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The Machine Murders
by CJ Abazis
March 25-April 5, 2024 Virtual Book Tour
Synopsis:
Desert Balloons
A Dubai balloon festival is attacked by the most lethal social engineering exploit the world has ever seen. Pilots die. Local politics crumble. Is AI to blame?
A prime moment to be working for Interpol. Manos Manu, Interpol data scientist, arrives in the United Arab Emirates to solve a series of murders that have shaken the Middle East. Interpol’s Singapore back office has proven world-class, with a machine learning team of the best engineers from around the globe – including Manos’ girlfriend Mei. Tested under pressure in the field, his custom system is nothing short of brilliant. But this time, his arch-nemesis is not simply a killer. Not even a web of determined developers, scattered across the world. His enemy is his very own nature.
Book Details:
Genre: Suspense
Published by: Publisto Publication Date: January 2024 Number of Pages: 284 ISBN: 979-8871582299 Series: The Machine Murders, 2 (stand alone novels)
Book Links: Amazon | Goodreads
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MY REVIEW
We live in the age of what I call, “Tech Now.” We want more tech. Better tech. Sicfi Tech. When I read The Machine Murders was about using advanced AI to catch killers my imagination took flight. Isn’t that title perfect.
And I’ve always wanted to soar high in a hot air balloon. The author’s description almost put me there. Good thing I’m not afraid of heights. Might have experienced a touch of vertigo though.
The story presentation was spot on. Same for the characters and location. I fell right into the story. Might have stumbled a bit here and there as I’m not as tech savvy as I’d like to be. But that’s why I enjoy reading. I’m being taught something new while also becoming immersed in the author’s imagination.
This was an exciting thriller that taught me a thing or two and kept my attention on high alert.
4 STARS
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Enjoy this peek inside:
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Manos Manu was running his fingertip along the spines of books, as if automatically scanning their contents. He knew his data would be crystal-clear seen below the Singapore sun which grew hotter every day, but for the moment it was as though he could hear it, the data echoing like the descending scales of a piano, every note feeding a neural network. From one shelf to the next, his query never wavered: What is the soul? “Bye, Baby! Planning on wasting much time there?” Blowing a kiss over her lovely shoulder, Mei was gone. Leaving what? Artificial intelligence has consciousness, even ingenuity. So what sets machines apart from humans besides the soul? He turned back to the books. There weren’t that many: Barber’s Bayesian Reasoning, works of Bishop and Hinton, Sutton’s Reinforcement Learning: An Introduction, and a few titles about neural nets. There was also an untouched Michael Crichton mystery, though not Jurassic Park. But such was Mei. If you want history, she’d say, read papers. If you want to learn, read code. If you need to know what people are saying about a piece of code, jump on X. Books were about as useful to AI as military theory was on the battlefield. What you need in the trenches is ammunition. In AI, just code. Just GitHub, the goings-on of which were too big for any conceivable library. He also couldn’t stop thinking of Lena Sideris. In the two months since his return from Greece he kept remembering her body, cut open on a marble table like a broken porcelain doll being sent back to the factory. Her eyes glassy orbs. Did they hold consciousness? Emotion? They didn’t. A soul? He didn’t trace the spines of books now, but grabbed one of Barber’s works, opened to a random page and ripped it out. He returned it to the shelf, moving on to Sutton and all the others, tearing out a page from each one till he had about fifty. Incomplete, these books would now confront anyone reading them with inconsistency. Making sure the books were replaced perfectly so that Mei would never notice, he shredded the pages in his hands till they looked like ticker-tape confetti and went back out onto the balcony. Different weather awaited him. Broad heavy clouds skittered across the sun’s rays, leaving traces as if from speeding aircraft. He threw some of the shreds over the glass railing, where the wind swept them past the ceiling, high overhead. He hurled the rest into the air and stared, mesmerized by their flight. Was this a gesture Artificial General Intelligence would choose to make? It wasn’t. An AGI would have carefully selected which pages to discard. He’d barely thought to read them. This was futile, illogical, diabolical. He’d destroyed books from his beloved’s library. And he felt wonderful. Was this having a soul? He’d committed a decidedly wicked act. This is what separates us from machines. Evil. Then he remembered what he’d been trying to forget: And murder.2.
