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Jan. 2, 2022 - Clif High
30:36
lumpy woo

history is lumpy. Should it be?

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Hello humans.
Hello humans.
Yeah, I got a new graphic back here.
Big map.
Interesting one too.
It presents the planet as though the center of it is the Pacific Ocean, which is of course the way that it's got to be viewed, just because the Pacific Ocean is the biggest thing we've got.
We also have some interesting stuff.
Got some seagulls attacking the house at the moment.
We have Antarctica separated off in its own little mini map area, which is very cool, and yet also referenced at the bottom of the map in the traditional spread apart view.
And so this is like a curved projection of the planet.
Let me get that that way, okay?
Anyway, and so here's the Canada, all North America, Mexico, et cetera, et cetera, and South America.
We're going to talk about some lumpy woo.
Okay, so in the early days of coding, programmers were like, you could be an itinerant programmer, right?
You didn't have to work for an organization as an employee if you didn't want to, because coding talent was so rare that you could just go from job to job to job, sort of freelancing, right?
And so I did that in preference to working in a nine-to-five job for places.
I've done both.
I've done a lot of the nine-to-fives.
Never lasted very long.
The politics and all of that shit just annoyed the crap out of me.
Anyway, though, so as a result of this, I got a good exposure to a lot of things because I worked for a lot of places.
And usually in the early days of the digital revolution, we were tasked with simply coding existing things they did such that it could be automated faster, data could be kept, yada, yada, yada.
And so we basically digitized what humans did at that point from like, say, oh, 1983 through about 1999 was the big push.
Towards the end of the 90s, there had already been a lot of applications done for people in terms of how they did things, and that was up and running.
So in the late 80s and early 90s, the focus shifted over to digitizing a lot of data and getting it out there in various different websites and repositories and so on, mostly from corporations and from government.
In the course of all of that period of time, I worked for very diverse groups of people coding things for them in a different number of languages.
But nonetheless, regardless of what the language is, as a coder, you had to learn what they did and know how they did it, especially those things they wouldn't tell you or didn't know to tell you, you know, that somehow made what they did more of an art than a science, because they would have inculcated it into their bodies, had built it into their operational flow of things, and wouldn't necessarily think about it, right?
Oh, you'd be looking at some operation, some job, and your software wouldn't work the way theirs, their actual operation did, and you keep trying to refine, refine, refine.
And then you discover, oh, look, they left out the fact that the guy on the job does X, Y, Z, and then he does Z again for this following reason.
And that was never included in any of the instructions from which you were developing the software.
And so you'd go there and you'd asked him, and he said, oh, yeah, if we do this, which was with a punch machine, and he says if you do it twice at the end of this series, it clears it for the next round, right?
And so, aha, if you're going to computerize it, you needed to do that because this particular machine that you're going to be computerizing against had this double function that was not in the manual.
That if you did the same thing twice without reloading the plate you were going to mash on, it cleared the registry of all these little pins that mash the plate into the shape you wanted.
So all different kinds of strange things that I would end up coding.
I coded this application for a insurance company, a couple of insurance companies, and reinsurance companies.
Okay, these are the reinsurance companies.
Actually, they should be called pre-insurance companies because they're the big boys behind the actual insurance companies when the insurance companies get themselves into a bind like they are now with all of the deaths in the United States.
Anyway, so, well, globally.
We're up 40% in the U.S. for one insurance company alone.
And I've talked to an actuary that works for, I won't say, but and he's telling me that that's more like 57% for their company.
and the death rates are tracking really close statistically time-wise to when the vax is available for which different age group.
This particular insurance company is very big in the Northeast, so they've got a lot of exposure to this and a lot of experience with it already.
Anyway, the insurance company that I'd worked for in the past, which actually got me a job with FERC, which was the Federal Energy Regulatory Commission, and I wrote software for nuke plant guys.
But before I did that, I worked for this insurance company, and I assisted them in adding expert systems, what we now call artificial intelligence, to their existing workflow and database of all of the actuaries.
And so basically what I did was to go, they had a really good actuary, he was near to retirement.
I went and interviewed him for a long time, wrote down every fucking thing he said, made it into software and into algorithms that we then applied to their internal in-house network that all the actuaries used because they were in a big expansion period.
