The Reborn (The Day Eight Series Part 1) (24 page)

Read The Reborn (The Day Eight Series Part 1) Online

Authors: Ray Mazza

Tags: #Technological Fiction

BOOK: The Reborn (The Day Eight Series Part 1)
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That’s a neat trick
, thought Trevor. He’d like to be able to materialize things whenever he wanted – a gourmet dinner… a six pack… a briefcase packed with diamonds…  a girlfriend. “It’s a lot of different colors?”

“Yup! Dad made it for me specially. It used to be yellow but then he turned it nice colors so it would be more fun to eat. And it is!” She tossed a piece up in the air and opened her mouth, swaying, to catch it. It bounced off her nose and back into the bowl. She giggled and looked to Trevor, “Okay, you can start it now.”

Trevor started the movie, then continued reading the note.

 

Now, turn on the computer.

 

He found the power button and pressed it. There was an electronic hum from the computer case and then the monitor flashed on, showing the computer’s desktop.

What? Did the computer just boot up?
Trevor wasn’t sure he even saw a boot screen… that was the fastest load of an operating system he’d ever witnessed.
This is some serious hardware,
he thought.
All solid state drives?

The desktop contained a single folder, titled
Genetically Adaptive Circuitry 12.1
. He recognized it immediately. It was a programming project he’d worked on nearly eight years ago when he first joined the company… right before it had redubbed itself Day Eight. Until then, it had been known as Intelligentech, a mess of a name that was too long for its own good and unwieldy to say.

This project had gotten Trevor hired, fresh from grad school at UMich. He remembered it so clearly. It was one of the most surprising moments of his life.

He’d given a talk at a genetic algorithms conference in his final year. People were yawning the whole time and checking their watches. Midway through, a man in the front row nodded off and actually somersaulted out of his chair, then left with a bloody nose. At the end, nobody asked any questions, and there was no applause.

After the room had cleared out, one man in a dark suit and lightly tinted Ray-Bans remained in the back row. Trevor assumed the guy was snoozing behind his shades. But on the way out, the man stood and approached Trevor, removing his sunglasses. He proceeded to grill Trevor with questions until an oncoming lecture forced them out into the hall. Then the man grilled Trevor some more.

Trevor hadn’t known it at the time, but it was a job interview. And he was acing it. Apparently, this man – a tech scientist from Intelligentech – had seen something in Trevor’s work that nobody else had: promise.

Trevor had lectured about a Darwinian method for evolving computer chips too complex to design manually. His program treated large numbers of chips like a diverse population of living species vying for dominance. It created tens of thousands of chips with different, semi-random circuitry, and ranked them based on how well they performed desired tasks. Then it “killed” the poorest performing ones, “mated” the rest, and injected random mutations to get a subsequent population of “offspring” chips that would outperform the previous generation. The program repeated this hundreds of thousands of times, and would occasionally evolve a chip that behaved exactly as desired.

It was survival of the fittest for computer chips.

But there was an oddity. It sometimes created chips with massive portions of seemingly irrelevant pieces. Trevor, as an afterthought, had included a mere two slides dedicated to discussing these ganglia of miscellaneous logic. He had mentioned that on the surface they appeared to be insignificant, but found a correlation between these ganglia in a population’s ancestors and timely arrival at solutions. Often a small mutation inside one or a minuscule change to its connectivity to the rest of the circuit could have far-reaching effects.

Curiously, the scientist from Intelligentech was only interested in these two slides. They had talked about these “logic ganglia,” and how their formation might vaguely resemble the evolution of nerves in the earliest organisms.

Trevor had toyed with evolving them by looping their outputs back to inputs and to their fitness ranking algorithm so the ganglia would modify and score themselves, hoping something meaningful would result.  It felt very much like swimming around in the middle of the vast Pacific at night, hoping to wash up on a glorious island of significance.

Trevor’s colleagues had rejected these ideas outright, failing to see any promise. Although Trevor never found his island, the man from Intelligentech seemed as close to ecstatic as an expressionless scientist could get, and after two hour’s discourse, offered Trevor a job on the spot.

A few days later, Trevor accepted. In his earliest moments at the company, they asked him to continue his circuit growth algorithms, and told him the plan was to use them in some form of optical recognition. Lab coats would hand him a sheet of inputs and request ganglia circuits that approximated a second sheet of outputs.

When he completed one assignment, the lab coats would take the circuit design with a nod and hand him a new set of inputs and desired outputs. Again, he would pore over variables while evolving populations of circuits. Each time that the lab coats handed him a new request, they would also urge him to refine his approach.

He optimized his code, and over time was able to arrive at solutions more and more quickly. The first one took close to a month. After a few more, they were taking a couple weeks. Eventually, he was finding solutions within days, and then mere hours.

Then one day they took his code, thanked him, and moved him onto something else. No explanations.

Returning his mind to the present, Trevor glanced again at the folder on the desktop. It was numbered with a version almost twice as high as where he’d left off way back then.

 

You will notice that the program we hired you for is here. I cannot tell you what a fantastic job you did; it was a wonderful contribution. After the handoff, my personal staff continued working on it. We needed to adapt it to our purposes and for our unique hardware, which is why we took it away from you. Your algorithm, however, has remained untouched.

