Chasm Waxing: A Startup, Cyber-Thriller

by BMichaelsAuthor

  Becca struggled to get back to her seat without drawing attention to herself. “You can erase all of that.” Phew, I’m glad my voice didn’t squeak, she thought.

  Josh penned his own architectural diagram. “It sounds like you’re very familiar with AI. CyberAI is a comprehensive cybersecurity suite that touches network security, application security, server security, monitoring, smart availability throttling, and automated incident response.”

  Becca laughed. “How in the world do you remember everything you do?”

  Josh grinned. “I don’t. The AI does. Anyway, one of my special focus areas was mitigating insider threats—rogue system administrators and hackers that game the system to get elevated privileges. If a system administrator—like Edward Snowden—starts reviewing a lot of files or searching in directories where he’s never been; CyberAI raises alarms.

  “I’m trying to enhance the AI. I want it to predict when someone will be a threat. The General really likes that part. And of course, we integrate with virus and malware detection software. I’ve already told you about how we ingest data from the web and social media sites. I’m also trying to create an AI-bot that crawls the Deep Web. So that’s a broad overview.” The Deep Web was the portion of the Internet that was not indexed by search engines.

  “Our AI Kernel is at the heart of all of this. We developed it to work with various AI algorithms. Until last month, the AI Kernel focused on text—you know with NLP—and machine learning. The algorithms operated on both the log files and the web data. Of course, the perfect NLP would read a book, and understand it as well as a human reader. My NLP is far from perfect.”

  Josh told Becca about his MIT coursework and the genesis of CyberAI.

  “I used one-third of the security log data to train my NLP machine learning algorithms. Then, I used the other two-thirds of the data to see if I could detect the attacks. I repeated this process of training and testing, training and testing, until I was achieving good results. My CTO, Vish Kumar, focused—”

  “Vish Kumar from Graphica Intelligence? He’s a rock star!”

  “Yeah,” replied Josh, “He came to us after Graphica’s acquisition. Vish’s team is superb at packet inspection and employing graph analytic algorithms to identify threats. His stuff is slick. It proactively identifies suspicious packets from compromised hosts.”

  Graph analysis used graph theory to find meaningful relationships in social networks and other graph data. Graph analysis of your Facebook friends or Twitter followers could lead to discovering unexpected connections and relationships.

  “So why are you in my office?” said Becca, semi-sarcastically. “It sounds like you got this all figured out.”

  Josh huffed. “Hardly, CyberAI only recognizes 83% of cyber-threats. That’s too low. It doesn’t do enough to minimize the need for human security administrators. Now Vish’s stuff is killing it, but my software isn’t improving in its recognition capabilities. And that’s just recognition. I’m not moving any closer to where I really want CyberAI to go—prediction. And beyond that, I want to enable discovery. I think the future of AI is extending human intelligence, not replacing it.

  “I’ve gotten the last drop of blood from improvements to NLP using standard machine learning techniques. Last month, I decided to add another group of algorithms to our AI kernel—deep learning. Do you know anything about deep learning?”

  “Yes, but act like I don’t,” she replied, channeling her inner General Shields. Becca did draw a line. She wouldn’t instruct Josh to pretend she was his grandma.

  “Deep learning is a particular type of machine learning. Deep learning algorithms break problems up into many different layers. Statistical calculations are performed on the data at each layer. The information can be images, sound, text, graphs, and so on. These calculations determine the essential similarities, or features, for each layer.

  “Deep learning is inspired by learning in the human brain. The multiple layers of a deep learning algorithm are called a, ‘neural network.’ Powerful neural networks, trained by deep learning, are behind some of the biggest breakthroughs in AI in the last decade.”

  “Breakthroughs like what?” asked Becca.

  “Breakthroughs like self-driving cars. Autonomous vehicles must recognize and react to obstacles and road conditions. Breakthroughs like speech recognition in digital personal assistants. Apple’s Siri, Microsoft’s Cortana, and Amazon’s Echo have to understand speech to answer questions and process commands. Breakthroughs like Facebook’s software that automatically tags and categorizes images without human help.”

  “That’s interesting. I didn’t realize that deep learning was so pervasive.”

  “Who knew, right?” said Josh. “Deep learning is becoming a new computing model. It’s going to impact every industry. Not only is it going to allow voice commands to be a new input for computing, but visual computing will take off. Kids will play chess against the computer with a real chess board.

  “Despite all its promise, I’m struggling to get any meaningful impact from my neural network that understands English. I’ve crawled the Internet with spiders, just like the Atom search engine. I’ve assembled an enormous amount of text to use as a training set. While my hardware certainly can’t compare with Nucleus’ server farm, my dad was a big investor in NVIDIA. He gave me two, first-generation NVIDIA DGX-1 deep learning supercomputers.

  “I used this data to train the neural network to understand the text. Then, I applied the neural network to the log files I’ve used in the past. When I combined my old machine learning stuff, with new deep learning algorithms; the AI’s inference results only improved .08%. That’s why my demo for General Shields went so poorly.”

