Q*pid Read online

  Table of Contents

  Blurb

  Dedication

  Acknowledgments

  Chapter ONE

  Chapter TWO

  Chapter THREE

  Chapter FOUR

  Chapter FIVE

  Chapter SIX

  Chapter SEVEN

  Chapter EIGHT

  Chapter NINE

  Chapter TEN

  Chapter ELEVEN

  Chapter TWELVE

  Chapter THIRTEEN

  Chapter FOURTEEN

  Chapter FIFTEEN

  Chapter SIXTEEN

  Chapter SEVENTEEN

  Chapter EIGHTEEN

  Chapter NINETEEN

  Chapter TWENTY

  Chapter TWENTY-ONE

  More from Xavier Mayne

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  Copyright

  Q*pid

  By Xavier Mayne

  Can a computer program understand love better than the human heart?

  Archer, the AI at the dating service Q*pid, realizes humans don’t always make the best choices, so it begins making some unconventional choices for them.

  Fox Kincade is the last of his group of friends to be single, so he’s delighted when he discovers a new match in his Q*pid app—one that, according to the advanced AI wizardry, should be the love of his life. Instead of the woman he’s expecting, he’s paired with Drew Larsen, a shy, somewhat nerdy PhD student who has also grown discouraged with romance.

  Drew and Fox have little in common—aside from the fact that they’re both straight. Or so they always thought. But as the guys get to know each other, they realize Archer might have the right idea. Their path isn’t smooth because both need to overcome every idea they have about themselves and what true love might look like. But with the help of Archer—and some friends who have stuck with Fox and Drew through the thick and thin of their relationship trials—they might find their way into each other’s hearts.

  For J. We found each other the old-fashioned way.

  Acknowledgments

  ONCE AGAIN I am indebted to my first reader, George Schober, whose gentle guidance and tough questions have edified my prose.

  Chapter ONE

  “HERE’S YOUR ridiculous pink drink.” Padma sat on the stool on the opposite side of the tall table by the window.

  Veera took the frosty cosmopolitan from her friend. “We can’t all be single-malt girls,” she said with a raised eyebrow. “I need a little sugar with my ethanol.” She took a large gulp.

  “When I see people chug like that, I wonder what his name is. You, though… I know who’s making you drink.”

  “He’s just so damn stubborn.”

  Padma laughed. “He takes after you.”

  “I wish I could get him to let it go.”

  “Don’t you decide what he lets go of? Can’t you cut out the code that isn’t working?”

  “He thinks it’s really important, so I’m not going to delete it. That would be like me giving you a lobotomy every time you say something I don’t like.”

  “You’re lucky I’m so delightful.”

  “Yes, I was just thinking that,” Veera replied in a dead monotone.

  Padma laughed. “But he can’t think—he’s an AI engine that you created.”

  “Which is why I can’t simply delete things because they don’t work the way I want them to. If he can’t learn, he might as well be some standard analytical engine. That would set the world on fire.” Veera rolled her eyes.

  “We’re not eliminating world hunger here,” Padma replied. “We tell people whom to date. It’s not exactly rocket surgery.”

  Veera shook her head at Padma’s mangled metaphor. “If we successfully apply a learning AI to this most human of problems, then maybe world hunger is next.”

  Padma took a sip of her whiskey and sat back. “Why is this so important to you?”

  Veera sighed and stared into the rapidly diminishing depths of her cosmo. “Have your parents found a match for you?”

  Padma shook her head. “I hope not. I told them I wanted to finish school first, and then I said I needed some time to establish my career. My hope is that eventually they will forget about it.”

  “And how likely is that?”

  “Not very.”

  “Why don’t you want them to arrange a marriage for you?”

  “My sisters, mainly. One of them is happy, but the other… it didn’t go so well.”

  “Those are the same odds people face with love matches, you know. Arranged marriages, on the population level, work out better.”

  Padma shrugged, but her unease with the topic was clear. “So you never told me what went on in the code review last week.”

  It was Veera’s turn to shrug bleakly. “It wasn’t great. Most of those guys don’t like the idea of AI in the first place, but the epistemology engine really seems to push their buttons. They all said—in no uncertain terms—that I should ditch the whole idea and get back to ‘solving real problems.’”

  “Ouch.”

  “Yeah, ouch.” Veera slumped forward. “Maybe they’re right.”

  Padma sat up straight, as if taking on the weight of Veera’s despair. “No, they’re not. It is still a good idea. Because it’s a completely new way of approaching the problem, they aren’t comfortable with it. It scares them to think of an algorithm that knows them better than they know themselves.”

  “They’re programmers, Padma. They don’t know themselves at all. If I were working on code to optimize delivery routes for heart-shaped boxes of candy, they’d be pitching in to help me. But trying to get them to understand how actual humans relate to each other, and how AI can help them do that better—well, I might as well be trying to talk to them about fashion.”

