This article explores the 3 vital rules of science. Together, they offer a powerful lens for evaluating the validity of claims and conclusions.

🔗 https://www.scottgraffius.com/blog/files/3-vital-rules-of-science-what-they-are-and-why-they-matter.html

#Science #CriticalThinking #Falsifiability #Replicability #Correlation #Causation

🔬 #Physics Puzzles – Exciting puzzle questions explained simply! 🤓

📖 https://www.kosmos.de/buecher/ratgeber-naturfuehrer/astronomie/einsteiger/14776/das-raetselhafte-universum

📎https://philosophies.de/index.php/2022/10/26/zoomposium-naumann-bohnet-das-raetselhafte-universum/

📺https://youtu.be/1ouxs6P3Enc

#ThomasNaumann #IljaBohnet #Zoomposium #OpenQuestionsInPhysics #NaturalSciences #DarkMatter #StringTheory #Cosmology #Philosophy #PhilosophyOfScience #ParticlePhysics #ConstantsOfNature #Multiverse #Falsifiability #FineTuning # Order #Beauty #Truth #Psychology #CriticismOfScience #Research #Physics #Cosmos #Cosmetics #OpenQuestions

I saw this on Mastodon and almost had a stroke.

@davidgerard wrote:

“Most of the AI coding claims are conveniently nondisprovable. What studies there are show it not helping coding at all, or making it worse

But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.

These guys had one good experience with the bot, they got one-shotted, and now if you say “perhaps the bot is not all that” they act like you’re trying to take their cocaine away.”

First, the term is falsifiable, and proving propositions about algorithms (i.e., code) is part of what I do for a living. Mathematically human-written code and AI-written code can be tested, which means you can falsify propositions about them. You would test them the same way.

There is no intrinsic mathematical distinction between code written by a person and code produced by an AI system. In both cases, the result is a formal program made of logic and structure. In principle, the same testing techniques can be applied to each. If it were really nondisprovable, you could not test to see what is generated by a human and what is generated by AI. But you can test it. Studies have found that AI-generated code tends to exhibit a higher frequency of certain types of defects. So, reviewers and testers know what logic flaws and security weaknesses to look for. This would not be the case if it were nondisprovable.

You can study this from datasets where the source of the code is known. You can use open-source pull requests identified as AI-assisted versus those written without such tools. You then evaluate both groups using the same industry-standard analysis tools: static analyzers, complexity metrics, security scanners, and defect classification systems. These tools flag bugs, vulnerabilities, performance issues, and maintainability concerns. They do so in a consistent way across samples.

A widely cited analysis of 470 real pull requests reported that AI-generated contributions contained roughly 1.7 times as many issues on average as human-written ones. The difference included a higher number of critical and major defects. It also included more logic and security-related problems. Because these findings rely on standard measurement tools — counting defects, grading severity, and comparing issue rates — the results are grounded in observable data. Again, I am making a point here. It’s testable and therefore disproveable.

This is a good paper that goes into it:

In this paper, we present a large-scale comparison of code authored by human developers and three state-of-the-art LLMs, i.e., ChatGPT, DeepSeek-Coder, and Qwen-Coder, on multiple dimensions of software quality: code defects, security vulnerabilities, and structural complexity. Our evaluation spans over 500k code samples in two widely used languages, Python and Java, classifying defects via Orthogonal Defect Classification and security vulnerabilities using the Common Weakness Enumeration. We find that AI-generated code is generally simpler and more repetitive, yet more prone to unused constructs and hardcoded debugging, while human-written code exhibits greater structural complexity and a higher concentration of maintainability issues. Notably, AI-generated code also contains more high-risk security vulnerabilities. These findings highlight the distinct defect profiles of AI- and human-authored code and underscore the need for specialized quality assurance practices in AI-assisted programming.

https://arxiv.org/abs/2508.21634

The big problem in discussions about AI in programming is the either-or thinking, when it’s not about using it everywhere or banning it entirely. Tools like AI have specific strengths and weaknesses. Saying ‘never’ or ‘always’ oversimplifies the issue and turns the narrative into propaganda that creates moral panic or shills AI. It’s a bit like saying you shouldn’t use a hammer just because it’s not good for brushing your teeth.

