Avogadro Corp. s-1 Read online

  “The answer,” David went on, as he paced back and forth in the front of the room, “is that it depends on the person you’re sending it to. So ELOPe customizes its analysis not just to what the sender is asking for, but for what the recipient is motivated by.”

  David noticed that Rebecca Smith was standing in the doorway listening to the presentation. In a sharp tailored suit, and with her reputation hovering about her like an invisible aura, the Avogadro CEO made for an imposing presence. Only her warm smile left a welcoming space in which an ordinary guy like David could stand.

  She nodded to David as she came in and took her seat at the head of the table.

  Kenneth asked, “But what you’re describing, how does it work? Natural language processing ability of computers doesn’t even come close to being able to understand the semantics of human language. Have you had some miracle breakthrough?”

  “At the heart of how this works is the field of recommendation algorithms,” David explained. “Sean hired me not because I knew anything about language analysis but because I was a leading competitor in the Netflix competition. Netflix recommends movies that you’d enjoy watching. The better Netflix can do this, the more you as a customer enjoy using Netflix’s movie rental service. Several years ago, Netflix offered a million dollar prize to anyone who could beat their own algorithm by ten percent.”

  “What’s amazing and even counterintuitive about recommendation algorithms is that they don’t depend on understanding anything about the movie. Netflix does not, for example, have a staff of people watching movies to categorize and rate them, just to find the latest sci-fi space action thriller that I happen to like. Instead, they rely on a technique called collaborative filtering, where they find other customers just like me, and then see how those customers rated a given movie to predict how I’ll rate it. Sean’s insight was that since natural language analysis struggles to understand semantics, it would be best to start with an approach that doesn’t rely on understanding, but instead one which utilizes patterns.”

  When David received nods from the audience, he went on. “That’s what ELOPe does. It looks at the language used by millions of email users. It looks at the language received by people, and how they reacted. Did they react positively or negatively? Compiled over thousands of emails per person, and millions of people, we can find a cluster of users just like the intended recipient of an email, and see how they respond to variations of language and ideas to find the best way to present information and make compelling arguments.”

  Now there were some puzzled looks and half raised hands as people around the room tried to ask questions. David forestalled them with a raised hand, and went on. “Hold the questions for a second, and let me give you a simple example. Let’s imagine that a person called Abe, whenever he received an email mentioning kids, responded with a negative response.”

  David gestured back and forth with his hands, getting into the example. “Now imagine that ELOPe has to predict whether a new email about to be sent would be received positively or negatively by Abe. If that new email also mentioned kids, it’s a good bet that it will be received negatively. If Abe was your boss, and you were going to ask him for vacation time, it’s probably not a good idea to use spending time with your kids as justification.”

  He heard a few chuckles.

  “So is there is no semantic analysis?” Rebecca asked. “We don’t know why Abe dislikes kids?”

  “No, we have no idea why Abe feels the way he does,” David answered with a smile. “We just observe the pattern of behavior.”

  “What if my manager hadn’t received any emails about kids?” Sean protested. “How could we predict how he would respond?”

  David smiled, knowing that Sean knew the answer, and was just helping him along. “Let’s say we have another user, Bob. Bob hasn’t received any emails about kids. However, ELOPe notices that Bob, Abe, and about a hundred other people have responded similarly to most topics, topics such as the activities they do on the weekend, the vacations they take, how they choose to spend their time. Let’s say that this group of people are ninety-five percent similar. That is, across all the topics they’ve responded to, they are ninety-five percent likely to have similar sentiment in their response: negative or positive. This is what we call a user cluster.”

  The executives around the room nodded in understanding, and David went on.

  “If other members of the user cluster received emails about kids,” David explained, “and they all responded negatively, then ELOPe will be ninety-five percent certain that Bob will respond negatively. Of course, it’s rarely so cut and dry, and it is a statistical prediction, which means that five percent of the time ELOPe will be wrong, but most of the time it will be right. So Sean, if your boss was Bob, I wouldn’t recommend mentioning kids when you ask for vacation.”

  David waited for few chuckles from the audience. “Joking aside, ELOPe is working, and we’ve tested it with users. On average, favorable sentiment in reply emails increases twenty-three percent with ELOPe turned on compared to the baseline. That’s twenty-three percent more vacations granted, twenty-three percent more people agreeing to go on dates, twenty-three percent more people getting their work requests granted.”

  Rebecca stared at David. “Wait a second. Going on dates? If that’s the case, you’ve got a woman out on a date with someone she wouldn’t have otherwise been with. That sounds manipulative and risky.”

  Kenneth looked startled by Rebecca’s objection, and started talking quietly to Sean, sitting next to him.

  David felt his internal danger meter flare into the red, and his stomach threatened to leap into his throat. Oh, the dating example was so damn controversial. The next few minutes would make or break his project. If Kenneth and Rebecca decided against the project, it would be impossible for Sean to give him the support he needed to get his project released.