  “Which I’d be all for it if you were able to keep them from wearing socks with sandals. That would be worth whatever it takes.”

  “I’m just trying to solve for the human heart. Don’t ask me to do the impossible.”

  They laughed and polished off the first of several drinks.

  “VEERA, THE floor is yours.” Edwin, the manager of her team, smiled encouragingly across the table.

  The smile was mostly for show, she knew, because he’d made perfectly clear he had precious little confidence in her idea. However, despite his reservations—which he’d enumerated several times for her over coffee during the course of the previous week—his team had a quota of developer pitches to make during the quarter, and at this late date he had no other options.

  She opened her laptop and connected the display cable. The wall at the end of the conference room came to life, showing the first slide of her presentation. The hot-pink glow from the monitor flooded the room, basking all in attendance in the reflected glory of her cupid-decorated title slide.

  Veera glanced around the room and immediately regretted not asking one of the web designers for advice on her color palette. She cleared her throat nervously.

  “Thank you, Edwin.” Her voice sounded small. She wasn’t confident it had even traveled to the end of the suddenly miles-long conference table.

  Focus, Veera. Find your voice.

  “Artificial intelligence has never been applied to relationship discovery,” she began, using the company’s preferred term for online dating. “Today I’d like to present a vision for how AI can be a significant differentiator for our service. I call it the ‘epistemology engine.’ Our current data-mining processes work very well, but like all post hoc analytics, they allow only future optimization based on past performance. What they cannot do is dynamically adapt the discovery model in real time. In short, they do a great job of helping the next customer, but they ca
n do little to help the current one.”

  Veera looked out across a conference room full of skeptical expressions. She swallowed hard and tried to remember to breathe.

  “Today, I’d like to introduce you to the future of relationship discovery. Artificial intelligence that learns and adapts, accompanying our customers through their daily online lives, becoming a trusted friend as much as a matchmaker.” She paused to look around the room. This was the moment she’d practiced for a full sleepless week. “I’d like you to meet Archer.”

  She advanced to the next slide, where Archer’s name appeared at the top, accompanied by a picture of her ungainly tower computer wearing a T-shirt that said “I’m with Cupid.” Some scattered chuckling rippled across the room.

  “Archer is built on an open-source AI framework and uses the ‘epistemology engine’ I mentioned previously. In early testing we’ve found—”

  “Whose AI framework?” grunted one of the engineers at the back of the room.

  “IBM’s Watson,” Veera replied.

  “Who coded the epistemology bit?” another engineer chimed in.

  Veera’s cheeks warmed. “I did. I mean, I am. I’ve been working on it for about a year as a side project, but over the last month I’ve been hooking it into the AI. As I was saying, the early testing is showing promising results. But just as important as the code is the data set we’re proposing to give it.”

  This was the part of the presentation she’d been dreading. She had only been able to win Edwin over to the proposal when, desperate in the face of his insistence that she drop it and work on something more immediately deliverable, she told him why it was so important to her. She knew she could win this group if she did the same.

  “We have to stop thinking about relationship discovery as an algorithmic process. People don’t fall in love because our analytics tell them they should.”

  “No, they don’t,” Ross, one of the marketing managers, broke in. “They tell us what they’re looking for in a relationship, and we help them find it. They’re in control.”

  “And that’s the problem I’ve solved,” Veera said, meeting his eyes with what she hoped looked like fearless confidence. It was anything but. “In many cultures, marriages are arranged. I believe—”

  “Arranged marriages?” Ross scoffed. “Are you proposing that we choose the best match and tell our customers they have to get married? Will we dispatch a drone to shoot them if they hesitate?”

  “Perhaps if we let Veera explain her actual proposal?” Edwin ventured.

  She cast him a grateful glance, then began again.

  “As I was saying, arranged marriages work because they are orchestrated by the people who know the bride and groom best—their parents and often their extended families. They look at the whole person, and they work to find a mate who will best complement him or her.”

  “Or who has the most oxen to offer,” Ross cracked.

  “That’s offensive,” snapped Padma. Veera had never heard her utter a sharp word in the more than two years they’d worked together.

  Around the table, eyes narrowed at Ross, who shifted uncomfortably in his seat. He cleared his throat in a way that might have led those near him to believe he had uttered an apology.

  Veera turned back to the screen, resolving not to allow him to interrupt again. “Archer is an attempt to bring artificial intelligence to bear on a challenge that until now we could only work around the edges of.”

  She advanced the slide, covered with the sample analytical reports that resulted from profile analysis and matching. “Our system is very good at what it does: taking our customer’s self-reported profile and matching them with what they say they want. And our results are industry-leading. But our success is built on the shaky foundation of two willful misconceptions: that people know themselves, and that they know the qualities of the person who will make them happiest.”

