Augmented intelligence pietro_leo_sole24_ore_school

Artificial Intelligence
to make precise decisions
July 13, 2017
Pietro Leo
Executive Architect & CTO
Chief scientist, and research strategist IBM Italy
IBM Academy of Technology Leadership Team
pieroleo.com

DATA
INFORMATION
KNOWLEDGE
WISDOM

10
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
friend’s
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

11
Video: http://www.digitaltrends.com/home/grush-toothbrush-wins-americas-greatest-makers/

Source: http://www.bloomberg.com/video/meet-the-world-s-most-connected-man-
Vs~LzkbkR7yhjza~7nji1g.html
Meet the
World's Most
Connected Man

16
Image source: http://personalexcellence.co/blog/i deal-beauty/

17
City
Lifestyle
ZIPcode
Costal vs Inland Marital status
Generation
Location
Family Size
Gender
Income Level
Competitors
Age
Loyalty & Card
Activity
Revenue Size
Life Stages
Eductation
Legal status
Sector
Industry

18
City
Lifestyle
ZIPcode
Generation
Location
Family Size
Gender
Income Level
Competitors
Age
Loyalty & Card
Activity
Revenue Size
Life Stages
Eductation
Legal status
Sector
Industry
Subscriptions
Date on Site
Wish List
Size of
Network
Check-ins
App usage duration
Number of Apps on Device
Deposits/Withdrawals
Device Usage
Purchase History
Following
Followers
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

19
City
Lifestyle
ZIPcode
Generation
Location
Family Size
Gender
Income Level
Competitors
Age
Loyalty & Card
Activity
Revenue Size
Life Stages
Eductation
Legal status
Sector
Industry
Subscriptions
Date on Site
Wish List
Size of
Network
Check-ins
App usage duration
Device Usage
Purchase History
Following
Followers
Likes
History of Hashtags
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
Dialog
Intent
Latent Semantic Analysis
Phonemes
Ontology Analysis
Linguistics
Image Tags
Question Analysis
Self-transcendent
Affective Status

20
City
Lifestyle
ZIPcode
Generation
Location
Family Size
Gender
Income Level
Competitors
Age
Loyalty & Card
Activity
Revenue Size
Life Stages
Eductation
Legal status
Sector
Industry
Subscriptions
Date on Site
Wish List
Size of
Network
Check-ins
App usage duration
Device Usage
Purchase History
Following
Followers
Likes
History of Hashtags
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
Dialog
Intent
Latent Semantic Analysis
Phonemes
Ontology Analysis
Linguistics
Image Tags
Question Analysis
Self-transcendent
Affective Status
X-rays (CT scans)
sound (ultrasound),
magnetism (MRI),
Radioactive (SPECT, PET)
light (endoscopy, OCT)
Bio-Images
Clinical/Biochemical
DataMicrobiome
EnvironmentDNA
Proteome
Steps
Nutrition
Genetics
Runs
Food
Source: Bipartisan Policy Center,
“F” as in Fat: HowObesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)
Internet of Body
BMI

Rapid growth of exogenous data is transforming healthcare
6 Terabytes
60%
Exogenous Factors
1100 Terabytes
Volume, Variety, Velocity, Veracity:
Educational records, Employment Status,
Social Security Accounts, Mental Health
Records, Caseworker Files, Fitbits, Home
Monitoring Systems, and more…
0.4 Terabytes
Electronic 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 Levels
Physical Activity
Other vital signs Social
Interaction
Affinity (retail)
Sleep Pattern

> 2.5 Trillion PDF Files
in the World
Majority with public and private
enterprises and institutions.
Enterprise HYPERDATA
23
Multi-Modal Rich data: Text, Tables, Images, Audio, Video, Formats,
Hierarchy….

Leveraging the Explosion of Data in Medicine
An Impossible Task Without Analytics and New advanced Artificial Intelligence
Computing Models
1000
FactsperDecision
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

26
Body Mass Index (BMI)
Mass (weight - Kg) /
height (cm) x height (cm)
You are “Normal” if your BMI is
between 18.5 and 24.99
Adolphe Quetelet, 1832

27
Practice Pearls:
• BMI - Body mass
index is a strong and
independent risk factor
for being diagnosed
with type 2 diabetes
mellitus
• Type 2 diabetes risk
may be incrementally
higher in those with a
higher body mass index
• Understanding the risk
factors helps to shorten
the time to diagnosis
and treatment
How precise could be a “simple” signal

© 2017 International Business Machines Corporation
The way to
find information
The way to
make precise decisions
BigData++