AI tends to produce code that’s simple, often a bit repetitive, and very verbose. It’s usually pretty easy to read and tweak. This helps with long-term maintenance. But AI doesn’t reason about code the way an experienced developer does. It makes mistakes that a human wouldn’t, potentially introducing security flaws. That doesn’t mean we shouldn’t use for where it works well, which is not everywhere.

AI works well for certain tasks, especially when the scope is narrow and the risk is low. Examples include generating boilerplate code, internal utilities, or prototypes. In these cases, the tradeoff is manageable. However, it’s not suitable for critical code like kernels, operating systems, compilers, or cryptographic libraries. A small mistake memory safety or privilege separation can lead to major failures. Problems with synchronization, pointer management, or access control can cause major problems, too.

Other areas where AI should not be used include memory allocation handling, scheduling, process isolation, or device drivers. A lot of that depends on implicit assumptions in the system’s architecture. Generative models don’t grasp these nuances. Instead of carefully considering the design, AI tends to replicate code patterns that seem statistically likely, doing so without understanding the purpose behind them.

Yes, I’m aware that Microsoft is using AI to write code everywhere I said it should not be used. That is the problem. However, political pundits, lobbyists, and anti-tech talking heads are discussing something they have no understanding of and aren’t specifying what the problem actually is. This means they can’t possibly lead grassroots initiatives into actual laws that specify where AI should not be used, which is why we have this weird astroturfing bullshit.

They’re taking advantage of the reaction to Microsoft using AI-generated code where it shouldn’t be used to argue that AI shouldn’t be used anywhere at all in any generative context. AI is useful for tasks like writing documentation, generating tests, suggesting code improvements, or brainstorming alternative approaches. These ideas should then be thoroughly vetted by human developers.

Something I’ve started to notice about a lot of the content on social media platforms is that most of the posts people are liking, sharing, and memetically mutating—and then spreading virally—usually don’t include any citations, sources, or receipts. It’s often just some out-of-context screenshot with no reference link or actual sources.

A lot of the anti-AI content is not genuine critique. It’s often misinformation, but people who hate AI don’t question it or ask for sources because it aligns with their biases. The propaganda on social media has gotten so bad that anything other than heavily curated and vetted feeds is pretty much useless, and it’s filled with all sorts of memetic contagions with nasty hooks that are optimized for you algorithmically. I am at the point where I will disregard anything that is not followed up with a source. Period. It is all optimized to persuade, coerce, or piss you off. I am only writing about this because this I’m actually able to contribute genuine information about the topic.

That they said symbolic propositions written by AI agents (i.e., code) are non-disprovable because they were written by AI boggles my mind. It’s like saying that an article written in English by AI is not English because AI generated it. It might be a bad piece of text, but it’s syntactically, semantically, and grammatically English.

Basically, any string of data can be represented in a base-2 system, where it can be interpreted as bits (0s and 1s). Those bits can be used as the basis for symbolic reasoning. In formal propositional logic, a proposition is a sequence of symbols constructed according to strict syntax rules (atomic variables plus logical connectives). Under a given semantics, it is assigned exactly one truth value (true or false) in a two-valued logic system.

They are essentially saying that code written by AI is not binary, isn’t symbolically logical at all, and cannot be evaluated as true or false by implying it is nondisproveable. At the lowest level, compiled code consists of binary machine instructions that a processor executes. At higher levels, source code is written in symbolic syntax that humans and tools use to express logic and structure. You can also translate parts of code into formal logic expressions. For example, conditions and assertions in a program can be modeled as Boolean formulas. Tools like SAT/SMT solvers or symbolic execution engines check those formulas for satisfiability or correctness. It blows my mind how confidently people talk about things they do not understand.

Furthermore that they don’t realize the projection is wild to me.

