If you can't beat em, join em

If You Can’t Beat ‘Em – NSTAC ETSV – John S. Eberhardt III – 25 Oct 2016

If You Can’t Beat ‘Em, Join ‘Emor

We Better Make Friends with the Robots While We Still Can

John S. Eberhardt III

Adjunct Professor - Volgenau School of Engineering, George Mason University

Chief Technology Officer - ATA, LLC

25 October 2016


Professional Background

• Adjunct Professor at the Volgenau School, George Mason University

• Chief Technology Officer, ATA (Full Stack Data Science)• Former Partner and Founder, 3E Services (data consulting)• Former Founder and Chief Scientist at Decision Q Corp

(machine learning)• 48 publications and conference presentations

Disclaimer: This presentation represents the personal opinions of Mr. Eberhardt, based upon his professional experience. It should not be viewed as a formal recommendation, and does not represent the institutional views of either George Mason University or ATA, LLC.


BiographyJohn is a Data Scientist with over 20 years of experience in the Analytical Sector. John has led the development of multiple advanced analytical products and methods, managing teams of scientists and engineers to rapidly create customer-centered analytical solutions.

With one patent and six patent applications in process and over 35 publications, John is a thought leader in advanced analytics with experience in machine learning, statistical algorithms, and user interface design for decision support in Security, Healthcare, Financial Services, Life Sciences, and Consumer Products.

John has developed over 20 analytical solutions in clinical decision support, cyber security, molecular diagnostics, risk management, and product marketing including award winning healthcare quality applications. John has applied his expertise with the Federal Aviation Administration, Department of Defense, Altamira, Roche, Genentech, Novartis, Walter Reed Army Medical Center, Memorial Sloan Kettering, the University of Wisconsin, University of Mississippi, and Thomas Jefferson University among others.

John has a BA Cum Laude from Duke University in Economics and History.


Understanding the NSTAC ETSV Scoping

• Seeks to engage experts to determine the impact of emerging technologies on National Security and Emergency Preparedness• What do we expect to emerge in the next three to seven years that

will be available to commercial and government markets?• What are the dependencies between these technologies and

expected timelines?• Which technologies are potentially most valuable to the NS/EP

mission? • What challenges may arise from not adopting these technologies?


Key Questions I Will Focus On

• What disruptive technologies are on the way over the next decade, and where are they in development?

• What common traits do they share? What can we learn?• How might these increase risk in the NS/EP domain?• How might these technologies also be used to reduce risk in

the NS/EP domain?


What Can We Expect in the Coming Decade?M

atur

ityFu

nctio

nal

Conc

eptu

al

TimingNow Someday

Semantic Technologies

Pervasive Semantic Technologies

IPv6

IoT

Autonomous Devices

Open Source Hardware

Blockchain

Deep Learning

Weak AI (Sentience)

Pervasive Blockchain

Pervasive Deep Learning

Pervasive Lattice Encryption

Quantum Computing

Biocomputing

Strong AI (Sapience, Turing complete)

So what is the theme of all this stuff? Man made technology will have an increasing degree of autonomy and intelligence.


What does autonomy mean for NS/EP?

• Expanding attack space• Just outright bigger

• Autonomous tech creates more scalar instability• Potential for Emergent Behaviors• Malicious: Intentional abuse of autonomy for attack• Unexpected: Unexpected behaviors produce damage

without a malicious actor• Need for decentralized data and response


What is scalar instability?

• Stable/Unstable – How quickly the fundamental governing principles of the system change – sports rules are stable, relationships in the securities markets are not

• Scalar/Non-Scalar – Are there physical restrictions on system scaling (human height is restricted by biology, building height by the strength of steel)

• Human beings are “designed” to operate in stable, non-scalar systems by our evolutionary history – information systems are not

• How do we quantify these axes?

