Here's Chapter 2 (finally!). If I had been this bad at estimating schedules when I was a freelance tech writer, I'd be living under a highway bridge in Plaquemines Parish, Louisiana. But once again, my characters gave me the one-finger salute and did what they damn well pleased. So
mea culpa
, y'all,
mea maxima culpa
. I promise to try to do better.
Ivan powered up his cell phone as soon as he landed at SFO; it immediately lit up and sounded off with a Presidential Alert:
Shitstorm here. Call NOW
. It was from Brian, of course; he bragged about hacking the Wireless Emergency Alert system but had never used it on Ivan. Ivan hit Brian's speed dial.
"Ivan! Where are you?"
"Wheels down at SFO. What's the shitstorm?"
"Kimberly's got a nasty bug, randomly fucking up responses. Half a dozen of the beta sites so far. We've put together a search-and-destroy team, you, me, and Jean FitzHenry."
"Jesus Brian, I'm just getting off an 11-hour flight—"
"You're booked on an American red-eye to Boston in..." Ivan heard Brian typing on his laptop. "...ninety minutes. Check in online and download your boarding pass to your phone. You should be able to get through Immigration and Customs in plenty of time at this hour. Don't worry about your checked luggage, just bring your carry-on and laptop; we'll have somebody pick up your luggage tomorrow. You can buy more skivvies at Wal-Mart." He said all that without pausing to breathe; it was obvious there was no point in arguing.
Ivan couldn't see any sleep in his near future. "Wal-Mart my ass! If I have to buy underwear I'm going first class. Tarzhay, baby!" When Brian didn't laugh, he knew the problem was really serious.
"Look, Ivan, there's a lot riding on us. We've got to find and fix this fucking problem as soon as we can. The VCs are getting antsy and Jeremy's sweating bullets. The whole company might be riding on this. Try to sleep on the way, you're going to need it."
Ivan knew when he was beat. "Okay, okay. Got your toolkit?"
"Of course, bro, never leave home without it."
"Good. I've got my laptop, I'll download the test suite and anything else I need. See you at Logan."
Brian's toolkit was a collection of apps, some he wrote himself and some he gathered—not all from totally legitimate sources—that let him look at and even diddle with networks and computers whether or not he was an authorized user. The tools could be used for nefarious purposes, but as far as Ivan knew he used them strictly to support Golkonda's development and testing needs.
The test suite was what Brian, Ivan, and the director of marketing believed was a representative sample of the sort of projects customers would use Kimberly for; they ranged from unrealistically simple to really convoluted. The databases were encrypted in the cloud. He also downloaded the anticipated results—it was well-nigh impossible to define a "correct" result in data mining, especially when you throw AI into the mix. The test suite databases were sufficiently skewed to produce the anticipated result every run.
When Ivan got to the gate, he opened his laptop. He listened to voice mails from his brother in Colorado and sister in Illinois; he'd call them from Boston. He didn't even open the rest of the text messages. They could wait until the 5-hour flight to Boston if the plane had wi-fi.
He started getting antsy when boarding started and Brian and Jean weren't at the gate yet; they got there, out of breath, with three minutes to spare. Brian was angry; no, he was livid. "Fucking TSA," he wheezed. "I swear to God they jones on making you miss your flight. And if you complain, you might wind up on their fucking no-fly list!" He handed me my boarding pass. "You're in first class since you've been flying for days; Jean and I are back in steerage."
They dragged their carry-ons onto the jetway, the last to board. As Ivan put his bag in the overhead bin, he could hear Brian bitching to a flight attendant about having to stow his bag several rows away from his seat as if the inconvenience was a violation of his civil rights. Ivan was relieved when he shut up before they called the airport police to toss him off.
Ivan was concerned as he strapped into his seat. Brian wasn't usually that obnoxious; his stress level had to be off the charts. The problem obviously was serious, but he was too tired to worry about it. He fell asleep before they reached cruising altitude and slept fitfully most of the way to Boston. A bathroom call woke him up once, then a troublesome dream a bit later; he drifted back to sleep both times, fretting about how to find Fumiko. He resolved to start trying as soon as they got to their hotel.
_________
Data mining is the catch phrase for the search for valuable nuggets of information buried in heretofore undiscovered patterns or relationships in terabytes of often seemingly unrelated data—in a beery rant following a particularly unsatisfactory meeting with marketing, Ivan called data mining "a misleading metaphoric moniker slapped on by hucksters". Marketing mavens were delighted to polish its image as the latest holy grail for companies searching for ways to prosper, whether they needed another flashy play for Increased Shareholder Value or a means of survival, to avoid being brushed aside by competitors with better products or VC-fueled startups driven by young wizards working hundred-hour weeks.
First-generation data miners needed crazy-expensive supercomputers. Second-generation systems brought the cost down a lot, but were still so expensive that most boards of directors were reluctant to invest so much in something not guaranteed to produce an acceptable return. Third-generation systems were supposed to bring the cost down almost to impulse-purchase level—impulse purchases for businesses and other organizations that didn't suffer from a penchant for penny pinching, that is—but the anticipated breakthrough always seemed to be at least a year off.
Golkonda's first product was Argentum, a data mining system with a local front end and back end in the cloud. Thanks to some proprietary pre-processing in the cloud, the final steps of analysis and formulation of responses could run on a cluster of two or three servers. It made data mining affordable for mid-size companies and institutions, but few were ready to take the risk of going with an untried company and product.
A year later came Auricle, which was faster, could handle larger databases, and still ran on the same (comparatively) minimal system as Argentum. Another company in the market—well, more accurately the market leader, led by its ambitious, aggressive founder—took great exception to the name Auricle, filing suit claiming it would "confuse all potential customers in both the database and cloud computing markets." To avoid expensive litigation, Golkonda relaunched the product as Aurum.
Those at Golkonda familiar with the politicians' mantra "I don't care what you say about me, just be sure to spell my name right." welcomed the ensuing publicity. Some cynics even suspected that the allegedly confusing name was chosen deliberately. Thanks to the public exposure, Aurum was somewhat more successful than Argentum, although it never earned back its development and marketing costs (including, of course, the expense of changing the name).
But both Argentum and Aurum were actually stalking horses for the third product, Kimberly; they admirably served their purpose of giving Golkonda visibility in the data mining market. Kimberly was what Jeremy intended all along to be their flagship product. It was remarkably faster than all existing products, ran on much smaller systems, and could deal with multi-petabyte data sets. It built a first-pass index in the cloud, and performed one of Jeremy's innovations called
relation clustering and pruning
. These results were then encrypted and sent to the local servers, where final processing took place. The index was deleted from the cloud when the local cluster reported receiving all the data.
The final processing included another innovation of Jeremy's, an artificial intelligence module that made inferences based on the nature of the dataset, the specified goals submitted as part of the project, and any history it had of previous projects submitted by the owner. It then built (or refined) its own model of what sorts of intelligence the owner typically looked for. Marketing literature called this
heuristic goal seeking
. As the AI model grew more detailed (and, it was hoped, more accurate), Kimberly could actually propose further projects aimed at similar or related goals.
Golkonda negotiated confidential non-disclosure agreements with six companies carefully selected to represent the most important potential market segments, then sent engineers to install beta versions of Kimberly. For more than a week the response was enthusiastic, generating much valuable feedback, breathless buzz in the blogosphere, and optimism at Golkonda. It promised to disrupt the market the no less than Snapchat or Spotify. Speculation about an IPO began.
Then the shitstorm hit: Kimberly started going bonkers.
_________