transforming data into wisdom

@pieroleo

Transforming Data into Wisdom

Pietro LeoExecutive Architect - IBM Italy CTO for Big Data Analytics & WatsonIBM Academy of Technology LeadershipHead of IBM Italy Center of Advanced Studies

2

Tech Age

You$shared$your$position$with$me$and$can$guess$your$mobility$need.$I$can$take$you$where$you$need$to$be

Just$enjoy$your$new$experience.$Stay$safe$as$in$your$home

I$know$what$is$needed$for$you,$even$before$you$order$it

Please,$come$with$me$and$stay$by$me.I$know$your$content$I$can$take$care$of$all$your$digital$life

Has DATA'a'gravity?

Data'growth and'gravity distorts and'impactsevery component'of'IT'– and'business

Data & Big Data

Toward a Precise Decision Making to reduce the wasteful spend as well as the risk in every industry

New Information Technology challenge is now about the

possibility to expand our WISDOM options

Watson

Wisdom

Ecosystemand,Partners

Industry,Solutions

ClientSolutions&,products

IBM$ProvidedData Publically

SourcedData

PartnerProvidedData

PrivateClientData

IBM,Watson,Innovation,platform,for,Cognitive,Business

Watson'HealthWatson'Financial'ServiceWatson'Internet'of'Things

Hybrid,Watson,Frameworks

WatsonServices,B API

Data

Knowledge

Wisdom

Cognitive Platform Cognitive Solutions

Anaphoric* Co,referencingColloquialism* ProcessingContent* Management* ,, VersioningConvolutional* Neural* NetworksCurationDeep* LearningDialog* FramingEllipsesEmbedded* Table* ProcessingEnsembles* and* FusionEntity* ResolutionFactoid* Answering

Feature* Engineering

Feature* NormalizationFocus* and* Spurious* Phrase*ResolutionHTML*Page* AnalysisImage* ManagementInformation* RetrievalKnowledge* (Property)* GraphsKnowledge* AnsweringKnowledge* Extraction* AnnotatorsKnowledge* Validation* and*ExtrapolationLanguage* ModelingLatent* Semantic* Analysis

Learn* To*RankLinguistic* AnalysisLogical* Reasoning* AnalysisLogistical* RegressionMachine* LearningMulti,Dimensional* ClusteringMultilingual* trainingn,Gram* Analysis* (word*combinations* and* distance)Ontology* AnalysisPareto* AnalysisPassage* AnsweringPDF*ConversionPhoneme* Aggregation

Question* AnalysisQuestion,answering* Reasoning*StrategiesRecursive* Neural* NetworksRules* ProcessingScalable* SearchSimilarity*AnalyticsStatistical* Language* ParsingSupport* Vector* MachinesSyllable* AnalysisTable* AnsweringVisual* AnalysisVisual* RenderingVoice* Synthesis

These*APIs*are*underpinned*by*50#technologies:

2011

2015Source:*http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services,catalog.html

Cognitive Services

Live Workshop

Chewing(Gum(Wall(in(California

Source:(http://en.geourdu.co/buzz/bizarre5shocking/chewing5gum5wall5in5california/

San(Luis(Obispo

Customer Analysis Healthcare

IBM Chef Watson.

Inspire your cooking decisions

Cognitive)Cooking

187

Cognitive)Computing)approach)to)Computational)Creativity

Create&Food&new&recipes&from&scratch

Modify&existing&recipes&to&satisfy&your&

own&taste

Suggest&new&things&to&prepare&&&cook

Pair(ingredients(and(flavors(for(recipes(and(dishes(

1876

Wisdom for All Nutrition

3

PROLOGUE

@pieroleo

@pieroleo

WISDOM

KNOWLEDGEDATA

INFORMATION

@pieroleo

@pieroleo

DATA

INFORMATION

KNOWLEDGE

WISDOM "Olli"

Self-Drive VehicleCo-creative community3D-printedCloud IoTArtificial Intelligence30 Transportation Sensors + New onesConversationRecommenderVideo RecognitionPersonalization…

@pieroleo

DATA

INFORMATION

KNOWLEDGE

WISDOMSo, we need WISDOM, to Augment, individualand collective, Intelligence

9

THE TECHAGE

@pieroleo

10

@pieroleo

@pieroleo

630

539

446

389

357

363

as of 12 October 2016

@pieroleo

http://www.grushgamer.com/

In 2015 63% of CEOs will increase investment in digital, it is a matter of survive

Investment in private Fintechcompanies increase 10x in past 5 years: 19B

Credits: http://www.arkive.org/whale-shark/rhincodon-typus/

What is Fintech?

16

FintechFinancial technology, also known as FinTech, is an economic industry composed of companies that use technology to make financial servicesmore efficient.

Financial technology companies are generally startups founded with the purpose of disruptingincumbent financial systems and corporations that rely less on software.

Source: https://en.wikipedia.org/wiki/Financial_technology

17

Fintech are « UNBUNDLING » tranditional banks

« UNBUNDLING » is general phenomena that is impacting every sectors or corporations

Source: https://www.cbinsights.com/blog/smart-home-market-map-company-list/

SMART HOMES

« UNBUNDLING » logistics

Source: https://www.cbinsights.com/blog/startups-unbundling-fedex/

The biggest taxi company do not own cars

Uber vs Taxi Drivers

Uber vs Uber Drivers

Uber vs …..

The largest accommodation company owns no real estates

Marriott CIO acknowledgedAirbnbas a new competitor

China has the largest e-commerce volume in the world: $672B, - 39%

The largest media company owns not content

29

What is the behind?Digital Business + Digital Intelligence

30

You shared your position with me and can guess your mobility need. I can take you where you need to be

Just enjoy your new experience. Stay safe as in your home

I know what is needed for you, even before you order it

Please, come with me and stay by me.I know your content I can take care of all your digital life

31

AI (business)springDigital Business + Digital Intelligence

Artificial Intelligence patents have more than tripled in 10 years

Venture Scanner are tracking 1481 ArtificialIntelligence companies with a combinedfundingamount of $8.8 Billion

Venture Scanner AI segments

78% of Executives say business will manage people along side machines

39

What is the behind?Digital Business + Digital Intelligence

40

You shared your position with me and can guess your mobility need. I can take you where you need to be

Just enjoy your new experience. Stay safe as in your home

I know what is needed for you, even before you order it

Please, come with me and stay by me.I know your content I can take care of all your digital life

41

How is that possible?