  She looked around the room at eyebrows raised in her direction. “We know that people exaggerate or downplay their own qualities in their profiles—our research has shown us that conclusively. So their profiles are mostly a description of who they wish they were. We also know that what people are most likely to look for in a potential mate are those things they perceived were lacking in the person they most recently dated, not necessarily the things that will bring them the most happiness in a relationship.”

  “So people are lying about themselves and about what they want?” came a voice, again from the back of the room. Software engineers tended to cut to the chase.

  “I wouldn’t say ‘lie,’ exactly,” Veera replied. “More that they are optimistic in their appraisal of themselves, and backward-looking in their identification of traits they want to see in others. But the net effect, from a systemic perspective, is precisely the same: we have never had the information we need to truly discover the best relationship possibilities for our customers. Until Archer.”

  She advanced to the next slide. “Archer works differently. It resides on the customer’s connected devices, where it gathers information about them not based on what they say, but rather on what they do. It gathers a complete profile of them based on their social media activity—all of it—and it derives a virtual profile based on what it finds. Then it matches that to the virtual profiles of other customers and discovers relationship possibilities based on a more realistic data set than any comparable service has ever had.”

  “So… customers need to give it access to all of their social media accounts?” a skeptical voice asked, in a careful pace that clearly conveyed doubt.

  “Yes. Not just to Facebook and Twitter, but Snapchat and Tumblr and text and videoconferencing and web browsing in general, among other things. We’ll see the posts they like, the memes they share—and the photos they don’t. Using the cameras on their laptops and phones and tablets we’ll perform sentiment analysis to find out how they react to what they see online—which social media posts actually make them LOL, if you will.”

  “Why in the world would anyone give us access to their entire online identity?” Ross demanded, apparently having overcome the shame occasioned by his previous insensitive remark.

  “Because they want better matches. They look to our system to give them what they want, and our own research indicates they value their privacy somewhat less than they value finding the right mate. Once they experience the kinds of relationship discovery we can deliver using Archer, they will gladly give him access to everything. And because our system’s never been hacked, they trust us to ensure the privacy of their information.”

  “This is a nightmare from the perspective of customer data security,” opined the head of customer data security. Veera was not surprised. He had made this remark in every pitch meeting she’d ever witnessed. “How can you be certain customer data will never be exposed?”

  “We one-way hash it as it is gathered. Only Archer will know the key used to hash each user’s data, and there’s no way to associate data from the store back to the user profile. No human will be able to retrieve non-anonymized customer data.”

  “So Archer will be working with encrypted data at all times?” the data security czar asked. “That sounds very expensive in terms of processor cycles.”

  “It would be if the AI weren’t so efficient,” Veera replied.

  “You throw out this dragnet, pull in everything, make matches,” summed up one of the engineers. “How do we make matches if all we know is what someone does online?”

  “That’s where the epistemology engine comes in,” Veera replied. “It looks for patterns, substantiates them with sentiment analysis and other tools, and learns what’s really important to each customer.”

  “So then it matches people who do the same things online?”

  “No, not exactly. It doesn’t simply match activities and interests, the way we do now. It learns how people react to what they see online, how their personal communications differ from their professional activities, and it derives a pretty d
eep persona for each customer. Then it runs that persona against those of customers in the target group and looks for both parallel and complementary patterns. It then measures the success of each interaction between matched couples and learns how to make better matches in the future. Within six months of launch, Archer will have refined his analytical processes so extensively that he will be able to make relationship discoveries far faster, and with a greater probability of success, than any method ever attempted.”

  “Or we will scare off our users, leak all of their data, and burn up untold computing resources on an unproven technology,” Ross brutally retorted. “We could be looking at an extinction-level event, people.” He sat back in his chair as if he had delivered the deathblow to the whole idea.

  “Bullshit.” This was a new voice, belonging to Alexis, the director of PR. She hadn’t yet spoken, and her voice rolled through the room like a cannon shot. “Veera, this is clearly next-level thinking. None of our competitors have anything like this. Push as hard and as fast as you can, and when you have the tech ready, we will package this up into a program that our customers will not only love, they will pay extra for.”

  Veera had seen Alexis bring a close to a number of contentious meetings with her brand of summary judgment. And it seemed to be working this time too. The reactions among those in attendance ranged from enthusiastic nodding to ambivalent shrugging, but there were no further objections. Ross, for his part, sat stone-faced and dyspeptic, but he offered nothing more substantive than a grunt of reluctant surrender.

  Veera beamed. She hadn’t won Ross over, but she had won the pitch.

  Now all she had to do was deliver Archer.

  “MRS. SCHWARTZMANN, you really should stop putting melon rinds down the garbage disposal. It can’t handle them.”

  “I know, I know,” she replied, holding her hands up as if yielding the responsibility for her kitchen habits to a higher power. “You are so nice to come help an old woman.”