  Becca carefully examined CyberAI’s architecture. After some long moments in thought, she said, “I believe you need a better training set. You need a purer corpus of text that serves as your ground truth. My guess is that there are too many semantic differences in your Internet text. Your neural network isn’t understanding the English well enough.”

  Josh looked impressed. “I’ve got a pretty sophisticated annotation layer that allows me to add nuances and labels to the corpora. But I agree; that’s where I need to focus.”

  “Why not use the Bible?” suggested Becca. “The Bible has a vast amount of text and many different versions. They all carry the same ideas using different words. That can help you with the semantics. If you ever want to move beyond English, there’s a Bible for every language. Plus, you have a massive amount of commentaries written in different time periods. There’s a shared meaning between all of this corpora to help you with semantics and labels. It’s like a built-in annotation layer. It’ll give you many more reliable hooks.”

  With a droll smile, Josh replied, “The Bible? Are you some kind of Holy Roller? Hacker, computer programmer, hunter, and Bible thumper?”

  Becca scowled at Josh.

  “What? I’m just having fun.”

  She maintained her stare. “No, I’m not a Bible-thumper. Far from it. I just happen to know a lot about the Bible. My dad was a Pentecostal preacher in Texas. He fed me the Bible for breakfast. He has a vast library of Bibles, Bible dictionaries, Bible handbooks, Bible commentaries; he’s really smart about Scripture.”

  “What’s a Pentecostal?”

  “Pentecostals are non-Catholics. Think Billy Graham, not the Pope. By that, I mean they are Protestants. Pentecostals emphasize a more active role for the Holy Spirit in their faith. So to them, God still speaks, God still heals—things like that. A lot of TV preachers are Pentecostals.”

  “Hmm,” replied Josh. “What does your mom do?”

  His question threw Becca off. “My mom died of ovarian cancer when I was 10.” Then God disappeared, she thought.

  “Oh, I’m sorry,” said Josh. “My mom and dad divorced when I was starting high school, but it’s nothing like losing your mom. I run the Jewish race—not the religion—if you know what I mean.”

  Becca di
dn’t catch Josh’s humor. She didn’t want to talk any more about her mom, dad, or religion. “With all the variations of text, I think you can use the Bible as a Rosetta stone to train your neural network.”

  “I believe you’re right. I think employing different translations of the Bible and the commentaries as training data for my recurrent neural network makes a lot of sense. I’d hypothesize that the text will be of higher fidelity. It should really help improve my word vectors. I’m going to start architecting and training the software tonight.

  “Once I get it trained, I can see if the neural network improves the recognition of cyber-events. Then, I can start thinking about replacing the G-Master. I’m excited about that use case. I do want to head in the area of stronger AI. I’ve been trying to expand Vish’s vision beyond cybersecurity. In time, I want to out-do the Atom search engine. I’d like to perform discovery—not search. And I’d like to make it social discovery in a VR world.”

  Josh had long believed that a significant proportion of Atom search engine traffic could be displaced by an AI that discovered things for its users. Users would no longer have to type explicit search terms. Nucleus wouldn’t like it, but maybe they’d buy his company.

  Josh didn’t view discovery and search as mutually exclusive, but he felt that if he implemented discovery in a compelling manner, the Atom search engine would not be the only game in town. He believed that discovery could be especially compelling if coupled with VR or Augmented Reality—AR.

  VR and AR were similar. The primary difference was that the VR head-mounts completely shut out the real world. VR was total immersion. AR superimposed digital content, like words or graphics, over the physical world. It wasn’t fully immersive; you could still interact with your physical environment. In the same way that the computing experience on a phone differed from a PC; VR and AR were distinct computing platforms.

  Becca re-engaged in the conversation. “Wow, that’s exciting! I want my computer to discover things for me. Then, I can quit searching. I get it. So, to summarize our meeting, we’re not in love with our current AI—it’s just an open source engine we’ve trained for cyber-events. Our entire architecture is modular; all of our intellectual property centers on G-Bridge. My co-worker, Ali, will welcome focusing on other things rather than recognizing cyber-events. You just have to talk to Samantha about how all this happens from a business perspective. That’s above my paygrade. I suppose you can work out a licensing agreement or something.”

  “Right,” said Josh, “I’ll shoot her an email later tonight and copy you on it. When I’m done, maybe we could get together? I’ll show you what I’ve got. By the way, is it happy hour yet?”

  “Hardly,” laughed Becca. “That sounds like a plan.”

  Becca’s mind wandered. “I’ve been thinking. You’re a smart guy. Maybe you know the answer. I understand how Gamification Systems, CyberAI, and the thought-based VR controller company—Prosthetic Thought—were down-selected for the Accelerator.

  “And now, I get what the General was thinking when he suggested we’re more complimentary than competitive. But why do you think Shields also funded a drone company and a wireless power company? It doesn’t make any sense to me.”

