The Future of AI, NowAndi Sama

Chief Information Officer

75% of

commercial

enterprise apps

will use AI by 2020

In 2018, blended AI will

disrupt your customer

service and sales strategy

85% of CIOs

will be piloting AI

programs by 2020

Knowledge workers spend

80% of their time

searching and preparing

data... NOT on innovating

with Data Science and AI

2017 IDC report: http://idcdocserv.com/US42072917e_IBM

Innovation Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.

Peak of Inflated Expectations: Early publicity produces a number of success stories — often accompanied by scores of failures. Some companies take action; many do not.

Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.

Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious.

Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology's broad market applicability and relevance are clearly paying off.

Gartner Hypecyle for Emerging Technology July 2018: IoT & AI Platform

About10-Years

Name: SophiaWho am I?: Social humanoid

robotMy parent?: Hong Kong-

based company Hanson Robotics

Born: Activated on April 19, 2015

Shown to public: SXSW festival, March 2016 (USA)

Capabilities: Display 62+ facial expressions

Data Science Artificial Intelligence: Machine Learning

Regression

Clustering

Self Driving Car

Autonomous Vehicle

Learn to win by Observing

Watson IoT Platform

Source: SWG Insight, Edisi Q3 2017

Watson IoT Edge Analytics Watson Realtime Insights Watson Machine Learning Blockchain Spatial Analytics (Automatic routing given

GPS coordinates) The Weather Channel

IoT Devices

Industry 4.0 - Analyze data stream from IoT devices e.g. Supply Chain AutomationIBM Watson IoT Platform with Blockchain and Machine Learning

A group of young people standing around a party

An

dro

id M

ob

ile A

pp

by

An

dre

w W

idja

ja

Ba

se m

od

el: h

ttp

s://

git

hu

b.c

om

/ben

iz/d

eep

det

ect

Cross Industry – (Image Captioning) Translate Video/Image to meaningful textTrigger selected commercial (iklan) for exampleIBM Watson Visual Recognition, Watson Studio

A Street with a sign for Cars and People

An

dro

id M

ob

ile A

pp

by

An

dre

w W

idja

ja

Ba

se m

od

el: h

ttp

s://

git

hu

b.c

om

/ben

iz/d

eep

det

ect

Cross Industry – (Image Captioning) Translate Video/Image to meaningful textTrigger selected commercial (iklan) for exampleIBM Watson Visual Recognition, Watson Studio

IoT-Enabled device running Machine Learning model “Face Recognition” locally Embedded Computing NVidia Jetson TX2 Supercomputer

7.5 Watt Hardware 256 GPU cores, 8GB RAM

Neural Network Model can be trained by Watson Studio Programmed in Python State-of-the-art Face Recognition

Library with dlib

WatsonStudio

Cross Industry – Realtime Face RecognitionWatson Studio deployed to Edge IoT Device

Source http://www.thejakartapost.com/travel/2018/08/04/jakpost-guide-to-2018-asian-games-west-java.html

Cross Industry – Classify/Understand Natural Language as humans doIBM Watson Knowledge Studio, Natural Language Classifier/Understanding (NLC/NLU)

IBM Watson Studio

IBM Object Storage

Dat

aset

(lag

u2

BC

L)M

od

elin

gIn

fere

nce

Cross Industry – Text Generation e.g. for Natural Language ProcessingWatson Studio

Quick Modelling• Epoch: 300• Loss: 0.2280• Acc: 91.83%

Generated Texts, given first few words

cintaku hanya untukmu dengan kesungguhankumimpi indahku yang kujalani sanubari ku

aku cinta dia tapi takdir memisahkanlupakanlah daku tinggalkan ini inginku mengulang

malam malam yang indah ada aku ada yangsayangku cintaku selalu

sedih hatiku bintangkamu kutunggu yang paling hot kuhujan rintik sang waktu bisa kitakuterima saja aku akan cinta yg

rembulan indah pun pernah muda saatnyaindahnya oh oh indahnya serasa

berbunga tinggalkan ini inginku mengulang

IBM Watson Studio

IBM Object Storage

Dat

aset

(Go

ogl

e N

ews)

Mo

del

ing

Infe

ren

ce

Cross Industry – Text Meaning in Context Watson Studio

Library• Gensim for NLP

+-

WomanKing

Man

Monarch, 61.89%

Princess, 59.02%

Queen, 71.18%*)

