transforming data into wisdom
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@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
@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
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
« 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
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
Venture Scanner are tracking 1481 ArtificialIntelligence companies with a combinedfundingamount of $8.8 Billion
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
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
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
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
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
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
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
@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
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
BIG DATA & ANALYTICS APPROACH
Analyze information as is, cleanse as needed
Large amount of messy
information
Hypothesis Question
DataAnswer
TRADITIONAL APPROACH
Start with hypothesis andtest against selected data
BIG DATA & ANALYTICS APPROACH
Explore all data andidentify correlations
Data Exploration
CorrelationInsight
Repository InsightAnalysisData
TRADITIONAL APPROACH
Analyze data after it’s been processed and landed in a warehouse or mart
Data
Insight
Analysis
BIG DATA & ANALYTICS APPROACH
Analyze data in motion as it’s generated, in real-time
@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
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
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?
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/
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
Ecosystemand Partners
Industry Solutions
ClientSolutions
IBM ProvidedData Publically
SourcedData
PartnerProvidedData
PrivateClientData
IBM Watson Innovation platform for Cognitive Business
Health
Financial
Cross
Public FilingsPatentsMedical JournalsU.S. Geological Survey…
AppleTwitterQuest Diagnostics…
MedtronicUnder ArmourJohnson & JohnsonThomson Reuters…
Watson HealthWatson Financial ServiceWatson Internet of Things
Hybrid Watson Frameworks
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
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
Ecosystemand Partners
Industry Solutions
ClientSolutions
IBM ProvidedData Publically
SourcedData
PartnerProvidedData
PrivateClientData
IBM Watson Innovation platform for Cognitive Business
Watson HealthWatson Financial ServiceWatson Internet of Things
Hybrid Watson Frameworks
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
@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
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
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 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
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
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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
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
How?Why?
Who? What?
Customer Analytics challenge:build a 360°Integrated Customer View… and more
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
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Watson App Gallery – News ExplorerAPIs used: AlchemyData Newshttp://news-‐explorer.mybluemix.net/
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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
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Examples of Semanticclassifiers for images and video
Automatic recognition of sports and activity categories
http://ibm64f.pok.ibm.com/imars/systemv/indexAA
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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/
Ecosystemand Partners
Industry Solutions
ClientSolutions
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
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
Ecosystemand Partners
Industry Solutions
ClientSolutions
IBM ProvidedData Publically
SourcedData
PartnerProvidedData
PrivateClientData
An example of industrial-oriented platform: Watson Health
Watson HealthWatson Financial ServiceWatson Internet of Things
Data
Knowledge
Wisdom
Public FilingsPatents
Medical Journals
AppleTwitterQuest Diagnostics
MedtronicUnder ArmourJohnson & JohnsonTEVA
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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
Watson Health’s aim is to create an open industry platform utilizing keycapabilities and partnerships to help improve Healthcare
Watson Cloud
PARTNERSHIPS
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
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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/
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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
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
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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
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
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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
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
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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!
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
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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
Chief ArtificialIntelligence Officer
Chief Data Scientist
Chief InformationOfficer
Chief DataOfficer
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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.
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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
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“…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
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