers. This is a fact. So, yeah, if we can help those people out: great.

If this is all directed by a human vision and just helping out to make certain processes easier? It is fine.

However, something that is just 100% AI? That is dumb and sucks. And it sucks even more that people's fanart, fanfics, and also commercial work online got stolen for it.

And yet... Yeah, I am sorry, I am afraid I have to join the camp of: "I am afraid criminalizing taking the training data is a really bad idea." Because yeah... It is fucking shitty how Facebook, Microsoft, Google, OpenAI and whatever are using this stolen data to create programs to make themselves richer and what not, while not even making their models open source. BUT... If we outlawed it, the only people being capable of even creating such algorithms that absolutely can help in some processes would be big media corporations that already own a ton of data for training (so basically Disney, Warner and Universal) who would then get a monopoly. And that would actually be a bad thing. So, like... both variations suck. There is no good solution, I am afraid.

And mind you, Disney, Warner, and Universal would still not pay their artists for it. lol

However, that does not mean, you should not bully the companies who are using this stolen data right now without making their models open source! And also please, please bully Hasbro and Riot and whoever for using AI Art in their merchandise. Bully them hard. They have a lot of money and they deserve to be bullied!

But yeah. Generally speaking: Please, please, as I will always say... inform yourself on these topics. Do not hate on stuff without understanding what it actually is. Most topics in life are nuanced. Not all. But many.

[couldtheyescortelliewilliams]

Propaganda:

It’s literally designed to do stuff like this. Also it survived getting infected by a spore fungus thing before. Also it would totally get attached, despite how much it would claim otherwise.

/

/

/

How would they have handled the Firefly hospital massacre:

Ngl probably the same way Joel did

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/

Do you want to submit a potential protector for Ellie? Click here if you do!

[cyborgized]

Cybernetics discussed on Sealab 2021

[torpublishinggroup]

Murderbot is MurderBACK in the next installment of Martha Wells’s NYT bestselling Murderbot Diaries series System Collapse 🤖🚀

WHAT’S IT ABOUT

Following the events of Network Effect, our favorite lethally cybernetic television fiend has done the previously unthinkable: agreed to accompany the sentient spaceship Perihelion (dubbed ART by Murderbot, short for Asshole Research Transport) and crew on its next mission. 

Unfortunately, they’re not going to get too far. 

Having failed to harvest dangerous artifacts from their target planet by way of Murderbot misadventure, the Barish-Estranza corporation is much angered and determined to recoup their considerable losses. And when you’re a lethally opportunistic space corp, blood and muscle are valuable currency. 

Murderbot, ART’s crew, and the Preservation humans have planetside work to do as Barish-Estranza seeks to claim the planet’s beleaguered colony as a conscripted workforce. 

But for Murderbot, the challenge is as internal as it is external. Something is deeply, deeply wrong with it. Normal operational parameters are unmet, but with the corp’s SecUnit-heavy persuasion teams en route, Murderbot needs to resolve its issues, and fast!

[alephzdraws]

That Freak of Nature AU

I can imagine Sonic becoming increasingly exasperated trying to explain to Shadow how to function as close to a normal mobian as physically possible so that G.U.N. has a harder time discovering him and Shadow just goes "what's the point of all this? I don't get it. I hate this skin already, get it off me."

Like trying to remind him to blink so he doesn't stare deep into peoples' souls as much, and he does, but with a third eyelid so he doesn't lose sight (Immediately freaks Sonic out on the spot).

Or attempting to explain to him that he can't just morph his body around in public to get around the discomfort of existing in a form he doesn't like (if you're unaware, the Doom Morph in this AU is Shadow's true base form and he can shapeshift into a hedgehog. Pretty much the reverse of SxS Gens).

---

No, splitting your face open to scare away people you don't like isn't a good idea. You're trying to stay hidden from those guys who want to finish the job of killing you.

No Shadow, eating people is wrong, even if you're an alien. Yes, even if they're bad people like the ones who killed your favourite human.

Try not to make weapons out of your or my limbs unless you really need to protect yourself.