Technology ingredients to make precise decisions: driving
new Capability for Business
Artificial Intelligence
Range of techniques
including natural
language
understanding,
knowledge, reasoning
and planning, for
advanced tasks
Cognitive Computing
Leverage a combination
decision-making
and reasoning strategies over
deep domain models and
evidence-based
explanations, using AI/
Machine Learning tools.
Machine Learning
Statistical analysis for
pattern recognition to
make data-driven
predictions

Research at the heart of core AI
Comprehension:
From video and text to rich
human perception
Learning and Reasoning:
From scalable machine learning
to making a case
Interaction:
Understanding language, tone,
emotion and context
“A green bird sitting on top
of a bowl”

Hype Cycle for
Emerging
Technologies,
2016 (Gartner)

https://www.ibm.com/annualreport/2016/images/dow nloads/IBM-Annual-R eport-2016.pdf
Augmenting
DECISIONS

Assistant
Tools
Collaborator
Coach
Mediator
Emerging types of Cognitive Systems
Augment Decision Making is opening to new forms of
collaboration between humans and machines

34
Radiologist Oncologist
Sales Assistant Tax Advisor

35
Chef Designer
Musicist Movie Director

Opportunity for
decision-making
support
2025
Augmenting decisions opens new opportunities on top of traditional IT
Traditional global
IT spend
Source: IBM analysis presented to the Investor Briefings
~$2T
~$1.2T

37
Top outcomes from cognitive initiatives vary by industry
Finance
49% Increased market agility
46% Improved customer service
43% Increased customer
engagement
43% Improved productivity &
efficiency
42% Improved security &
compliance, reduced risk
Retail
56% Personalized customer / user
experience
56% Increased customer engagement
56% Improved decision making &
planning
56% Reduced costs
Health
66% Accelerated innovation of
new products / services
66% Improved productivity & efficiency
64% Improved security & compliance,
reduced risk
62% Reduced costs
Manufacturing
64% Improved decision making
& planning
58% Improved productivity &
efficiency
54% Improved security &
compliance, reduced risk
49% Enhanced thelearning
experience
Government/Education
54% Personalized customer / user
experience
37% Improved decision making &
planning
36% Improved productivity & efficiency
33% Increased customer engagement
Professional Services
40% Reduced costs
36% Personalized customer/user
experience
36% Expanded ecosystem
34% 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/

IBM Watson is the most advanced Artificial Intelligence & Machine Learning platform to
support Decision Making in Business
Toward a Precise Decision Making to reduce the wasteful
spend as well as the risk in every industry
Watson
:
Cognitive System

IBM Cognitive Computing
45
Nazioni
100+
Applications
già nel mercato
6.000
Ricercatori e
Specialisti in IBM
8
Lingue
200
Università
organizzano corsi su Watson
500+
Partners
Che integranoWatson
API & Hybrid
Cognitive
Frameworks
20
Industrie
80.000
Sviluppatori
costruiscono applicazioni
con Watson
Watson Health
5.000 Dipendenti,6B$ di
investimento
Watson Internet
Of Things
1000 Dipendenti, 3B$di
investimento
Watson Finantial
Services
3 Unità di Business
Verticali
200M
Cittadini
60M
Pazienti
30B
Immagini
1.2M
Abstract
Medici
60+
Soluzioni

Who: Current top players (prevalent) competitive directions and approaches
Personalized Service /
Content Aggregation
Industry-oriented / Professions Specific
Outcomes via cognitive Solutions
Core Business Cognitive /
Enhance Experiences
IBM (Health,
Finance, …)
API SERVICES /
PLATFORM
AWS
Microsoft
Goggle
Amazon (Alexa)
Facebook
IBM BlueMix

42
Keyword Extraction,
Entity Extraction,
Sentiment Analysis,
Concept Tagging,
Conversation Intents Entities Dialogues
Personality Big5 Personality Traits Needs Values
Language Tone Emotion Social propensities Language styles
Translate Conversational News Custom TranslationPatents
Language Deep
Understanding
Relation Extraction,
Taxonomy Classification,
Author Extraction…..
Custom Analysis
Speech-to-text
Custom pronunciations Voice Transformation
Expressive Voice
Voice synthesis
Keyword Spotting Telephony Broadband
Vision Face Recognition Image Similarity Image Classification
Custom eyes
Source: https://www.ibm.com/watson/developercloud/services-catalog.html
WATSON
Kind of skills

43
https://www.technologyreview.com/s/603895/customer-service-chatbots-are-about-to-become-frighteningly-realistic/
The movements of Soul
Machines’sdigital facesare
produced by simulating the
anatomy and mechanicsof
muscles and other tissues of th
human face.
Soul Machines
The avatarscan read the
facial expressionsof a
person talking to them,
using a device’s front-
facing camera
Soul Machines
made NADIA, a
chatbot for the
Australian
government to
help people get
information about
disability
services.

