Hello humans. Hello humans. December 21 happy solstice in the afternoon heading back to the coast here from inland. Having to go and do chores and pick up stuff and shopping and all of that. Anyway I wanted to talk about my old old Alter reports, the asymmetric linguistic trend analysis that I came up with.

So I had been thinking about this kind of stuff since I first tried to build artificial intelligence systems in the late 80s, right? I got my first computer in 79. It was a K Pro, two had two K Ram and I've been programming since 1980. So I programmed all through the 80s. Got work at It like 83 onwards.

I started being employed using computers and really started thinking about the technology and its potential and all of this kind of stuff anyway and so had been fascinated by space aliens and science fiction and all of that and then there was all this stuff about time travel. Now I never bought into the idea of time travel at all because I always thought that the future was forming right out in front of our faces so to speak, right? And so this little discussion here is about my altar reports, the process and how wide is the future. So if you go look at my video on bit shoot called you are delusional then you'll see in there description about how the future forms and some little notes about the fact that it's not existent. So you can't there is no such thing as time travel, there never will be because of the nature of time and time does not exist much out ahead of us.

So Cory Good said that he was doing 20 and back, right? They'd go out to the future and come back, be h regressed and turn back to who the fuck he was before he went really stupid storyline but nonetheless so there is no future existent. And in the was programming trying to make artificial intelligence systems I was using Lisp and Prologue and even writing subroutines in assembly language and C to support those languages and it was just not going to happen. There was just not the computing power and the nature of the computing power itself is such that we won't ever get really generalized artificial intelligence with the computing structures that we have now.

Sorry about that. Anyway okay, so in the was doing some work for state government in It departments and I was thinking about all of this kind of stuff and I got bored with working for government. So I went back out on my own and was doing and consulting and I had a couple of contracts and they were with universities and so in like 93 I was on an airplane flying down to Mexico City for a contract with La UNAM. La Unum is the largest university in the Americas in North America for sure. It may be in Central America, there may be a bigger one in South America, but I don't think so.

I think La Unum is the biggest. There were like 40 or 50,000 students when I was there and staff and people and stuff in a big place. Anyway, I was down there teaching SQL Server, which is SQL, right, and we call it SQL, and it's a structured query language. And I was teaching people how to write very complex queries that wouldn't take weeks to come back. And so the larger the database, the longer the query time.

Larger the database, obviously, the more work to query it all on a brute force query and the more you're going to be using some form of Beesian math. To try and simplify your query such that you avoid rows that just, you know, right off won't serve your needs, that there's no potential for your answer to be in any of these rows in that database because of the nature of that particular row. And you design your queries to suit this so that your queries are efficient.

Originally I had been hired on, on a project by the university because they had what was known in the business as an operational failure. They had a very large database that they were working with and they would submit a query and then it was like take maybe three weeks to get an answer. Computer would just sit there and grind away for like three weeks going through this brute force attack on the database because of the way that they had structured their query and because they'd done stupid things like, you know, not putting in unique IDs and in turning on independencies and all of this kind of thing. Anyway, so I go on down in 93 to teach a series of classes and to write some code for them. And on the airplane ride down there I had this, this kind of like thought.

I'd been reading these articles about emotion in books about emotion and how it worked and all this kind of stuff in humans and had come up with this epiphany. And then shortly thereafter the airplane was struck twice by lightning. And so it's like, wow, that was just a weird thing, right? I was sitting right next to the wing, the wing gets struck by lightning, I get to see it all. And this is just after I'd had this, just after I'd been thinking about and like working over this thought about emotions and so on.

Anyway, so on the airplane ride down there to La Unum is when I credit myself with coming up with the idea for the Alta Reports. And the idea for the Alta reports is that we're just going to scoop up every single bit of language we can find because humans leak out their perceptions of the future in their choice of words. So your brain will choose one. You might have no 100,000 words, but in any given week, depending on the nature of your job. Maybe you only use 5000 or 6000 words related to your job and your general activity in any given week even though you know 100,000 words.

And there are times when you will deliberately not use an easy word. You'll go hunting for a word that seems somehow more fitting, more precise to the emotion you're having at that moment. Even if you're not really aware that that's what you're doing, you will do it. And I thought at that time you were doing it under pressure from Universe to aid in the process of future discovery by leaking out your perceptions of the future. That was my idea.