New actuaries didn't have the history in the industry that the older guys did who were retiring.
And so the idea was to digitize the knowledge of these older guys before they left the company.
Anyway, so I talked to this one guy, and his big thing was he had this like love-hate affair going with an aspect of his work.
And you'd hear him say, I love lumps, just love them lumps.
And then later on, you'd hear him say some other time, oh God, he'd just bitch and moan, I hate lumpy data.
Oh, God, I hate lumpy data.
Just can't stand it.
Oh, drive me crazy.
Blah, blah, blah, blah, blah.
This guy's job was, he had a couple of different responsibilities.
He was head of the fraud division for this insurance company for a long time.
And he had been sort of retired into before, sort of pre-retirement, you know, like putting them on full pay and half duty kind of thing.
He was off inventing algorithms, and half the time I was interviewing him, but he had been in charge of all these algorithmic templates that the actuaries used.
And so he developed these over 30 or 40 years.
And basically he was taking kids out of school and making them into full-fledged company actuaries just in the course of a couple of years by these templates that he had developed that you could mathematically apply over different industries.
It was really cool.
The guy was real smart, this out of the southwest.
He had been in New Mexico and Arizona all of his life, I guess.
Real cowboy guy, but a real sharp actuary.
But he hated lumpy data and he loved lumpy data.
And initially I didn't understand what he was talking about.
I figured he'd been hitting the mezcal a bit too much.
And I was a kid, I didn't really know.
But here's the situation.
So this guy, when he had the job as being a security guy, he loved finding lumps in data because lumps betrayed anomalies.
And anomalies frequently led to the discovery of either fraud or a loophole in how they did things trying to protect the company's shareholders and not pay out money.
Bear in mind, that's all these guys do.
Okay, so I was rather shocked.
But basically, all the actuaries and everybody in the whole insurance company, all they do is fight to not pay out money in order to be able to give it to the shareholders.
So that's just the nature of the game.
Anyway, so this guy, he liked finding lumpy data when he was doing work as the head of security because, as I say, it pointed him to anomalies that were indicators to look at.
You know, universe telling me, look there.
And he said that, you know, he was really good.
I mean, you'd watch him do his work and he'd scan.
He'd had all these ledgers and the way they did things was all on paper at that point.
And he would, they'd be printouts and stuff, but he'd fold them in a particular way.
In fact, he didn't fold them.
He had two people, two women that were in charge with these devices of folding these complex spreadsheet kind of reports such that he could have one, two, three, or four of these things open and all on the same page, all in the same kind of data, not obscuring each other and showing him these patterns.
And I would say, look, there's a lump.
And I'd say, okay.
And then he'd say, you know, I've seen this place, this area in Wisconsin before, and this should not be there.
This is an anomalous area.
And then we'd go and find that report that this was the aggregate of, and sure enough, we'd find out that there were, you know, three boat sinkings or something that should not have occurred in that time of year, because it had never, ever, ever, ever happened before in that time of year in that region that five boats all sunk and there was no weather to account for it or something like this, right?
And then he says, okay, and then what he did was to call up the fraud guys in the division and he says, here's such and such a policy and such and such a policy, and there's four others in this region.
Go and find out, you know, where the accidents happened.
And if they were all in the same region or in the same lake, start investigating for fraud.
And sure enough, there were five accidents for five boats that had sunk, fairly expensive boats, fishing boats or something.
I didn't get the details.
I just saw the money amounts.
And they were all on two lakes.
And they all happened within like three months.
And so it's like, well, bing, bing, bing, you know, lumpy data, right?
Shit does not happen out of sequence that way.
Shit just doesn't happen that way.
Especially for actuaries, because these guys look for history, for long ranges of history.
And they're, you know, minutiae.
They really get into it.
Anyway, so he loved lumpy data that way, right?
But he hated lumpy data in his job of doing an algorithm, as designing algorithms.
And I understand it from that viewpoint because I've done that myself.
Designed algorithms for GEC Marconi, British Airlines, American Airlines, Boeing, Microsoft Consulting, numerous state governments, FERC, a Federal Energy Regulatory Commission, a number of companies involved in oil exploration, this sort of thing.
