Chasm Waxing: A Startup, Cyber-Thriller (15 page)

Samantha stopped outside of Becca’s
office. “How’s it going?”

“
Good,” said Becca. “I
feel
excellent
about next week’s demo for Gecko. I’ve got to talk to Saul
about a couple of things, but everything is working. And Ali tells
he’s 100% sure of his code changes. We’re going to run through the
demo tomorrow morning.”

“
Ok,” replied Samantha.
“Why don’t you and Ali run through the demo with me tomorrow
afternoon. I want to see it work for myself. Will you also look at
Lou’s PowerPoint for Gecko? I want you to do a sanity check on the
technical details. We’re going to start with the PowerPoint, you’re
going to do the demo, and then finish back up with the
PowerPoint.”
Lou Skaist
was Gamification’s
Vice
President of Sales.

“
Sure,”
replied
Becca.

“
How’s the CyberAI stuff
coming?”

“
Josh just told me it’s
going well. Once we get this demo done, I can start to work with
him more closely. I’m going to have to get Saul more
involved.”

Samantha nodded her approval and began
to unlock her office door.

“
Why was Velocity here?”
asked Becca. Becca was sure Samantha heard her. The CEO breezed
into her office and shut the door. A few moments later, Becca’s
phone buzzed with a text from Josh.

‘
Are you available
tonight???’ Becca replied, ‘
tnite
not best…who about 2moro?’ Then,
noticing the misspelling, she texted, ‘grrr—how, not who!

’

6:15 p.m. (EDT), Friday, July 31, 2020
– North Laurel, MD

Josh Adler’s
Apartment

Becca thanked her Uber
driver and knocked on Josh’s door. She imagined the Founder and CEO
of CyberAI lived in something bigger. Maybe the startup life was
not as
glamorous
as she thought.

“
How are you,” said Josh.
“Wow, you should wear a sundress more often!”

“
It’s better than a
flannel?”

“
Much,” Josh replied, with
a broad smile and a wink.

“
Thanks for being so
understanding and stopping by to see the progress I’m
making.
I haven’t left this
apartment since Tuesday
. I want to knock
this out as quickly as
possible,
so
I can show you a proper first
date.”

Josh Adler’s three bedroom apartment
was more like a computer lab, with a kitchen and couch. Flashing
green, blue, and red lights flickered everywhere among the line of
rackmount servers. Wires ran from all angles. Whiteboards,
chock-full of diagrams and mathematical equations, adorned every
wall. There wasn’t a personal picture or memento to be
found.

“
Why isn’t all this stuff
at your office?”

“
My dad gave me most of
this equipment—when we were still talking—and I didn’t want to make
it a part of the company. Sometimes, when I’m in a zone, I need to
be in my
own
place, with my
own
stuff.”

Josh brought Becca to
his
central
work area. Becca scooted a chair next to him. “Look at this.
Becca, the computer is learning! I’ve trained the neural network on
Bibles and
commentaries. I’ve also
included
some ancient texts, like Philo
and Josephus.”

Josh pointed to the two
NVIDIA DGX-1s in the server rack. “These
boxes
processed the text and
created the neural
network,
using
my
deep
learning algorithm. Because the math
of deep
learning
is relatively
straightforward
, GPUs are much faster
at
creating
neural networks than CPUs. My neural network
is still a work in progress. It’s not
perfect.
I want to ask General Shields if
I can get time on NSA
supercomputers
. But the results are
still
astonishing
.” Josh maximized a window
on his computer screen. The monitor displayed a bar
chart.

“
I’ve gone from 83%
recognition of
cyber
-events to 91.5%. And I’ve only
been working on this for a week! My last demo for General Shields
was such a disaster, because month-over-month, the results improved
less than one percent. Now I can show him
substantial
progress. And it’s all
due to you.”

Becca smiled. “Josh, you
did this. Not me.
These results
are epic. You’re getting close to your 10X metric of
95%.”

“
Yep.
Thanks,
Becca. I meant that
without you, I
would never
have selected the training material that I used.
I might have picked Shakespeare or Plato, but
definitely
not the
Bible.
Anyway
, this is only the half of it. Look at this—actually, it’s
cooler in VR. Hold on.”

Josh grabbed two VR
head-mounts and four hand-held wireless controllers. They both put
on the
VR goggles
and gripped their controllers.
Josh
properly
positioned themselves among
the room’s external VR cameras and sensors. “You remember that the
CyberAI software scans the web and social media for cyber-threats?
Then it incorporates that information into our predictive
modeling?”

“
Yes,” replied
Becca.

“
The bar chart you just
saw included the data from the Internet.”

“
Ok, I think I’m following
you. Go on.”

“
I was curious to see if
the neural network could determine meaning beyond cybersecurity. So
I relaxed the parameters of the search, and told my query engine to
ingest everything—
no
parameters.”

Josh clicked a button on
his VR controller. Becca and Josh’s immersive VR world sprang
to
life
.
The VR headset completely blocked out the real world. In front of
them, a large word cloud of variably sized and differently colored
words appeared. Some words
included,
‘#,’ to denote that they were
hashtags.

