How to do the percolator dance

Cajmere “Percolator” | Insomniac

The birth of our underground brand Factory 93 not only brought on an adrenaline rush reminiscent of the renegade warehouse era of raving—on which Insomniac was founded—but it also had us thinking back to all the people, places and parties that made this whole operation possible. And with that came a burning desire to crack open our collection and dust off the classic records we couldn’t live without. Through our From the Crates series, we’ll be breaking out both seminal and obscure cuts alike, imparting some knowledge in the process.

“Coffee Pot (It’s Time for the Percolator),” as it’s officially named—or “Time for the Percolator” less formally, or simply “Percolator”—is a crucial second-wave house jam and one of the most instantly recognizable records you’ll likely ever jack to. You’ve almost definitely heard it, but if you haven’t, you must truly ask yourself: Have I been to a real house party?

Before he went by Cajmere or Green Velvet or any of his other alien aliases, Chicago native Curtis Jones was studying chemical engineering and quit school to start producing music, running a label, and DJing as a full-time gig.

“They told me that I needed to have a hobby, so I started off with music,” he told the Village Voice in 2012. “From there, I just fell in love with it, so I started to produce stuff. That transitioned into me performing out of necessity, because people wanted to see me perform ‘The Percolator.’” It was his first mega-hit and a dancefloor staple, but surprisingly, it didn’t catch fire out of the gate. This weird little gem needed a few stabs before getting the takeoff it needed.

House music had been, well, percolating in Chicago for the better part of a decade when Jones rolled up his sleeves in the studio in the early ‘90s. This was also before house music got really BIG, vocal-heavy, and piano-driven by the mid ‘90s. Jones was embracing its minimal, nascent raw sound (and also was admittedly broke, as it goes for many younger artists, so rawness was sort of a logistical reality, as well). Also, as Jones described to 5Mag, the initial explosion of the house scene had started to fade down by the end of the ‘80s, as far as parties in Chicago. “We all went to the parties in the ’80s—huge parties with thousands of people and a lot of excitement. That had kind of died off.” And so, the scene seemed due for a new vanguard to breathe some fresh air into the nightlife.

The first mix of “Percolator” would become known as “Keep Movin’,” but it didn’t really stick with dancefloors, even though it’s nearly identical. The vocal is different (“Keep movin’ / I got news for you, honey” in place of “It’s time for tha percolator”)—but sonically, it’s just an alternate mix of what would go on to evolve into “Percolator.”

The sounds on display in “Percolator” are simple: vocal (“it’s time for tha per-cu-lator”), a drum machine, and that slippery, squeaky synth. It’s track-y to the point of silliness, with its militant drumline patterns and the opposite of any deeper melodic or classic song structure—which, many have argued, laid the template and created the jumping-off point for the aggressive sounds of ghetto house and Baltimore club, a raunchier, tougher and more explicitly ass-driven sound, which was about to branch out.

“Keep Movin’” was cut to wax in 1991 on Chicago imprint Clubhouse Records, which was, as Jones explained, “run by Hula and K. Fingers. They had some hits like “Nu Nu” from Lidell Townsell, but they were working mainly on remixing major artists like the Fresh Prince [Will Smith].”

For whatever reason, “Keep Movin’” didn’t seem to break out. So, Jones just kept producing new work and, as became his wont, returned to rework, remix, and re-record elements of the song multiple times, until he found the iteration that really clicked.

“The sound of Curtis Jones speaking, ‘It’s time for the percolayda,’ is a bit like the Amen Break. It might seem simple on the surface, but the magic in the original can’t be duplicated.”

As he told the Voice, “Because it got so overlooked, I was like, ‘Let me try it again!’ [laughs] I put it on an EP with three other tracks. The standout track was ‘Chit-Chat,’ which [New Jersey DJ] Tony Humphries used to play a lot. Because it was so popular, I decided to do remixes of all the songs on the EP, so that goes to the third version of it, where I came up with ‘The Percolator.’”

The Computer Blog’s Imitation Experiment, a series devoted to recreating famous tracks, tried to recreate “Percolator.” They found that there’s something about the vocal that’s impossible to replicate. “The sound of Curtis Jones speaking ‘it’s time for the percolayda’ is a bit like the Amen Break. It might seem simple on the surface, but the magic in the original can’t be duplicated.”

Jones got a lot of help from his engineer (and the mastering by local legend Mark Richardson). As Jones told The Quietus in 2012, “The studio engineer was… well, let’s just say, he was very good at the technical stuff,” remembers Jones. “He could make anything sound like you wanted it to. And we were working on this remix, and he said something like, ‘I need to go and put the percolator on.’ And I thought, ‘Yes! That’s it! It’s time for the percolator!’” And thus, the vocal of a house classic was born.

This time around, however, the record hit, which surprised Jones, who felt it wasn’t the best mix of the song, and who was now already tired of the tune altogether (“Ehh. It’s not as good as the first version of it,” he’d admit decades later). This success not only pushed Jones to the foreground and helped kick off his career, which is still going strong (he’s headlining festivals well into his 50s), but it inspired a “Percolator” dance, which means it was cemented in the local dance culture for a long time to come. It also helped inspire entire scenes to develop. Its simplicity made it accessible and fluid to cross over into various music communities in the Chicago area. Its influence can’t really be overstated.

Reaching his personal saturation point for his own creation, Jones eventually grew even more tired of the record, as is understandable for anyone who has to hear their own work ad nauseum.

“There was a point in my life where I didn’t feel the way I do now, and that perspective helped me get beyond it and understand that if fans want to hear [‘Percolator’], there’s nothing wrong with that. Hearing you play this one song that you made will make someone so happy. I have to admit, there was a point in my life that if I heard ‘Percolator’ one more time… [laughs] I mean, there were even tracks about ‘Percolator’!”



CajmereGreen VelvetHouse

17 Songs that Launched the Blackest Dance Crazes

Dance and music have always been two of the most defining aspects of black culture.

As renowned poet laureate Andre Benjamin once said, “All the fresh styles always start off as a good little hood thing” – you know, before they hit Hollywood and get oversaturated to death.