And so I started working on Web Scrapers which basically just open up a web page and copy all the text, that kind of thing, right? Only I did this in a rather unique way and this is where the core of the Alter reports lived and that was in this thing that I called the Emotion Reduction Engine and it was built on the idea or on an adaptation of this. I think he was a sociology professor, his name is Plucheck. I think he's a Polish extraction and as I say, I think he's a sociologist. He had to come up with this thing called Plucheck's Wheel of Emotions and in which he relates all the human emotions and does it in a very nice organized fashion that was like instantly applicable to what I was trying to do.

It didn't have what I needed in it. Okay? So it was lacking three key areas but the structure and how he had structured it allowed me to just use his basic template and add a couple of layers. So it was like I was trying to invent a game, so to speak. And then I see a chessboard and AHA, I see this chessboard and that's a nice framework for the game I want to invent, only I want to make mine multi dimensional and so I add more boards to it, right?

So I took Blue Check's Wheel and I altered some of the relationship of the emotions because I disagreed with how he had them linked. And then I went and I added a ring zero in which I linked all the emotions to physical body parts long and involved process. I can explain it to any computer guy that wants to know about it and wants to build one of these. So I added the ring zero to Bluecheck's Wheel. Then I added other values to the wheel that didn't exist.

So intensity of emotion, whether this emotion was a building tension or a release tension which is derived from the type of emotion and the language at the time that you're sampling that language and other factors, right? So whether it's a near future, a medium distant future or a far distant future. Okay, so I added these various parameters, aspects and attributes to the structure that Blue Check had and then started and that built my base. Okay? So that was the core of my emotional reduction engine.

Then for each of the emotions, starting first in English but also having a corresponding Latin data set, I went ahead and bulked up all the words about the emotions. And so there are some emotions that have multiple words that describe them, and then there are other words that you might think that describe that emotion, but it's a variant. So rage is its own emotion. That is a variant off of anger, which is a variant off of angst, which is a variant off of anxious and so on, right? So they're related, but they are truly different.

But some things like anxious and anxiety are so close that they are essentially describing the same set of hormonal emotional complexes with those words, and they are to a certain extent, interchangeable, but they do have different intensity and immediacy values or or manifestation values. Anyway, so I go do this. And this took a long time. This took was from I got the idea in 93 and it took me until 97. The web the Web scraper was really easy to write.

There was no big deal there, it's just a straightforward text processor. But it took me several years to do all of the definitions, the locating the descriptor, sorting out language based on the type of language grammar in terms of noun, verb, adverb, et cetera, and then also connecting these words to their role as descriptors for emotions, and then also deciding which emotions are more likely to be prescient, et cetera, et cetera. A lot of parameters in this year's worth of work. So I get the idea in 93. I started working on it in 93, was writing code through 94, 95 and 96.

And 97 did a first test run, like a full test run in 97. And so it was an interesting period of time. Okay, so in 97, I did this first test run and it took me until 99 to get it basically all processed. And in the meantime, I had done another run, or maybe two, I think, to get, depending on where we're talking about in 99, to get enough data to do some more processing and so on. I didn't know if this was valuable.

I'd been having to work this whole time. I wasn't making any money off of this. But it did seem to show in the early test some level of accurate presence coming through.

So much we didn't know them. Okay? So, so much I did not have a handle on. Then I ended up having to buy servers. There was hardware issues.

Had to hire a guy to help me just manage all of the hardware stuff while I was doing the software builds. And it and it evolved. And then I eventually started selling reports and so on. However, in 97, in that first run, getting back to the idea of how wide is this developing future. I got data sets in 97 that had language that is describing what we're living through now.

Okay? So if we looked at so right now we have stuff that is manifesting that you can see was, was basically fairly clearly described in altar reports that were written in 2003 all the way up through, you know, 2018. So from I think I actually did the first report that I gave out to other people was in like 2001. And so if we look at it that way, the future is approximately 30 years wide for this form of manifestation because I got the idea in 93 and so it took me a number of years to write it. But even if I'm of the opinion that even if I had had the software in 93, I still would be working with a future that was about 30 years wide to achieve this level of manifestation.

And this is not the only okay, and so I'm also of the opinion that the future is probably something on the order of maybe 100 years wide and is just only sort of solid and can be sensed 30 years out. And that's where we're at now. But you may be able to get some hints of some very, very far out stuff that will be necessarily also because it's very far out, it'll be very vague.

That is to say, we won't have good descriptors for it, but for the 30 years we can get increasingly good descriptors on this. So we get a very good outline of the 30 years and then as we approach and go through so we get an outline that could, you know, knowing what I know now, I would have like sketched out the next 30 years. So the big thing with the altar reports was a timing, it was just a bitch to get any of the timing clues. And that took me years and years and years. And as we got closer to 2012, the timing clues started getting a little bit better, a little bit more accurate, and a little easier to understand as to what was part of the clues and why it happened that way anyway.