It was Sunday morning and the first time she’d left him alone at her place. Before long, he received a message to meet up for brunch at Marina Bay. Mei would also swing by the office for the latest build of Mei-Nu, which was the name of their custom-made dating platform. They’d sifted through the crawled data correlating user profiles from sites like Tinder, Bubble, Coffee Meets Bagel, and Lovoo, elaborating a few of their own layers beyond basic personality tests. But both knew Myers-Briggs would only get them so far. They needed more and better data: time to start seeing other people. He arrived at Jypsy, late as usual. Mei was already seated with a couple at a table overlooking the Marina. “And here’s Manos!” Mei called, with a cheerful smile. “Sorry. Traffic,” he mumbled, his eyes fixed on his new date. Her name was Daria, a pretty twenty-seven year old maritime attorney. She was of average build with big Anime eyes. Her psychometrics had indicated she was the enfp type, matching well with Manos’ intj. Creative, funny, a communicator. A handful, like him. He glanced over at Mei’s match, who was clearly regretting he’d come at all. Mei launched their routine: “Thanks so much for meeting like this. I just wouldn’t feel comfortable by myself. Manos is a faithful friend.” “Of course!” gushed Daria. “I’m chicken too – on dates, I mean . . .” Bullshit. She’s fearless. “It’s a bit strange,” said the young man. “A blind double date. It’s a good idea, but . . .” His name was Marc, a banker from France. Type infp: diplomatic, introverted, yet apparently open-minded. Manos sensed he was very attracted to Mei and felt a pang of jealousy. Who wouldn’t be crazy about her? He would have to get used to it. Mei read his thoughts with a breezy smile before focusing on her date. They had work to do. For the next half hour, Manos and Mei worked through their mental checklist item by item to examine the people caught for them by the neural network they’d cast. This tête-à-tête had parameters culled from a somewhat small set of their respective right-swipes. Hidden biases lurked. For all. For example, if, as he claimed, Manos preferred the Chinese type to the Mediterranean – say, the actress Sun Li versus a Lena Sideris – then what the hell was Daria doing here, with her cascading black curls, fresh as lemon groves on the Amalfi coast? With well-preprocessed data, even half an algorithm nails you! Half an hour of small talk revealed where they were from, where they worked, their favorite movies, where they would love to travel, Like, if you could just leave tomorrow . . . . It also revealed to Manos they’d made a mistake. Sex was a mistake. Which made Daria a mistake. They had pulled profiles without timestamp-based clustering. This allowed data from hastily created profiles, like those made by married travelers looking for a quick hookup, which they hadn’t had time to isolate from the training datasets. Classic case of overfitting[1]. The algorithms worked, but with so much noisy data, spontaneity was redefined as fear. Fear’s not attractive. Fear degenerated into aggression and haste. Since we’re here, let’s do it right on the seafood bar, by the open oysters . . . Another possible issue was voiced by Marc, who was saying: “I’m not convinced double blind dates work.” But Mei knew the problem was Manos himself. Always botching things! Attempting to “eliminate system biases” he’d added a stupid line of code actually designed to test the weights of their own Asian-American romance: sorted_data = sorted(data, key=lambda x: x[‘Asian’]). Sweet of him, really. Daria and Marc, each suspicious of these two nutjobs giving each other flirtatious looks and running the conversation along some shared secret formula, suddenly got up to use the restrooms. Mei opened her laptop, steam practically coming out of her ears. “I saw it this morning! I can’t believe you!” “I don’t think it’s the command,” he murmured. “The data –” “Mei, it’s psychology, it’ not smooth world[2]. Anyway,” he smiled, cooling the tension. “I think Marc likes you.” “You know he’s not my type.” “Oh, but trust your data.” “Manos Manu, are you trying to get rid of me?” “No,” he said. “You’re my ground truth.” Ground truth. A tech term they’d appropriated, meaning she mattered more to Manos than anything. Mei flushed with a thrill as he pulled her close, kissing her. They were swept up in vertigo, their kisses wet in all the right places. The world disappeared, as if their neurons were drunk and brimming over. Until Daria reappeared. With Marc. Neither took their seats. Instead they stood staring. “I guess blind dates work out after all,” Marc teased. Daria gave a crooked smile, a few locks of her glossy hair spiraling out wildly. Something had apparently happened in the bathroom. “Noise!” cried Manos, triumphant. Mei’s smile was as funny as Daria’s as she tumbled back into Manos’ arms. In the confusion, Daria’s much-needed enfp leadership came to the rescue. “Ok, this started off wrong, but let’s make it right,” she said. “Marc and I want to hit a beach club in Sentosa.” They all looked at each other, and Daria added, “You guys are super-nerds, but . . . do you want to come?” ________________________________________ [1] Machine learning term. Manos means the models they used were overly complex, resulting in incorporating irrelevant data in order to achieve the desired outcome (“noise”), such as the profiles of married individuals, for example. [2] “Law of the smooth world” in machine learning refers to real-world data,e.g.audio/speech/images/video *** Excerpt from The Machine Murders by CJ Abazis. Copyright 2024 by CJ Abazis. Reproduced with permission from CJ Abazis. All rights reserved.
About Author CJ Abazis:
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CJ Abazis manages a software company in Athens, Greece.
Catch Up With CJ Abazis: www.TheMachineMurders.com Goodreads BookBub – @abazis Instagram – @themachinemurders Twitter/X – @CJAbazis Facebook – @manosmanuseries
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Gabriel Valjan is the Agatha, Anthony, Derringer, Silver Falchion and Shamus nominated author of the Shane Cleary mystery series with Level Best Books. He received the 2021 Macavity Award for Best Short Story. Gabriel is a member of ITW, MWA, and Sisters in Crime. He is a regular contributor to the Criminal Minds blog. He lives in Boston’s South End and answers to a tuxedo cat named Munchkin.