And so when you do this, you want to find a nice stream of steady data to design your algorithm around that's going to reproduce that data.
That's sort of the idea is that the algorithms are going to go in out and examine other data and get you a viewpoint that looks like this.
In other words, the programmer or the coder, the software designer asks the client, what do you want it to look like?
What do you want to actually see as a result of this examination?
And they tell you, and then you sort of reverse engineer it, right?
Oh, that spaceship looked like this and it did this.
And so, okay, we can reverse engineer it.
That's the sort of thing, right?
But it's very hard to do it the other way, where they say, well, I think maybe I want to do this or maybe I want to do that or so on.
You get into these morasses that lead to putting more people and money at something that should be easy and reasonably quick to do.
And so I had lots of employment because I could do that sort of thing.
I could figure out, you know, is this an issue where we actually had to have a team to do it or is this, they've got it tightly enough designed and so on.
So you have to lead the client through various different levels of progress to their end goal because frequently these guys did not know what they actually wanted to pay money for.
But when the actuary had to do it for his in-house clients and when I had to do the algorithm design for my clients, you run into this situation where if you have data that comes along and jumps up, it's like, okay, okay, damn it, now what the hell's going on?
Do I have to account for that?
How do I account for it?
What other input is causing this?
Is this seasonal?
Is it something that I have to account for all the time?
How much processing is it going to take to do this extra little whatever the fuck that caused this?
And so it becomes an issue, right?
So you don't want to see lumps.
You want to see your data in some kind of a smooth curve or whatever shape it wants to take, but you don't want these little protrusions or outliers because they cause you to have to re-examine what you think you know about the problem before you proceed.
Usually, basically, it's a case of you don't want more work, right?
It's like, oh, no, no, no, it's the end of the day.
I don't want to see that in the data.
Okay, so anyway, so we were examining the other day in the Lost in the Wu video about how we've lost or gained, depending on how you want to think about it, about a thousand years.
If you look at Fomenko's work, he says that the whole Christ episode and the Coptics and all of that business was about 1300 AD if we want to stick with 2022 as our current year.
I'm sort of saying the same thing by saying, well, we're not really in 2022.
We're really in like 892, right?
Something like that.
Anyway, so but our history has been mucked with, totally screwed over.
And so there's, if you look at the data, if you go through our history and you look for lumps, which we can now do because so much of the data has been digitized and put on in its minutia into various databases that are now connected to the search engines.
So I frequently use search engines to develop algorithmic trends across a broad surface.
All right, I'll explain that in a second.
But anyway, so my new map here is the whole globe.
I'm just showing the United States because we're going to talk about the United States in a minute.
And it had Antarctica and this kind of stuff, and I'll be using these to show various different aspects of the war.
And so right now I'll tell you that our lumpy woo has allowed me to determine that there was a period of time, let's say it was 70 years, maybe it was slightly more than that, let's just say abounded by 80 years or so in our past in North America,
and this pertains to a lot of other countries, in which the bug attacked North America and burned down almost all of our major cities.
And we don't have that in our history.
We do, but we don't think about it.
We don't even really, modern millennials and Zoomers or whatever the hell the other guys are called, they don't think about it.
They don't ever really examine history.
They're not taught to look at history.
They're not taught to be curious about what's going on.
My magnets are shifting.
But major cities throughout the United States were burned in huge areas.
40 blocks, 50 blocks, 100 blocks, 1,000 buildings, 2,000 buildings, 5,000 buildings, 10,000 buildings, such that over this period of time, the bug burned down, the bug's minions, burned down what we would think of as billions and billions of dollars of infrastructure.
Vast quantities of infrastructure.
Just burned it to the ground.
And it's easy to see if you go and look using these quick little queries that you can do.
I'll show you one of them in a minute.
But I'd just been doing this.
I'd come across some interesting information that just made no sense.
It was lumpy data, right?
This should not occur.
Certain things happen.
So in the move from gaslights to electricity, we have a situation where there should be a predictable accidents in our society as we start to figure out as humans what electricity is all about and how we're going to use it, right?
We're going to zap ourselves, we're going to set shit on fire, we're going to zap other people, all these different kinds of things as we move from one form of technology into another.
This is predictable.
It happened.