Please don't take over my body unless I need you to. I don't wanna have to "explain" that to the public, and it's kind of existentially terrifying for me to lose control (Don't tell anyone I said that).

I understand that your "quills" are technically your tentacles and kinda wanna move on their own, but you're sorta freaking out those people across the street.

I know it sucks walking around in the wrong form, but it's either that, you hang out in my body like a weird alien parasite, or G.U.N. takes you.

[thenightfolknetwork]

I have a problem. Somebody asked me if I was pro-AI, and I said yes, of course, every sensible person is. "A person's a person, whether they were coded or conceived or came to life through other means," I didn't say, because I felt it was obvious. Now I've found out that people use "AI" to refer to non-sentient computer models, so that means I gave off the COMPLETE wrong impression, and it's mortifying. How do I deal with this, and how do I prevent it from happening again?

I completely understand your frustration, reader. The use of the term “AI” to refer to generative algorithms has had several unfortunate side effects, not least among them being the confusion this terminology causes for genuinely sentient programmes and otherwise coded consciousnesses.

I would first like to note that there is no way to completely prevent such miscommunication from happening. There is a hard limit to how much we can control other people's perceptions of us, after all, and language has always had a rather slippery relationship with meaning. All you can do is try your best, and to speak up when you think things have gone awry.

In this specific situation, the simplest way to avoid this confusion is to speak precisely and ask others to do the same. If a person asks whether you are pro-AI, ask them to clarify whether they're referring to sentient coded consciousnesses, or to a glorified spell checker.

If you have the energy, and if you think it would be productive, you might also push back a little on the terminology they're using. Encourage them to specify either “coded consciousnesses” when speaking about actual people, or “generative AI” when speaking about the automated plagiarism machines.

Otherwise, the best thing you can do here is to lead by example. Be clear and precise in your own language, continue to advocate for the rights and dignities of coded consciousnesses where appropriate, and be prepared to correct any misunderstandings as they arise. That's really all any of us can do.

[For more creaturely advice, check out Monstrous Agonies on your podcast platform of choice, or visit monstrousproductions.org for more info]

[browsethestacks]

Adventure Time

Art by Chris Samnee

[compneuropapers]

Interesting Papers for Week 3, 2025

Synaptic weight dynamics underlying memory consolidation: Implications for learning rules, circuit organization, and circuit function. Bhasin, B. J., Raymond, J. L., & Goldman, M. S. (2024). Proceedings of the National Academy of Sciences, 121(41), e2406010121.

Characterization of the temporal stability of ToM and pain functional brain networks carry distinct developmental signatures during naturalistic viewing. Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. (2024). Scientific Reports, 14, 22479.

Connectomic reconstruction predicts visual features used for navigation. Garner, D., Kind, E., Lai, J. Y. H., Nern, A., Zhao, A., Houghton, L., … Kim, S. S. (2024). Nature, 634(8032), 181–190.

Socialization causes long-lasting behavioral changes. Gil-Martí, B., Isidro-Mézcua, J., Poza-Rodriguez, A., Asti Tello, G. S., Treves, G., Turiégano, E., … Martin, F. A. (2024). Scientific Reports, 14, 22302.

Neural pathways and computations that achieve stable contrast processing tuned to natural scenes. Gür, B., Ramirez, L., Cornean, J., Thurn, F., Molina-Obando, S., Ramos-Traslosheros, G., & Silies, M. (2024). Nature Communications, 15, 8580.

Lack of optimistic bias during social evaluation learning reflects reduced positive self-beliefs in depression and social anxiety, but via distinct mechanisms. Hoffmann, J. A., Hobbs, C., Moutoussis, M., & Button, K. S. (2024). Scientific Reports, 14, 22471.

Causal involvement of dorsomedial prefrontal cortex in learning the predictability of observable actions. Kang, P., Moisa, M., Lindström, B., Soutschek, A., Ruff, C. C., & Tobler, P. N. (2024). Nature Communications, 15, 8305.

A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection. Kikumoto, A., Bhandari, A., Shibata, K., & Badre, D. (2024). Nature Communications, 15, 8513.