It’s cool though, we know where they started. With us.

But before the viral fame and celebrities dancing offbeat on daytime talk shows, all dance crazes begin with two things – a super catchy song and entertaining moves.

Let’s look back at 17 songs that launched 17 dances that took over black culture – some briefly, others, eternally.

And before y’all throw tantrums in the comments, remember: To qualify for this list, the dances MUST have a specific song associated with it. So no goofy Flossin’ dancing here.

Honorable mention: Sugarhill Gang’s “Apache (Jump on It)”

17. Da Butt

The dance: Butts n’ black folk! Obviously, we’re gonna be here for this one. This classic from the School Daze film has lived on in butt-shaking infamy. This dance was SCANDALOUS back in 1988. Then Miley Cyrus went and invented twerking in 2013 and we just couldn’t keep up.

Level of difficulty: Can you bend over? Can you roll your booty in PG-13 fashion? Then you can do this dance.

The song that started it all: E.U., “Da Butt”

16. Hit the Quan

The dance: Y’all thought Rich Homie Quan’s biggest contribution to hip-hop was his actual music? Playa please. A random dance he did in his video caught fire, iLoveMemphis made a song about it and here we are.

Level of difficulty: This is an easy one. Squat down like you’re potty training, wave your arms around like a kid pretending to be an airplane, and there you go.

The song that started it all: iLove Memphis, “Hit the Quan”

15. Walk It Out

The dance: If you weren’t around in 2006, it’s kinda hard to understand how big this song was. Unk  even dragged Andre 3000 out of his underground bunker for the remix! That’s a modern miracle. The dance itself doesn’t have much to it – as long as you can bounce around like you’re stepping on hot coals, you’re gucci.

Level of difficulty: Not too bad. If you can walk on your tiptoes you can do this.

The song that started it all: DJ Unk, “Walk It Out”

14. JuJu on that Beat

The dance: You know you’re old when you start asking little kids to dance for your enjoyment. The song is ridiculous (“You ugly! You your daddy’s son!”) and the dance looks like a synchronized seizure but  laughing at children is the best part of being an adult. It beats paying bills.

Level of difficulty: Other than the Running Man part, I have no idea what’s going on here. So I’d say it’s pretty difficult.

The song that started it all: Zay Hilfigerrr & Zayion McCall, “JuJu on That Beat”

13. Stanky Legg

The dance: Remember in the 00s when grown men wore oversized T-shirts that hung down to their knees? Imagine getting tangled up in those bedsheets and fighting your way out. Boom, hit dance. Thanks, GS Boyz.

Level of difficulty: Easy. Pretend one leg has a mind of its own and you just freestyle up top.

The song that started it all: GS Boyz, “Stanky Legg”

12. The Nae Nae

The dance: Silentó’s song caught fire in 2015 – you know you’ve got something when Ellen is doing the dance on her talk show. That also means it got SUPER OLD SUPER QUICK. But hey, at least it was something to keep your nieces and nephews entertained at the cookouts for a summer or two.

Level of difficulty: Not too bad. It’s like a simpler version of The Quan. Look, if *I* can do it, with my knees sounding like Rice Krispie Treats the entire time, it’s got to be easy.

The song that started it all: Silentó, “Watch Me (Whip/Nae Nae)”

11. Tootsie Roll


You KNOW you just said that in your head. The 69 Boyz had us tearing up the school dances with this one, which more or less was just a knockoff version of the Butterfly.

Level of difficulty: Move your knees in and out and suddenly that tootsie is rollin’. We kept things simple in the 90s.

The song that started it all: The 69 Boyz, “Toostie Roll”

10. Da Dip

The dance: Listen, this dance is absolutely stupid. Look at that gif – it’s like an ad for back pain medication. But the song is SO INCREDIBLY ADDICTIVE that it disguises how goofy the dance is. Freak Nasty, you are truly a musical genius.

Level of difficulty: Put your hand up on my hip. When I dip, you dip, we dip. What else you need to know?

The song that started it all: Freak Nasty, “Da Dip”

9. Swag Surfin

The dance: Like Da Dip, this is more about Fast Life Yungstaz’s song than the actual dance, which is just a bunch of people rocking back and forth. But there’s something so oddly inspiring about it. You know how we used to lock arms at church when singing “We Shall Overcome?” This is hip-hop’s version of that.

Level of difficulty: The easiest dance in the history of dances. Rock side to side on beat and you’re clean like dish detergent.

The song that started it all: Fast Life Yungstaz, “Swag Surfin'”

8. Teach Me How to Dougie

The dance: My generation knows Doug E. Fresh as the Human Beat Box. But for this current generation, he’s the Dougie guy. As long as the checks keep clearing I’m sure Doug E. doesn’t care. The dance is actually pretty dope too.

Level of difficulty: It’s not as hard as it looks! It’s basically a two-step with unnecessary arm movements. In fact, video exists of me doing this dance paiiiinfully sloooooowly. But if you think I’m sharing it here, you crazy. I’m sure it will resurface during my 2020 presidential campaign when y’all look to derail my White House run. Haters.

The song that started it all: Cali Swag District, “Teach Me How to Dougie”

7. Cupid Shuffle

The dance: If you’ve EVER been to a black function in your life, you know this one. The second the beat drops, GET OUT OF THE WAY because the stampede of aunties in kitten heels might end your life. And there’s always one person who has to get extra cute on the “walk it by yourself” part. Stop overachieving!

Level of difficulty: A piece of cupid cake. Just listen to the instructions.  He’s literally telling you how to move! Mess that up and it’s your own fault.

The song that started it all: Cupid, “Cupid Shuffle”

6. Humpty Dance

The dance: The Humpty Dance was THE dance of my childhood but I could never figure it out for myself because I wasn’t sure what they were doing and there were no YouTube instructional videos back then. YEAH I’M OLD. But seriously, look at the dance – it’s like they’re trying not to get blown over by a gust of wind.

Level of difficulty: I’m gonna say SUPREME DIFFICULTY cuz it’s 2019 and I’m still not sure what Humpty n’ dem are doing. The code has not been cracked.