46
I am going to
New York
next May
Man
Walking, go
around
vest
Where and When will you be using this jacket?
I'll find a jacket that fits those conditions.Are you
looking for a men's or women'sjacket?
Okay, I got it. What will you use this jacket for?
What styles are you looking for?
Conversation
https://www.thenorthface.com/xps

47
I am going to
New York
next May
Where and
When will
you be using
this jacket?
I'll find a jacket
that fits those
conditions. Are
you looking for a
men's or women's
jacket?
https://www.thenorthface.com/xps
Man
Okay, I got it.
What will you
use this
jacket for?
Walking,
go around
What styles
are you
looking for?
vest

48
It will be more and more a
bots vs bots marketing battle!
Our personal BOTS will buy
for us, #Brands should
convince them NOT us!

Watson
Oncology
A collaboration between IBM and
Memorial Sloan Kettering (MSK). Watson
for Oncology utilizes MSK curated
literature and rationales, as well as over
290 medical journals, over 200
textbooks, and 12 million pages of text to
support decisions.
• Analyzes the patient's medical record
• Identifies potential evidence-backed
treatment options
• Finds and provides supporting evidence
from a wide variety of sources

51
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

86%
Accuracy

• Identification of masses in breast MRI
images >93% (1)
• Detection of calcified plaques in coronary
arteries from CT images > 90% (2)
• Automatic Detection of Aortic Dissection
in Contrast-Enhanced CT > 83% (3)
• Melanoma recognition in Dermoscopic
Images >84% Roc curve (sensivity
>95%)
IBM Research Works from the International Symposium on Biomedical Imaging 2017
(1) Hadad, Omer at ali - (2) Tang, Hui at ali. (3) Dehghan, Ehsan at ali (4) Moradi, Mehdi at ali. (5) Ben-Ari, Rami at ali. (6) Roy, Pallab at ali. (7) edai,
Suman at ali (8) Coleccala et ali.
• Labeling Doppler images with aortic stenosis
>78% (4)
• Detection of Architectural distortion in
Mammograms > 80% (5)
• Diabetic Retinopathy detection in Colour
Fundus Images >86% (6)
• Multi-Stage Segmentation of the Fovea in
Retinal Fundus Images Error <14 pixel (7)

7/18/175
I.R.C.C.S. CASA SOLLIEVO della SOFFERENZA
Opera di San Pio da Pietrelcina

Memories are a bridge among generations
7/18
Tales

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 relationbut a
deep intimacywith
them
63

https://watsonads.com
Watson Ads
16

66
MARCHESA
A dress that think
JASONGRECH
Fashion zeitgeist

Food Knowledge Database
Combinatorial
Designer
Cognitive
Assessor
Dynamic
Planner
Peer Produced
Inspiration Set
Novel
Customized
Recipe
Cognitive
Cooking System
67
How does Cognitive Cooking work?
Raw Data
- Recipes
- Recipes contexts
- Chemical/Flavour Data
- Hedonic psychophysics
- Background knowledge (e.g.
Wikipedia for regional
cuisines, etc)
...
- Bayesian surprise
- Flavor Pleasantness
...
Data-driven
Decisions
106
>1015-23

Watson Chef with Bon Appétit
Live at: https://www.ibmchefwatson.com/tupler

70
Creations from the Cognitive Collection – Designed by JASONGRECH and IBM Watson
Source: https://www.ibm.com/blogs/think/2016/08/cognitive-fa

71
Source: https://www.ibm.com/blogs/think/2016/08/cognitive-fa

Source: https://www.ibm.com/blogs/think/2016/08/cognitive-movie-trailer/
1) A visual analysis
2) An audio analysis
3) An analysis of eachscene’s composition
IBM Research Takes Watson to
Hollywood with the First “Cognitive
Movie Trailer”

Watson /Presentation Title / Date74
Watson
Platform

75 IBM Cognitive Cloud | Electrolux Digital Summit 2017
Cloud Infrastructure
A highly scalable, security
enabled infrastructure
Data
Tools to prepare data
for cognitive
AI
Cognitive building blocks
for developers
Applications, solutions
and services
Targeted solutions for
enterprise businesses
IBM delivers an architecture
engineered for disruption
Cognitive Systems
leverage machine
learning to predict
meaning in features of
human language (spoken,
written, visual) and
related forms of human
reasoning