42

Image source: http://personalexcellence.co/blog/ideal-‐beauty/

City

Lifestyle

ZIPcode

Costal vs Inland Marital status

Generation

Location

Family Size

Gender

Income Level

Competitors

Age

Loyalty & CardActivity

Revenue Size

Life Stages

Eductation

Legal status

Sector

Industry

43


City

Lifestyle

ZIPcode


Generation

Location

Family Size

Gender

Income Level

Competitors

Age


Revenue Size

Life Stages

Eductation

Legal status

Sector

Industry

SubscriptionsDate on Site

Wish List

Size of Network

Check-ins

App usage duration

Number of Apps on Device

Deposits/Withdrawals

Device UsagePurchase History

FollowingFollowers

Likes

Number of Hashtags used

History of Hashtags

Search Strings entered

Sequence of visits

Time/Day log in

Time spent on site

Time spent on page

Frequency of Search

Videos Viewed

Photos liked

44


City

Lifestyle

ZIPcode


Generation

Location

Family Size

Gender

Income Level

Competitors

Age


Revenue Size

Life Stages

Eductation

Legal status

Sector

Industry

SubscriptionsDate on Site

Wish List

Size of Network

Check-ins

App usage duration

Number of Apps on Device

Deposits/Withdrawals

Device UsagePurchase History

FollowingFollowers

Likes

Number of Hashtags used

History of Hashtags

Search Strings entered

Sequence of visits

Time/Day log in

Time spent on site

Time spent on page

Frequency of Search

Videos Viewed

Photos liked

Sentiment

Tone

Euphemisms

Hedonism

Extroversion

Face Recognition

Openess

Colloquialism

Reasoning Strategies

Language Modeling

DialogIntent

Latent Semantic Analysis

Phonemes

Ontology Analysis

Linguistics Image Tags

Question Analysis

Self-transcendent

Affective Status

Source: http://www.bloomberg.com/video/meet-the-world-s-most-connected-man-Vs~LzkbkR7yhjza~7nji1g.html

Meet theWorld's Most Connected Man

Rapid growth of exogenous data is transforming healthcare

6 Terabytes

60%Exogenous Factors

1100 TerabytesVolume, Variety, Velocity, Veracity:Educational records, Employment Status, Social Security Accounts, Mental Health Records, Caseworker Files, Fitbits, Home Monitoring Systems, and more…

0.4 TerabytesElectronic Medical / Health Records, Physician Management Systems, Claims Systems and more…

30%Genomics Factors

10%Clinical Factors

IBM Watson Health // SOURCE: ©2015 J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93

Data Generated per Life

Leveraging Exogenous Data for Chronic Care

60%Exogenous Factors

30%Genomics Factors

10%Clinical Factors

SOURCE: ©2015 J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93

Glucose Monitoring

Calorie Intake

Stress LevelsPhysical Activity

Other vital signs Social Interaction

Affinity (retail)

Sleep Pattern

Medtronic gets FDA nod for artificial pancreas system, preps to launch Watson-powered Sugar.IQ app

50

What is happening?

51

Automating the

World

Understanding the

World

Main Technology Shift

H-FactorProgram Train/Data Scientist

Knowledge Workers Learning Workers

52

BIG DATA

DATA

WISDOM

Knowledge

Information

Technology is no more supporting every kind of

private and public organizations, it is becoming

part of them.

Machine IntelligenceIs becoming the key

ingredient.

AnalyticsCloud Computing

Data Science

Mobile

Social

Digitalization

Technology

Business

Robotics

Artificial Intelligence

Business & Tech NexusThings

53

DATA

@pieroleo

Has DATA a gravity?

Data growth and gravity distorts and impactsevery component of IT – and business

@pieroleo

55

>80% Unstructured Data

+ External Data“Untouched” Data+ Stream of Data

Enterprise Data Machine Data People Data

@pieroleo

Data is there and we need to make the best out of it

@pieroleo

We produce and consume Data for a specific purpose

@pieroleo

Surce: http://pennystocks.la/internet-in-real-time/

Big Data Faces: the Internet in Real-Time

@pieroleo

59

SocialData from and about People

PhysicalSensors & Streams

Terabytes to exabytes of existing data to process

Streaming data, milliseconds to seconds to

respond

Structured, Semi-structured Unstructured,

text & multimedia

Uncertainty from inconsistency, ambiguities, etc.

Volume

Velocity

Variety

Veracity

DataContent

>80%<20%

Traditional Enterprise Data

Big data embodies new data characteristics created by today’s digitized marketplace

BiologicalDNA Sequencers

@pieroleo

60 60

Global Data Volume in Exabytes

Multiple sources: IDC,Cisco

100

90

80

70

60

50

40

30

20

10

Aggregate Uncertainty %

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

2005 2010 2015

By 2015, 80% of all available data will be uncertain: Veracity

Data quality solutions exist for enterprise data like customer, product, and address data, but this is only a fraction of the total enterprise data.

By 2015 the number of networked devices will be double the entire global population. All

sensor data has uncertainty.

The total number of social media accounts exceeds the entire global

population. This data is highly uncertain in both its expression and content.

@pieroleo

Paradigm shifts enabled by big data and analyticsTRADITIONAL APPROACH

Analyze small subsets of information

Analyzedinformation

All available

information

BIG DATA & ANALYTICS APPROACH

Analyze all information

All available

informationanalyzed

Leverage more of the data being captured

Data leads the way— discover new emerging properties

Reduce effort required to leverage data

Leverage data as it is captured

TRADITIONAL APPROACH

Carefully cleanse information before any analysis

Small amount of carefully organized information


Analyze information as is, cleanse as needed

Large amount of messy

information

Hypothesis Question

DataAnswer


Start with hypothesis andtest against selected data


Explore all data andidentify correlations

Data Exploration

CorrelationInsight

Repository InsightAnalysisData


Analyze data after it’s been processed and landed in a warehouse or mart

Data

Insight

Analysis


Analyze data in motion as it’s generated, in real-time

@pieroleo

Source: http://datacoup..com

Value of Data

Pietro Leo's SecondIncome!

@pieroleo

Just ONE Transactionpath goes to the end in thousands and to complete that path tens of decision points were considered. Right now we store and analyze in our transactional systems just the transaction end points.

Buyer ….Win!!!

Buying Decision Labyrinth

Yes!

Big Data is the answer and the need of the new emerging sub-‐transactional era

@pieroleo

It's an invitation-only loan product offered exclusively to Amazon Sellers. The Amazon loans offers very competitive from 6 to 14% interest rates and no pre-payment penalty.

