“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 into Valentine’s day with me.”
Josh flashed his virtual smile again. “My VR-self is smooth like that,” he snickered. “The February 14 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.
“Now, what you’d like to see is as little variance as possible. Ideally, you wouldn’t see any green or red differences. Everything would be a black, ‘zero.’ If there were no variances, you’d be a god. All your predictions would be coming exactly true. Now, come down here.”
They moved to the meta-column dated with today’s date, July 31, 2020. After that, the meta-column only recorded predicted values. “My backtest shows that the deep learning algorithm would have been very useful in predicting closing prices for the next day, especially as time advanced.”
Becca looked to her left. She noticed that while there were more red and green variances in January; each month, more black zeros were visible. July contained the most black zeroes. “This month was the best month so far. Even where there are red or green numbers, the variances are decreasing.”
“That’s precisely right. The algorithm is learning. It’s getting better and better at predicting the closing prices. My dad would kill for this software.”
“I guess I should invest in Facebook stock,” said Becca.
“Yes. Not only could you invest in the stock, but you could make a fortune in options. The algorithm gives you a high degree of confidence in tomorrow’s closing price.”
“So, you are going to quit your job as CEO and trade on Wall Street?” asked Becca—half serious.
“No, my dad has done that forever. I admire him. He’s a billionaire. That’s awesome. But I want to change the world. This is exactly what I want my algorithms to enable—discovery. I don’t want to watch closing prices and charts for a living. I want to create ever more valuable closing prices for my publicly traded AI corporation.
&
nbsp; “I want my company to predict your future interests. It could be something like a movie, a pair of shoes, your future husband—anything. Your discovery service will run in the background and be an extension of you—your extended intelligence.
“It could do your chores. It could execute smart contracts for you on the Blockchain. It could purchase your groceries, because it talks to your refrigerator and knows you’re low on eggs. The possibilities are endless. But the bottom line is that you would do a lot less searching and a lot more discovering. The more the AI knows about you, the better predictions your discovery service would render. Advertisers would love it. Some would even just target ads to the discovery service.”
“I don’t understand how it’s predicting the future with such a high degree of accuracy?” asked Becca. “It only has information that is available to us. I mean, you didn’t program it to figure out tomorrow’s closing price of Facebook.”
“That’s just it, Becca. I don’t know how it’s doing it either. I didn’t program an algorithm to bet on football or determine closing prices. I just used a deep learning algorithm to create a neural network. That neural network was designed to understand and learn from English text. I can’t predict what the AI is going to learn. Deep learning offers a new way to program computers. My only job is to make the deep learning algorithms understand text better.”
“How do you do that?” asked Becca.
“I tell the algorithm it’s doing a good job or bad job with supervised learning. Then, I’ll turn it loose and see what it learns without my feedback. That’s called unsupervised learning. Also, I want to improve each layer of the neural network that processes the text…characters, prefixes, suffixes, grammatical relationships, word vectors, sentences—all the way to semantics, ontologies, concepts, and themes.
“There are some other things I want to tweak. I want to make it multi-modal. Then the AI could learn from text and pictures, for example. I also want to see how much the neural network creation improves with faster computers. I’m sure General Shields is going to love this, and he’ll get me some time on the NSA supercomputers.”
“I know he’ll love it,” said Becca. “Now let me try. TextWorld, display something I don’t know.”
Josh looked chagrined. “Becca, that’s not going to—”
Before Josh could finish his statement, they were immersed in a number of words and images. These included: Area 51, Aliens, Bigfoot, the Bermuda Triangle, Hillary Clinton pictured with the King of Saudi Arabia, and an old Harrison Ford picture. It was snapped from the set of the film, Raiders of the Lost Ark. Becca selected the image of Indiana Jones. “My dad and I loved that movie.” She expected to see little-known movie trivia, or obscure facts about Harrison Ford.
A word cloud did appear. And there were topics that Becca expected. “Look at everything I don’t know,” huffed Becca. “And you were just about to tell me that my question wouldn’t work. I can’t believe you’d do that on Valentine’s Day.”
Josh laughed.
Upon closer examination, Becca saw a topic she didn’t anticipate—an unknown unknown. She reached out and picked the ‘Location of the Ark of the Covenant’ topic.
The Past/ Present/ Future Grid appeared.
The Ark had fascinated Becca since she watched the movie. “Dad told me that this was not just a movie mystery. This is a real historical enigma. No one knows what actually happened to the Ark. It disappeared.”
“Yeah, it was lost from history in 587 BC,” said Josh, reading from the Present column. Both of their eyes immediately gravitated to the Future column. On the far right side of the Future column stood a glistening white and gold building.
The building reminded Becca of the Acropolis, but it was more rectangular and tall. Two colossal pillars, probably three or four stories high, guarded the entrance to the building. There was a large stone structure in front of the building, surrounded by a number of courtyards.
