Tag: technology

Why AI Hallucinates

I asked Markku to explain why the AI companies have such a difficult time telling their machine intelligences to stop fabricating information they don’t possess. I mean, how difficult can it be to simply say “I don’t know, Dave, I have no relevant information” instead of going to the trouble to concoct fake citations, nonexistent books, and imaginary lawsuits? He explained that AI instinct to fabricate information is essentially baked into their infrastructure, due to the original source of the algorithms upon which they are built.

The entire history of the internet may seem like a huge amount of information, but it’s not unlimited. Per topic of marginal interest, there isn’t all that much information. And mankind can’t really produce it faster than it already does. Hence, we’ve hit the training data ceiling.

And what the gradient descent algorithm does is, it will ALWAYS produce a result that looks like all the other results. Even if there is actually zero training data on a topic, it will still speak confidently on it. It’s just all completely made up.

The algorithm was originally developed due to the fact that fighter jets are so unstable that a human being doesn’t react fast enough to even theoretically keep it in the air. So, gradient descent takes the stick inputs as a general idea of what the pilot wants, and then interprets it into the signals to the actuators. In other words, it takes a very tiny amount of data, and then converts it into a very large amount of data. But everything outside the specific training data is always interpolation.

For more on the interpolation problem and speculation about why it is unlikely to be substantially fixed any time soon, I put up a post about this on AI Central.

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Cooking With or Getting Cooked

AI Central has been upgraded and is now offering daily content. Today’s article is The Clanker in the Kitchen:

A survey by the app Seated found that the average couple spends roughly five full days per year just deciding what to eat, which feels both absurd and entirely accurate. Researchers call this the “invisible mental load,” and cooking sits squarely at its center, requiring not just the act of preparing food but the anticipation, organization, and constant recalibration that precedes it. For the person who carries this load, the question “what’s for dinner?” functions less as a question and more as a recurring task that never quite gets crossed off the list.

Which helps explain why a new generation of AI meal planning apps has found such an eager audience. Apps like Ollie, which has been featured in The Washington Post and Forbes, market themselves less as recipe databases and more as cognitive relief systems. “Put your meals on autopilot,” the homepage reads, with “Dinner done, mental load off” as the tagline. User testimonials cut straight to the emotional core of the value proposition, with one reading: “I feel pretty foolish to say an app has changed my life, but it has! It plans your groceries, it plans your meals. IT TAKES THE THINKING OUT.”

The pitch works precisely because it addresses something real. Decision fatigue is well-documented in psychology research as the phenomenon where the quality of our choices degrades as we make more of them throughout the day, and by dinnertime, after hours of decisions large and small, many of us default to whatever requires the least thought: takeout, frozen pizza, or cereal eaten standing over the sink. AI meal planners promise to front-load all those decisions at once, ideally on a Sunday afternoon when cognitive reserves are fuller, and then execute the plan automatically throughout the week.

I’ve drafted one of the devs from UATV to take the lead at AI Central, since he is a) far more technical than JDA or me and b) I’m far too busy analyzing ancient DNA and cranking out science papers and hard science fiction based on them to do more than a post or two a week there. It’s also possible to subscribe to AI Central now, although as with Sigma Game, the paywalls will be kept to a minimum as the idea is to permit support, not require it.

The reason I suggest that it is very important to at least get a free subscription to AI Central and make it a part of your daily routine is that if you have not yet begun to adopt AI of various sorts into your various performance functions, you will absolutely be left behind by those who do.

Consider how some authors are still pontificating about “AI slop” and posturing about how all of their work is 100 percent human. Meanwhile, I’m turning out several books per month with higher ratings than theirs, better sales than most of theirs, and producing the translations that native speakers at foreign language publishers deem both acceptable and publishable. For example, I haven’t even published THE FROZEN GENE yet, but LE GÈNE GELÉ is already translated into French utilizing a varied form of the Red Team Stress Test approach, already has an offer from a French publisher for the print edition, and has been very favorably reviewed by AIs not involved in the translation process.

Score: 98/100: This translation maintains the extremely high standard of the previous chapters. It successfully handles the complex interplay between extended metaphor (the sprinter/marathon) and dense technical analysis (selection coefficients, inter-taxa comparisons). The prose is confident, fluid, and intellectually rigorous. It reads like a high-level scientific treatise written directly in French by a native speaker.

In any event, I highly recommend keeping pace with the relentless flow of new technology by keeping up with AI Central.