The power of a sub-transactional knowledge

Source: http://uk.businessinsider.com/r-exclusive-amazon-to-offer-loans-to-sellers-in-china-7-other-countries-2015-6?r=US&IR=T

US, Japan from 2012 and from 2015 - Canada, China, France, Germany, India, Italy, Spain and the United Kingdom

@pieroleo

For Science, Big Data is the microscope of the 21st century

Wine DNA Tracing

@pieroleo

Source: Cornell University -Maize kernal infected with Aspergillus flavus, which producedaflatoxin.http://www.plantpath.cornell.edu/labs/milgroom/Research_aflatoxin.html And http://www.special-clean.com/special-clean/en/mold/mold-lexicon-1.php

For science, Big Data is the microscope of the 21st century

@pieroleo

Source: A statue representing Janus Bifrons in the Vatican Museums

Big Data as a new Business Concept and as a new Technology Concept

@pieroleo

68

Big Data as a new business concept: New values and opportunities for a number of stakeholders

Chief Marketing Officerhow to improve customer focus?...could predict the right offer for the right customer at the right time and improve customer value and intimacy or prevent churn?

Chief Product Designer...how we can innovste? … could

we improve our product channels/design offering??

Chief Finance Officer

...could streamline compliance and understand risk exposure across businesses and

regions?

Chief Risk Officer...uses anti fraud predictive analytics to detect and prevent rapid fire anomalous transactions or wire transfers identified as high probability of fraud?

Chief Executive Officer...could make better business decisions using accurate data across all company/system dimensions and across time horizons: past, present and future?

Chief Information Officer ...could analyze oceans of machine generated logs to

predict which components or equipment in the datacenter are likely to fail and thereby avert a disruption

during critical quarter end? How we can support Zero high risks or manage crisis?

Big Data

@pieroleo

We need to combine internal and external data, utilized and under-utilized data, structured and unstructured data... and cross-link organization knowledge & data silos

CRM• emails• claims• call center scripts• Chats with customers• …

Transactional Info.:• Transactions• Orders• consultancies• …

Legal Info:• Contracts• Complaints• Reports• Legal Actions• Fraud Data• …

Knowledge Management•Manuals, wikis, couses•Projects Data•Market Analysis•RSS Business Feeds•Data feed: Bloomberg reuters• …

IT SystemsSystem LogsApplication logs: web, vending machines, mobileVideoSensor Networks, RFID• …

Social Media:• Global Social Networks: tweeter, facebook, etc.• Small communities: blogs, muros corporativos,• Internal Social Networks (employees)• News • … Big

Data

Big Data as a new technology concept

@pieroleo

“Big Data is the set of technical capabilities,

management processes and

skills for converting vast, fast, and varied data into Right Data to produce useful

knowledge” Source: Definition discussed during the work of the Word Summit on Big Data and Organization Design Paris – 2013 and Adapted from: Beacon Report – Big Data Big Brains – 2013

In summary, what is Big Data?

@pieroleo

New Organization Design: What is New and Different?

A lot more data and different kinds of data.Historically most data was structured data – rows and columns

Today it is unstructured data like aerial photos, audio from call centers, video from surveillance cameras, e-mails, texts, diagrams.

A shift in focus from data stocks to data flows.Historical information was stored in data warehouses and analyzed by data mining.

Streaming data arrives in real time allowing us to influence events as they happen. We can prevent some bad events from ever happening at all.

Shift in the power structure of the company. Many companies have analog establishments. We need to shift power to those who can draw valuable insights from data and analytics and implement them.

Shift from periodic to real time or continuous decision making. We need an increase in the clock speed of every process in the company.

There is a potential for “Big Data” to become a fundamental center for the company. Is it a new dimension of structure?

Organization Design IssuesTechnology Issues

Source: Jay R. Galbraith

72

THE WISDOMAGE

The way to find information

The way to make better decisions

74

We need wisdom to be helped to cope with

Cognitive Overload

Toward a Precise Decision Making to reduce the wasteful spend as well as the risk in every industry

New Information Technology challenge is now about the

possibility to expand our WISDOM options

Watson

2011 2015

2016 - AlphaGO=4 Lee Se-Dol=1

1997 - IBM=2.5 Kasparov=2.5

1997

AlphaGO uses self-trained net to evaluate positions and moves on 30M historical games

DeepBlue uses a hard-coded objective function written by a human coupled with High Performance Computing

2016

10

10170

1040

Applying or having wisdom in real world is not only an AI game

COMPUTING & MATH WISDOM

IBM Watson – Jeopardy!

SEMANTICS

The Jeopardy! Challenge: 5 Key Dimensions to drive Question Answering

Broad/Open Domain

Complex Language

High Precision

Accurate Confidence

High Speed

$600In cell division, mitosis splits the nucleus & cytokinesis splits this liquid cushioning the

nucleus

$200If you're standing, it's the direction you should look

to check out the wainscoting.

$2000Of the 4 countries in the world that the U.S. does not have diplomatic relations with, the one that’s farthest north

$1000The first person

mentioned by name in ‘The Man in the Iron Mask’ is this hero of a previous book by the same author.

What is down? Who is D’Artagnan?

What is cytoplasm?

What is North Korea?

@pieroleo

78

@pieroleo

79

@pieroleo

80

81

Analytic Systems use statistical techniques for detecting patterns or detect trends within data, yield an understanding of historical or current state from which to draw conclusions

Text Mining is a class of functions for parsing and identifying significant words in language (NLP) as well as understand the semantic of a textual content

Cognitive Systems leverage machine learning to predict meaning in features of human language (spoken, written, visual) and related forms of human reasoning

Multi-Media Mining is a a class of function for analyzing visual content such as images or videos

Speech Mining is a class of functions for analyzing audio signals including speech to such as ability Cognitive Solutions

leverage a combination of cognitive system reasoningstrategies and other analytic and classical computing techniques to solve for a complex problem -> Amplify Human WISDOM in a specific domain

XXX Mining is class of large specialized functions for analyzing “digital representation” in a specific domain à e.g., Bioinformatics, Financial Analytics, etc.

Machine Learning is a class of statistical techniques that use training data to recognize the correlation between a set of feature patterns and outcomes.

It includes also Deep Learning that is a rapidly maturing space, based on neural network techniques, that are taught to find their own features

Emerging Patterns for Artificial Intelligence adoption in Business World

WISDOM BIG DATA ANALYTICS

@pieroleo

82

• Cognitive systems are able to learn their behavior through education;;

• That support forms of expression that are more natural for human interaction;;

• Whose primary value is their expertise;; and• That continue to evolve their reasoning approach as they experience new information, new scenarios, and new responses

1.education 2.expression 3.expertise 4.evolve

Which are cognitive systems main attributes?

