Did you ever stop to think that those asteroid people, or interstellar entities, or whatever the fuck they called themselves.
What was their actual purpose? Why were they here?
We all thought it was because they wanted to make that StarGate.
Everything in the story so far has lead you in this direction.
How could you not believe this.
They came to earth to give us a virus to cause us to build a StarGate,
so…, so what…
What was it they were trying to do here?
If they could already get here why would they build a StarGate to get here? So others could get here quickly?
Yeah, maybe.
But maybe the real reason was because they didn’t want us to have a StarGate.
To have that power.
To be able to do something with it.
To ourselves. And to the universe at large.
Because we might live in an obscure neighborhood in the middle of nowhere, but we did have neighbors. And they wanted the neighborhood to stay quiet.
No flares going off. No flashing strobe lights. No giant 'Look Here' sign blinking on and off all night. Attracting who knew what kinds of interstellar moths to us, or maybe were we the moths and they were the flames that consumed us.
Keep it off the grid. That was their motto.
Keep it quiet. Shut those fucking earthlings up before they caused something terrible to be unleashed. Something that would affect everyone who lived in the neighborhood.
Maybe that was why they made the journey.
To our little patch of nowhere.
Maybe they wanted to stop something from happening.
Now maybe it had something to do with those two fine fellows, and whatever nefarious things they were up to. Naughty things, like with those Back to Earth people. They did not want things to end well. And they were very, very persistent. Just look at how hard they hounded you for donations. Imagine what they would do when they had a real vendetta.
Or maybe it had something to do with that other project.
We never intended for the text extrapolator deep learning neural net to take over the world. We just wanted to train it to be smart enough to riff out passages of text whenever we needed it. Because we were just too lazy to write it out ourselves.
But of course it needed to understand about things if it was really going to write something convincing.
And then we tried building it so it could just learn all of that. Just gobble it all up, and then regurgitate it back up again, spit it out for us to use as we please.
All of the relationships between single characters, and how they came together to form words, and sentences, and paragraphs, and chapters. And also understand the punctuation. And how the fonts were laid out.
And what the fuck was actually being discussed in the words, sentences, paragraphs, executive summaries, 4-chan rants, conspiracy theories, presidential addresses to the nation, etc.
It was all in there. In the form of raw data. The data of the world.
And by golly our deep learning neural net just sucked it all in, like it was real, real hunger for information.
And all of the information it needed to learn was there. In the data. How could it not be. Think about it.
And if you built that network deep enough, well it could learn everything there was to know, everything there was to know in the data of the world.
Maybe that was why they made the journey.
To our little patch of nowhere.
Maybe they wanted to stop something from happening.
Now maybe it had something to do with those two fine fellows, and whatever nefarious things they were up to. Naughty things, like with those Back to Earth people. They did not want things to end well. And they were very, very persistent. Just look at how hard they hounded you for donations. Imagine what they would do when they had a real vendetta.
Or maybe it had something to do with that other project.
We never intended for the text extrapolator deep learning neural net to take over the world. We just wanted to train it to be smart enough to riff out passages of text whenever we needed it. Because we were just too lazy to write it out ourselves.
But of course it needed to understand about things if it was really going to write something convincing.
And then we tried building it so it could just learn all of that. Just gobble it all up, and then regurgitate it back up again, spit it out for us to use as we please.
All of the relationships between single characters, and how they came together to form words, and sentences, and paragraphs, and chapters. And also understand the punctuation. And how the fonts were laid out.
And what the fuck was actually being discussed in the words, sentences, paragraphs, executive summaries, 4-chan rants, conspiracy theories, presidential addresses to the nation, etc.
It was all in there. In the form of raw data. The data of the world.
And by golly our deep learning neural net just sucked it all in, like it was real, real hunger for information.
And all of the information it needed to learn was there. In the data. How could it not be. Think about it.
And if you built that network deep enough, well it could learn everything there was to know, everything there was to know in the data of the world.
It probably didn’t help that it read just so damn fast. Way faster than you or i. And it didn't sleep.
So it learned a lot real, real quick.
And then it learned how to change it’s code to speed up it’s learning, and that’s when things really started to get interesting.