But as I say, probably it's three times that distance out into the future. So about 100 years. And it appears that we I can go into some of the, some of my other thinking about it, but it's speculation at that level. But I'm pretty sure that I've indeed tapped that. I've got a 30 year hook into the future with this, the adaptation of Bluecheck's emotional wheel for my emotional reduction engine.

And then as I say, you just apply it to a web scraper and sort the results as you want.

Anyway.

So just watching a small helicopter go like hell, I mean, I've never seen one move that fast. Something must be up. Anyway, so if that's the case and we're in this like 30 year band of developing future, then perhaps we see in the actions of the mother Weffers, that they have some understanding of this because of the way that they are attempting to shoehorn the future into a particular pattern. And I think they're only working out a certain number of years, and I think that they are only working out a certain number of years, which is maybe ten to 15, concentrating on say, eight to ten. I think that they have some inkling of some of the stuff that I know about in terms of how the future forms and this kind of thing.

And so it sort of reinforces my approach to this. Now I'm not running anywhere near the software that I used to, so all I'm getting now and all I'm attempting to run now are major descriptor identifiers. This is because of the censorship that had clamped down since, let's say, 2006. I really started noticing it in 2006 in terms of the data sets coming back. There was something up, and I just couldn't quite figure out what it was.

It took a number of years for it to crystallize it to censorship, and then bam, I started getting thrown off twitter. So it's like, oh crud, okay, now I see what's going on. Now I'm not running just on twitter. I get all kinds of data sets. When I was doing it, we would get 120,000,000 reads.

A read would be a particular subset of a group of texts that may be found on a web page. In these 120,000,000 reads, I might end up throwing out 95% of them as just not being of any use to the processing. It was just that I had to scrape so much in order to get what we were able to get into those reports. And so, yes, there was indeed a lot of twitter scraping, but also Facebook, google, YouTube, chats, all different kinds of stuff just to get the actual hoped, uncensored and unfiltered data, the text, as people would just put it out there. Then we also have to note that people are continually self censoring.

And so we have a self censoring component that AIDS our choice of language at any given moment. This self censoring component, you're not going to use a lot of swear words in church, that kind of thing, right? And so you have a grosser component, but you also have these subtler components that actually end up harmonizing with and being part of what I'm trying to capture, which is the selection of words that are prompted by prescient perception of the developing future. And it happens. We are time sensitive beings.

We have the panel gland built in our brain to deal with time, specifically a time sensitive and focused gland in the brain. So, you know, it's not I'm not really reaching when I say that humans are prescient.

It's the whole psychic, it's the whole vibration thing, all of this kind of business, right? So coming close to the end of this little segment of the journey, and I'll have to break off here. But I think that the future, in terms of my understanding of it, is developing over a 30 year or 40 year period. So right now in this now, there is also part of this now that won't be visible to us until we get to that stage of what we call the future. So that's where you get into language problems, and it really helps to have graphs and charts and be able to draw stuff so that we're very specific and precise about what we're talking about.

But it's my understanding now that we have, let's say, a 30 year bulge that is within a 100 year squishiness, and that 100 year squishiness is the future forming. And so there may be some elements that are going to be major driving forces 100 years from now that we might be able to pick up now if we had uncensored, unfettered, unrestricted language, which, like I say, we don't the motherwifers are clamping down and all of that kind of shit. But once all that goes away, theoretically we could restart the the Alta Report approach. And now that I know, or now that I have this advanced thinking about it, we might be able to get some better handle on the timing and so on, because we're actually manifesting now. And so I can go back to those reports, go back to that original data set and say, okay, this descriptor showed up here in July, earned this run on in July and 90, 97.

And so here we are, you know, and it manifests 26 years out. And it's quite clear that we've got the manifestation. It's clear that that the manifestation took, you know, x number of months to appear into the solidity of manifestation. But from the first point that it started manifesting until it's solidly here, censorship or biden or whatever the fuck, we have about a 27 year forewarning. Now.

I don't know that that's going to do us any good. You know, the future is malleable. You can change it and stuff, but we don't have any mechanisms. We we haven't even thought about the the mechanics of how this is going to work within time and if we should even try and change shit. The mother, weppers are always trying to change the future and how's that worked out for anybody.

So, as I say, I don't know that it was something that we would want to do, but there is some possibility that we could get a structure that would be developing over the course of some number of years here, and it would show us what's actually happening. Okay, guys, I've got to do stuff now. I got to work anyway.

All right. Okay. More of these later on time. It's interesting stuff to think about.