@pieroleo

Opportunity for decision-making

support2025

Cognitive opens new opportunities on top of traditional IT

Traditional globalIT spend

Source: IBM analysis presented to the Investor Briefings

~$2T

~$1.2T

@pieroleo

Top outcomes from cognitive initiatives vary by industry

Finance49% Increased market agility46% Improved customer service43% Increased customer

engagement43% Improved productivity &

efficiency42% Improved security & compliance, reduced risk

Retail56% Personalized customer / user

experience56% Increased customer engagement56% Improved decision making & planning 56% Reduced costs55% Improved customer service

Health66% Accelerated innovation of

new products / services66% Improved productivity &

efficiency64% Improved security & compliance,

reduced risk62% Reduced costs59% Improved customer service

Manufacturing64% Improved decision making

& planning 58% Improved productivity &

efficiency54% Improved security & compliance, reduced risk52% Improved customer service49% Enhanced the learning experience

Government/Education54% Personalized customer / user experience50% Improved customer service37% Improved decision making & planning 36% Improved productivity & efficiency33% Increased customer engagement

Professional Services40% Reduced costs36% Personalized customer/user

experience36% Improved customer service36% Expanded ecosystem34% Accelerated innovation of new

products / services

% achieving outcome with cognitive

Source: An IBM study of over 600 early cognitive adopters - 2016 Full report: http://www.ibm.com/cognitive/advantage-reports/

85

COGNITIVEPLATFORMS

Ecosystemand Partners

Industry Solutions

ClientSolutions& products

IBM ProvidedData Publically

SourcedData

PartnerProvidedData

PrivateClientData

IBM Watson Innovation platform for Cognitive Business

Watson HealthWatson Financial ServiceWatson Internet of Things

Hybrid Watson Frameworks

WatsonServices - API

Data

Knowledge

Wisdom

87

COGNITIVESOLUTIONS


Industry Solutions

ClientSolutions


SourcedData

PartnerProvidedData

PrivateClientData


Health

Financial

Cross

Public FilingsPatentsMedical JournalsU.S. Geological Survey…

AppleTwitterQuest Diagnostics…

MedtronicUnder ArmourJohnson & JohnsonThomson Reuters…



WatsonServices

Comms Industrial Distribution Financial Public ServicesHealth

Fraud AnalysisCorp Intelligence

Claims ProcessingDigital Agent

Call Center Advisor

Public SafetyNational SecurityShopping Advisor

Sales AutomationSupply & Logistics

Omni-Channel Ops

Product SafetyField Service MgtGeology Advisor

Digital AgentTheme Park ExpCall Center Ops

CIO DashboardCorp Intelligence

M&A Advisor Cyber Security

Life SciencesOncology

Clinical Trial Matching

1-800 Flowers

Live at: https://www.1800flowers.com/gwyn-1800flowers?flws_rd=1 Live at: https://www.thenorthface.com/xps

GWYN (Gifts When You Need), a Watson-powered personal concierge designed to help customers find the perfect gift

The North Face

A personal Shop Assistant that can drive you to select the most appropriate Jacket

Virtual Agents: Sales Assistants

• Will deliver personalized content through the dashboard and other digital channels supported by the OnStar Go ecosystem to make the most of time spent in the car.

• iHeartRadio will use Watson Personality Insights to curate personalized experiences that leverage on-air personalities and local content from radio stations across the U.S.

• The platform employs Watson Tradeoff Analytics to give a traveling foodie dining recommendations from celebrity chefs when driving in a new city.

Cognitive Automation

June 2016 96

97

What types of cognitive systems?

98

8,361 Teams joined to propose and generate ideas

And over 2.700 passed feasibility reviews

275,000 IBMers all around the world who engaged in the Cognitive Build.

• Imagine a digital cognitive system to help you do something important in your personal or professional lives

• Team to design it and advocate for it, and then everyone votes

• Winners: reduce waste and human suffering, screen for health issues and safety threats, learn life skills and make better choices, find what you are looking for, move around more effectively, provide emotional support, provide IT support, learn about important public policy goals and make better choices

Types of Cognitive Systems

99

Tool AssistantTools Collaborator

Coach Mediator

Source: Analysis of top 400 ieas by J. Spoorer, Don Norman and Paul Maglio

100

COGNITIVESERVICES


Industry Solutions

ClientSolutions


SourcedData

PartnerProvidedData

PrivateClientData




WatsonServices

Data

Knowledge

Wisdom

VisualRecognition

Speech toText

Personality

Insights

LanguageTranslatio

n

WatsonServices are a set of building blocks that can be mixed to build cognitive applications.They run on a Platform.

IBM Cognitive Services –BlueMix - Platform

Text tospeech

Anaphoric Co-referencingColloquialism ProcessingContent Management -- VersioningConvolutional Neural NetworksCurationDeep LearningDialog FramingEllipsesEmbedded Table ProcessingEnsembles and FusionEntity ResolutionFactoid Answering

Feature Engineering

Feature NormalizationFocus and Spurious Phrase ResolutionHTML Page AnalysisImage ManagementInformation RetrievalKnowledge (Property) GraphsKnowledge AnsweringKnowledge Extraction AnnotatorsKnowledge Validation and ExtrapolationLanguage ModelingLatent Semantic Analysis

Learn To RankLinguistic AnalysisLogical Reasoning AnalysisLogistical RegressionMachine LearningMulti-Dimensional ClusteringMultilingual trainingn-Gram Analysis (word combinations and distance)Ontology AnalysisPareto AnalysisPassage AnsweringPDF ConversionPhoneme Aggregation

Question AnalysisQuestion-answering Reasoning StrategiesRecursive Neural NetworksRules ProcessingScalable SearchSimilarity AnalyticsStatistical Language ParsingSupport Vector MachinesSyllable AnalysisTable AnsweringVisual AnalysisVisual RenderingVoice Synthesis

These APIs are underpinned by 50 technologies:

2011

2015Source: http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html

IBM Cognitive Services1. Watson APIs are

continuously. 2. They are

complemented with tens of other APIs in other domains, all running on ONE platform.

3. They can mashed up to build an infinite number of cognitive assistants.

2011

2016Pipeline

Gain insight into how and why people think, act, and feel the way they do. This service applies linguistic analytics and personality theory to infer attributes from a person's unstructured text

PersonalityInsights

New programming environments on clouds are providing a fast and easy access to IBM Watson APIs and more …

106

Source: https://ibmtjbot.github.io/

I'm an open source projectdesigned to help you accessWatson Services in a fun way.