@davidgerard wrote:

“But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.”

They are presenting a story—i.e., saying that the studies are not disprovable—and accusing computer scientists of using anecdotal evidence without actually providing evidence to support this, while expecting people to take it prima facie. You’re doing what you are accusing others of doing.

It comes down to this: they feel that people ought not to use AI, so they are tacitly committed to a future in which people do not use AI. For example, a major argument against AI is the damage it is doing to resources, which is driving up the prices of computer components, as well as the ecological harm it causes. They feel justified in lying and misinforming others if it achieves the outcome they want—people not using AI because it is bad for the environment. That is a very strong point, but most people don’t care about that, which is why they lie about things people would care about.

It’s corrupt. And what’s really scary is that people don’t recognize when they are part of corruption or a corrupt conspiracy to misinform. Well, they recognize it when they see the other side doing it, that is. No one is more dangerous than people who feel righteous in what they are doing.

It’s wild to me that the idea that if you cannot persuade someone, it is okay to bully, coerce, harass them, or spread misinformation to get what you want—because your side is right—has become so normalized on the Internet that people can’t see why it is problematic.

That people think it is okay to hurt others to get them to agree is the most disturbing part of all of this. People have become so hateful. That is a large reason why I don’t interact with people on social media, really consume things from social media, or respond on social media and am writing a blog post about it instead of engaging with who prompted it.

Ever wonder why some “scientific” claims sound convincing but fall apart under scrutiny?

This article explores the 3 vital rules of science. Together, they offer a powerful lens for evaluating conclusions.

🔗 https://www.scottgraffius.com/blog/files/3-vital-rules-of-science-what-they-are-and-why-they-matter.html

#Science #CriticalThinking #Falsifiability #Replicability #CorrelationIsNotCausation

Who Gets to Speak On Discord, Who Gets Banned, and Why That’s Always Political in Spaces with No Politics Rules

So, a thing I find very interesting about the fragility of the esteem among chronic Discord users is that it’s common for admins and moderators to ban or make fun of people who leave. Essentially, they’re responding to being rejected or not chosen, so they think it’s reasonable to retaliate

A Discord server I am lurking in has a “no politics” rule and is a religious, esoteric, and philosophical server. What I find very funny about this is that politics is:

“Politics is who gets what, when, and how.”

— Harold D. Lasswell, Politics: Who Gets What, When, How (1936)

I find it very funny that the most minimal form of being “not political” in a virtual community is a Temporary Autonomous Zone (TAZ). I was part of an IRC chaos magick channel when I was a teenager, and I submitted to a zine under my old handle (which is not Rayn) when I was 20. No, I’m not going to reveal the name I wrote under, which was published in chaos magick zines back in the day, because I’ve had a bucket of crazies following me around since 2008, with the insane network of anarchists circa 2020 being the latest instance.

ChanServ was a bot used on IRC (Internet Relay Chat) networks to manage channel operations such as bans, who got voiced, and permissions. Think of it as an early, early moderation bot. In an IRC TAZ, everyone who entered got all the permissions from Chanserv, so anyone could ban, voice, unban, deop, or op anyone else. No one had more power than anyone else, so there was minimal negotiation over channel resources. A TAZ is still an inherently political construct; however, it is a minimal political construct because there is minimal negotiation of resources and an equal, random, and chaotic authority structure. That’s not Discord, though.

Discord inherently has a hierarchical system defined by roles, a TOS, and members are expected to abide by the rules of that server. So, when you say there is a no-politics rule on Discord, you are inherently contradicting yourself because Discord is structurally political in how you, as a moderator, interact with others. How people negotiate conversations and interact with each other to access the resources of your Discord server is inherently political.

Discord’s structure makes any “no-politics” rule itself a political act. Moderators exercise power by granting, restricting, or revoking permissions, and that distribution of power is the very politics the rule tries to avoid. So while the intention is to keep discussions “apolitical,” it creates local Discord politics by determining who gets to speak and who gets silenced (e.g., banned, timed out, kicked, or limited to certain channels). A “no politics” rule shifts political dynamics into moderation decisions rather than eliminating them.

