Enterprise Artificial Intelligence

Size: px

Start display at page:

Download "Enterprise Artificial Intelligence"

Fay Henderson
6 years ago
Views:

1 Ron Bodkin VP and GM, Artificial Intelligence Enterprise Artificial Intelligence Applications and Approaches Teradata 2016 Teradata

Evolution of Analytics Prescriptive Predictive Descriptive REPORTING What is happening? 1990s ANALYZING Is it real? Why is it happening? PREDICTING What are the hidden patterns? What will happen next?

2 Evolution of Analytics Prescriptive Predictive Descriptive REPORTING What is happening? 1990s ANALYZING Is it real? Why is it happening? PREDICTING What are the hidden patterns? What will happen next? OPERATIONALIZING What is happening right now? 2000s ACTIVATING Make it happen with automation. MACHINE LEARNING Self-learning systems with linear regression. 2010s ARTIFICIAL INTELLIGENCE Deep learning. 2

3 The Resurgence of Artificial Intelligence Explosion of data Rapid increase in GPU capability Rapid progress in research and applications using neural networks. Significant technology investments. By 2019, deep learning will provide best-in-class performance for demand, fraud, and failure prediction. Gartner Paying with Your Face Face-detecting systems in China now authorize payments 3

4 4 What is AI?

5 Stunning Success in Image Recognition Source: Intel 5

6 The Resurgence of AI 6

7 Deep Learning (add visual for CxO) How is it different? Multiple layers in neural network with intermediate data representations to facilitate dimensional reduction. Interpret non-linear relationships in the data. Derive patterns from data with very high dimensionality. Why do we care? Ability to create value with little or no domain knowledge required. Ability to incorporate data from across multiple, seemingly unrelated sources. Ability to tolerate very noisy data. 7

8 Agenda Context Applications Conclusions 8

9 Mobile Personalization Source: Google Generalize rules (e.g., categories of interest) Memorize exceptions (e.g., common pairs) Google Play Store production and other leading digital companies Projects in banking, telco, retail 9

10 Banking Anti-Fraud: Business Drivers Goal: fraud detection across products Trends Evolution of new payment methods Mobile payments exploding Fraud evolving rapidly, increased sophistication Traditional approach is hand-written rules Cost, delay and customer impact of false positives 10

11 Banking Anti-Fraud: Solution Approach Phased implementation approach Simulated result Champion/challenger testing Production deployment Significant improvements over traditional rules-based techniques Techniques Boosted Decision Trees Recurrent Neural Networks Generative Adversarial Networks 11

12 Key Requirement: Model Interpretation We have deployed LIME (Locally Interpretable Model Explanation) for customers Improves trust Compliance with EU s General Data Protection Regulation (GDPR) 12

13 Agenda Market Context Applications Conclusions 13

14 Challenges to deliver AI use cases Strategic Technology Broad hype and interest Business processes must change Few applications in enterprise Point solution limitations Research Driven Specialized Hardware Evolving Frameworks Academic Code Operationalization Data Talent & Ethics Transparency Integration of AI output to broader ecosystem Deployment of largescale GPU clusters Systems Integration Access & generation of labeled data Governance & Quality Volume & Variety Training data curation Shortage of AI talent beyond academia Disruption of workplace may cause dramatic new business workflows, labor markets and ethically issues Teradata

Focus First on Pilot into Production Production

Hand Over Outcome: System scaled, application teams

Live Handover Cross-Functional Teams Production

Define business opportunity, understand data

generate data Outcome: Proposed solution approach

business process (Champion/Challenger), analysis,

learnings, improvement Analyze Data Live Test

Teams Engineer Discovery/Insights Activities:

15 Focus First on Pilot into Production Production Activities: Go Live, Analytics Ops integration, Hand Over Outcome: System scaled, application teams and ops trained and operating AI as a Service Go Live Handover Cross-Functional Teams Production Investigate Assessment Test Validate Activities: Define business opportunity, understand data available, test model approaches, potentially generate data Outcome: Proposed solution approach Live Test Activities: Integration into live business process (Champion/Challenger), analysis, iteration Outcome: Benefit measurement, live learnings, improvement Analyze Data Live Test Insights Integration Simulate Cross-Functional Teams Engineer Discovery/Insights Activities: Architecture selection, software engineering of model and simulation Outcome: Predicted impact of model 15

16 Analytics Ops for Cross-Functional AI Teams Application Integration A/B Testing Automated Training & Scoring Test and Deployment Constant Monitoring 16

Our Approach Teradata Labs Teradata Deep Learning Community Dozens of Experts in Deep Learning, Image/Audio/Video Processing, Computer Vision, GPU Practitioners 200+ Practitioners delivering

17 Our Approach Teradata Labs Teradata Deep Learning Community Dozens of Experts in Deep Learning, Image/Audio/Video Processing, Computer Vision, GPU Practitioners 200+ Practitioners delivering Artificial Intelligence Business Value on Customer Projects Experts 500+ Solution Architects, Business Consultants and Software Engineers with knowledge of Artificial Intelligence Tools, Techniques and Technologies. Deep expertise in retail and across industries. Industry Collaborations Academic Collaborations Analytics Ops Interest Data Management 17

18 18 Industry Timeline Projection

19 Conclusions AI moving beyond labs to production 19 A strategy and roadmap is critical Pilot now to build Capabilities

20 Thank you! Ron Bodkin 20

fraud examination resulting in cost savings Techniques: Image Processing Natural

21 Automated Check Processing Handwritten check volume is decreasing however processing checks has many fixed costs Handwriting recognition to reduce manual processing and fraud examination resulting in cost savings Techniques: Image Processing Natural Language Processing ImageMagick Processing Handwriting Recognition Check Images To Hadoop 21

22 Finding a Needle in a Haystack with Deep Learning High-tech manufacturing example: Process technology is becoming exponentially complex à Approaching 1M process variables that can influence yield & quality. Substrate RMA Fab WAT Probe Assembly Final Test System Test Customer FDC PCM BIN PARAM 10k s of Parameters BIN PARAM RMA BIN PARAM BIN 10k s of Parameters 10k s of Parameters 10k s of Parameters 22

The Rise of Engineering-Driven Analytics. Richard Rovner VP Marketing

The Rise of Engineering-Driven Analytics Richard Rovner VP Marketing MathWorks @RichardRovner The Rise of Engineering-Driven Analytics The Rise of Engineering-Driven Analytics Limited users, scope & technology