Crown Prince, 54.99%

Prince, 53.77%

Most Similar

Doesn’t Match

Tea

Blueberry, apple,

cucumber

Papaya, tea, guava

Orange, banana, tomato

*) Confidence level, utilizing pre-trained word2vec model based on GoogleNews dataset

H/W S/W (2.27)Socks Shoes (2.52)

Sky Earth (3.49)Saving Loan (3.88)

How Similar/Different

Cross Industry – Conversation with the machine (Chat)

Cross Industry – Natural Language ProcessingIBM Watson Assistant (Watson Conversation)

Watson Personality Insight

Source: SWG Insight, Edisi Q1 2018

Data Sourcetext, social media, etc

Human Capital - Identify Psychological TraitsWatson Personality Insight

Customerwho churned

Patterns:1) Very high usage of data and only data2) Usage of the phone only from 14-16th days3) Almost no usage during the 30 days period

1

2

3

One Customer

Day

s o

f th

e 3

0-d

ays

per

iod

10 Variables

# incoming calls

# outgoing calls

Sum duration incoming calls

Sum duration outgoing calls

Volume download

Volume upload

Duration data connection

# data connection

# MT SMS

# MO SMS

Max:1

Min: 0

1st day

2nd day

3rd day

Telco/Cross-Industry – Classification e.g. Customer’s Churn AnalyticsIBM Watson Studio or Watson Neural Network Modeler

Watson Visual RecognitionWatson Machine Learning

Data Source(video, images)

ClassificationAccording to

trained modelAnalyzes the photos that

customers submit a body damage claim, Applying the same

classification logic as highly experienced damage assessment

advisors

Identify selected part of a tree

Cross Industry - Tag, Classify, and Train Visual ContentIBM Watson Machine Learning, Watson Visual Recognition

Digit Prediction

Classificationdigit in range

[0..9]

Unseen Data

Neural Network modelcreated by IBM NN Modeler

Recognizing Handwritten Digit

Cross Industry - Classify Image/Text/AudioIBM Watson Studio or Watson Neural Network Modeler

Data prep made easy

Guided exploration

Understand outcomes

Share insights

Cross Industry - Advanced Analytics without complexityIBM Watson Analytics

Platform for Development, Deployment, & Model Management

Enabling Team Productivity & Collaboration: Domain Expert, Data Scientist, Data Engineer, Developer

End-to-end AI workflow: Connect/Access/ Search/Find/Prepare data for Analysis, Build & Train model, deploy model, monitor + analyze & manage

Available in Cloud, On-Premise, Desktop

Cross Industry - Advanced Analytics with FlexibilityIBM Watson Studio (Data Science Experience)

Financial ServicesDerive insights based on customer activity, investigate fraudulent behavior, create secure data transparency across global entities

Reduce readmissions, reduce time to comply with FDA regulations, reduce time to locate information for citizens and professionals

Increase document retrieval accuracy, improve search capabilities, share information while complying with sensitive data rules, dramatically increase agent productivity and customer satisfaction, reduce claim loss ratios and fraud, improve campaign effectiveness

Analyze social media and customer input for product development, identify product issues faster, reduce financial penalties and maintenance costs, improve supply chain consistency and deliver up to date information to procurement

Reduce time to find information for product investigations, improve access to patient and research data globally

Improve speed of regulation updates, connect citizens with public information, identify contraband activity, search and analyze complains, arrest records…

Improve product analysis, improve stocking decisions based on customer input, translate survey information in to actionable intelligence

Improve call center resolution rates, detect likely churn candidates, consolidate and normalize information after M&A activity

Healthcare

Insurance

Manufacturing

Pharma

Public Sector

Retail / CPG

Telco

Use Cases for Data Science – for Various Industries

If Your Data is Bad,Your Machine Learning tools are Useless

Data Scientists spend 80% of their time to cleanse data, before training the Predictive Model

It’s the Problem, data scientists complain about most

Harvard Business ReviewApril 2018

Data Science Journey

Harvard Business Review, April 2018

Determine Objective

Invest time for Quality

Data

Maintain Audit Trail

Assign PIC for Data Quality

Assign Independent

QA

Clarify Objectives of using Machine Learning, Assess whether you have the right data to support

Lowering the cost / Removing Bias / Improving existing decision process

Quality level, Data Cleansing, de-duplication

Possibly generating new data

About 6 months before building machine learning model

Original training data, data for training, steps to generate original data training data