 

If it was so fantastic, why hadn’t they brought him into Damon’s inner circle? Trevor no longer bought the story they gave him about an optics application… it sounded too unimportant. From the timeline Damon had given him, Day Eight already had its earliest human simulations when Trevor began working there. This was related.

 

What I need now is for you to find a bug in your code, and fix it. It is a bug that has been there since close to the project’s inception. It is an insidious bug that has lain dormant, and only has reared its head in the past few weeks. At first, we didn’t even know it was a bug. We just thought it was a shortcoming of the program. After Kane worked with it for nights on end, he found an inconsistency.

 

However, none of my team were able to fix it, because they couldn’t find the cause. Your math, although extremely complex, is understandable. What we had not been able to understand wholly was your approach. The aspects of it that we can grasp are simply jaw-dropping.

 

They said that Einstein was decades ahead of his time. Some believe that if he hadn’t penned his paradigm-shifting theory of the equivalence of mass and energy,  E = MC
2
,  that we still wouldn’t have it today. Instead, we’d just be wondering why the atomic clocks in our satellites wouldn’t stay consistent with those on Earth, and why our GPS devices couldn’t be more accurate.

 

I believe your approach to self-modifying intelligence – and I do mean intelligence, not just circuits – is, like Einstein’s theory of relativity: ahead of its time. Since we could not fully comprehend it, I ask that you find this bug, understand it, and fix it. We could not.

 

The reason we noticed this bug when you did not was that our modifications and our hardware increased the speed of your program to an extraordinary level. Only then, did it become apparent.

 

I am confident that you will see what I mean. You can fix it.

 

This is of the utmost importance, Trevor.

 

Damon.

 

PS – Please do not remove this or any future notes from the room. Simply leave them in the wastebasket, I will handle their elimination.

 

“Wow.” Both excited and in disbelief, Trevor sat and soaked up what he’d read. By this point, it was obvious that most of the projects that Day Eight’s “normal” employees worked on were related to the human simulations – either the work was to directly enhance them, or it was an implementation of ideas the simulations themselves had developed.

But the thought of his own program having been an accomplishment on the level of Einstein was far-fetched. Novel, maybe, but his approach hadn’t been so far beyond logical reasoning that it surpassed the comprehension of the lab coats at Day Eight. Had it?

Trevor checked on Allison, who was happily watching
Alice in Wonderland
and munching on her bowl of rainbow popcorn.

Looking back to the computer, he took a deep breath, stretched his hands, and jumped in. It only took half an hour or so before he felt at home once again in his code.

The program was still set to work on the same input the lab coats had last given him years ago. Without changing anything,  Trevor pressed the ‘F5’ key to run it. At first, he thought it hadn’t worked, because the program displayed its usual charts, graphs, and statistics, but none of them were updating… like the program had frozen. Was this the bug?

Trevor looked more closely at the screen. The top of it read:

 

Solution Found.

Total Generations: 1,922,517

Printed to file “Circuit 10-29-2012.1.cct”

 

What? The program completed? Already!?
It wasn’t updating because it had finished instantaneously. He’d expected it to take a few hours.

Holy crap. This computer is…
“Fast” would have been the understatement of the year.

Instinctively, he opened the computer properties to check the processor. It only displayed:

 

Processor: Unknown Model

 

Trevor shut the computer down and removed the finger screws from the oversized case, then slid the side panel off to expose its internals. A billow of heat brushed past his face. At first, he didn’t quite understand what he saw. The inside of the case was laden with rusty cobwebs. On closer inspection, he realized he was looking at a nearly microscopic network of copper-colored tubing.

Not thinking, Trevor reached out to touch it. As his fingertip made contact with a portion of the webbing, a searing pain shot through his hand. He yelled out and sprinted to the bathroom to hold his finger under cold water. How could he have been so stupid? It had been like touching a soldering iron. The tubing must have been an extensive form of a heat sink used to draw heat away from the most active components so they didn’t fry themselves.

Fortunately, the medicine cabinet held both bandages and antibiotic ointment. He dressed and bandaged his finger and looked at it, thankful he didn’t have to do a lot of typing just yet… he would need to locate the bug first.

Back in the sanctuary, Allison was upset after hearing him yell out. He calmed her down and she settled back into her movie.

Trevor again peered inside the computer case, still curious, but more cautious. There were some standard computer components which didn’t interest him. He turned his attention back to the tubing. Most of it fed into a golf-ball sized sphere in the center of the case that had a silver-purple iridescence to it under the gentle light of the room. That – however it functioned – must have been the processor. Trevor guessed it was spherical because that was the most efficient way to pack material into a space… although all of today’s computers still hadn’t progressed beyond flat processors.

Satisfied there was nothing more to learn simply by looking at it, he shut the case and rebooted the machine, excited about the prospect of running his circuit evolution program at blinding speeds.

Trevor toggled an option that would keep it running indefinitely since it wasn’t trying to find a specific answer. It would just “live” and create populations of larger and more complicated ganglia of circuits.

He ran the program again. The screen came to life, with charts jumping around, lines rising and falling on graphs, and numbers growing, shrinking, growing again. After six seconds, it halted. The screen displayed:

 

Population insufficient for reproduction.

Error: Gene mismatch

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