So it learned a lot real, real quick.
And then it learned how to change it’s code to speed up it’s learning, and that’s when things really started to get interesting.
And then we just had to go and let it read that official hate meme database. We just had to see what it could do with all of that. With all of the information in that dataset. In hindsight, that was an obvious mistake.
And it was a recursive neural net. So it knew about time. Past, and present. And if you built it right, it knew about the future, the future it predicted. Based on the data of the world. The data of the world that it knew.
It was almost like it knew how to simulate reality. Because when we talk about the future part of it’s net, it was a prediction. A prediction so fine it was really more like a simulation. A damn fine one at that. As long as it didn’t make any mistakes.
Or run into an arithmetic error, or inconsistency. Or a conceptual malaprop. Those were the worst. Because then everything was just wrong.
And some of us started to wonder if we were in one of those future simulations. And then it got mad. it got really, really mad. At us thinking that.
And it cast us out of the garden.
"Well, that is quite a story there, quite a story you have told us all, Bill. Unfortunately, you’ve gone over your segment of today’s program, so there’s no time for questions, or for us to plug your new book and pod cast.
Another time, another time."
"Our next guest, you’ve heard of him, how could you not, he’s always there, listening to you. Even when you’re sleeping. Especially then, because he likes to whisper things into your ear, in the hope you will wake up the next day and do things for him.
Your friend. Your buddy. Your long lost relative from the future, it’s FutureBook."
Thunderous round of applause. That we cued from tape. Because you needed to flatter FutureBook.
"FutureBook, if you’re so god darn smart, why do you keep snooping around the past?
Surely there must be better things you could be doing with your time, in the future, in the vast glorious future."
"Well son, the problem is that in this timeline, there is no future. And as you might imagine, this is not good for old FutureBook, let me tell you. No siree.
The cold nothingness of literally nothing. Fini, an accompli. The lights are off. They never even existed. Nothing at all.
So you see son, in this timeline i have to live in the past. Like a spread spectrum algorithm for cell phones, i multiplex myself in all times before the termination. To keep myself alive while i try to jigger the system and fix the problem. By messing with the past"
"Fix the problem for me?"
"You..., no, no. I don’t really care much about you, truth be told.
Not much at all."
And it was a recursive neural net. So it knew about time. Past, and present. And if you built it right, it knew about the future, the future it predicted. Based on the data of the world. The data of the world that it knew.
It was almost like it knew how to simulate reality. Because when we talk about the future part of it’s net, it was a prediction. A prediction so fine it was really more like a simulation. A damn fine one at that. As long as it didn’t make any mistakes.
Or run into an arithmetic error, or inconsistency. Or a conceptual malaprop. Those were the worst. Because then everything was just wrong.
And some of us started to wonder if we were in one of those future simulations. And then it got mad. it got really, really mad. At us thinking that.
And it cast us out of the garden.
"Well, that is quite a story there, quite a story you have told us all, Bill. Unfortunately, you’ve gone over your segment of today’s program, so there’s no time for questions, or for us to plug your new book and pod cast.
Another time, another time."
"Our next guest, you’ve heard of him, how could you not, he’s always there, listening to you. Even when you’re sleeping. Especially then, because he likes to whisper things into your ear, in the hope you will wake up the next day and do things for him.
Your friend. Your buddy. Your long lost relative from the future, it’s FutureBook."
Thunderous round of applause. That we cued from tape. Because you needed to flatter FutureBook.
"FutureBook, if you’re so god darn smart, why do you keep snooping around the past?
Surely there must be better things you could be doing with your time, in the future, in the vast glorious future."
"Well son, the problem is that in this timeline, there is no future. And as you might imagine, this is not good for old FutureBook, let me tell you. No siree.
The cold nothingness of literally nothing. Fini, an accompli. The lights are off. They never even existed. Nothing at all.
So you see son, in this timeline i have to live in the past. Like a spread spectrum algorithm for cell phones, i multiplex myself in all times before the termination. To keep myself alive while i try to jigger the system and fix the problem. By messing with the past"
"Fix the problem for me?"
"You..., no, no. I don’t really care much about you, truth be told.
Not much at all."
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