Understand the bias & limitations

Set & enforce standard for data quality

Lead to find & eliminate root cause of error

Internal Quality Assurance (QA) department or 3rd

Party

Shop for data

Curate data

Manage policies

Shape data

Build dashboards

Build ML models

Auto-optimize models

Streaming pipelines

Build data apps

IBM Watson Data PlatformIntegrated, unified self-service experience

Articles on AIwww.facebook.com/SinergiWahanaGemilang

Andi Sama

Chief Information Officer

andisama@swgemilang.com

Formal EducationMaster: Bina Nusantara, 2006 - 2008

(Executive Program in Management)Master: STTI Benarif Indonesia, 1997 - 1998

(Computer Science)Bachelor: Bina Nusantara, 1987 - 1992

(Computer Engineering)

Executive Education2002: Owen Graduate School of Management

University of Vanderbilt, Nashville, TN USA(Banking Operation & Technology)

Professional Career2010-Now: Sinergi Wahana Gemilang

CIO- IBM Software VAD- Esri Partner- Autodesk Gold VAR

2000-2010: Mitra Integrasi KomputindoIT Integration Architect, CTO, Managed Services Head

1990-2000: IBM Indonesia, IBM Asia Pacific Developer, Technical Advisor, Technical Sales

Current Research InterestsData Science & Machine LearningInternet of ThingsBlockchainQuantum Computing

www.facebook.com/andisama www.facebook.com/SinergiWahanaGemilangwww.swgemilang.com SWG Insight App on Appstore & Playstore

Speaker Profile

More Info on…

IBM Data Science SWG Insight on Playstore or Appstore SWG Website or SWG Facebook page

BACKUP Slides

Supervised Learning Unsupervised Learning Reinforcement Learninng

Semi Supervised Learning

Machine Learning Types

Source: Hands-On Machine Learning with Scikit-Learn and TensorFlow

Traditional Programming vs Machine Learning

Source: MIT Lecture 11 – Machine Learning, 6.0002, 2016

Traditional Programming Machine Learning

Computer Output

Data

Program/Algorithm

ComputerProgram/Algorithm

Data

Output

{[“Gunawan”, “M”, 45, 3200], [“May”, “F”, 31, 2400],[“Elle”, “F”, 27, 1925],[“Bob”, “M”, 36, 2375],…[“Boy”, “M”, 23, 1800]}

Y = ax + b

Y

Y

{[“Gunawan”, “M”, 45, 3200], [“May”, “F”, 31, 2400],[“Elle”, “F”, 27, 1925],[“Bob”, “M”, 36, 2375],…[“Boy”, “M”, 23, 1800]}

Y = ax + b

Machine Learning

TrainingEngine

(Train/Fit)

ModelTrainingData set

TestingData set

(Validation)

Target Output/Label

New (Unseen)Data set

Feedback/Iteration to minimize error (improve prediction accuracy)

70-80%

20-30%

Create Model Use Model

Inference Engine

(Predict)

AvailableData set

• “Target Classes” e.g. for Classification or “Target Values” for Regression if it is a Supervised Learning

• There are no target output in Unsupervised Learning (e.g. Clustering)

Output

Data Science Venn Diagram v2.0

Machine Learning as part of Data Science, is the

combination of computer science + math & statistics

disciplines.

Unicorn in the middle indicates that Data

Scientists are hard to find, as hard as unicorns.

ComputerScience

Math &Statistics

Subject Matter Expert

TraditionalSoftware

TraditionalResearch

MachineLearning

Unicorn(Data Scientist)

Data Science

©Stephen Geringer, Raleigh NC (2014)

ForwardPropagation

ForwardPropagation

“Human Face”

“Human Face”

Smaller, Varied N(New, Unseen Data)

= ?

Feedback/Iterationto minimize error

(improve prediction accuracy)

“Dog”Target Output Labels

USE (PREDICT)Deep Neural Network

(Architecture, Trained Weights)

Number ofepoch

GeneratesTrainedModel

Network Architecture (e.g. ResNet-152),

Initial Weights (e.g. Xavier),Activation Function (e.g. Sigmoid/ReLu)

BackwardPropagation

e.g. using SGD(Stochastic Gradient Descent)

TRAIN (FIT)Deep Neural Network

(Architecture, Weights,Activation Functions)

Learning rate,Update Weights

“Target Output Labels” e.g. for Classification or “Target Values” for Regression if within Supervised Learning

There are no target outputs in Unsupervised Learning (e.g. Clustering)

Large N(Training Dataset, Xi)

Typical Deep Learning Worklflow,with Supervising Learning (labeled data)