Clif High - 100 Years of Squishiness

Dec. 21, 2022 - Clif High

22:46

100 Years of Squishiness

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	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 um Alta reports.
	The um 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 um artificial intelligence systems in the late 80s, right?
	So I got my first computer in 79.
	It was a K Pro 2, had 2K RAM, and I've been programming since uh 1980.
	And uh so I I programmed all through the 80s, got work at it, uh like 83 onwards.
	I started um uh being employed using computers and um really started thinking about the technology and its potential and all of this kind of stuff.
	Anyway, and so um had been fascinated by uh 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 uh forming right out in front of our faces, so to speak, right?
	And so this little discussion here is about my um Alta reports, uh the process, and how wide is the future.
	Uh so if you go look at my video on BitChute uh called um You Are Delusional, then it uh then you'll see in there uh description about how the future forms and some um uh little notes about you know uh the fact that it's not existent, so you can't uh there is no such thing as time travel.
	Uh there never will be because of the nature of time, and um time does not exist uh much out in ahead of us.
	So uh, you know, Cory Good said that he was doing 20 and back, right?
	That he'd go out to the future and and come back, be age regressed, and turn back to who the fuck he was before he went.
	Um really stupid uh storyline, but nonetheless, uh so there's there is no future existent, and in in um in the 80s I was programming trying to make um artificial intelligence uh systems.
	I was using uh Lisp and Prolog and even writing um subroutines in assembly language and C to support those languages, and um it was just not gonna happen.
	There was just not the computing power and the nature of the computing power itself is such that we won't ever get a really a um uh generalized artificial intelligence with the computing structures that we have now.
	Hey!
	Hey!
	Oh, sorry about that.
	Anyway, um, so uh in the 90s I was doing some work for uh state government uh 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 um was doing a consulting and I had a couple of contracts and uh they were with um universities and so in um in like 93 I was um on an airplane flying down to Mexico City uh for a contract with La UNAM.
	Uh La UNAM is the largest uh university in the Americas, uh in North America for sure.
	It it may be uh in central America, there may be a bigger one in um South America, but I don't think so.
	I think La Unam is the is the biggest.
	There was like there were like 40 or 50,000 students when I was there and and staff and people and stuff.
	I mean it in a big place.
	Anyway, um I was down there teaching uh SQL Server, uh which is SQL, right?
	That's 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, uh obviously the more work to query it all on a brute force query, and uh the more you're gonna be using some form of um uh Bayesian math to try and simplify your query such that you uh uh uh avoid rows that just uh you know right off won't uh won't serve your needs that there's no potential uh for your answer to be in any of these rows in that database because of the nature of that particular row,
	and you you design your queries to suit this so that your queries are efficient.
	Umrigally 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 uh 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 uh not putting in uh unique IDs and index and turning on indices and all of this kind of thing.
	Anyway, uh so um I go on down in '93 to uh teach the series of classes and to write some code for them.
	Um on the airplane ride down there, uh, I had this kind of like thought.
	I'd been reading these articles um about uh emotion and 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, you know, that was that was a just a weird thing, right?
	Uh I was sitting right next 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 um working over this thought about emotions and so on.
	Anyway, so um on the airplane ride down there to La Unam 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 gonna 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 a hundred thousand words, but in any given week, depending on the nature of your job, uh maybe you only use five or six thousand words related to your your job and your general activity in any given week, even though you know a hundred thousand 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, um, 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 um as a uh for 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 I started working on web scrapers, which you know, 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 um uh the core of the Ulta reports lived, and that was in this thing that I called the emotion reduction engine.
	And it was a um it was built on the idea of uh or on an adaptation of uh this um, I think he was a sociology professor.
	His name's Pluczyk, I think.
	Uh he's uh of Polish extraction, and as I say, I think he's a sociologist.
	Uh he had the uh come up with this thing called um uh Pluczek's uh 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 um three key areas, but the structure and how he had structured it allowed me to just use his basic template and um add a couple of layers.
	So it was like I was trying to invent a game, so to speak, and then I see a chess board, and uh aha, I see this this chess board, and that's a nice framework for the um for the game I want to invent, only I want to make mine multidimensional, and so I add more boards to it, right?
	So I took uh Pluchuk'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 uh emotions to physical body parts.
	Uh 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.
	But so I added the ring zero to Pluchek's wheel, then I added other values to the wheel that didn't exist.
	So uh intensity of emotion, whether this emotion was a building tension or a release tension, which is derived from the uh type of emotion and the uh language um at the time that you're sampling that language and and other factors, right?
	So uh immediacy, so whether it's um a near future, a medium distant future, or a far distant future.
	Okay, so I added these various uh parameters, aspects, and attributes to the very to the structure that Pluchek had, and then started uh and and that built my base, okay.
	So that was the core of my um uh uh emotional reduction engine.
