It was still early evening but it was already dusk in Toronto. Rain had started to fall. Whipped by the wind, it drenched the few pedestrians scurrying along Queen’s Quay West, toward the Jack Layton Ferry Terminal, to board the last ferries headed for the Toronto Islands, which formed a massive breakwater for the city’s modern harbor on Lake Ontario.
From the sheltered terrace of Quantumnetic’s top floor office building in Ontario’s capital city, Stephen Kesl watched the men and women rushing home, newspapers held over heads as makeshift umbrellas. He shook his head, puzzled by their lack of preparedness for the storm. They should have listened to the Canadian Weather Service’s forecast. Not because the weathermen were infallible, but because the service now relied on the forecasts provided by Weatherbot, his company’s proprietary machine learning application.
Of course the machine learning program would’ve been useless if it weren’t for the invention of quantum computers. Kesl’s company, Quantumnetics, had the most advanced with its 2000 qubits processor, capable of 100 quadrillion operations per second. Running on the quantum processing beast, Weatherbot never stopped analyzing data and most importantly, always getting smarter. The combination was perfect for deep learning projects like forecasting the weather.
Weatherbot didn’t need to be programed to predict weather. Instead it taught itself to forecast accurately, in a way no human could fathom, from massive amounts of accumulated past and live data from around the world. Since coming on line in August, it had demonstrated its ability to predict accurately, hour by hour, ten significant weather variables for Toronto, five days into the future. Now that the beta testing was finished for Canada’s largest metropolitan area, Kesl was certain by the end of the year the Meteorological Service of Canada would ditch its old computer model forecasting and spend its money on services provided by Weatherbot.
Kesl stepped back inside. The raw icy wind blowing in from the lake carried a dense, cold fog with it. He didn’t need his deep learning machine to tell him he’d get sick if he stayed outside in this bad weather with nothing more than a polo shirt, slacks and sandals. He went exactly two steps past the sliding glass doors and stopped. He always did this, no matter what room he entered. Everyone who asked him about this strange habit received a well-practiced, self-deprecating smile for an answer.
Kesl automatically checked the exits. A single door led in and out of the room from offices beyond this one. Behind the desk was an elevator that went down to the parking garage. He had the only pass code. The rest of the room was spare, with a tiled floor and white walls. There was no art on the walls. He couldn’t stand the distraction. An ergonomic standing work desk was the only furniture. It had a single keyboard and a microphone but no mouse, and faced a gigantic interactive glass screen on the nearest wall. A message alert blinked on the glass screen in a lurid red. “Hangman, who’s calling?” Kesl asked.
“A 16th century Elizabethan poet, spy and playwright,” Hangman answered, this time in a tenor voice that was a mixture of the three operatic tenors – José Carreras, Placido Domingo and Luciano Pavarotti. Early on, when Hangman was teaching itself the basics of human speech, Kesl had trained Hangman to mimic the cadence, accent and mannerisms of famous people, including opera singers. Kesl loved opera because of the mathematical precision the musical genre demanded.
He smiled at Hangman’s literary allusion. The digital intelligence had given all of his special contacts arcane references. It was a game they played that tested Kesl’s eidetic memory against Hangman’s. This one was easy. “Christopher Marlowe,” he said. Chris was part of Kesl’s private Bigfoot data network and the man he had come to respect and rely on in vetting the deluge of reports of Bigfoot encounters. “Put him through.”
Chris’ lined face peered at him through the screen. Kesl knew the man was seeing his own features – dark skin, cheeks cross-hatched with slim white scars from the surgeries, black hair and whiskerless chin.
“This had better be good, Chris. I’m due at the governor’s in thirty minutes for drinks, although I don’t drink, idle chitchat, and to push for a government contract for Weatherbot’s new role as forecaster in chief for Canada’s Weather Service. Then I have a meeting with my board of directors and the Royal Bank of Canada for funding a new initiative into AI to eliminate credit cards – they should really stop issuing credit cards though, what do you think? It’s a digital currency world now. Never mind. Then home for dinner and Jeanine and reading stories to the kids.” Kesl said everything in a breathless rush that those who knew him were used to.
Chris waited for him to come to the end. “Take a look at this.” A picture opened up on the screen, the computer automatically adjusting the pixels to enhance the image.
Kesl took one look at the green-hued Sasquatch and gasped. For a moment it seemed his brilliant mind couldn’t take in what he saw – the vacant stare in the cold, gray eyes; the mortal wound in its upper torso. The creature couldn’t be dead. Wasn’t supposed to be dead. But it was unmistakably dead. This information sent Kesl’s mind into a hopeless task, like a computer that’s been programmed to compute the highest prime number. After a period of time, he couldn’t begin to figure out how long, he became aware of sound coming from a direction in front of him. The noise became familiar. Then, Chris’ voice came to him as though through a long tunnel.
“Stephen! You OK? You look as if you lost a best friend or something.”
Kesl’s eyesight returned. His brain started to function again and he focused on the smaller picture of Chris, in the lower left hand corner of the screen, as more pics rolled in. Afraid that a peek at the dead Sasquatch would send him back into another fugue state. This was all wrong.
“What happened?” he asked to give himself more time to figure out the next step.
“I have a hunting friend, Bob Nitschke, who found it.”
“Did he kill it?” Kesl asked, the words grating in his mouth.
“It was already dying when he saw it. He sent me the text pictures you’re looking at. He also got everything on a trail cam he set up. That’s not important. The important thing is we’ve got us a real Sasquatch. This is what we’ve all been waiting for, proof positive. They really exist. This is going to blow open Bigfoot research world wide.”
Kesl balked at that suggestion. The Sasquatch Research Association was the last group who needed to know this Sasquatch had been found. He needed Chris’s cooperation to keep the lid on this for now. “Do you know where the body is? Tell me he entered the coordinates in his phone?”
Chris nodded enthusiastically. “I’m supposed to meet him at home in a couple of hours.”
Kesl’s mind had cleared completely and he was thinking more lucidly, the shock of the dead beast no longer affecting him. “Go to the site immediately and secure the body. Grab the trail cam. I’ll come to Escanaba … Where the hell is it? … Oh yeah … I’ll handle Bob … What’s his address?”
Chris gave him the information.
The last three pics came through. Kesl started at the sight of a woman bending over the body, shooting and running away. Another fugue state threatened and he counted backward from twenty to zero in Russian, repeated the sequence in Urdu, and a third time in French. His mind calm once more he wondered aloud, “What the hell is this? … Who is that? Do you know? Do you know?”
He saw Chris waiting patiently for him to wind down, knowing his friend was familiar with his speed talking and thinking. “Okay, here’s what you do … call your friend, Bob, and tell him I’m coming to see him … then get the body. I’ll meet up with you as soon as possible.”