MOAB

Chapter 30: Galene @ 1.2x nhs

Chapter 30: Galene @ 1.2x nhs

“Okay, so I had another brain fart.”

“You keep using that word. I do not think it means what you think it means.” Greg smiled at her, giving her his full attention and support.

She had gathered Greg and Mel for her presentation. They were in the meeting room, which was practically never used by Greg. It had a large and ridiculously expensive meeting table and all the necessary gadgets and screens. It was a perfectly good room going to waste. Gal made a mental note to encourage him to use it more often, it made him look even more important.

“I was watching a movie with you, and you said you feed the movies into the AI so it gives back shortened versions of them. Naturally, I kept thinking about ways to optimise that. You have to understand, what we call Artificial Intelligence can also be called Artificial Idiocy. Computers are powerful, but extremely dumb. Sorry Mel, but you know what I mean. You can tell it to analyse each frame in real time, but if it doesn’t have clear instructions on what to do, it can’t make decisions.”

Mel brushed her comment away with a delicate gesture.

“I’m with you so far. Indeed, sometimes the algos spit out gibberish, I’ve seen it many times,” Greg nodded.

“I’m only gonna mention him once, so pay attention. I had an ex, he was a wannabe filmmaker. Always toying around with a camera, with sounds, editing, all of it. It was his passion. He sucked, but he was passionate, no one could deny him that. We broke up cause he was a loser but I remember something about music scores. He’d say to me, music in movies is completely cliche, but it is cliche for a reason: It works. It works across boundaries, across language, across race, across culture. If you sit a man down to watch a movie in a foreign language and he can hear the music score, he will know what to feel. He will know what the scene is about.”

“You’re gonna take the music score into account!” Greg jumped up. He sat back down, motioning her to carry on her presentation.

“Precisely! I tried it out a couple of times and it seems to work great. With the old algorithm, it sometimes trimmed scenes that were important but didn’t have dialogue or a lot of motion or camera motion. Now, it can identify a music swell for example and it will never trim that scene down because it’s important.”

“This seems like a brilliant idea,” Mel said with her soothing voice. “Well done.”

“So, ba-dum ba-dum,” she rapped the desk and clicked the slide.

A mockup of an app showed up, with fanfare sounds worthy of a Hollywood film. The logo was a draft but passable, it said ‘TimeShave.’

“Get it?” Gal looked at them expectantly.

“Yes, timesaver,” Greg said. “She has a quantum brain and I pick up stuff rather quickly, if you haven’t noticed.”

Gal lost her stream of thought and fangirled over Mel for a minute. “I know, right? It’s so… I wanna play with it someday,” she sighed.

“Timeshaving,” Greg nudged her back.

“Right. Timeshaving. Obviously, the timeshaving takes a lot of processing power, but it only needs to run once. I suggest a service that users input requests, for example ‘Gone With the Wind,’ always wanted to see that one and never got around to, and the service trims it down without leaving anything important out. So, our example is a four hour movie, plus it’s an old one. Slower cuts, more panning shots, more travelling shots. The algorithm I made can shave itsy-bitsy frames here and there, or even entire seconds in those large panning shots we mentioned, and present a tighter movie. It shaves about four minutes per hour, which in this case is times four ending up with sixteen minutes less run time. In ‘Gone With the Wind,’ the result is twice that because of the slower cuts I mentioned.”

Mel and Greg nodded, thinking. “So it’s a paid service?” he asked.

“Well, yeah, processing time isn’t free. Over time, there will be a library of content that is timeshaved-” she paused for effect and wiggled her eyebrow at him, “-and revenue increases since you reuse the already timeshaved content for new users.”

“Target groups?” Greg asked, being helpful.

Gal recited. “Businessmen, timesaving fanatics, owners of self-driving cars because we already have a service that streams content to them depending on the estimated travel time…” she trailed off. “Damn, forgot the last one.”

Mel added, “Startuppers, perhaps?”

Gal pointed at her, “Startuppers! f**k. Good one. Hey, you are good at inspiring people, maybe you should make a career out of it.” Gal pulled out the notepad and wrote it down.

“Nice,” Greg said. “I’m definitely interested!”

Galene sat down, all energy gone from her shoulders. “Hit me. Tell me what a fugly idea it is. Just lay it out for me, don’t hold back.”

“It’s brilliant,” Melpomene said.

“Really?”

“Really.”

“So it’s not stupid?”

“It is not stupid, I assure you. The logistics and the market interest need to be tested of course, but it shows potential,” Mel said.

“You’ve run simulations in your mind already, haven’t you?” Gal said as a matter-of-fact.

Greg turned to his Muse. “You can do that?”

“Yes, I can, and I have. The data is insufficient, but it shows promise,” Mel assured her.

Greg clapped his hands. “Nice! Okay, I say go for it. I’ll get you the marketing department head on the phone so they can handle the tests. There’s no need for you to waste time on the business plan.”

Gal perked up. “You’d do that for me?”

“Of course. It’s a great idea. Not sure about the name though,” he teased.

“The name stays!”