  Josh paused. “I guess I haven’t ever thought about it. I think Flashcharge will wirelessly power the drones. I’ve talked to the Swarmbot guys. Their focus is on cheap drones and robots that provide swarm intelligence—link ants or bees. But, I don’t know what the NSA needs with swarming robots. Their expertise is in signals intelligence and encryption.”

  Chapter 12 – Becca’s Story

  12:20 p.m. (EDT), Thursday, July 30, 2020 – Baltimore, MD

  National Aquarium, 501 East Pratt St.

  Becca nearly choked on her sandwich. She gasped loudly at the uncomfortable question from her father, Elisha Roberts.

  Earlier in the morning, she Ubered the 20-plus mile drive from Columbia to the National Aquarium. Becca and her dad spent the morning touring the huge complex. The Dolphin Discovery Exhibit, which housed eight Atlantic bottlenose dolphins, consumed a good portion of their morning together. She loved the dolphin’s intelligence and grace.

  The father and daughter were on the top floor of the aquarium. They’d just strolled through the Sea Cliffs Exhibit and grabbed a box lunch from the Harbor Market Counter. They sat at a table overlooking Baltimore’s historic, Inner Harbor. Watching this same harbor during the War of 1812, Francis Scott Key wrote, “The Star-Spangled Banner.”

  Elisha Roberts was in town to be a guest pastor at a Charismatic church, just outside downtown Baltimore. Other than some doctrinal beliefs related to the Holy Spirit, little separated Charismatics from Pentecostals. They were both significant constituents of Evangelicals—the group Becca felt only received their news from Fox and the Drudge Report.

  “Dad, I haven’t found a church that I like. I’ve been working almost 24/7 for Gamification,” said Becca, with a snarky tone.

  Becca was telling a half-truth. She hadn’t found a church. But, she also wasn’t looking. Becca’s heart hurt when she thought about church. There were so many fond memories. Memories of her mom and dad attending church and church activities, like picnics and campouts. Texas was a lifetime ago.

  The brow-beating question irritated her. She loved her father. But Becca maintained a slowly simmering bitterness—or maybe it was disappointment—with her dad. She was definitely angry at God, if He existed.

  Pastor’s kids didn’t just go to church on Sundays. They lived church daily. Elisha preached at two services on Sunday. After that, there was the Wednesday night service. Church members were always dropping by the house. Despite the busyness, those were the happiest days of Becca’s life.

  Then her mom died. Elisha Roberts, an up-and-coming healing preacher, could get other people healed—but not her mom. Susan Roberts was in the prime of her life, when the cancer attacked the very ovaries that gave Becca breath. Becca stopped believing in her dad and God.

  After Susan’s death, Elisha had to leave Texas. There were too many memories for him to process every day. Elisha suffered a protracted season of depression. A friend in Tennessee hired Elisha to be an associate pastor of his Memphis church.

  This was a huge demotion, but Elisha didn’t feel right about pastoring other people. He was struggling to pastor himself. Recently, when his friend retired from the ministry, Elisha was elected senior pastor.

  Her mom’s death and the subsequent move, robbed Becca of her very identity. She took up hacking as a coping mechanism to escape the pain. Hacking, and later programming, had a certainty to it. It was simple cause and effect. Do this. Don’t do that. Learning the rules was all consuming. But, at least there were rules. If something broke, Becca could fix it. And, she didn’t have to think about her mom.

  In her early years, Becca wasn’t covering her hacking tracks very well. At the age of 14, the Memphis FBI paid a visit to Elisha. Becca got off with a warning. A motherly FBI agent took an interest in Becca. They remained close. She helped Becca land a job as a part-time hacker for the FBI. Becca moved out of her dad’s house when she was 18.

  Elisha smiled, “Alright, Alright. No more church attendance quizzes. Have you shot any wild pigs recently?”

  Becca was relieved to change the subject. “No, but I’ve found something I really enjoy. I became the chief game designer for a project we’re doing at Gamification. I love it! I got to create an entire world—monsters, castles, weapons. I hope to do more of it.”

  “That’s wonderful. Why’d you like it so much?”

  “I’m bored with hacking, programming, and security. Making a game is like nothing I’ve ever done. I love being able to author an entire world. It helps me see everything differently.”

  “Do you think that you can keep creating games for the company?”

  “I don’t know. Samantha has told me repeatedly that our goal is to integrate with games made by other firms. The game I designed is only a prototype. I unders
tand. We’ll see.”

  “That’s great honey. It’s nice to see you happy. Are you dating anyone?”

  Becca scrunched her nose, elevating the glasses on her face. “No. There’s not much time for dating. I just met someone, but I don’t know…”

  Becca’s phone buzzed.

  It was a text from Josh. ‘This new AI is rocken! TTL.’ Becca’s eyes danced. She quickly put the phone down.

  “How are you, Dad? How was your prayer meeting last night?”

  “Awesome! We had a great time of prayer and fellowship at the Refuge Bible Church. It’s just down the street. My sermon was on Daniel 12:4. An angel told Daniel to seal his book ‘until the time of the end; many shall run to and fro and knowledge shall increase.’ Isn’t that a perfect description of our Big Data era?”