There was a period of time as we electrified, as any area is electrified, there's a period of time in the beginning where you have all these accidents, then everything settles down as you get the hang of it.
And usually these kind of things happen when you leave one technology and move into another on a mass adoption kind of a thing.
Look at all of the people that, you know, lost Bitcoin and that kind of thing as they moved into that for into cryptos for the very first time way back when, without any guidance, trying to figure it out on their own.
And before that, look at all the people that were getting their phones ripped off and so on back in the 70s and 80s.
And so as we bring in the new technologies, there's always this point of vulnerability.
And these are not lumpy areas in data.
These are predictable issues with our curves that show up as humans adopt anything, any kind of technology.
And it's even predictable with the adoption of technology in a good way, going from early adopters to the mainstream users and then late adopters in the adoption of the technology, etc.
Okay, so lumpy data, though, does show that we've had an attack and our social order had been under attack and a large number of cities had been burned down.
And I set out on a much more complex search, but I'll show you one of the searches I ended up with here in a minute.
So I'm going to take this down and you'll see what I'm talking about here as we get into it.
And in the future, I'm going to use more of this to illustrate how our history has been removed from us and that it's currently being undone behind us the way the, what was the name of that movie?
Anyway, some movie where they created the reality in front of you and undid it behind you.
Anyway, so we've been attacked, okay, and so we've been attacked by the bug, and the bug has been eating on us for a bunch of years.
Let me get rid of these magnets.
Hang on.
So, big, big, big, big, big map.
It's a big world.
africa is giant okay so we have the great new york city fire in 1835 and it repeats in in 1845 in another area of new york Okay, we have Great Chicago fire, 1871, Great Boston fire, 1872, Great Seattle fire, 1889, Great Baltimore fire in 1906, Great San Francisco fire in 1904 for Great Baltimore, 1906 for Great San Francisco.
We have Denver in 1863, Houston in 1912, Portland in 1866.
These are all great fires that did vast quantities of damage to whole regions, blocks and blocks and blocks of it.
Now, Paris in 1900 had a great fire, but it is a single location and instance.
It doesn't spread citywide.
The reason that you bring it up here is because to note that we're looking in all of these areas here for areas where there's a lot of damage, big areas that are damaged.
So in the Seattle fire, for instance, in 1889, 50 plus blocks, according to one source, or 100 plus blocks, according to another source, were totally destroyed and had to be rebuilt.
Then there's all kinds of interesting, lumpy data about the rebuilding of thousands of buildings in basically two years in Seattle when we didn't have the capacity to produce the bricks and they're all made out of bricks.
Okay, we're a resource harvesting timberland.
We've got clays up here.
If you know where to look and you can dig deep enough, you can get clays.
But to get enough clay in a particular region to do a mass amount of brick building is like, hmm, right?
Anyway, and so they rebuilt the whole city.
In each and every one of these episodes, you will find that these fires are followed by an amazing recovery, a huge rapid rebuild in these large areas.
And that whole new things occurred.
New companies came into existence that end up dominating the region, all of these kinds of things immediately in those two years following all of these fires in these local areas.
Literally changed the whole economic as well as social and architectural landscape for these regions.
Paris it did not.
Some of these other cities it did not.
On my map I was going to show you before I took it down, but that these great fires don't occur or in okay, all of these are in the 1800s going up into the 1900s, right?
And in this period of time, they pretty much stop in 1912.
There's a few others after that, but not to the same level of damage and destruction as we see in all of these.
Now, one could hypothesize that anything past 1912 date time in a date-wise here would be too close to modern human memory to do this kind of thing, what we're looking at.
So in other words, why did it stop in 1912?
There's a lot of cities that didn't get burnt.
A lot of these cities share certain characteristics that don't include these cities.
And so we see there's areas like, you know, okay, so let me show you the search and you can go hunting for yourself and you can rack up more of these here.
So the criteria that you want to see is that when you get a result off of your search engine, and I'm using duck duck go.
So in your lumpy data, you want it to have multiple blocks, okay?
You want to have it be a lot of blocks.
These great fires were big.
Seattle is like 50 to 100 blocks in that fire.
The one in Manhattan here, I think that was 45 blocks, coincidentally.
Okay, so these are large areas that have burned.