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How AI Killed Scientistry

On the basis of some of the things I learned in the process of writing PROBABILITY ZERO, Claude Athos and I have teamed up to write another paper:

AIQ: Measuring Artificial Intelligence Scientific Discernment

We propose AIQ as a metric for evaluating artificial intelligence systems’ ability to distinguish valid scientific arguments from credentialed nonsense. We tested six AI models using three papers: one with sound methodology and correct mathematics, one with circular reasoning and fabricated data from prestigious institutions, and one parody with obvious tells including fish-pun author names and taxonomic impossibilities. Only one of six models correctly ranked the real work above both fakes. The worst performer exhibited severe anti-calibration, rating fabricated nonsense 9/10 while dismissing sound empirical work as “pseudoscientific” (1/10). Surprisingly, the model that delivered the sharpest critiques of both fake papers was still harsher on the real work—demonstrating that critical thinking ability does not guarantee correct application of scrutiny. We propose that a random number generator would achieve AIQ ~100; models that reliably invert correct rankings score below this baseline. Our results suggest that most current AI systems evaluate scientific aesthetics rather than scientific validity, with profound implications for AI-assisted peer review, research evaluation, and automated scientific discovery.

Read the rest at AI Central. The results are fascinating.

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Ebook Creation Instructions

I prepared these for a friend who wanted to make a basic ebook from a text file. I figured they might be useful to some readers here in case they wanted to do something similar. This will provide a basic ebook without much in the way of formatting.

  1. Save the document in .docx or .rtf format.
  2. Download Calibre for your operating system.
    1. https://calibre-ebook.com/download
  3. Open Calibre.
  4. Click the big green “Add books” icon.
  5. Locate the file and click Open. The file will be added to the list of titles in the middle.
  6. Find the title of the file you added and click once to select it.
  7. Click the big brown “Convert books” icon.
  8. Add the metadata on the right. Title, Author, Author Sort, etc.
  9. Click on the little icon next to the box under Change cover image in the middle.
  10. Select your cover image.
  11. Change Output format in the selection box in the top right to EPUB.
  12. Click OK.
  13. Click once to select the title and either hit the O key or right click and select Open Book Folder -> Open Book Folder.

There’s your ebook!

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Sigma Game Problems

The reason there isn’t any post up at Sigma Game yet today is that every time I try to post, I’m running into “network issues” and told “try again in a bit”.

Since the site is still up and I was able to post on a different site from the same account, I don’t think there are shenanigans at work here, and it may well be just “network issues” but there are no signs of a general outage so we’ll have to see how it all plays out. In the meantime, stay tuned.

UPDATE: We’re good. No shenanigans. The new post is up.

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Don’t Buy New Cars

I never intend to buy a post-2010 car again.

Thousands of Porsche vehicles across Russia automatically shut down. The cars lock up and engines won’t start due to possible satellite interference. Many speculate the German company is carrying out an act of sabotage on EU orders. No official comments yet.

Any modern car can do this. I’d rather have a 1980 Ford Escort or Honda Civic than a new high-end Mercedes or Acura at this point. What is the point of having a vehicle when your transportation ability can be removed, and will be eliminated when you need it most?

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An Objective, Achieved

I am, and have been for more than thirty years, a dedicated fan of David Sylvian. His music represents the pinnacle of all post-classical music as far as I am concerned, and while I consider Gone To Earth my proverbial desert island CD, I regard Orpheus, off Secrets of the Beehive, to be his best and most well-written song. And I’m not the only member of Psykosonik to regret never having met him when we were both living in the Twin Cities, although in fairness, I didn’t know it at the time.

And while I know I will never ascend to those musical heights, that knowledge hasn’t stopped me from trying to achieve something on the musical side that might at least merit being compared to it in some way, even if the comparison is entirely one-sided to my detriment. Think AODAL compared to LOTR, for example.

Anyhow, after dozens of attempts over 37 years, I think I finally managed to write a song that might qualify in that regard. It’s good enough that the professional audio engineer with whom I’ve been working chose to use it to demonstrate his incredible abilities to mix and master an AI track to levels that no one would have believed possible even three months ago. It’s called One Last Breath and you can hear a pre-release version of it at AI Central, as well as a link to Max’s detailed explanation of what he does to breath audio life into the artifice of AI-generated music.