Information Dense Domains• Financial Markets• Biology• Networking• Cosmology

• Weather• Chemistry• Physics


Stable/Unstable – Scalar/Non-Scalar

SCALABILITY HighLong-Tail

LowGuassian (Lack of Physical Scalar Constraint)

STAB

ILIT

Y(S

tabi

lity

of O

ntol

ogy

– Lo

ng D

ata)

Low

Hete

roge

neou

sHi

ghHo

mog

eneo

us

Gambling(Lottery, Roulette)

IP Networking

MS Office

Stock MarketVirology

Prop Cat, Liability InsuranceMeteorology

Sports Chemistry

Human Biology

Gambling(Cards, Horse Racing)

Personal Lines Insurance

Commercial Banking Transactions

Nuclear Physics(Governing principles,reaction)

People who don’t understand this difference are called “suckers”

Very different

The 2008 financial crisis happened because financial institutions didn’t understand the difference.


What Is Emergent Behavior?

• Emergent Behavior defined as: “the arising of novel and coherent structures, patterns and properties during the process of self-organization in complex systems” (1)

• Emergent behavior occurs when larger systems emerge from smaller, autonomous units• Both sentient (environmentally aware) and sapient (self

aware) units• May be the result of autonomous units conducting local

and global optimization• “Bad” events happen when local and global optima are

disconnected (global optima are difficult – even with sapience)


Examples of Emergent Behavior

• Environmental pollution• Bank panics/Financial panics• Program Trading in the 1987 stock crash (2)

• Facilitated by the DOT program of the NYSE• Systems behaved rationally locally but irrationally globally• Data was coming too quickly for humans “One notable problem was

the difficulty gathering information in the rapidly changing and chaotic environment.” 2

• DDOS and SMURF attacks• Routers, switches, and DNS servers behave rationally• Malicious actors use locally rational behavior to creat

global sub-optimization


How Is This Changing Our Risk Space?

• Mesh networking of autonomous home appliances• Self driving cars• Autonomous drones• Advanced mobile devices with pervasive data access• Cheap or free advanced mobile encryption moving

information out of reach• Blockchain transactions moving out of public spaces

• Tensions between all of the above

• Increasing convergence between the digital and physical world – cyber events have real world consequences


Can Emerging Technologies Make Us Safer?

• If you can’t beat ‘em, join ‘em• We can’t legislate the risk away – these technologies are

more pervasive than guns and drugs, and easier to get• We need to build semi-autonomous security technology

that mirrors and responds to the risky behaviors• Must be capable of learning and adapting to their

environment without human supervision• Programmed to communicate among each other and

understand both local and global maxima and minima• Data and semantic interoperability facilitates signaling• Understanding the fundamental mathematics of

emergence• Which of these new technologies can we harness?


Scenario

• Massive DDOS on several major financial institutions enabled by weak network security in free coffee shop WiFi

• Classic Response: Each bank SOC identifies anomalous traffic patterns, intervenes manually

• Autonomous Future Response – Positive Emergent Behavior• “Smart” routers collect their own data• “Smart” routers do on-board analysis• “Smart” routers participate in decentralized trust network• “Smart” routers share data and analysis with each other• “Smart” routers identify the pattern• “Smart” routers identify that global optima may override

local instructions and deny traffic en masse• Decentralized, automated incident response – an organic SOC


Emerging Technologies Make It Possible

• Pervasive semantic technologies• Self regulating ontologies and taxonomies

• Big Data – global sharing so that autonomous devices can analyze and benchmark globally and locally• Need their own autonomous communications channel for

comparing evidence – NOT a C2 channel• Deep learning – complex inferences under conditions of

uncertainty• Weak and strong AI – ability to respond appropriately and

form judgments, not just fulfil logic• Blockchain for distributed trust amongst autonomous devices


Recommendations – How Do We Get There?

• Research on the fundamental nature of autonomous, intelligent technologies• Mathematics of emergent behavior

• Development of self-evolving, emergent, machine-to-machine communications methodologies• Includes the next generation of semantic technologies

• Research into applied Artificial Intelligence and Autonomous devices• Development of simulation environments for study of

emergent behaviors


Thank You!

Thank you for your time today!

If you want to reach me:[email protected]@johneberhardt.com415-254-7996

“Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.”

Antoine de Saint-Exupéry

mailto:[email protected]

mailto:[email protected]


References

1. Goldstein, Jeffrey, Emergence: Complexity and Organization 1 (1): 49–72, doi:10.1207/s15327000em0101_4

2. Mark Carlson, Board of Governors of the Federal Reserve, November 2006