The song that started it all: Digital Underground, “Humpty Dance”

5. Crank Dat

The dance: Ugh, Soulja Boy. The godfather of all the garbage Internet rappers and patient zero for today’s mumble rap menace. He’s even out here selling knockoff Nintendos! His reign of terror started with this dance, which birthed 1,000 clones. Wasn’t there even a Crank Dat Lion King at one point? If Simba and Nala start cranking that in the new movie I’m writing a strongly worded email to Disney Pictures.

Level of difficulty: Can’t front, this dance is a lot harder than it looks and the steps aren’t that intuitive. Props for pulling off all those moves in jeans that baggy.

The song that started it all: Soulja Boy, “Crank Dat (Soulja Boy)”

4. Wobble

The dance: The tale of “Wobble” is so weird. I remember when VIC dropped the song circa 2008 or so and no one batted an eye. Y’all were too busy listening to Tha Carter III or something. But three or four years later, it suddenly became the premier black wedding line dance song. This “new” dance was older than the ring bearer!

Level of difficulty: Jump back and forth, wave your arms like you’re checking your armpits to see if you need deodorant. Step back and forth, wave your arms like you’re checking your armpits to see if you need deodorant. Easy. Unless you need deodorant.

The song that started it all: VIC, “Wobble”

3. The Percolator

The dance: If you aren’t familiar with the House dance scene, you probably have no idea what this is. What a poor, deprived childhood you had then. You missed out on flailing around like a maniac to the sounds of a bubbling coffee pot. No wonder our knees are so bad today.

Level of difficulty: I swear most of the songs on this list are just versions of the Butterfly. This is no different. But instead of the Butterfly, this is the drunkest, sweatiest moth you’ve ever seen.

The song that started it all: Cajmere, “Coffee Pot (It’s Time For The Percolator)”

2. Cha Cha Slide

The dance: The Ghetto Hokey Pokey!

Put your left foot in, take your left foot out. Put your right foot in, take your right foot out.




Level of difficulty: Like several of the dances on the list, the song is instructional. If you can’t get this one down, you’re just not paying attention. If you don’t know what to do when he says EVERY BODY CLAP YO HANDS, there is no hope for you.

The song that started it all: Mr. C The Slide Man, “Cha-Cha Slide”

1. Electric Slide

The dance: If you have an ounce of melanin in your pores you know this had to be No. 1. Outdated? Maybe. Needlessly complicated? I think so. Are the lyrics sneakily describing a sex toy? Lord, I hope not.

But is this song a black institution? DEFINITELY.

If your family function doesn’t have at least one round of the Electric Slide, your potato salad is probably loaded with raisins and your mac and cheese came out of a box and not the oven. ABORT.

Level of difficulty: Not gonna lie to y’all, I never figured out how to pull this off correctly. True story – at my wedding, the entire group (from the wedding party to the guests) participated in a mass Electric Slide. More than 100 people sliding in sync – truly a thing of beauty.

Except for me. I was sliding in the opposite direction. Pray for me.

The song that started it all: Marcia Griffiths, “Electric Boogie”

What are your favorite songs that spawned insanely popular dances? Let us know below.

Complex systems. Getting to the Critical Level / Sudo Null IT News

If you've spent any time thinking about complex systems, you probably understand the importance of networks. Networks rule our world. From the chemical reactions within the cell, to the network of ecosystem relationships, trade and political networks that shape the course of history.

Or consider this article you are reading. You probably found it in social network , downloaded it from computer network and are currently deciphering the meaning with your neural network .

But no matter how much I thought about networks over the years, until recently I did not understand the importance of a simple diffusion .

This is our topic for today: how, how chaotic everything moves and spreads. Some examples to whet your appetite:

  • Infectious diseases that spread from carrier to carrier within a population.
  • Memes spreading across the follower graph on social networks. nine0020
  • Forest fire.
  • Ideas and practices permeating culture.
  • Neutron cascade in enriched uranium.

A quick note about form.

Unlike all my previous work, this essay is interactive [the original article contains interactive examples with sliders and buttons that control objects on the screen — approx. lane].

So let's get started. The first task is to develop a visual vocabulary for distribution across networks. nine0003

I am sure that all of you know the basis of networks, that is, nodes + edges. To explore diffusion, all you have to do is mark some nodes as active . Or, as epidemiologists like to say, infected :

This activation or infection spreads through the network from node to node according to the rules we will develop below.

Real networks tend to be much larger than this simple network of seven nodes. They are also much more confusing. But for simplicity, we will build a toy model here to study the lattice, that is, the lattice network (lattice network). nine0003

(What the grid lacks in realism, it makes up for in being easy to draw ;)

Except where otherwise noted, there are four neighbors at the nodes of the network, for example:

And you need to imagine that these grids extend endlessly in all directions. In other words, we are not interested in behavior that occurs only at the edges of the network or in small populations.

Given that the gratings are so ordered, we can simplify them down to pixels. For example, these two images represent the same network:

In one behavior, the active node always spreads the infection to its (uninfected) neighbors. But it's boring. Much more interesting things happen when the transmission is probabilistic .

In model SIR (Susceptible-Infected-Removed), a node can be in three states:

  • Susceptible (Susceptible)
  • Infected
  • Removed

Here's how the interactive simulation works per.]:

  • Nodes start as susceptible, except for a few nodes that start as infected.
  • At each time step, infected nodes get a chance to transmit the infection to each of their susceptible neighbors with a probability equal to the transmission rate.
  • Infected nodes then go into the "deleted" state, meaning they are no longer able to infect others or infect themselves.

In the context of disease, removal may mean that the person has died or that they have developed immunity to the pathogen. We say they are "removed" from the simulation because nothing else happens to them. nine0003

Depending on what we are trying to model, a different model than SIR may be needed.