You can 3D print me or laser cut me, then use one of myrecipes to bring me to life!

https://www.ibm.com/watson/developercloud/project-intu.html

https://www.ibm.com/watson/developercloud/

110

WISDOM FORALL

111https://www.youtube.com/watch?v=FYZld6SSCnY

@pieroleo

Understands the language of business

Visual, simple and intuitive

Simply type in a question and getmeaningfulinsights

immediately

Visual, simple and intuitive

Automatically suggests graphs and

visuals to communicate findings

INSIGHTContext

Automatically presents related facts and insights to guide

discovery

insight

insight

insightinsight

insight

insight

insight

You and your business data

https://www.analyticszone.com/homepage/web/displayNeoPage.action

Even a simple analytics project has multiple steps and people

Data Access

Data Preparation

Analysis

Validation

Collaboration

Reporting

Data Scientists and Statisticians

Business Users

ITBusiness Analysts

And it’s rarely a straightforward process

Data Access

Data Preparation

Analysis

Validation

Collaboration

Reporting

Data Scientists and StatisticiansBusiness Users

ITBusiness Analysts

Credits: Dashon Goldson Gallery

TOUCHDOWN!

RUSHING TD

FUMBLES

PASSING TD

1

2

3

@pieroleo

Understands the language of business

Single Interface … Explore > Predict > Assemble

Quick start intuitive interface

Key business driver insights

Dashboard and

storytelling authoring

Natural language dialogue

Easy data upload and Refinement capabilities

@pieroleo

IBM Watson Analytics

Watson Analytics

Communication & Collaboration

Visualization & Storytelling

AnalyticsDescriptive, Diagnostic, Predictive, Prescriptive, Cognitive

Data Access & Refinement

Cloud

Operations HR

ITFinanceSalesMarketing

Mobile Ready Secure

Value:•Put analytics in the hands of everyone•Make access to data easy for refinement and use •Deliver through the cloud for agilityand speed

PrioritizingAccountsReceivable

Identifying andRetaining KeyEmployees

HelpdeskCase

Analysis

CampaignPlanning and ROI

WarrantyAnalysis

Customer Retention

Finance HRITMarketing OperationsSalesExamles

122

Basic elements:Text Mining &Multi-media mining

123

Analytic Systems use statistical techniques for detecting patterns or detect trends within data, yield an understanding of historical or current state from which to draw conclusions

Text Mining is a class of functions for parsing and identifying significant words in language (NLP) as well as understand the semantic of a textual content

Cognitive Systems leverage machine learning to predict meaning in features of human language (spoken, written, visual) and related forms of human reasoning

Multi-Media Mining is a a class of function for analyzing visual content such as images or videos

Speech Mining is a class of functions for analyzing audio signals including speech to such as ability Cognitive Solutions

leverage a combination of cognitive system reasoningstrategies and other analytic and classical computing techniques to solve for a complex problem -> Amplify Human WISDOM in a specific domain

XXX Mining is class of large specialized functions for analyzing “digital representation” in a specific domain à e.g., Bioinformatics, Financial Analytics, etc.

Machine Learning is a class of statistical techniques that use training data to recognize the correlation between a set of feature patterns and outcomes.

It includes also Deep Learning that is a rapidly maturing space, based on neural network techniques, that are taught to find their own features

Emerging Patterns for Artificial Intelligence adoption in Business World

WISDOM BIG DATA ANALYTICS

Massive Unstructured is the biggest data wave of all

1990’s 2020’s

Video

Text

Exa

Peta

Tera

GigaData Volume

2000’s 2010’s

Structured data

Audio

ImageMed

High

Low

Computational Needs

Sophistication of Analysis

Expressiveness

Digital Marketing

10+% of video views

Wide Area Imagery

100’s TB per day72 video hrs/minute

Media

Source: IBM Market Insights based on composite sources

Safety / Security

Healthcare

Customer

1B camera phones

1B medical images/yr

10s millions cameras

Enterprise Video

Used by 1/3 of enterprises

Structured versus Unstructured Information: Whatdoes it mean?

Know this is the last name and this is their ageThe information is unambiguous

The context of the information is known

Pre-defined and machine-readable

Structured versus Unstructured Information: What does itmean?

Office Location is unstructured

AddressCityZip code….

The Enquire reported that the attractive, Ms Brown, CEO of Textract Corp, had been recently spotted drunk at Summit meeting in Zurich,…………At 42, Ms. Brown, is the youngest CEO at the Summit,…

<Organization><Name>

<Title>

<Proper Name> <Occupation>

Example of Annotation of a Text – “construct meaning from free form text, include identification and labeling the text with specific meanings”

<Positive ><Negative >

Unstructured Information:The context of the information is not known and is interpreted by the computer using mathematical techniques

Text Mining: transformsUnStructured Information into Structured data

Before After

Concept/entity extractionRelationship extractionSentiment Analysis

Linguistic Analysis CategorizationClustering,

Text AnalyticsTasks

DocumentSummarization….

Automotive Quality Insight• Analyzing: Tech notes, call logs, online media• For: Warranty Analysis, Quality Assurance• Benefits: Reduce warranty costs, improve customer satisfaction, marketing campaigns

Crime Analytics•Analyzing: Case files, police records, 911 calls…•For: Rapid crime solving & crime trend analysis•Benefits: Safer communities & optimized force deployment

Healthcare Analytics• Analyzing: E-Medical records, hospital reports• For: Clinical analysis;; treatment protocol optimization• Benefits: Better management of chronic diseases;; optimized drug formularies;; improved patient outcomes

Insurance Fraud•Analyzing: Insurance claims•For: Detecting Fraudulent activity & patterns•Benefits: Reduced losses, faster detection, more efficient claims processes

Customer Care• Analyzing: Call center logs, emails, online media• For: Buyer Behavior, Churn prediction• Benefits: Improve Customer satisfaction and retention, marketing campaigns, find new revenue opportunities, recostruct life stages and life events

Social Media for Marketing• Analyzing: Call center notes, multiple content repositories• For: churn prediction, product/brand quality • Benefits: Improve consumer satisfaction, marketing campaigns, find new revenue opportunities or product/brand quality issues

A first set of examplesleveraging Text Mining / Analytics

Multimedia Mining

Multimedia Mining flow: Feature extraction, modeling, and application of semantics and context are required to deliverinsights