  “Hold on. Maybe I chose a bad example.” David held up both hands, to forestall any more objections. “Who’s taken a Myers-Briggs personality workshop?”

  Everyone held up their hand or nodded in assent as David expected. The Myers-Briggs personality work or something similar was standard fare for every manager in every large corporation. Then he continued, “Now, what was the purpose of the workshop? It’s not just to find out you were an introvert or extravert, right?”

  “No, it’s to learn to work effectively with others,” Sean provided helpfully.

  “Working more effectively means what?” David paused. “It means learning how others communicate and think. It means learning who is likely to appreciate a data-driven argument versus an emotional argument. It means learning who is likely to want to think out loud, versus who wants to see the arguments written down and have time to respond.” David looked at the group, forcing himself to stay upbeat and chipper even though he feared that the group opinion could easily go against him and the project at this point. “Is that manipulative? Do we take a Myers-Briggs workshop to manipulate people, or to be able to work effectively with them, and spend less time in arguments and disagreements?”

  A few of the VPs turned and looked at Rebecca, waiting for the CEO to respond. Rebecca slowly nodded and agreed, “It’s not manipulative, it’s helpful. I can see that. I’ve been through more than my fair share of those workshops.”

  “If two people took one of those workshops together, they’d get to know how each other think. I don’t know if it’s been studied, but perhaps two people who have taken a Myers-Briggs workshop are also more likely to have a successful date together. What we’re doing with ELOPe is giving everyone the same benefits they would get from one of those workshops. We’re enabling people to be more effective communicators and collaborators. Who doesn’t want to be a better communicator? Who doesn’t want the people they work with to be better communicators?” David saw with relief that the tension in the group had dropped palatably.

  “Remember, we’re measuring sentiment in these messages,” he went on,
pacing back and forth in front of the display again. “It’s not just a grudging assent: people are having and maintaining more effective and cooperative ongoing communication when our tool is enabled. We’re empowering people, giving everyone the equivalent of what they would get in an expensive management workshop. Once, spell checking was the big innovation that leveled the playing field between people of good or bad spelling ability. Now we are leveling the playing field for people for writing — enabling people of all writing abilities to create powerful, well crafted communications.”

  There was quiet for a minute, then one of the executives asked, “What’s the timetable for releasing this?”

  With that question, all the remaining tension went out of the room. Discussion went on for another fifteen minutes, but the topics were all implementation details and business return on investment questions.

  At the end of David’s presentation, Sean walked him to the conference room door while the executives milled around and helped themselves to another round of coffee and food. “Good job,” he said privately to David, as he ushered him out. “I’m confident they’ll endorse the next phase.”

  As the door closed behind him, David leaned against the wall outside the conference room. The presentation had been more draining than he realized. Then he chuckled. The dating example had been contentious, but it was better to raise it and address it early than leave it as a lingering issue. He was sure the presentation had won them over. The language analysis he ran last night in ELOPe against his presentation predicted a ninety-three percent favorable response.

  * * *

  “Look Gary, you know as well as I do that it doesn’t make sense to optimize until after we’re done.”

  While David was at the big presentation with the big wigs, Mike was stuck having to defend their resource utilization with Gary Mitchell. Mike wondered if David had somehow arranged the time of the meeting with Gary to conflict with the executive briefing just so that David wouldn’t have to go. Give him a thorny bug to fix, a new architecture to design, and he’d be happy. Give him a team of developers to motivate, and that would be just fine. But he hated playing organizational politics. David was definitely going to owe him one for this.

  “Of course, we’ll only use a fraction of the number of servers after we optimize. However, we’re only going to optimize when the algorithm is done. If we start optimizing now, it’ll hurt our ability to improve the algorithm. This is basic computer science.” It was like talking to a wall, the words just bounced off.

  “Mike, Mike, Mike.”

  Mike rolled his eyes at Gary’s condescending tone, a safe maneuver since Gary couldn’t even be bothered to look at him. Mike studied Gary across the expansive desk. Gary leaned back in his car, arms stretched behind his head, white dress shirt stretched over his belly, jowls hanging down under his chin. He appeared to be studying the ceiling. Mike thought that Gary would be more at home as the VP at a place like General Motors. Only a big glass ashtray and cigar was missing. He wondered, not for the first time, how Gary had ended up at Avogadro.

  “I know your project got special approval from Sean to use production servers. Servers that keep Avogadro’s day to day operations running, I might remind you.” Gary finally heaved himself upright and looked at Mike. He pointed a fat finger at Mike before going on. “You’re eating up so much damn memory and bandwidth on the AvoMail servers that I’ve had to bring in additional capacity. You think your project is mana from Heaven, but that’s what every R&D team thinks. Meanwhile, I gotta keep things running here, and your one measly experiment is making us run critically short of spare capacity.”

  “Gary, we…”

  Gary ran right over him. “Approval from Sean or not, I’m in charge of Communication Products, and I’ve got ultimate responsibility for ensuring absolutely zero downtime. I’m telling you that you’ve got two weeks to get your project resources down, or I’m bouncing you off our production servers.”