If we are simulating the spread of measles or a wildfire outbreak, SIR is perfect. But suppose we are simulating the spread of a new cultural practice, such as meditation. At first the node (person) is receptive because it has never done it before. Then, if he starts meditating (perhaps after hearing about it from a friend), we will model him as infected. But if he stops practicing, he will not die and fall out of the simulation, because in the future he can easily pick up this habit again. So he goes back to the receptive state. nine0003

This is model SIS (Susceptible-Infected-Susceptible). There are two parameters in the classical model: transfer rate and recovery rate. However, in the simulations for this article, I decided to simplify by omitting the recovery rate parameter. Instead, the infected node automatically reverts to a susceptible state at the next time step, unless it is infected by one of its neighbors. In addition, we allow the node infected at step n to infect itself at step n+1 with a probability equal to the transmission rate. nine0003


As you can see, this is very different from the SIR model.

Because nodes are never removed, even a very small and limited lattice can sustain a SIS infection for a long time. The infection simply jumps from node to node and back again.

Despite their differences, SIR and SIS are remarkably interchangeable for our purposes. Therefore, for the rest of the article, we will focus on SIS - mainly because it is more tenacious and, therefore, more interesting to work with. nine0003

After playing around with the SIR and SIS models, you might notice something about the longevity of the infection. At very low transmission rates, such as 10%, the infection tends to die out. While at higher values, such as 50%, the infection remains alive and takes over most of the network. If the network were infinite, we could imagine that it continues and spreads forever.

Such boundless diffusion has many names: "viral", "nuclear", or (in the title of this article) critical .

It turns out that there is a specific breaking point that separates subcritical networks (doomed to extinction) from supercritical networks (capable of infinite growth). This tipping point is called the critical threshold , and is a fairly common feature of diffusion processes in normal networks.

The exact value of the critical threshold differs between networks. What is common is the presence of of this value.

[In the interactive demo from the original article, you can manually try to find the critical network threshold by changing the baud rate. It is somewhere between 22% and 23% - approx. trans.]

At 22% (and below), the infection eventually dies out. At 23% (and above), the initial infection sometimes dies out, but in most cases it manages to survive and spread long enough to ensure its existence forever.

(By the way, there is a whole scientific area dedicated to finding these critical thresholds for different network topologies. For a quick acquaintance, I recommend quickly scrolling through the Wikipedia article on the percolation threshold). nine0003

In general, here's how it works: below a critical threshold, any terminal infection in the network is guaranteed (with probability 1) to eventually die out. But above a critical threshold, there is a probability (p > 0) that the infection will continue forever, and in doing so will spread arbitrarily far from the original site.

Note, however, that the supercritical network does not guarantee that the infection will continue forever. In fact, it often fades out, especially in the very early stages of the simulation. Let's see how it goes. nine0003

Suppose we started with one infected node and four neighbors. In the first simulation step, the infection has 5 independent chances of spreading (including the chance to "spread" to itself in the next step):

Now suppose the transmission rate is 50%. In this case, in the first step, we toss a coin five times. And if five heads fall, the infection will be destroyed. This happens about 3% of the time - and that's just the first step. An infection that survives the first step has some (usually smaller) chance of dying out in the second step, some (even smaller) chance of dying out in the third step, and so on.

Thus, even when the network is supercritical - if the transfer rate is 99% - there is a chance that the infection will disappear.

But the important thing is that it is not always will fade away. If we add the probability of all steps fading to infinity, the result is less than 1. In other words, with a non-zero probability, the infection will continue forever. That's what it means for a network to be supercritical.

Up to this point, all of our simulations started with a small piece of pre-infected nodes in the center. nine0003

But what if you start from scratch? Next, we model spontaneous activation, a process by which a susceptible node becomes infected by chance (not from one of its neighbors).

This is called model SISa . The letter “a” means “automatic”.

A new parameter, the rate of spontaneous activation, appears in the SISa simulation, which changes the rate of spontaneous infection (the transmission rate parameter we saw earlier is also present).

What does it take to spread the infection throughout the network? nine0003


You may have noticed in the simulation that increasing the rate of spontaneous activation does not change whether the infection takes over the entire network or not. Only the baud rate determines whether the network is subcritical or supercritical. And when the network is subcritical (baud rate less than or equal to 22%), no infection can spread to the entire grid, no matter how often it starts.

It's like lighting a fire in a wet field. You can set a few dry leaves on fire, but the flames will quickly go out because the rest of the terrain isn't flammable enough (subcritical). While in a very dry field (supercritical) one spark is enough to start a fire. nine0003

Similar things happen in the realm of ideas and inventions. Often the world is not ready for an idea, in which case it can be invented again and again, but it does not catch the masses. On the other hand, the world may be completely ready for an invention (large latent demand), and as soon as it is born, it is accepted by everyone. In the middle are ideas that are invented in several places and distributed locally, but not enough for any single version to cover the entire network at once. In this last category we find, for example, agriculture and writing, which were independently invented by different human civilizations about ten and three times, respectively. nine0003

Suppose we make some nodes completely invulnerable, that is, immune to activation. It is as if they are initially in a remote state, and the SIS (a) model is launched on the remaining nodes.

The immunity slider controls the percentage of hosts that are removed. Try changing its value (while the model is running!) and see how it affects the state of the network, whether it is supercritical or not.


Changing the number of non-receptive nodes completely changes the picture, whether the network will be sub- or super-critical. And it's not hard to see why. With a large number of immune hosts, the infection has less opportunity to spread to new hosts. nine0003

It turns out that this has a number of very important practical implications.

One of them is to prevent the spread of forest fires. At the local level, each person must take their own precautions (for example, never leave open flames unattended). But on a large scale, isolated outbreaks are inevitable. Thus, another method of protection is to provide enough "breaks" (in the network of flammable materials) so that the outbreak does not cover the entire network. This function is performed by clearings:

Another outbreak that needs to be stopped is an infectious disease. Here the concept of herd immunity is introduced . This is the idea that some people cannot be vaccinated (for example, they have a compromised immune system), but if enough people are immune to an infection, the disease cannot spread indefinitely. In other words, enough of the population should be vaccinated to bring the population from supercritical to subcritical. When this happens, one patient can still become infected (for example, after traveling to another region), but without a supercritical network to grow in, the disease will only infect a small handful of people. nine0003

Finally, the concept of immune nodes explains what happens in a nuclear reactor. In a chain reaction, a decaying uranium-235 atom releases about three neutrons, which cause (on average) more than one U-235 atom to fission. The new neutrons then cause further fission of the atoms, and so on exponentially:

When building a bomb, the whole point is to ensure that the exponential growth continues unhindered. But in a power plant, the goal is to produce power without killing everyone around. 9 are used for this.0013 control rods made of a material capable of absorbing neutrons (for example, silver or boron). Since they absorb rather than release neutrons, they act as immune nodes in our simulation, thereby preventing the reactor core from going supercritical.

