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Podcast | Disinfolklore Detector - Live Demo

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A listener asked: “I’m definitely interested to hear more in detail about how you use AI.”

This week’s work was precipitated by a new model, which Anthropic dropped last week. Opus 5.5 has, for reasons that will become clear, become a bit of a revolutionary model. By model I mean a neural network algorithm. I am describing the architecture and the algorithm behind what many of us see as a chatbot.

But there is a different way to use the model behind the chatbot. You reach it through a part of the computer that those of us old enough will remember from the 1980s and MS-DOS. It involves all of these little instructions. On a Mac it’s called Terminal. It is a part of our computer which is there, and which most of us never go to. It lets us reach these models in a way you cannot easily reach them through a chatbot.

Some of you will also remember that I have been going through the principles of Infolklore. I use many words from the transcripts of Volya, and I am trying to derive the principles of Infolklore from the way many of us present on Volya. Last week’s episode carried the latest of them.

The Meta Main Game

The aim of all of my work is what I call the Meta Main Game. Within about ten years, the assisted cognition we already experience is likely to be mainstreamed directly inside our minds. We experience it now by interfacing with the internet, through chatbots, through web browsers, through this very app.

Whether that is done through connectors implanted in us or through light, I don’t know. Obviously Facebook and Elmo are working on implants. I am agnostic about whatever technology is used. I hope it isn’t done through implants, because I think that’s pretty gross.

My goal is to prepare for that moment. I want to prepare a module that an interpreter of sensory data will use, through assisted cognition, to determine quickly whether the data she is encountering is Disinfolklore, Infolklore or Neutral. It should work for humans or for computers.

So it is an intermediary through which data streams. It lets us make decisions very quickly in the face of what I expect to be ever-accelerating vectors of information and sensory data. By sensory data I mean not just text, and not just music. I mean everything we encounter in our reality, including conversations, what people are telling us, and what we are feeling.

Disinfolklore, as we know, is folklore-like story weaponised to inject lies, exploitative mental models and coercive control into minds. Infolklore is its positive twin. Its divine twin, if you like. The same archetypal forms, used to protect communities and rebuild shared understanding.

Neutral is neither. It is a menu. It is an account of something that has no archetypal content. There is nothing good about it and nothing bad about it. It is just sensory data.

So the detector I am working towards must be able to tell these three types apart. The Disinfolklore Detector is the Meta Main Game’s first working form, and I am building it now. This case records one week of that building.

In this week a cheaper, much more efficient model let me run many different operations at once, as simultaneous conversations with the machine. Each of these operations has its own window on one state-of-the-art Mac. One window, the operator, keeps the others and the long-running jobs in order.

Visualise a big screen, or several screens. Each has one of these MS-DOS-like windows open in it. The one in the top left-hand corner is the operator, which helps organise all the others.

Two latent dreams

Two latent dreams lie behind all of my work, and behind my work this week.

One is a question I asked in the London law firms where I worked in the early 2000s. The other is what I conceived at a conference on artificial neural networks in Long Beach, California, in December 2017. That is where the Meta Main Game began.

Both needed people and resources, compute and money, ideas and technical capacities which I did not have. The first needed a firm’s worth of them. The second needed someone who could help build and train a neural network.

Now the machine I have available to me can be directed in plain spoken and written English. All it takes from me is a clear mind, one that can keep hold of all of these different vectors, all of these different aspects of the project.

So this week’s lesson is really about that mind. Multiplying the conversations drew me back into the loop, the place of the person who checks and steers what an automated system does. There the work was plain. I was keeping thirteen windows in step, 24/7. Fifty-eight of my messages to those windows that week were the same instruction: just carry on.

The operator window took that work over, a step at a time, as I taught it to report in terms I can see. Window titles, and a list of what to type where. What stayed mine were the rulings on meaning, the permissions and the signatures.

The case closes where the Meta Main Game began, in Luhansk and Long Beach in December 2017. It closes on the open question of whether I can, in fact, build this.

What I am building

The Meta Main Game is a means of assisted cognition. On 14 July I put it like this: “In the vision I have for the Meta Main Game, in, say, ten years, humans will have the capacity to add on cognitive modules, which will immediately enable them to see” in different ways. They will filter data in ways not available to us today. Those ways may not even be necessary for us today, but may well be necessary in ten years’ time.

Seeing through these modules is not just about reading. The module is meant to work on all sensory data: what we read, hear and see. At this point it is a hypothesis. It is an idea to work towards.

Three kinds of work feed it. The main one is Automating Disinfolklore. The second is what I call Cryptotypic Engineering. The third I deal with mainly in my Power of Mana work, at PowerofMana.net: the Reversion to the M-N- sound. That is the linguistics and the meaning work. I draw on it whenever I talk about Mana.

The engine of Automating Disinfolklore is a large language model that runs on a state-of-the-art Mac. The model I am running at the moment is a Meta model, Glimmer, released a few weeks ago. The work I do will be adaptable to whichever locally hosted large language models come along.

Glimmer reads a text under one codebook, the written rules for every verdict, and one brief, the instructions it is given for each reading. It judges what it reads with the Code of Positive Trolls: Generosity, Ethical Discipline, Patience, Perseverance, Focus and Insight.

Those are the criteria. If a text conforms to those six, it is Infolklore. If it violates any of them, it is Disinfolklore. If it triggers none of them, there is no archetypal content. It is just talking about the weather. Not the weather in a Jane Austen novel. Just the weather. There might be symbolic content there, but generally speaking there isn’t.

My rulings on real texts are the standard Glimmer is measured against. So I am teaching it to perceive reality like me. Mine is the only ruling.

Two bodies of Infolklore

The engine learns first on two bodies of Infolklore, about 47 million words in all.

One is a spoken reservoir, which we know about. It is an archive of broadcasts in natural spoken English by native English speakers. This show is among them. The other is an archive of two Ukrainian newsrooms writing in russian, in the form of the structured article.

Later it will meet Disinfolklore at scale, in an archive of about 290 million texts from state influence operations around the globe, from war bloggers and from fringe platforms. Dravidian, Hindi, Chinese, russian, MAGA.

The engine reaches people first through the Disinfolklore Detector. It is planned in five stages, from a page on my Mac, for me alone, to a tool that other peop…



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