Presaccadic Attention Enhances and Reshapes the Contrast Sensitivity Function Differentially around the Visual Field. Kwak, Y., Zhao, Y., Lu, Z.-L., Hanning, N. M., & Carrasco, M. (2024). eNeuro, 11(9), ENEURO.0243-24.2024.

Transformation of neural coding for vibrotactile stimuli along the ascending somatosensory pathway. Lee, K.-S., Loutit, A. J., de Thomas Wagner, D., Sanders, M., Prsa, M., & Huber, D. (2024). Neuron, 112(19), 3343-3353.e7.

Inhibitory plasticity supports replay generalization in the hippocampus. Liao, Z., Terada, S., Raikov, I. G., Hadjiabadi, D., Szoboszlay, M., Soltesz, I., & Losonczy, A. (2024). Nature Neuroscience, 27(10), 1987–1998.

Third-party punishment-like behavior in a rat model. Mikami, K., Kigami, Y., Doi, T., Choudhury, M. E., Nishikawa, Y., Takahashi, R., … Tanaka, J. (2024). Scientific Reports, 14, 22310.

The morphospace of the brain-cognition organisation. Pacella, V., Nozais, V., Talozzi, L., Abdallah, M., Wassermann, D., Forkel, S. J., & Thiebaut de Schotten, M. (2024). Nature Communications, 15, 8452.

A Drosophila computational brain model reveals sensorimotor processing. Shiu, P. K., Sterne, G. R., Spiller, N., Franconville, R., Sandoval, A., Zhou, J., … Scott, K. (2024). Nature, 634(8032), 210–219.

Decision-making shapes dynamic inter-areal communication within macaque ventral frontal cortex. Stoll, F. M., & Rudebeck, P. H. (2024). Current Biology, 34(19), 4526-4538.e5.

Intrinsic Motivation in Dynamical Control Systems. Tiomkin, S., Nemenman, I., Polani, D., & Tishby, N. (2024). PRX Life, 2(3), 033009.

Coding of self and environment by Pacinian neurons in freely moving animals. Turecek, J., & Ginty, D. D. (2024). Neuron, 112(19), 3267-3277.e6.

The role of training variability for model-based and model-free learning of an arbitrary visuomotor mapping. Velázquez-Vargas, C. A., Daw, N. D., & Taylor, J. A. (2024). PLOS Computational Biology, 20(9), e1012471.

Rejecting unfairness enhances the implicit sense of agency in the human brain. Wang, Y., & Zhou, J. (2024). Scientific Reports, 14, 22822.

Impaired motor-to-sensory transformation mediates auditory hallucinations. Yang, F., Zhu, H., Cao, X., Li, H., Fang, X., Yu, L., … Tian, X. (2024). PLOS Biology, 22(10), e3002836.

[mindblowingscience]

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the Event Horizon Telescope, they now predict, among other things, that the black hole at the center of our Milky Way is spinning at near top speed. The astronomers have published their results and methodology in three papers in the journal Astronomy & Astrophysics.

Continue Reading.

[verilog-official]

hey i need some help

I'm developing a video compression algorithm and I'm trying to figure out how to encode it in a way that actually looks good.

basically, each frame is made up of a grid of 5x8 pixel tiles, each cell being one of 16 tiles. 8 of these tiles can be anything, while the other 8 are hard-coded.

so far, my algorithm simply compares each tile of the input frame to each hard-coded tile, and the 8 tiles that match the least are set to the "custom" tiles, and the other ones that match the hard-coded ones more are set to those hard-coded ones.

this works okay, but doesn't account for if two input frame tiles are the same thing or similar, it would be better to re-use custom tiles (eg, if the whole screen is black — due to the limitations of the screen I'm using, a solid black tile must be a custom tile, but a solid white one can be hard coded).

speed isn't that important, as each frame is only 80x16 pixels at the most, with one bit per pixel, and each tile is 5x8 pixels, for a grid of 16x2 tiles.

TL;DR: I need help writing an algorithm that can arrange 16 tiles into a 16x2 grid, while also determining the best pattern to set 8 of those tiles to, while leaving the other 8 constant.