So the trick of a nuclear reactor is to keep the reaction near a critical threshold by moving the control rods back and forth and ensure that whenever something goes wrong, the rods go down into the core and stop it. nine0003

The degree of a node is the number of its neighbors. Up to this point, we have considered networks of the 4th degree. But what happens if you change this setting?

For example, you can connect each node not only to four immediate neighbors, but also to four more diagonally. In such a network, the degree will be 8.

Lattices with degrees 4 and 8 are well symmetrical. But with a degree of 5 (for example), a problem arises: which five neighbors to choose? In this case, we choose four nearest neighbors (N, E, S, W) and then randomly choose one neighbor from the set {NE, SE, SW, NW}. The choice is made independently for each node at each time step. nine0003


Again, it's not hard to see what's going on here. When each node has more neighbors, then the chances of infection spreading increase - and thus the network is more likely to become critical.

However, the consequences can be unexpected, as we will see below.

So far, our networks have been completely homogeneous. Each node looks like any other. But what if we change the conditions and allow different node states throughout the network? nine0003

For example, let's try to model cities. To do this, we will increase the density in some parts of the network (a higher degree of nodes). We do this on the basis of the data that city dwellers have a wider social circle and more social interactions than people outside cities.

In our model, susceptible nodes are colored based on their grade. Nodes in "countryside" have degree 4 (and are colored light grey), while nodes in "urban" have higher degrees (and are colored darker), starting at degree 5 in the outskirts and ending at 8 in the city center . nine0003

Try to choose such a propagation speed that the activation covers the cities, and then does not go beyond their borders.

I find this simulation both obvious and surprising. Of course , cities are better at maintaining a cultural level than rural areas - everyone knows this. What surprises me is that some of this cultural diversity emerges simply from the topology of the social network.

This is an interesting point, I'll try to explain in more detail. nine0003

Here we are dealing with forms of culture that are transmitted simply and directly from person to person. For example, manners, parlor games, fashion trends, linguistic trends, small group rituals, and products that spread by word of mouth, plus whole packets of information that we call ideas.

(Note: the dissemination of information between people is extremely complicated by the mass media. It is easier to imagine some technologically primitive environment, for example, Ancient Greece, where almost every spark of culture was transmitted by interaction in physical space). nine0003

From the simulation above, I learned that there are ideas and cultural practices that can take root and spread in the city, but they simply cannot spread to the countryside (mathematically they cannot). These are the same ideas and the same people. It's not that the villagers are somehow "not far off": when interacting with the same idea, they have exactly the same chances of picking it up as the townspeople. It's just that the idea itself can't go viral in the countryside because there aren't many connections for it to spread. nine0003

This is perhaps easiest to see in the realm of fashion—clothes, hair, etc. In the fashion web, we can fix the edge of the lattice when two people notice each other's outfits. In the city center, each person can see more than 1000 other people every day - on the street, in the subway, in a crowded restaurant, etc. In the countryside, by contrast, each person can see only a couple of dozen others. Based on only this difference, the city is able to support more fashion trends. And only the most compelling trends—with the highest transmission rates—will be able to gain a foothold outside the city. nine0003

We tend to think that if the idea is good, it will eventually reach everyone, and if the idea is bad, it will disappear. Of course, this is true in extreme cases, but in between there are a bunch of ideas and practices that can only go viral on certain networks. It's really amazing.

Not only cities

Here we consider the influence of network density . It is defined for a given set of nodes as the number of the actual edges divided by the number potential edges . That is, the percentage of possible connections that actually exist.

So we have seen that network density is higher in urban centers than in rural areas. But cities are not the only places where we find dense networks.

Secondary schools are an interesting example. For example, for a particular district, let's compare the network that exists among schoolchildren with the network that exists among their parents. Same geographic area, same population, but one network is many times denser than the other. Therefore, it is not surprising that fashion and linguistic trends spread much faster among teenagers. nine0003

Similarly, elite networks tend to be much denser than non-elite networks - a fact underestimated in my opinion (people who are popular or influential spend more time networking and therefore have more "neighbors" than in ordinary people). Based on the simulations above, we expect elite networks to support some cultural forms that the mainstream cannot support, simply based on mathematical laws, based on the average degree of the network. I leave you to think about what these cultural forms might be. nine0003

Finally, we can apply this idea to the Internet by modeling it as a huge and very dense city. Not surprisingly, many new kinds of culture are thriving on the internet that simply cannot be sustained in purely spatial networks: niche hobbies, better design standards, greater awareness of injustice, etc. And it's not just nice things. Just as early cities were breeding grounds for diseases that could not spread at low population densities, so the internet is a breeding ground for malignant cultural forms such as clickbait, fake news, and artificial outrage. nine0003

"Having the right expert at the right time is often the most valuable resource for creative problem solving." - Michael Nielsen, "Inventing Discoveries"

We often think of discovery or invention as a process that takes place in the mind of a single genius. He is struck by a flash of inspiration and - eureka! - all of a sudden we get a new way to measure volume. Or the equation of gravity. Or a light bulb.

But if we take the point of view of a lone inventor at the moment of discovery, then we are looking at phenomenon from the point of view of node . While it would be more correct to interpret the invention as a network phenomenon.

The network is important in at least two respects. First, already existing ideas must penetrate into the consciousness of the inventor . These are quotations from a new article, a bibliographic section of a new book - the giants on whose shoulders Newton stood. Secondly, the network is critical to bringing the new idea back to the world; an invention that has not spread is hardly worth calling an "invention" at all. Thus, for both of these reasons, it makes sense to model invention—or, more broadly, the growth of knowledge—as a process of diffusion. nine0003

In a moment, I will present a rough simulation of how knowledge can spread and grow within a network. But first I must explain.