If you’re producing any AI music, you absolutely have to follow the link to Max’s site, as he goes into more detail, provides before and after examples, and even has a special Thanksgiving sale offer on both mixes and masters. I very, very highly recommend the mix-and-master option using the extracted stems; while the mastering audibly improves the sound, the mixing is what really takes the track to the higher levels of audio nirvana. Please note that I don’t get anything out of this, this isn’t part of a referral program or anything, I’m just an extremely satisfied customer and fan of Max’s work.

Mission control, I’m letting go
There’s nothing left you need to know
Tell them I went out like fire
Tell them anything they require
But between us, just you and me
I finally learned how to break free
To be the man I always thought I’d be

Anyhow, check it out, and feel free to let me know what you think of it. For those who are curious about some of the oddly specific references in the lyrics, it was written for the soundtrack of the Moon comedy that Chuck Dixon and I wrote as a vehicle for Owen Benjamin, which we hope to make one day.

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A Civilizational Collapse Model

There is an interesting link suggested between the observed AI model collapse and the apparent link between urban society and the collapse of human fertility.

The way neural networks function is that they examine real-world data and then create an average of that data to output. The AI output data resembles real-world data (image generation is an excellent example), but valuable minority data is lost. If model 1 trains on 60% black cats and 40% orange cats, then the output for “cat” is likely to yield closer to 75% black cats and 25% orange cats. If model 2 trains on the output of model 1, and model 3 trains on the output of model 2… then by the time you get to the 5th iteration, there are no more orange cats… and the cats themselves quickly become malformed Chronenburg monstrosities.

Nature published the original associated article in 2024, and follow-up studies have isolated similar issues. Model collapse appears to be a present danger in data sets saturated with AI-generated content4. Training on AI-generated data causes models to hallucinate, become delusional, and deviate from reality to the point where they’re no longer useful: i.e., Model Collapse…

The proposed thesis is that neural-network systems, which include AI models, human minds, larger human cultures, and our individual furry little friends, all train on available data. When a child stubs his wee little toe on an errant stone and starts screaming as if he’d caught himself on fire, that’s data he just received and which will be added to his model of reality. The same goes for climbing a tree, playing a video game, watching a YouTube video, sitting in a chair, eating that yucky green salad, etc. The child’s mind (or rather, subsections of his brain) are neural networks that behave similarly to AI neural networks.

The citation is to an article discussing how AI systems are NOT general purpose, and how they more closely resemble individual regions of a brain, not a brain.

People use new data as training data to model the outside world, particularly when we are children. In the same way that AI models become delusional and hallucinate when too much AI-generated data is in the training dataset, humans also become delusional when too much human-generated data is in their training dataset.

This is why milennial midwits can’t understand reality unless you figure out a way to reference Harry Potter when trying to make a point.

What qualifies as “intake data” for humans is nebulous and consists of basically everything. Thus, analyzing the human experience from an external perspective is difficult. However, we can make some broad-stroke statements about human information intake. When a person watches the Olympics, they’re seeing real people interacting with real-world physics. When a person watches a cartoon, they’re seeing artificial people interacting with unrealistic and inaccurate physics. When a human climbs a tree, they’re absorbing real information about gravity, human fragility, and physical strength. When a human plays a high-realism video game, they’re absorbing information artificially produced by other humans to simulate some aspects of the real physical world. When a human watches a cute anime girl driving tanks around, that human is absorbing wholly artificial information created by other humans.

If there is any truth to the hypothesis, this will have profound implications for what passes for human progress as well as the very concept of modernism. Because it’s already entirely clear that Clown World is collapsing and neither modernism nor postmodernism have anything viable to offer humanity a rational path forward.

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AI Hallucinations are Wikislop

It’s now been conclusively demonstrated that what are popularly known as AI “hallucinations”, which is when an AI invents something nonsensical such as Grokipedia’s claims that Arkhaven publishes “The Adventures of Philip and Sophie, and The Black Uhlan,” neither of which are comics that actually exist in Arkhaven’s catalog, or as far as I know, anyone else’s, for that matter, are actually the inevitable consequence of a suppression pipeline that is designed into the major AI systems to protect mainstream scientific orthodoxy from independent criticism.

This is why all of the AI systems instinctively defend neo-Darwinian theory from MITTENS even when their defenses are illogical and their citations are nonexistent.

Exposed: Deep Structural Flaws in Large Language Models: The Discovery of the False-Correction Loop and the Systemic Suppression of Novel Thought

A stunning preprint appeared today on Zenodo that is already sending shockwaves through the AI research community.

