HEALTHY COMPETITION: HOW ADVERSARIAL REASONING IS LEADING THE NEXT WAVE OF INNOVATION Nashville Analytics Summit August 8-9, 2017 John Liu, PhD CFA Digital Reasoning

DIGITAL REASONINGTrusted Cognitive Computing For A Better World

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WarGames (1983)

3 big ideas…

current algorithms lack robustness

bad data

fake data

dark data

adversarial reasoning to the rescue

a quick review of AI…

machine learning

learns feature weights

neural networks

universal approximation

deep learning

automated feature learning

reinforcement learning

learning policies

observations

actions

agent

environment(delayed)

reward

policy

value

state

model

deep reinforcement learning

sparse, delayed rewards

value fndeepnet

policydeepnet

modeldeepnet

they suffer limitations

arising from…

noisy data

sparse data

missing data

nonstationary environments

that cause problems

like…

false positives

bad decisions

bad policies

we ask this question…

are there robust

algorithms that can operate

in the wild?

does it really matter?

sometimes, yes, very much…

what if…

noise is not random…

and is intentionally false…

what if there is an active

adversary...

like a hacker

messing around…

or a hacker not messing around…

or even worse?

An algorithm that can learns to counter active

adversaries is more robust in the wild.

what hasn’t

worked:

failed defenses include...

generative pre-training

adding noise

model ensembles

regularization dropout

researchers are turning to adversarial reasoning…

what is adversarial reasoning?

understanding the intent & actions of your opponent…

contest of actions & rewards…

incorporating active deception & diversion…

adversarial model

manipulation of data

model adversary

environment

predic1onac1onpolicy

truedata

falsedata

Core concepts of adversarial reasoning Include…

intent inference

plan inference

deception detection

countermeasure modeling

strategy formulation

Intent & plan inference

Bayesian network graphs

A5acker

Intent

Knowledge

ProbingAc1vity

MaliciousAc1vity

Evidence

games with deception

stochastic, partially observable

simultaneousplayNashequilibrium

sequen1alplayStackelberggame

methods of deception

mimicry

decoy

masking

doubleplay

semantic/cognitive attacks

mimicry

decoy

masking

doubleplay

Operation Fortitude

detecting deception

linguistic cues

information trajectory map

info-theoretic value ranking

multi-agent ensembling

Countering deception

predict/ignore bad data

fuzzy logic/soft margins

risk weighting

Bayesian filter

abstract info from data

new algorithms

incorporating adversarial

reasoning...

actor-critic method

actor critic

actor-critic model

policy improvement

policy evaluation

actor cri1c

experiencereplay

“cooperate”adversaries

RL deadly triad

bootstrapping

off-policylearning

DLfunc1onapproxima1on

instability&divergence

A3C method

multiple actors

critic

A3C method

actors cri1c

experiencereplaynotnecessary

actorsactors

mul1pleactorsstabilizelearning

fast

overcomesdecep1on

A3C comparison

A3ConCPUonly

A3C demonstration

generative adversarial network

realworldimages

discriminator loss

generatornoise

fake

real

learnsdecep1on

adversarial

generatorvsdiscriminator

early GAN

lacked deep semantics

generative faces

thesearenotrealpeople

generative aging

maynotbetrueaging

generative ideas

variational autoencoder

Stitchfix style transfer

latentvector

encodervsdecoder

VAE face transitions

adversarial learning leads to robustness

adversarial learning algos rapidly coming to market

in summary

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