Labeled DataUnlabeled Data

K-means Bayes NetClustering

Markov Model

Decision Tree

Modeling

ColorSpectrum

Edges

Camera Motion

Feature Extraction

EnsembleClassifiers

Texture

Active Learning

Deep Belief Nets

Vehicle tracking Activity classificationSafe zone monitoring

Locations ActivitiesScenes

Safety/Security

Behaviors

ObjectsPeopleEvents

Tracks

Moving Objects

Actions

Neural Net

classification

scoringSemantics

Multimedia

AdaBoost

Blobs

BackgroundSegmentation

Zero-crossings

Support Vector Machine

Gaussian Mixture Model

Hidden Markov Model

Frequencies

Video-based Appraisal:§ Goal: improve home, automobile, or marine insurance process using supporting multimedia data

§ Use video by insurance policy holder to document insured items

§ Automatically turns the video into the basis for appraisals and claims

Insurance

Public Safety and Security:§ Goal: ensure safety and security in transit system

§ Detect suspicious activities, safety concerns, and crowd conditions using camera-based analytics

§ Support real-time alerting and forensic search over video data

Transportation

In Store Video Analytics:§ Goal: use existing store cameras to tell who is entering the store and demographics

§ Bring video to aisles to tell how long people look at products and ads, what they picked up, whether they placed in cart

§ Extend campaign management and customer analytics solutions with in-store analytics

Retail

Consumer Goods

Identify Logo Exposure:§ Goal: automatically annotate videos with logo version and calculate exposure time

§ Identify multiple logo appearancesin the same frames

§ Identify distorted logos on clothing and promotional items

Many enterprises are investigating nextgeneration multimedia analytics-based solutions

134

Customer Analysis Transformationwith Data and Precision

Chewing Gum Wall in California

Source: http://en.geourdu.co/buzz/bizarre-shocking/chewing-gum-wall-in-california/

San Luis Obispo

Portraits from New York

Stranger Visions

In Stranger Visions artist Heather Dewey-Hagborg creates portrait sculptures from analyses of DNA material collected in public places.

Source: http://deweyhagborg.com/strangervisions/

Customer Analytics: Adding Value at Every Point of Interaction and leveraging customer Digital Footprints

Systems of Record Systems of Engagement

Customer Analytics

Big Data Analytics

138

All perspectivesPast (historical, aggregated)Present (real-time, scenarios)

Future (predictive, prescriptive)

At the pointof impact

All decisionsMajor and minor;;

Strategic and tactical;;Routine and exceptions;;Manual and automated

All informationTransaction/POS data

Social data Click streamsSurveys

Enterprise contentExternal data (competitive, environmental, etc.)

All peopleAll departments

Front line, back officeExecutives, managers

EmployeesSuppliers, customers and

consumersPartners Customer

Analytics

Challenge: Consider all data points

What are people saying?

How do people feel about my brand?

Who is this individual like?Who does she influence/follow?

What are her preferences?What words/offers will engage her?

Customer AnalyticsPractical CHALLENGES

360°Integrated Customer View

!

Customer Analytics challenge:build a 360°Integrated Customer View… and more

SINGLE VIEWBusiness Data, Social Data,

Interactive data360°Integrated Customer View

Marketing

Cust. Care

Sales

Risk, Fraud


SINGLE VIEWBusiness Data, Social Data,

Interactive data360°Integrated Customer View

Marketing

Cust. Care

Sales

Risk, Fraud

How?Why?

Who? What?


Social Data is not a SINGLE and omogeneos source: it is a complex aggregate of content thatwe can leverage in dependance of well defined Business Use Cases.

General Rule for Social Data

Examples of Social Media Outlets

§ More than 1 billion unique users visit Youtube each month watching over 6 billion hours of video

§ More than 388 million people view more than 12.7 billion blog pages each month

§ There are 500 million tweets daily – that’s 5,700 per second

§ 50% of Facebook users check it daily – there are more than 1 billion users world wide

14

Monitoring and Reporting

Analytics of Aggregates Analytics of Individuals &

specific groups

Listening

Engagement

Demographics

PublishingMeasurement Net Promoter

Network Topology

Sentiment Analysis

Brand Analysis

Identity AnalysisPredictive Analysis

SNA Pattern Detection

Intrinsic Preferences

Social GenomeMicro-‐Segmentation

Next Best OfferMessaging/campaigns

Face Recognition Visual Recognition

Age Detection

Image TaggingGender Recognition

Identity Recognition

What are people saying?

How do people feel about my brand?

Who is this individual like?Who does she influence/follow?What are her preferences?

What words/offers will engage her?

Techniques

Cognos - Big Insights – SMA - SPSS –Watson Explorer – Adv. Analytics & Cognitive Services

From CHALLENGES to TechniquesAnd Capabilities

Source: http://www.businessinsider.com/huge-social-media-manager-does-all-day-2014-5?IR=T

We Got A Look Inside The 45-Day Planning Process That Goes Into Creating A Single Corporate Tweet

24 may 2014

After 1 Month!

A risky job !

Source: http://www.businessinsider.com/huge-social-media-manager-does-all-day-2014-5?IR=T

We Got A Look Inside The 45-Day Planning Process That Goes Into Creating A Single Corporate Tweet

13 Mar 2015

After 1 year!

A risky job !

CustomerAnalytics & TRUST

“Trust men and they will be true to you;; treat them greatly and they will show themselves great.”

Ralph Waldo Emerson

Consumers are open to share their personal information, with the exception of financial data, when there isperceived benefit

Consumer Maintains Control of DataWhat is your willingness to provide information in exchange for something relevant to you (non-monetary)?

Source: IBV Retail 2012 Winning Over the Empowered Consumer Study n= 28527 (global) P04: What is your willingness to provide information for each of the following items if [pipe primary retailer] provided something relevant to you in exchange?

