AGILE AND AUTOMATION CONCLAVE 2018 - Accenture · 2018-07-03 · Agile and Automation Conclave 2018 REFERENCES Books • Human + Machine: Reimagining Work in the Age of AI.Paul Daugherty

AGILE AND AUTOMATION CONCLAVE 2018

FUTURE OF ENTERPRISE AICHALLENGES AND OPPORTUNITIES

JANARDAN MISRA

Agile and Automation Conclave 2018

JANARDAN MISRATECHNOLOGY RESEARCH SR. PRINCIPAL, ACCENTURE LABS• 18+ years of R&D experience with contributions in areas of Unstructured

Data Analytics, Information Retrieval, Applied Machine Learning, and Complex

Adaptive Systems.

• 30+ peer reviewed research papers and a monograph.

• 40+ patents (issued and pending) across multiple geographies.


AGENDA

• ENABLING TECHNOLOGIES

• WORKFORCE IMPLICATIONS

• POTENTIAL PITFALLS

• CURRENT STATE OF AI

• CHALLENGES WITH TODAY’S AI

• EMERGING TECHNIQUES


CURRENT STATE OF AI

What can AI Do?

“If a typical person can do a mental task with < 1 second of thought, we can probably automate it using AI either now or in the near future”

-- Andrew Ng

• Often most helpful in complex environments

Probable Futures

Information

AI

Funnel


AI – CURRENT STATE (CONT.)

• Major Breakthroughs

• Key Paradigm

• Deep Neural Networks based Supervised Learning

Speech and Image Recognition Language TranslationPersonalized Recommendations Credit Card Fraud DetectionSpam Filtering Search


CHALLENGES WITH TODAY’S AI

Data Challenges• Effectiveness may come only with millions of data-points • Difficult to create ‘gold standard’ data set for training and validation

Engineering Challenges• Software Engineering for AI is still evolving! • Difficult to debug and incrementally improve in contrast to classical programming


CHALLENGES WITH TODAY’S AI (CONT.)

Functional Challenges

• Causal Inferencing• Learning causation beyond correlations

Are these two definitions equivalent?• “A number that is divisible only by

itself and 1” • “a natural number greater than 1

that cannot be formed by multiplying two smaller natural numbers”

Temperature and Ice-cream sales are correlated!

• Do high temperatures cause high sales or vice versa?

• Reasoning• Commonsense and open-ended

inferences• Comprehension

• Learning abstractions through definitions


EMERGING TECHNIQUES

COMPOSABLE AI SYSTEMS• Model vs Action

Composition

AI-SPECIFIC ARCHITECTURES• Domain Specific

ML models and hardware

NATURAL LANGUAGE PROCESSING• Conversational

and Q&A Agents

DEEP LEARNING + BIG DATA• Ability to learn

indefinitely as more data comes in

MISSION-CRITICAL AI• Acting in

Dynamic environments


ENABLING TECHNOLOGIES

Never-ending Active Learning• Continuously learn as you predict with human-in-the-loop

Transfer Learning • Knowledge Reuse• Example: To be able to learn on open data to solve closed enterprise problems

Unsupervised Learning• Learning autonomously without explicit training


WORKFORCE IMPLICATIONS

Data

Prediction

Judgement

Action

Output

Feedback

Employing Prediction Machines

• AI trainers vs designers

• Creative thinking vs routine execution

Experts-in-the-loop

• Complexity of judgements will be deciding

factor

• Skills to make right judgements will be critical

for the future workforces


POTENTIAL PITFALLS

Technical Debt

• Potentially high maintenance costs after quick design wins

Lack of Explainability

• Most successful AI techniques are opaque

• “Right to Explanation” – GDPR

Security Concerns

• Data Poisoning attacks

• Lack of robustness against adversaries

Ethical Concerns

• How to ensure fairness?


REFERENCESBooks• Human + Machine: Reimagining Work in the Age of AI. Paul Daugherty and H. James Wilson, Harvard

Business Review Press 2018

• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro

Domingos, Basic Books, 2018

• Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence. Jerry Kaplan,

Yale University Press, 2015

Articles• Artificial Intelligence and Life in 2030: One Hundred Year Study on Artificial Intelligence. Peter Stone et

al., Stanford University, 2016

• A Berkeley View of Systems Challenges for AI. Ion Stoica et al., arXiv.org, 2017

• What Artificial Intelligence Can and Can’t Do Right Now. Andrew Ng, HBR, 2016

• Future progress in Artificial Intelligence: A Survey of Expert Opinion. V. C. Müller and N. Bostrom, Springer

2016

• Deep Learning: A Critical Appraisal. Gary Marcus, arXiv.org, 2018

• What can Machine Learning Do? Workforce Implications. Eric B. and Tom Mitchell, Science, 2017


Q&A


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Thank you!

Janardan [email protected]

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AGILE AND AUTOMATION CONCLAVE 2018 - Accenture · 2018-07-03 · Agile and Automation Conclave 2018 REFERENCES Books • Human + Machine: Reimagining Work in the Age of AI.Paul Daugherty