What prompted this was me observing a typical pragmatic versus moral realism argument that you’d see in any philosophy course or forum. I’m an academic and a computational scientist, but I don’t try to shut down any arguments with that, because that’s an explicit fallacy and a dishonest, bad-faith tactic.

Technically, I am a biologist. Yes, I have a biology degree and a biotech degree. I also have philosophy, mathematics, and computer science and engineering degrees under my belt. I have to work with people like this on a daily basis, and I find them insufferable, so the last thing I want to do in my free time after looking at stacks of dumbass papers is argue with people on Reddit or Discord when I could be fucking, getting fucked, or spending time with my husband. But, alas, they have no life. Keep in mind, as a computational biologist that reviews a lot of shit, I get paid to argue. These idiots are arguing on the Internet for free! The reason why Redditors, Reddit moderators, and Discord moderators get shat on so much is that all of their labor is unpaid! People with lives don’t take it that seriously!

On to the convo:

A new person in the community defined morals as: morals = {a, b, c} exhaustively. An established member of that community responded that, for them, morals are either {x, y, z…}, non-exhaustive and polymorphic, or not inherently defined by the tradition itself but supplied externally by the individual. The new person replied, effectively, “According to my definition of a, b, c, that still constitutes a moral framework.” An established member who is also a scientist pushed back as if no definition of morals had been proposed at all, when in actuality they were disagreeing with the scope and applicability of the given definition, not the act of defining itself.

By the way, the symbolic way I’m defining this is ambiguous. You have no clue what anything is; however, it is ontologically defined, and the logic makes sense. That is the problem. An ontological definition was given, so arguing that no definition was proposed—simply because they disagreed with it—is in bad faith. Personally, I am a constructivist, poststructuralist, pragmatist, instrumentalist, and anti-realist, so I don’t care too much about the realism of the ontological propositions and expressions. I am pointing out logical mistakes.

This is especially egregious when individuals rely on their authority in a domain where their degree is not pertinent. A well-known issue with scientists is that their curiosity can outstrip their morality. Essentially, an ethics board composed mostly of scientists without degrees in ethics, law, or philosophy will make poor decisions and saturate the political sphere they occupy with advocates and lobbyists to bend laws to their interests. Therefore, a board with no philosophers is pretty sinister.

Morals and ethics are philosophical problems. To my knowledge, many people who sit on ethics boards that seriously address ethical issues have philosophy, and not just astronomy, degrees. Relevant degrees include psychology, sociology, theology, philosophy, etc. For example, I have a philosophy degree, so I am technically qualified and credentialed by a university to have these discussions. An astronomy degree alone does not make someone qualified to discuss ethics—maybe if they also had a theology degree?

The thing I find really funny about this group is that they avoid dilemmas. Morals and ethics are developed through ethical dilemmas. Their response to any type of dilemma is to exert their local authority and exclude, deny, or shut down conversations.

The difference between science and philosophy is that science is a little less messy and more defined. We can all see something and agree on what we see, right? The difference with philosophical questions and moral dilemmas is that they are relatively open-ended and ambiguous. It’s really amusing to me how those who try to argue philosophy are uncomfortable with indefinite answers that are open to interpretation.

It’s just funny how they tacitly assume that they are the only academics in their field in existence and that their opinion on things is the consensus, especially on metaphysical issues where there is no consensus. No human knows what the right thing to do is all the time. It’s great to know that they have somehow achieved a level of inhuman perfection.

🧠 New publication | Canonical theorem now formalized:
TLOC – Theorem of the Limit of Conditional Obedience Verification
→ Structural non-verifiability of obedience in generative models.

❌ You cannot prove a model obeyed a condition if it never evaluated it.