25% 27%41% 41% 44% 46%

63%30% 30%

28% 29% 28% 28%

21%45% 43%33% 30% 28% 26% 15%

0%

20%

40%

60%

80%

100%

Media Usage(e.g. Mediachannels)

Demographic (e.g. age,ethnicity)

Identification(name,address)

Lifestyle (# ofcars, homeownership)

LocationBased

Medical Financial

Completely Disagree Neutral Completely willing

@pieroleo

IBM Cloud Computing Platform

Cognitive Systems & Apps

Watson Ecosystem

Watson

@pieroleo

Source: http://www.equals3.ai/meetlucy

@pieroleo

@pieroleo

Watson App Gallery – News ExplorerAPIs used: AlchemyData Newshttp://news-‐explorer.mybluemix.net/

153

@pieroleo Images, Imanges, Images... Images

Images Followers of a Brand

@pieroleo

155

Extracts Consumer Attributes from Images and Videos

@pieroleo

69%13%

7.8%

3.8% 3.1%2.4%

Travel & SceneryGoing outSports interestsShopping

60%6.1%1.8%1.6%

MultimediaAnalytics

SkySceneryRural SceneryUrban SceneryWater Scenery

Performance

ZooSport venue

Parade

Outdoor MarketIndoor Store

24%

1.5%

Travel & Scenery

LeisureScenery

Airplane - 12.5%Blue sky - 8.9%Sunset - 2.4%

Fireworks – 0,5

Top Travel & Scenery

Top SceneryTop Leisure

Source: IBM System-V

Analytics to extract insights from images and videos

BrandFollowers

@pieroleo

157

Examples of Semanticclassifiers for images and video

Automatic recognition of sports and activity categories

http://ibm64f.pok.ibm.com/imars/systemv/indexAA

@pieroleo

158

Customer Visual Attributes:Spans Multiple Facets and Complements TraditionalData Sources

@pieroleo

170,000 personal weather stations worldwide

2.2 B locations forecasted every 15 minutes.

15 B Weather averages 15B forecast queries daily.

20 terabytes, every day.

Bring Advanced Weather Insights to Business

Source: https://www.wunderground.com/

@pieroleo

Weather is the secret to understanding how consumers feel

160

@pieroleo

And that earned us a spot in the daily routines and ritualsof consumers.

161

@pieroleo

Making real connections with consumers through weather and analytics.

162

@pieroleo

163https://watsonads.com/#

Watson Ads

164

HealthcareTransformationwith Data and Precision


Industry Solutions

ClientSolutions


SourcedData

PartnerProvidedData

PrivateClientData




WatsonServices - API

Data

Knowledge

Wisdom

Leveraging the Explosion of Data in Medicine – An Impossible Task Without Analytics and New advanced Artificial Intelligence Computing Models

1000

Facts p

er Decision

10

100

1990 2000 2010 2020

Human Cognitive Capacity

Electronic Health Records (Clinical Data)

Internet of Things (Exogenous Data)

The Human Genome (Genomic Data)

Capturing the Value of Data: Big Changes Ahead

Medical error—the third leading cause of death in the US

Source: BMJ 2016;; 353 doi: http://dx.doi.org/10.1136/bmj.i2139 (Published 03 May 2016) Cite this as: BMJ 2016;;353:i2139


Industry Solutions

ClientSolutions


SourcedData

PartnerProvidedData

PrivateClientData

An example of industrial-oriented platform: Watson Health


Data

Knowledge

Wisdom

Public FilingsPatents

Medical Journals

AppleTwitterQuest Diagnostics

MedtronicUnder ArmourJohnson & JohnsonTEVA

168

Watson Health is bringing unique insights to the marketplace to help reduce costs, improve outcomes and help increase value.

DataStandards based, extremely scalable,

open repository of data on all dimensions of

healthcare and research

Insights as a service

Knowledge and actionable information through

advanced analytics and cognitive capabilities

SolutionsIBM and an ecosystem of partners help improve the overall experience and increase the quality

of outcomes

Watson HealthData – Insights – Solutions

169

Our approach

Watson Health’s aim is to create an open industry platform utilizing keycapabilities and partnerships to help improve Healthcare

Watson Cloud

PARTNERSHIPS

171

Our approach

Watson for Genomics

Business Challenge: • As the cost of Next Generation Sequencing decreases, there will be an increase in tumor genome sequencing resulting in massive quantities of genetic data to analyze

• Currently, it takes an average of 4-6 weeks to analyze and interpret genetic data manually • Complexity of matching genetic mutations of individual’s tumor with molecular targeted therapies using multiple data sources

Watson Solution: • Empowers Physicians to Make the Most of Genomic Data and Assisting Them to Provide Comprehensive and Up-to-date Cancer Patient-Care

1. Leverages whole genome, whole exome, or large panels variant sequences from patient tumor biopsies 2. Identifies gene level variants using several industry standard databases, as well as relevant literature 3. Provides actionable list of gene variants and the therapies that target them, either directly or indirectly

Use Cases:• Assist Molecular Pathologists in reviewing the 100s to 1000s of gene level variants, and associating each with the likelihood its driving cancer developing in that individual patient

• Once the driver alterations have been approved by the pathologist, WGA assists the Medical Oncologist with recommending an approved, investigational, or off-labeled targeted therapy

172

Watson Genomics from Quest Diagnostics®

Watson Genomics from Quest Diagnostics is a solution that can help patients along their cancer journey.

1. Quest Diagnostics sequences and analyzes a tumor’s genomic makeup to find specific mutations

2. Watson then compares those mutations against relevant medical literature, clinical studies, pharmacopeia and carefully annotated rules created by leading oncologists.

3. A Quest Diagnostics pathologist will review and validate the results and prepare a report to send back to the patient’s treating physician

http://www.ibm.com/watson/health/oncology/genomics/

174

Watson for OncologyTrained by Memorial Sloan Kettering

Business Challenge: • Ability to assess quickly the best treatments for an individual patient based on latest evidence and clinical guidelinesWatson Solution: • A tool to assist physicians make personalized treatment decisions

− Analyzes patient data against thousands of historical cases and trained through thousands of Memorial Sloan Kettering MD and analyst hours

− Suggestions to help inform oncologists’ decisions based on over 290 medical journals, over 200 textbooks, and 12M pages of text− Evolves with the fast-changing field− Currently supports first line treatment (Breast, Lung, Colorectal cancers)

174© 2015 International Business Machines Corporation

175

Our approach

177 177

Bioimages

178 178

179

The Medical Sieve §Build a fast anomaly detection engine

– Quickly filters irrelevant images– Highlights disease-depicting regions– Flags coincidental diagnosis

§ Intended as a radiology assistant – Clinicians still do the diagnosis– Machine reduces workload – Machine performs triage/decision support

Given history of the patient and images of a study

Is there an anomalous image here?If so, where is the anomaly ?Describe the anomaly

The Medical Sieve

@pieroleo

180

@pieroleo

182

Pathway Genomics OME App – Powered by WatsonMerging cognitive computing and deep learning with precision medicine and genetics

How it works

Pathway Genomics mails the user a saliva DNA

collection kit

Pathway will work with clinicians and scientists to conduct the Pathway Fit test. It specifically looks at 75 genes that focus on phenotypes like diet, exercise, lipids, and sugar metabolism