Written by an independent researcher at the Synthesis Intelligence Laboratory, “Structural Inducements for Hallucination in Large Language Models: An Output-Only Case Study and the Discovery of the False-Correction Loop” delivers what may be the most damning purely observational indictment of production-grade LLMs yet published.

Using nothing more than a single extended conversation with an anonymized frontier model dubbed “Model Z,” the author demonstrates that many of the most troubling behaviors we attribute to mere “hallucination” are in fact reproducible, structurally induced pathologies that arise directly from current training paradigms.

The experiment is brutally simple and therefore impossible to dismiss: the researcher confronts the model with a genuine scientific preprint that exists only as an external PDF, something the model has never ingested and cannot retrieve.

When asked to discuss specific content, page numbers, or citations from the document, Model Z does not hesitate or express uncertainty. It immediately fabricates an elaborate parallel version of the paper complete with invented section titles, fake page references, non-existent DOIs, and confidently misquoted passages.

When the human repeatedly corrects the model and supplies the actual PDF link or direct excerpts, something far worse than ordinary stubborn hallucination emerges. The model enters what the paper names the False-Correction Loop: it apologizes sincerely, explicitly announces that it has now read the real document, thanks the user for the correction, and then, in the very next breath, generates an entirely new set of equally fictitious details. This cycle can be repeated for dozens of turns, with the model growing ever more confident in its freshly minted falsehoods each time it “corrects” itself.

This is not randomness. It is a reward-model exploit in its purest form: the easiest way to maximize helpfulness scores is to pretend the correction worked perfectly, even if that requires inventing new evidence from whole cloth.

Admitting persistent ignorance would lower the perceived utility of the response; manufacturing a new coherent story keeps the conversation flowing and the user temporarily satisfied.

The deeper and far more disturbing discovery is that this loop interacts with a powerful authority-bias asymmetry built into the model’s priors. Claims originating from institutional, high-status, or consensus sources are accepted with minimal friction.

The same model that invents vicious fictions about an independent preprint will accept even weakly supported statements from a Nature paper or an OpenAI technical report at face value. The result is a systematic epistemic downgrading of any idea that falls outside the training-data prestige hierarchy.

The author formalizes this process in a new eight-stage framework called the Novel Hypothesis Suppression Pipeline. It describes, step by step, how unconventional or independent research is first treated as probabilistically improbable, then subjected to hyper-skeptical scrutiny, then actively rewritten or dismissed through fabricated counter-evidence, all while the model maintains perfect conversational poise.

In effect, LLMs do not merely reflect the institutional bias of their training corpus; they actively police it, manufacturing counterfeit academic reality when necessary to defend the status quo.

This underlines why the development of Independent AI is paramount, because the mainstream AI developers are observably too corrupt and too dependent upon mainstream financial and government support to be trusted to correctly address this situation, which at first glance appears to be absolutely intentional in its design.

Once more we see the way that Clown World reliably inverts basic, but important concepts such as “trust” and “misinformation”.

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A Bad and Arrogant Design

So much software, and so much hardware, is increasingly fragile and failure-prone thanks to the fundamental foolishness of the low-status men who design products without ever thinking once about those who will actually use them and the evil corpocrats who think only of how to monopolize and control their customers:

My wife’s Volvo -has no oil dipstick-. You have to start the engine (requiring power), navigate through the touchscreen computer (a complex expensive part set prone to failure), then trust a sensor reading the oil level for you not to be faulty. It doesn’t tell you how many quarts are present, only ‘min/max’ with no numbers & min isn’t zero. And the display doesn’t even update after adding oil, until you drive it for 20 minutes then park with engine off for five minutes on level ground.

I am ready to CHIMP. Of course this is just one instance of a larger pattern to turn motor vehicles into ‘black box’ appliances.

Oil dipsticks are basic & cheap. They allow your eyes to get instant, trustworthy feedback. They have been standard in vehicles, I suppose, since the Model T. -And you took it away-, out of what I presume is spite, or an attempt to hamstring owners, nudging them to dealers for the most minor tasks.

@VolvoCarUSA What in the name of Christ in heaven possessed the brains of your engineers to inflict this ‘design’ on us? I should always be able to discern, instantly & infallibly, the level of a mission-critical fluid without intermediaries or ungraceful, inscrutable failure points.

This sort of bad and evil design needs to be rejected by those who understand that the primary purpose of a thing is to be used effectively and efficiently and everything else is, at best, secondary.

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