At the beginning of the simulation, there are four experts in each quadrant of the grid, arranged as follows:

Expert 1 has the first version of the idea — let's call it Idea 1.0. Expert 2 is a person who knows how to turn Idea 1.0 into Idea 2.0. Expert 3 knows how to convert Idea 2.0 into Idea 3.0. And finally, the fourth expert knows how to put the finishing touches to Idea 4.0. nine0003

This is similar to a technique like origami where techniques are developed and combined with other techniques to create more interesting designs. Or it may be a field of knowledge like physics in which more recent work builds on the fundamental work of predecessors.

The point of this simulation is that we need all four experts to contribute to the final version of the idea. And at each stage, the idea needs to be brought to the appropriate expert.

A few caveats. There are many unrealistic assumptions encoded in the simulation. Here are just a few:

  1. It is assumed that ideas cannot be preserved and transmitted except from person to person (i. e. there are no books and media).
  2. It is assumed that there are permanent experts in the population capable of generating ideas, although in reality many random factors influence the emergence of a discovery or invention.
  3. All four versions of the idea use the same set of SIS parameters (baud rate, immunity percentage, etc.), although it is probably more realistic to use different parameters for each version (1.0, 2.0, etc.)
  4. It is assumed that the N+1 idea always completely supersedes the N idea, although in practice both the old and the new versions circulate at the same time, with no clear winner.

… and many others.


This is a ridiculously simplified model of how knowledge actually grows. A lot of important details remained outside the model (see above). However, it captures an important essence of the process. And so we can, with reservations, talk about the growth of knowledge using our knowledge of diffusion. nine0003

In particular, the diffusion model provides insight into how to speed up the process : it is necessary to facilitate the exchange of ideas between expert nodes. This may mean clearing the network of dead nodes that interfere with diffusion. Or it could mean locating all the experts in a dense city or cluster where ideas spread quickly. Or just put them in the same room:

So... that's all I can say about diffusion.

But I have one last thought, and it is very important. It's about growth ( and stagnation ) of knowledge in scientific communities. This idea is different in tone and content from everything above, but I hope you will forgive me.

The illustration shows one of the most important positive feedback loops in the world (and has been for quite a long time):

The upstroke of the loop (K ⟶ T) is quite simple: we use new knowledge to develop new tools. For example, understanding the physics of semiconductors allows us to build computers. nine0003

However, the downward move requires some explanation. How does the development of technology lead to the growth of knowledge?

One way—perhaps the most direct—is when new technologies give us new ways of perceiving the world. For example, the best microscopes allow you to look deeper inside the cell, throwing up ideas for molecular biology. GPS trackers show how animals move. Sonar allows you to explore the oceans. Etc.

Undoubtedly, this is a vital mechanism, but there are at least two other paths from technology to knowledge. Maybe they are not so simple, but I think they are just as important:

First . Technology leads to economic abundance (that is, wealth), and this allows more people to engage in knowledge production.

If 90% of the population of your country is engaged in agriculture, and the remaining 10% are engaged in some form of trade (or war), then people have very little free time to think about the laws of nature. Perhaps that is why in the old days science was mainly promoted by children from wealthy families.

More than 50,000 Ph.D.s are graduating annually in the United States. Instead of a person going to work in a factory at the age of 18 (or earlier), a graduate student has to be funded until the age of 30, or perhaps until 40 - and even then it is not clear whether his work will bring any real economic effect. But it is necessary for a person to reach the cutting edge of his discipline, especially in such difficult areas as physics or biology. nine0003

The fact is that specialists are expensive from a systems point of view. And the ultimate source of social wealth that funds these professionals is new technology: the plow subsidizes the pen.

Second . New technologies, especially in the field of travel and communications, are changing the structure of social networks in which knowledge grows. In particular, it allows experts and specialists to interact more closely with each other.

Notable inventions here include the printing press, steamships and railroads (facilitating travel and/or sending mail over long distances), telephones, airplanes, and the Internet. All of these technologies contribute to increased network density, especially within specialized communities (where almost all knowledge growth occurs). For example, the networks of correspondence that arose among European scientists at the end of the Middle Ages, or how modern physicists use arXiv. nine0003

Ultimately, both of these paths are similar. Both increase the density of the network of specialists, which in turn leads to an increase in knowledge:

For many years I have been rather dismissive of higher education. A short stay in graduate school left a bad taste in my mouth. But now that I'm looking back and thinking (there is to abstract away all personal problems), I must conclude that college education is still extremely important.

Academic social networks (for example, research communities) are one of the most advanced and valuable structures created by our civilization. Nowhere have we accumulated a greater concentration of specialists focused on knowledge production. Nowhere have people developed a greater ability to understand and criticize each other's ideas. This is the beating heart of progress. It is in these networks that the fire of enlightenment burns most strongly. nine0003

But we cannot take progress for granted. If the irreproducibility crisis has taught us anything, it's that science can have systemic problems. This is a kind of degradation of the network.

Suppose we distinguish between two ways of doing science: real science and careerism . True science is practices that reliably produce knowledge. It is motivated by curiosity and characterized by honesty (Feynman: "You see, I just need to understand the world"). Careerism, in contrast, is motivated by professional ambition and is characterized by playing politics and scientific labels. It may look and act like science, but not produces reliable knowledge.

(Yes, that's an exaggerated dichotomy. Just a thought experiment. Don't judge me too hard. )

The fact is that when careerists take a place in the real research community, they spoil the work. They seek to promote themselves while the rest of the community tries to acquire and share new knowledge. Instead of striving for clarity, careerists complicate and confuse things in order to sound more impressive. They are engaged in (as Harry Frankfurt would say) scientific nonsense. And, therefore, we could model them as dead nodes, immune to the good faith exchange of information necessary for knowledge growth:

Perhaps the best model is one in which Career nodes are not only immune to knowledge, but actively spread fake knowledge . Fake knowledge can include non-meaningful results that are artificially inflated in importance, or truly false results that result from manipulation or fabricated data.