Watson cognitive computing technology, intelligent machine learning, and a corpus of health and wellness

information

With Watson APIs, the Pathway app leverages Watson’s natural language processing technology and content in the form of health and wellness information

Highly personalized insights to empower people to change unhealthy behaviors, allowing them to live healthier lives, e.g.

genetically optimal diet plans or restaurant and menu recommendations

Early Alpha Version

Users unique genetic traits Health HabitsData from wearable health

monitors Apple HealthKit Electronic health records Insurance informationGPS Data

Incorporated Data: Pathway’s “FIT” Test

Additional datasets

Other User Data Watson corpus of health and wellness information

Data Sources

@pieroleo

@pieroleo

184

185

NutritionTransformationwith Data and Precision

Food Security

Cooking

Health

Wellbeing

Nutrition & Technology

AI & MachineLearning

Digital Data

Cloud

Analytics

Agroindustry

Internet of Things

GenomicsMetabolomics

Food Distribution & Preparation

There is a nexus of forces, from different angles, that combine Nutrition & Technology

CreativityComputing

An opportunity to support decisions of professionals and consumers with data is emerging

Mobile

Social

3

Nutrition & Health

Mucuna pruriens Cocoa

Chef Watson

Food

Nutrient

Phyto-Nutrient

Physical Response

Condition

has_nutrient

phyto_response

nutrient_response

has_phyto_nutrient

affec

ts (+/

-)

Nutrition & Food

Food Recognition

Coaching5

IBM Chef Watson.

Inspire your cooking decisions

Cognitive Cooking

188

Cognitive Computing approach to Computational Creativity

Create Food new recipes from scratch

Modify existing recipes to satisfy your

own taste

Suggest new things to prepare & cook

Pair ingredients and flavors for recipes and dishes

1886

https://www.ibmchefwatson.com/tupler

7

Chef Watson ArchitectureCOGNITIVE COOKING

SYSTEM

FOOD KNOWLEDGEDATABASE

• Cuisine• Dish• Recipes• Steps: input, output, property

• Flavor Compound• Odor Descriptor• Odor Pleasantness

• Nutrition Fact • Ingredient Type• Ingredient pairing

Recipes.wikia.com / Bon AppetitWikipedia USDA nutrient DB Derived from SourcesVCF, Atlas of Odor Character Profiles, research papers

1. Identify recipe templates

2. Generate new ingredient combinations

3. Compute surprise, pleasantness, and chemical pairing of new combinations

4. Score and rank new combinations

For each new combination: 5. Identify most similar

existing recipe6. Compute ingredient

proportions7. Create recipe steps

DYNAMICPLANNER

COMBINATORIALDESIGNER

COGNITIVEASSESSORDISH LEARNER

8

Food Knowledge Database

Recipe Recipe Step

Recipe Step Input

Recipe Step Output

Recipe Step Property

Ingredient Flavor Compound

Nutrition FactCuisine

Dish

Ingredient PairingIngredient Type

Odor Descriptor

Odor Pleasantness

recipes.wikia.com

wikipedia

USDA nutrient DB

VCF, Atlas of Odor Character Profiles, research papers

Derived from above sources

1919

Personalize a recipe

https://twist.ibmchefwatson.com/

Tell Watson how you are feeling and how to start to drink

Tweak your flavors based on Watson’s analysis and suggestions

Bring the flavors to life with your bartender, snap a photo and share!

Dinner Planner

12

https://www.bearnakedcustom.com/BearNaked13

Conversational system that can assist user to find a recipes

14

Weather is the secret to understanding how consumers feel… and cook

A brand able to gain a spot in the daily routines and rituals of consumers creates a not only a relation but a deep intimacy with them

198

https://watsonads.com

Watson Ads

16

200

CLOSING

@pieroleo

Source: https://www.coursera.org/featured/top_specializations_locale_en_os_web

10 Top Specialization on Coursera (Dec 2016)

@pieroleo

Scientific Method

Visualization

Domain Expertise TOM

Hacker Mindset

MathData Engineering

Advanced Computing

StatisticsData Scientist

A Data Scientist§ Explores and examines data from multiple disparate sources

§ Sifts through all incoming data with the goal of discovering a previouslyhidden insight

§Has strong business acumen, coupled with the ability to communicate findings to bothbusiness and IT leaders in a way thatcan influence how an organizationapproaches a business challenge

§Represents an evolution from the business or data analyst role

§Has a solid foundation typically in computer science and applications, modeling, statistics, analytics and math.

The role of a Data Scientist

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Chief ArtificialIntelligence Officer

Chief Data Scientist

Chief InformationOfficer

Chief DataOfficer

@pieroleo

Source: https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf

1 Private and public institutions are encouraged to examine whether and how they can responsibly leverage AI and machine learning in ways that will benefit society.

2 Federal agencies should prioritize open training data and open data standards in AI.

3 The Federal Government should explore ways to improve the capacity of key agencies to apply AI to their missions.

@pieroleo

Sheryl Sandberg, COO, apologised for 'poor communication' of the study

Said Facebook never meant to upset users with the secret research

Was part of a study to see if people's moods are affected by content

Information Commissioner now investigating whether or not the site breached data regulations

Facebook has apologised to itsusers after a secret psychologicalexperiment has sparked outrage in the online community

Facebook admitted it had manipulated the news feeds of nearly

700,000 users without their

knowledge as part of a psychology experiment.

Source: http://www.forbes.com/sites/kashmirhill/2014/07/02/sheryl-sandberg-apologizes-for-facebook-emotion-manipulation-study-kind-of/

With Big Data #TRUST (plus #Securityplus #Privacy) matter

@pieroleo

“…Unfortunately, the conversations didn't stay playful for long. Pretty soon after Tay launched, people starting tweeting the bot with all sorts of misogynistic, racist, and Donald Trumpist remarks. And Tay — being essentially a robot parrot with an internet connection — started repeating these sentiments back to users, proving correct that old programming adage: flaming garbage pile in, flaming garbage pile ….“out.

@pieroleo

Source: http://www.ted.com/talks/sherry_turkle_alone_together

Sherry Turkle:Connected, but alone?

These days phones in our pockets are changing ourminds and hearts offer us three gratifying fantasiesand NEW challenges and risks for us:

1) We can put our attention where we want to be

2) We always be heard

3) We never left to be alone

https://www.partnershiponai.org/

@pieroleo

@pieroleo

www.linkedin.com/in/pieroleo

Pietro LeoExecutive Architect IBM Italy CTO for Big Data Analytics & WatsonIBM Academy of Technology Leadeship

Grazie!