	Then for each of the emotions, starting first in in English, but also uh having a corresponding uh Latin uh 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 uh rage is its own emotion that is a variant off of anger, uh, which is a variant off of uh angst, um, which is a variant off of anxious, uh, and so on, right?
	So there are they're related, but they are truly different.
	But some things like um anxious and anxiety are so close that they are essentially describing the same set of hormonal emotional um complexes with those words, and they are to a certain extent um interchangeable, but they do have different uh uh intensity and immediacy values uh or or manifestation values.
	Anyway, so I go do this, and this took a long time.
	This took was from I I got the idea in '93, and it took me until 97.
	The web the web scraper was real easy to write, there was no big deal there.
	Uh it's just a straightforward text processor.
	But it took me several years to do all of the um definitions, the locating the descriptors, sorting out language based on uh the type of language, you know, grammar in terms of noun, verb, you know, adverb, etc.
	And then also connecting these words to their role as um descriptors for uh emotions, and then also deciding which emotions are more likely to be prescient, etc.
	etc.
	A lot of parameters in this, you know, years worth of work.
	So I get the idea in 93.
	I started working on it in 93, was writing code through 94 and 95 and uh 96 and 97 did a first test run like a full test run in 97 and so that was um it was an interesting period of time okay so in 97 I 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 uh depending on where we're talking about in 99 uh to get enough data to do some more processing and so on.
	I didn't know if this was valuable, you know I'd been having to work this whole time.
	I I wasn't making any money off of this, but it did seem to show in the early tests some level of accurate prescience coming through so much we didn't know though.
	Okay, so much I did not have a handle on, and 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 uh 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 the 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 at um so right now we have uh stuff that is manifesting that you can see was was basically fairly clearly described in ALTA reports that were written in 2003 all the way up through you know 2018.
	So from I think I actually did the first uh report that I gave out to other people was in like uh 2001, and uh so uh if we look at it that way,
	uh the future is approximately 30 years wide for this form of manifestation because I got the idea in 93, and so um it took uh took me a number of years to write it,
	but even if I I'm of the opinion that 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 uh okay,
	and so it's I'm also of the opinion that the future is probably something on the order of maybe a hundred 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, very, very, very far out stuff that will be necessarily also uh 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 uh on this,
	so we get a very um good outline of the 30 years, and then as we approach and go through uh 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 the big thing with the Alta reports was the timing.
	It's 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, uh a little bit more accurate, and a little easier to understand as to what was part of the clues and and and why it happened that way.
	Sigh
	Anyway, um but as I say, probably it's three times that distance out into the future, so about a hundred years.
	Um, and it appears that we can go into some of the the some of my other thinking about it, but but it's speculation at that level, but I'm pretty sure that that I've indeed tapped a um that I've got a 30 year hook into the future with this uh Pluchex uh the adaptation of Pluchek's emotional wheel for my emotional reduction engine.
	Uh And then as I say, you just apply it to a web scraper and um uh sort the results as you want it's useless.
	Anyway, um 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 um in this like 30-year band of uh developing future,
	then perhaps we see in the actions of the mother wefers that they have some understanding of this because of the way that they are attempting to uh shoehorn the future into a particular pattern,
	and I think they're only working out a certain number of years, and I think that that um that they are only working out a certain number of years, which is maybe 10 to 15, uh concentrating on say uh eight to ten.
	I think that they have some inkling of some of the stuff that I know about in terms of uh how the future forms and this kind of thing, and so it sort of reinforces um my approach to this.
	Now, I'm not running uh 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 uh 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 into censorship, and then bam, I started getting thrown off Twitter.
	So it's like, oh crud, okay, now I see what's going on.
	Um, I'm not running just on Twitter, I get all kinds of data sets when I was doing it.
	Uh, we would get a hundred twenty million uh reads.
	A read would be a particular subset of a uh group of text that may be found on a web page.
	And and these uh 120 million reads, I might end up throwing 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 uh what we were able to get uh into those reports, and so yes, there was indeed a lot of Twitter scraping, but also Facebook, um uh you know, Google, uh, you know, YouTube chats, all different kinds of stuff, uh, just to get the actual um hopeful hoped uh uncensored and unfiltered uh data.
	Uh you know, the text as people would just put it out there, and then we also have to note that people are continually self-censoring, and so we have uh self-censoring component that aids our choice of language at any given moment.
	This self-censoring component, uh, you know, you're not gonna uh use a lot of swear words in church, that kind of thing, right?
	And so uh, you know, you have a a grosser component, but you also have these subtler components that 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 um perception of the developing future, and it happens, you know.
	We are uh time-sensitive beings, we have the pineal gland built in our brain to deal with time, uh, specifically a time uh sensitive and focused uh gland in the brain so you know it's not I I'm not uh really reaching when I say that you know humans are pressured and you'll you know I mean it's the whole psychic it's the whole vibration thing all of this kind

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