No matter how we model them, careerists can certainly stifle our scientific communities. nine0003

It's like a nuclear chain reaction that we badly need - we need an explosion of knowledge - only our enriched U-235 has too much of the non-reactive isotope U-238, which suppresses the chain reaction.

Of course, there is no clear distinction between careerists and real scientists. There is a bit of careerism in each of us. The question is how long the network will last before the spread of knowledge dies out.

Oh, you've read to the end. Thank you for reading. nine0003


CC0 all rights reserved. You can use this work as you see fit :).


  • to Kevin Kwok and Niki Case for thoughtful comments and suggestions on various versions of the draft.
  • to Nick Barr for moral support throughout the process and for the most helpful feedback on my work.
  • to Keith A. for pointing out to me the percolation phenomenon and the percolation threshold.
  • to Jeff Lonsdale for linking to this essay, which (despite its many shortcomings) was the main impetus for this post.

Interactive Essay Samples

  • All works by Nicky Case, especially "The Parable of the Polygons" (with Wee Hart) and "Designing a Better Voting Ballot". This is a high bar for what an interactive essay can be.
  • Exceptionally high-quality interactive descriptions of machine learning. nine0020
  • Bret Victor's classic Up and Down the Ladder of Abstraction. I wasn't very good at moving up the stairs, but there's always one more try left.

The Fermi paradox is not a paradox at all, but a question; what it consists of, and how to solve it (part 3) / Sudo Null IT News How the settlement of mankind in the Galaxy could look like.

In the last article, devoted to the so-called "Fermi Paradox" - and in fact, just the question of why we still have not found evidence of the existence of aliens in our Galaxy, given its huge size - we considered several hypotheses that explain reasons for the absence of such signals. Today we continue this hard work. nine0003

Theory of percolation [leakage]

The concept of the DSTART "generation ship" being developed with the support of the European Space Agency

Einstein published his "special theory of relativity" in 1905, attempting to relate the Newtonian laws of physics and Maxwell's equations to explain the nature of light. He argued that the speed of light is the physical limit of the speed at which a massive object can travel. nine0003

According to this theory, the closer an object's speed approaches the speed of light, the more energy must be expended to further increase its speed. When the speed of light is reached, this energy will become infinite, as will the mass of the object itself. In fact, it turns out that no material object can be accelerated to the speed of light.

And from all this it follows that traveling through the Galaxy at any significant speed requires extremely large energy consumption. nine0003

Answering Fermi's question "where is everyone", taking into account the factors of the gigantic size of the Galaxy and the time required to overcome such distances, has been tried more than once. In 1981, Carl Sagan and William Newman, in Galactic Civilizations: Population Dynamics and Interstellar Diffusion, suggested that signals from other civilizations might simply not have reached us yet. This will be true if the Galaxy is just beginning to be filled with various civilizations.

Developed and improved on their idea by Joffrey Landis in the 1993 work "The Fermi Paradox: A Solution with Percolation Theory". Percolation theory, related to mathematics and physical statistics, studies how a network changes when various connections within it are removed. If enough connections are removed, it will inevitably fall apart into separate clusters. Landis believes that the process of migration of human populations can be described in a similar way.

From his point of view, in a galaxy with an abundance of various life forms, not all of them will necessarily strive to expand. Some of them will try to colonize other worlds, while others will like to "stay at home". Such a variety of motives can be found even among different human groups, not to mention other types of intelligent beings. nine0003

In addition, the expansion of living spaces is associated with a serious expenditure of resources, and therefore cannot occur quickly or evenly. As a result, only a few of the intelligent beings will slowly and gradually spread through the Galaxy over limited distances. The greater these distances, the more difficult it will be to organize the exchange of information between the colonies, as a result of which their own cultures will arise on them - perhaps not prone to colonization.

Professor Adam Frank at work 2019of the year “The Fermi Paradox and the Aurora Effect: Settlement, Expansion and Stable States of Extraterrestrial Civilizations” with the team studied the issue of the likely expansion of extraterrestrial civilizations in the Galaxy, concluding that their settling should proceed in uneven clusters, since not all potentially habitable planets will be suitable for colonizers .

Let's try to understand how costly in terms of resources such resettlement will be. According to some estimates, it will take humanity 1,000 to 81,000 years to get to the closest star to the Sun, Proxima Centauri, located 4.24 light years away, using current technologies. Realistic future technologies reduce this time from a few decades to a little over a century. And when the colonists get to the planets of this system, manage not to die, settle there and send us a signal, it will take more than 4 years to go one way. It will take another 4 years to receive a response. nine0003

In addition, the cost of such a project would be too high. The Apollo program, which sent American astronauts to the moon, cost $25.4 billion from 1961 to 1973, which, adjusted for inflation, is now about $150 billion. The implementation of this program was preceded by the space programs Mercury and Gemini, which cost adjusted for inflation, at $2.3 billion and $10 billion. In total, from 1958 to 1972, it took $163 billion to send a person to the nearest satellite. It is planned to spend $35 billion on the modern Artemis project to deliver people to the moon and return them from there.

Interstellar travel will be much more expensive. In the 1950s and 1960s, the United States developed a project for a manned nuclear-pulse spacecraft ("explosive") "Orion". It was assumed that the ship will disperse the explosions of nuclear warheads. Such a system would allow the ship to reach 5% of the speed of light. However, taking into account inflation, the construction of such a ship at current prices would take about $ 2.75 trillion - this is 78% of US budget revenues in 2019 and 10% of the country's GDP.

What the Orion ship might look like

Another similar project was the Daedalus, developed from 1973 to 1977. The project involved the construction of a powerful two-stage unmanned spacecraft with thermonuclear engines in orbit around Jupiter. According to calculations, Daedalus was supposed to fly to Barnard's Star (one of the nearest stars) in 50 years, without slowing down, pass it along a flyby trajectory, collect information about the star and planets, and then transmit the research results to Earth via radio channel. nine0003

Artist's view of Daedalus project (Saturn V rocket is shown for scale).

the speed of light, and the second - up to a speed of 12% of the speed of light in 1. 8 years.Such a monster would weigh 60,000 megatons and cost $ 5,267 billion in 2012.