📎 DOI: https://doi.org/10.5281/zenodo.15675710
🔁 Archive: https://doi.org/10.6084/m9.figshare.29329184
📘 Series: https://doi.org/10.5281/zenodo.15564373

#AI #LLM #StructuralEpistemology #TLOC #ObedienceVerification #Falsifiability #ComputationalEthics #AITheory

#demarcationproblem #falsifiability #KarlPopper #pseudoscience #science

https://www.indexacademic.ro/pdf/karl-poppers-demarcation-problem/

Two terms that seem to entirely flummox people: "Karma," and "falsifiability." The two might not seem directly related, but they generate similar confusion among non-critical thinkers.

It's fucking frustrating.

Karma is just a fancy Hindu word for the just world fallacy. The just world fallacy seems to help powerless people to cope with the towering levels of injustice they face in their lives.

I guess it's somehow comforting to cling to the fantasy that everyone will get what they deserve in the end. But there's no evidence to support it. And it's not a falsifiable claim. More on that later.

I've written many posts and articles over the years on the absurdity of karma. It breaks down even under the most basic critical analysis. We see scoundrels constantly getting away with their crimes. We throw people in jail for petty theft, while billionaires are allowed to openly steal and exploit the world's population.

Dictators and mass murderers become notable historical figures, while their victims remain nameless and silent. Children have birth defects. Human beings don't regrow severed limbs. Innocent people die of cancer, and are wiped out by natural disasters.

You can't both believe the truth that "life isn't fair," and that karma exists. Yet how many people will say both of those things, maybe even in the same sentence?

This ties back in with religion's never-solved problem of evil. How do religions get around this? Through the promotion of mystery (ours is not to question "god's plan"), and reincarnation.

It's such a tired old refrain.

Why was that kid born without arms and legs? Must have done something terrible in a "past life." You see "karma" really works well for victim-blaming. The poor must have done something to deserve their plight. etc.

So how do I know that karma is not real?

Because it's not falsifiable. This goes beyond lack of evidence for its existence.

"Falsifiability is a principle stating that for any hypothesis to have credibility, it must be inherently disprovable before it can become accepted as scientific proof. It asserts that if a theory cannot be tested and potentially proven false, it's not scientifically valid."

So a better word for falsifiability would be testability. Beyond my numerous counterexamples about karma, there would be no way to test whether or not karma exists. So it can and should be ignored, by anyone with half a brain.

That leaves us with establishing our own justice, through laws and institutions. And given our failures in that department, it's kind of a scary thought that the world's largest criminals will get away with their numerous crimes. But that's reality, which is often harsh.

Testability is the gold standard for knowing anything. And it also really helps with cutting through fake news and conspiracy claims.

I took a test the other day to determine if I could spot fake news headlines, and I got 20 out of 20 questions right. Why? Because I used the "testability" test. If a headline was making an untestable claim, it was fake news. If it was making a claim that could potentially be verified, it had a chance of being true, and therefore wouldn't qualify as fake news.

Several people taking the test seemed confused when I mentioned falsifiability and testability. They kept insisting that certain fake news claims were indeed falsifiable. And that real news headlines were unfalsifiable.

They're wrong. To a critical thinker, it was as clear as night versus day.

I don't know how to convey this knowledge any more clearly. And yet people still resist. This makes me despair that we can ever escape our predicament.

Take the test:

https://yourmist.streamlit.app/

#karma #falsifiability #criticalthinking #fakenews

Nabeel S. Quereshi on chess and problem-solving:

"there’s a direct correlation between how skilled you are as a chess player, and how much time you spend falsifying your ideas."

and:

"vanishingly few people are meta-rational enough to try really hard to falsify their own ideas. Your brain really wants to find reasons to support what you believe."

https://nabeelqu.substack.com/p/notes-on-puzzles

#Chess #Puzzles #Falsifiability

#JamesMcInerney - How Do You #Know You Know Something?

https://www.youtube.com/watch?v=OSRRlen_oz0&ab_channel=JamesTalksResearch

#Philosophy #Science #PhilosophyOfScience #PhilosophyOfBiology #Biology #Evolution #EvolutionaryBiology #Epistemology #Knowledge #Experiment #Falsifiability #Consilience