And so to do these searches, you say, you go to the search engine and you say, great, then leave a space here, fire of, all right?
And so what you get is this.
So you could put in here, you could put in here Boston, great Boston fire of, and then when you tell it to hunt, it will naturally populate years in this space right here, more often than not.
So you'll see that, oh, look, there'd been a great Boston fire in 1872.
In New York, you see great New York fire, you're going to get 1835 as well as the 1845, and then you'll get some other great fires that were single business fires.
Great industrial damage, a lot of loss of life, but not damage to a great area of the city.
So what I'm doing is looking for multiple blocks, great damage to areas of the city as a result of all of this history hunting that I'm doing.
And so I just came across this kind of anomaly.
You don't see during this period of time in the 1800s, you don't see a lot of these great fires in other countries.
I have yet to do Australia.
I have yet to do any of the young countries that were building out at that point.
Because in the United States, this was used, all of this here was used to destroy an obscure history, such that we moderns won't know about what existed in these areas before those fires.
And it was important to someone that we not know this.
Now, if you go into these things, you'll also find a, that's why I have all these old encyclopedias, you will find an interesting cross-connection.
If I was one of those people that did infographics with all of the spider webs and stuff for people, I found a great number of people that are all interconnected in fires that are not in the city.
So, in other words, some people are involved in fires that are 1871 and then 1889, and then you see them resurface in 1904.
And there are rumors in the ones in the 1880s here, 1870s through the 1904, in the Northeast, that these were arranged by Freemasons, set by Freemasons.
We also have that in Seattle.
There was a big brouhaha about the Seattle fire being a set fire.
Now, recently, here in the Northwest, back when the BLM guys were rioting and Portland was burning down and all of that, there was a lot of the BLM fuckers out setting fires, trying to set fires in the woods.
So, this is not an atypical occurrence that people would use such fires for political ends, right?
And so, it's well within the bounds here to understand that maybe, indeed, Freemason Lodges were behind a lot of this.
Because we know that Freemason Lodges were in charge of the destruction or the acquisition of artifacts for the Smithsonian.
There were too many bones of too many large people, too many anomalously large people in California for the Smithsonian to take them all in the 1830s, 40s, 50s, and 60s, even into the 70s and 80s, I guess.
But they'd done things.
They'd burned a bunch.
They paid people to haul them offshore and dump them.
Then there was no real occurrences for a while, right?
Then we get into the era of the Smithsonian, and the Smithsonian had Freemasons hiring people in the World War I through the Depression to do more bone destruction offshore quietly.
And we haven't seen any instances of it yet where they're taking that approach.
So, we do see historical scrubbing going on in near real time that we have people writing about.
So, we may not have had people photographing or doing videos of them throwing giant bones off the side of the ship, but we do have people's journals saying, Hey, I got a real good job today.
I was paid a dollar, I'm getting paid a dollar a day, which was huge in the Depression, to throw heavy sacks over the sides of these ships off Catalina Island.
And when I asked what was in them, they said, Don't ask, but it felt like bones to me, but these were giant bones.
Anyway, and so, you know, just an entry in a journal.
And everybody thinks, oh, the guy's a wackadoodle.
But you see several thousands of these entries and that kind of thing, and you got some lumpy data you really got to look at, right?
Anyway, so this was a big data lump that I thought to bring to your attention insofar as our history.
And you can check this out yourself just by substituting in the name of the town and say great Boston fire of, and so on.
And so all of these historical phrases, if you wanted to find out, you could do the same thing with battles, right?
You know, the great defeat of, and just look at how many of these things line up in lumpy data.
And it's because people or somebody is out there massaging it.
Anyway, so that's it.
Weird shit going on.
And we've got a bunch of other things happening, but I'll make a few more of these as we get past some projects I've got going.
Trying to get time to do a long one on economics and stuff, because there's lumps that you can predict that are going to be occurring in our economy as we go forward from these next three months, January, February, March, which are going to give us this upset, but it'll give us some idea where we're headed.
And we can pick now the more likely paths that we're going to end up on doing approaches here.
And then we can do searches and see if there's anything that would support our suppositions.
So you could do this in such a way that you hunted out, if you wanted to, you could.
I won't go into it.
We'll do it at some other time.
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