“
Welcome to TextWorld,”
said Josh. “In TextWorld, these are the most important English
words and categories for the last 24 hours. My neural network
produced this list.”

“
You mean these are the
trending words?” asked Becca.

“
Exactly.”

In the virtual reality
space of TextWorld, Josh stepped forward and grabbed the word,
‘Sports.’ Becca watched the
virtual
Josh move in front of her. Her
eyes traced the whole of his virtual body. As Josh reached out for
Sports, the
old
word cloud dissolved, and a new word cloud
appeared. It contained a list of sports related words and topics.
“Let’s see what the computer predicts about—”

Becca burst forward and grasped,
‘Football.’ “I love football!”

The word cloud now entirely consisted
of American football terms. Becca selected, ‘Cowboys.’

“
Eww!” said Josh, a
diehard New York Giants fan.

Now, a three column grid
appeared in front of Becca and Josh. From left to right, each
column contained the labels of;
‘Past
,’ ‘Present,’ and ‘Future.’ Under
the labels, in each column, were minimized but readable web
pages—individual articles.

As Becca looked further
left, in the Past column, she could see older articles related to
the Dallas Cowboys. In the
center,
Present column,
she
saw
current
posts
. Most
covered the Cowboys training camp. It was occurring this month in
Oxnard, California.

“
So, you’ve created a
Virtual Search application?” asked Becca.

“
Sort of. But look in the
Future column.” The Future column resembled the
first word
cloud. It was
much less content rich; there were no pictures or videos. The
Future column only contained words and text files with
bold-type
headings.

Josh clutched the word,
‘Predicted Record,’ from the Future column. Everything dissolved.
Future predictions related to the Dallas Cowboys appeared.
“Based
on information available
right now, my AI
is predicting that the
Cowboys will finish with
11
wins and
five
losses.
It also sees
them winning the
division title. Dang it! The Giants need to get rid
of
that
stinking
coach.

“
TextWorld, display
current Vegas odds for 2020-2021 NFL season,” ordered Josh.
A
web page
from the Washington Post appeared in TextWorld. “Look,
according to today’s Vegas odds; the Cowboys are not favored to win
the NFL Eastern Division. So, if I trusted the predictive analytics
of my
deep
learning algorithm, I’d make this bet. Vegas odd’s makers are
not
bullish
enough on the
Cowboys
. I can
make
money.”

“
Wow, go Cowboys!” said
Becca. Josh groaned. “The question is, do you trust your deep
learning algorithm?”

Josh smiled from ear to
ear.
Those dimples,
thought Becca,
even
in
VR.

“
That’s where this gets
mind-boggling,” said Josh, excitedly. “TextWorld, display
file
backtest
.”

Becca now saw a massive
grid.
It looked like an
enormous spreadsheet. Her eyes first focused on
the multiple different colored numbers—there were black, green, and
red numerals. Then she widened the focus of her perspective. With
her larger point of view, Becca noticed that everything
fit within
three
large
rows,
spread
across her entire field of vision.

To their far left, each
row began with
a year-to-date
price chart
. From top to bottom, the
labels were, ‘DXY,’ ‘WTI,’ and ‘FB.’

Josh explained, “You’re
looking at the results
of
a
backtest
I ran last night. These charts
here…” He stepped closer to the three,
price-time charts
. The DXY chart
was in the row closest to his head, the WTI chart was
close to
his
chest, and the FB
chart
was in the row near his knees. Becca
followed.

Josh continued,
“These
charts
record the closing prices of the US dollar index, West Texas
Intermediate crude oil, and Facebook. All of them begin
on
January
1,
2020, and run through
today.
Looking at the charts; you can see
that the dollar has gone up, oil has
fallen
, and Facebook’s stock price has
been up and down. Recently, you can see that it hit new,
year-to-date highs.”

Becca examined the
charts
meticulously
. Each price-time chart
contained a line that touched the closing price for
every day
. There
were other lines as well. She could make out the 50 and
200-day
moving
averages and key Fibonacci levels.

“
I backtested my
deep
learning
algorithm on these three financial instruments—
a currency,
a commodity,
and
a stock.
A backtest calculates what the algorithm would’ve predicted.
Then it compares the prediction to what
actually
happened. If the
predictions are
right
, you know the algorithm is
working. Or,
at least
you know the algorithm would’ve
worked
in the
past.”

Josh
placed
his hand
on
the FB
row. He
started
walking to the right. There were
tons
of numbers. “Every trading day has
a meta-column. Josh shuffled to February 14, 2020.

Becca followed. “Aww, you
walked right in
to
Valentine’s day with me.”

Josh flashed his virtual
smile again. “My VR-self is smooth like that,” he snickered.
“The
February 1
4 meta-column has three different
numbers
. In this sub-column, are
the actual closing prices. This sub-column contains the
price
that the
AI would have predicted. And this sub-column records the daily
variance number.