  “Listen Gary, we can…” Mike started, but Gary interrupted him again.

  “No more ‘Listen Gary’,” he shouted. “We’re done here. I’ve had this discussion with David repeatedly. You’ve got two weeks. You go tell David. Goodbye.” Gary shooed him out of the office with his hand like an errant cat.

  Mike left Gary’s office, blew past Gary’s startled admin, and resisting the urge to slam doors, he walked back to the R&D building. He stalked down five floors, across a street and down a block, then up again, and finally through a maze of hallways in his own building, fuming with unspent anger.

  As Mike walked, he relaxed again, one benefit of the sprawling site. Avogadro Corp had expanded so much that they now spanned seven city blocks in the Northwest part of Portland, on the site of an old trucking company. A dozen buildings, most new, a few old, and constant construction.

  As the company and their profits had grown over the last fifteen years, they put up one new building after another, so fast that even the employees couldn’t keep track of who or what was where. Even Mike had seen three new buildings go up in his few years with the company.

  It was an ongoing source of curiosity among the employees to discover what the different buildings contained. While most of the office complex was quite normal, there were some oddball discoveries, like the telescope observatory on top of one of the buildings that could only be opened by certain, apparently randomly chosen, employee access cards. There was a billiard room that apparently changed floors and buildings. Mike had seen that one himself. Whether the trick was managed by having an actual room that moved, or by facilities staff moving the contents of the room, or by duplicating the room, no one knew. Of course the engineers at Avogadro couldn’t resist a puzzle, so they had done everything from hiding wifi nodes to RF encoding the furniture, with random results that just puzzled everyone even more.

  There was a half-serious belief among some of the employees that one of the executive team had a Winchester-house complex. Mike had visited the San Jose Winchester house once when he was in college. Built by Sarah Winchester, widow of the gun magnate William Winchester, she had the house under constant construction from 1884 to 1922, under the belief that she would die if the construction ever stopped. The thought that one of the Avogadro executives was plagued by a similar belief, and so was doing the same to the Avogadro campus always brought a smile to Mike’s face. On the whole, however, he thought that the oddball aspects of the site were more likely planned as a kind of game to entertain the engineers. It takes something extra to retain talent when you’re talking about a bunch of brilliant but easily bored geeks.

  As he crossed the second floor bridge back to the R&D building, Mike stopped smiling when he thought about telling David about the conversation. He wasn’t going to be happy to hear Gary’s ultimatum.

  Their recommendation algorithm, which sounded so simple when David explained it to a nontechnical person, depended on crunching vast quantities of data. Every email thread had to be analyzed and correlated with millions of other emails. Unlike movie recommendation algorithms, which could be analyzed and clustered using less than a hundred characteristics, it was orders of magnitude more complex to do the analysis on emails. It took them a thousand times more computation time, memory, and all important database access. Coming out of that meeting, there was no doubt that Gary had reached the limit of what he was going to allow their team to use.

  Unfortunately, Mike had lied to Gary. Mike shrugged, uncomfortable with himself. When had it become necessary to lie in his job? He didn’t like it. The reality was that he, David and other members of the team had been working for months trying to make their algorithm more efficient. Sadly, the current server-consuming behemoth was the best that they could do. No matter what they did, there weren’t going to be any more efficiency gains. Therefore, there was simply no way to meet Gary’s ultimatum.

  Nope, David was not going to be happy. Mike sighed. Unlike Mike, who usually took it all in stride, whether good or bad, David would get serio
usly upset.

  * * *

  A busy morning kept Mike hopping from one urgent issue to the next despite wanting desperately to talk to David. It wasn’t until hours later that Mike freed up. He ran into David’s office before anything else could interrupt either of them.

  “Got a minute?” Mike asked in a cautious tone.

  “Of course.”

  Like his own office next door, David’s office had room for three or four guests, as long as everyone was friendly and had used deodorant. A big whiteboard for collaboration spanned the wall behind Mike, and north facing windows held a view of heavily wooded Forest Park. Mike was sure the six month old office setup was less effective for working together than last year’s setup when everyone on the team was located in one big open space, but Mike enjoyed the change. Besides, it would all be different again next year.

  Mike recounted the meeting with Gary Mitchell, and saw David getting angry just listening to the story. “So then he kicked me out of his office without a chance to say anything else,” he concluded. “Besides, what else could I really say? You know there aren’t any efficiency improvements we can reasonably expect.”

  David sat at his desk, fingers steepled, staring into his darkened monitor. He hadn’t moved or said anything for the last minute. Mike knew that was always a bad sign. Tux the penguin, the Linux mascot, wobbled over David’s monitor in the ventilation system breeze. Mike remembered when Christine had bought the penguin for David after one of their first dates.

  “So two weeks. What do you want to do?” Mike prompted, after a minute of this painful silence.

  “Let’s pare down the number of people we have working on fixes and algorithm improvements,” David finally said, clearly having reached some internal conclusion. “How many people can you put full-time on performance?”