Another idea for an interstellar ship is based on the use of antimatter as The annihilation of matter and antimatter releases a huge amount of energy, and the resulting subatomic particles, pions and muons, can be thrown back using a magnetic nozzle, accelerating the rocket.0003

Unfortunately, today it would take about a trillion dollars to obtain just one gram of antimatter. And a two-stage antimatter rocket would require more than 815,000 tons of this substance - and that's just to fly to Proxima Centauri in 40 years.

Since fuel makes up the bulk of a rocket's mass, there are ideas for spacecraft that can generate their own fuel. Such, for example, is the Bassard interstellar ramjet engine - in fact, a ship that collects interstellar hydrogen with the help of a giant electromagnetic funnel and compresses it to a state in which nuclear fusion occurs. nine0003

Another concept, the Vacuum to Antimatter Rocket Interstellar Explorer System (VARIES), allows a ship to create its own fuel from the interstellar medium using powerful lasers powered by huge solar arrays. Laser beams should theoretically generate antimatter particles.

However, such projects cannot yet be called achievable either in terms of technology or in terms of finance.

Hypothesis of transcendence

Matryoshka brain

In 1958, Stanisław Ulam, a Polish and American mathematician and colleague of von Neumann, wrote about a conversation they had once had about accelerating progress. Then they first considered the issue of reaching the maximum speed of human development, or singularity.

This concept was popularized by Vernor Vinge, science fiction writer and professor of mathematics. In 1993, in the essay "The Coming Technological Singularity". The author believed that humanity is on the verge of creating a technology capable of producing creatures that are superior to humans in intelligence. Vinge predicted that such technology would emerge between 2005 and 2030 due to one of the following factors:

  • "awakening" of computers with superior human intelligence;
  • the emergence of large computer networks;
  • the advent of brain-computer interfaces, which will increase the intelligence of users;
  • biological enhancement of human intelligence.

He is echoed by the author and inventor Raymond Kurzweil, who believes that technological progress develops exponentially, and moreover, each new breakthrough reduces the time until the next breakthrough. nine0003

This kind of transcendence of living beings was discussed by Konstantin Tsiolkovsky in his 1932 essay "Is there a god?" .
This maturity is manifested in a perfect mind, deep knowledge of nature and technical power, which makes other celestial bodies accessible to the inhabitants of space.

Tsiolkovsky believed that space is the future and destiny of mankind, therefore, long before Fermi, he wondered why we have not yet met other living beings. He tried to answer this question in essay 1933 years "The planets are inhabited by living beings":

We are told: if they were, they would visit the Earth. My answer: maybe they will visit, but the time has not yet come for that. The wild Australians and Americans of ancient times waited for the Europeans to visit, but many millennia passed before they appeared. So we will wait sometime. Other planets may have long been mutually visited by their powerful inhabitants.

And to the question of why we do not see the results of the activities of aliens, Tsiolkovsky answers as follows:

Our means are very weak to perceive these signs. Our celestial neighbors understand that with a certain degree of development of knowledge, people themselves will undoubtedly prove to themselves that other planets are inhabited. In addition, it makes no sense to let the lower terrestrial animals know about this population of the planets, but also to the majority of mankind, in view of the low degree of its development. Wouldn't even this knowledge be harmful? Will pogroms and St. Bartholomew nights arise as a result of this?

In progress 1995 "Tsiolkovsky, Russian cosmism and extraterrestrial intelligence" Lytkin, Finney and Alepko described in detail the reasoning and inspiration of Tsiolkovsky. In fact, he suggested that the aliens may have evolved too much for us to recognize the results of their activities.

Already mentioned in the series of these articles, Kardashev believed that with the growth of civilizations, their development would be accompanied by an increase in energy consumption and the creation of ever larger structures (megastructures). Physicist John Barrow, on the contrary, noticed that human progress goes much more towards decreasing scales than towards increasing them. He presented his observations in a study of 1998 "Impossibility: The Limits of Science and the Science of Limits". Barrow proposed a classification of civilizations inverse to the Kardashev scale:

  • Type I-minus: a civilization capable of operating with objects whose scale coincides with the scale of individual individuals.
  • Type II-minus: work with the genetic code.
  • Type III-minus: work with matter at the molecular level.
  • Type IV-minus: work at the atomic level (nanotechnology).
  • nine0019 Type V-minus: work at the subatomic level (nucleons and nucleons).
  • Type VI-minus: manipulation of elementary particles (quarks, leptons).
  • Type Omega Minus: manipulation of the basic structure of space and time.

Forecasts about the technological progress of mankind and the search for an answer to the Fermi question have so far been best compared by the futurist John Smart in his 2002 work "The answer to the Fermi paradox: studying the mechanisms of universal transcendence." He supplemented this work in the 2011 essay "The Hypothesis of Transcendence: Sufficiently advanced civilizations inevitably leave our Universe." nine0003

In his opinion, the "Great Silence", or the absence of signs of life of aliens, can be explained by technological progress. He wrote that “the hypothesis of transcendence is that the universal process of evolutionary development leads all sufficiently advanced civilizations into some kind of “inner space”, a computationally optimal area of ​​ever-increasing density of space, time, energy and matter.

Smart suggested that transcendental aliens would inevitably enter black holes, since the latter are ideal sources of energy for calculations and all kinds of extreme physical experiments. The idea (recently confirmed) that black holes could be used as energy sources was first proposed by the physicist and mathematician Roger Penrose. nine0003

A similar idea was put forward by astronomer and astrophysicist Milan Cirkovic in his 2008 paper Arguments Against Empire. He built two models of the behavior of an extraterrestrial civilization - the "Empire", striving for expansion, and the "City", striving to optimize the use of available space.

He concluded that highly intelligent beings would rather optimize the space they already have than expand and colonize neighboring worlds. The development and improvement of advanced nano-, pico- and femtoscale technologies will help them in this. nine0003

The self-reproducing automata invented by von Neumann, which we mentioned earlier in the “berserker hypothesis”, also fit in here.

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