Closer to Becca than that structure, but still in the Future column, was an A-Map of the Middle East. The map was labeled in a typical manner. It had roads, highways, the Jordan and Nile Rivers, and nation-state names. The designated countries included portions of Syria, Lebanon, Jordan, and the northern part of Saudi Arabia. The entirety of Israel and Egypt was also portrayed.
The two ambled closer to the A-Map. “Woah,” said Becca, “is this showing us the hiding place of the Ark of the Covenant?”
“I don’t know,” replied Josh. “It’s a big area. Maybe, as I improve the algorithm and run it on faster computers, it’ll get more detailed. It’s interesting, though. And, at least you know your Cowboys are going to have a good year. Let’s get out of the Middle East. I don’t want to get run over by a camel.”
Becca laughed. “Alright, I’m hungry.”
*
Josh drove Becca to Georgetown for dinner. Georgetown was a historic neighborhood of Washington, DC. It was an extremely pleasant drive in Josh’s white, Faraday 777 GTS Convertible. Josh’s dad had bought him the car before he quit MIT. Faraday Motors was an upstart car company, based in the UK. They specialized in luxury, electric sports cars.
Josh thought it was cool that Flashcharge, another member of the Accelerator, was piloting their microwave power charging technology with Faraday. The trip was normally about 45 minutes, but Josh had a lead foot. All the Faraday’s cool apps enthralled Becca. The Faraday was truly a software defined car.
Becca asked Josh if he’d like to get on the speakerphone with her dad. They could ask him about the Ark of the Covenant. Josh agreed.
She introduced Josh as, ‘her friend.’ “Dad, what can you tell us about the Ark of the Covenant?”
“What?” asked Elisha. “It’s 8:45 on a Friday night, and you want to talk about the Bible? Have you guys been drinking?”
“No, not for at least another 20 minutes,” chuckled Becca. “Seriously Dad, Josh was showing me something related to a project he’s working on. And the Ark of the Covenant came up. Neither one of us knows too much about it. I know you do.”
“Alright,” said Elisha. “The Ark is first mentioned in Exodus, the second book of the Hebrew Bible after Genesis. Just about a year after the Exodus—you know, the parting of the Red Sea and all; God told Moses to build a gold-plated, rectangular box, made of acacia wood. He commanded the exact dimensions, something like four and one-half feet long, by two and one-half feet high, by two and one-half feet wide.
“The lid of the Ark was also to be covered with pure gold. Additionally, this lid was to include two large golden statues of cherubim angels, hammered from pure gold. God called this golden top of the Ark, the Mercy Seat. The Bible says that God’s presence—His glory—would dwell on the Mercy Seat, between the cherubim. God told Moses to place the Ten Commandments into the Ark.
“On the outside of the Ark, in the four corners, were placed four hooks. Through these hooks, two long poles could be inserted. Selected individuals from the tribe of Levi carried the Ark by holding the poles. You never wanted to touch the Ark directly.
“The Ark was to travel with the Hebrews. It signified that God was present with His people. God also directed Moses to build the Tabernacle and to put the Ark in its most prominent room—the Holy of Holies. For its day, the Tabernacle was a sophisticated tent. It could be torn down and set up quickly, as the Israelites traveled in the desert. 500 years later Solomon built the first Temple. Solomon was King David’s favored son. God said He didn’t want to dwell in a movable tent anymore. He wanted a permanent house.”
“That must have been the temple we saw in TextWorld?” asked Josh.
“But it was in the Future column?” said Becca.
“What?” asked Elisha.
“Never mind Dad. Keep going.”
“Just like in the Tabernacle, the Ark was housed the holiest place in the Temple, behind a curtain.”
“What do people think happened to the Ark?” asked Becca.
“Of course, no human alive knows for sure
. The Ark disappeared from the Bible after Nebuchadnezzar destroyed Solomon’s Temple in 587 BC. Many people say Nebuchadnezzar demolished it in the siege. Other people think that he took it to Babylon—present day Iraq. Persia conquered Babylon. Persia is present day Iran. So, some people say it’s there.
“Others think that brave Israelites, like Jeremiah, went in before the destruction of the Temple and hid the Ark. Maybe he buried it in a desert cave or underneath the Temple. There’s also a very persistent story about the Ark being in Ethiopia.
“Since the science of archeology started in the late 1800’s, archeologists have searched for the Ark. If you think the greatest artifacts ever discovered were the Rosetta Stone, the Dead Sea Scrolls, Pompeii, or Tutankhamun; unearthing the Ark is far bigger than all of those combined. But the most significant ramifications would be geopolitical.”
Chasm Waxing: A Startup, Cyber-Thriller Page 10