Cloud Infrastructure
A highly scalable, security
enabled infrastructure
Data
Tools to prepare data
for cognitive
AI
Cognitive building blocks
for developers
Applications, solutions
and services
Targeted solutions for
enterprise businesses
Ingestion
ConversationAPI
Storage Analytics Deployment Governance
Watson
Health
Solutions
Watson
Cyber
Security
Weather
IBM Services
& Ind.
Solutions
Watson
Virtual
Agent
Watson
Explore
and
Discover
IBM Risk
and
Compliance
Asset
Mgmt.
(Maximo)
Visual
Recognition
API Discovery
API
Speech
API
Compare
and Comply
API
Document
Conversion
API
DLaaS
API
Nat Language
Understanding
API
Nat Language
Classifier
API
Tone
Analyzer
API
Personal
Insight
API
Knowledge
Query
API
IBM delivers an architecture
engineered for disruption
Cloud Integration
Networking Security
Core
Enterprise
Infrastructure
Cognitive
Systems
Virtual
Servers
File StorageObject
Storage
Cognitive Micro-services DevOps Tooling
ISV Solutions Client Solutions

Data analyticsServe modelTrain model
Cognitive technologies transform data into augmented
intelligence that drives differentiated experiences and outcomes
Cognitive micro-services driven tooling
Curate
Training data
Conversation
API
Tone
Analyzer
API
Document
Conversion
API
Discovery
API
Personal
Insight
API
Nat Language
Understanding
API
Compare &
Comply
API
Visual Recognition
API
Nat Language
Classifier
API
DLaaS
API
Speech
API
Knowledge
Query
API
AI
https://developer.ibm.com/academic/ https://www.ibm.com/developerworks/

IBM Academic
Initiative
https://developer.ibm.com/ac
ademic/
References
Bluemix
https://www.ibm.com/cloud-
computing/bluemix/

Chief Artificial
Intelligence
Officer
Chief Data
Scientist
Chief
Information
Officer
Chief
Data
Officer
DATA INFORMATION KNOWLEDGE WISDOM
“A number” “A STREET
number”
“A map of a
City”
“A GPS root
recommendation
to go from A to B”

https://www.theguardian.com/technology/2016/sep/08/artificial-intelligence-beauty-contest-doesnt-like-black-people

https://www.partnershiponai.org/

Cognitive Principles
1. Purpose
2. Transparency
3. Skills
Source: https://www.ibm.com/ibm/responsibility/ibm_policies.html
The purpose of AI and cognitive systems developed and
applied by the IBM companyis to augment human
intelligence.
The IBM company will make clear: a) When and what
purpose of a cognitive solution; b) Major Data Used; c)
Protect Customer Data & Insightsownership.
IBM company will workto help students, workers and
citizens acquire the skills and knowledge to engage
safely, securely and effectively in a relationship with
cognitive systems, and to perform the new kinds of work
and jobs that will emerge in a cognitive economy.

Thank you
for your attention.
Pietro Leo
Executive Architect & CTO
Chief scientist, and research strategist IBM Italy
IBM Academy of Technology Leadership Team pieroleo.com

July 18,201785
1st Place Image
Source: COCO Challenge
https://www.ibm.com/blogs/bluemix/2016/12/watsons-image-
captioning-accuracy/
1st Place Speech
Watson says: “A green bird sitting on top of a bowl”
IBM Leadership in AI to understand our world
Watson error rate: 5.5%
Source: Switchboard conversational corpus
https://www.ibm.com/blogs/watson/2017/03/reachi
ng-new-records-in-speech-recognition/

Ready to use Affective Computing services in the Watson
Platform
Curate
Training data
Conversation
API
Tone
Analyzer
API
Document
Conversion
API
Discovery
API
Personal
Insight
API
Nat Language
Understanding
API
Compare &
Comply
API
Visual Recognition
API
Nat Language
Classifier
API
DLaaS
API
Speech
API
Knowledge
Query
API
AI
= Affective Service
Emotional Tone:
joy, fear, sadness, disgust, anger
Social Tone:
openness, conscientiousness, extraversion,
agreeableness, emotional range or
neuroticism
Language Tone:
Analytical, confidence, tentative
Customer Engagement Tone:
Sad, Frustrated, Satisfied, Excited, Polite,
Impolite, Sympathetic

Platform
Curate
Training data
Conversation
API
Document
Conversion
API
Discovery
API
Personality
Insight
API
Nat Language
Understanding
API
Compare &
Comply
API
Visual Recognition
API
Nat Language
Classifier
API
DLaaS
API
Speech
API
Knowledge
Query
API
AI
= Affective Service
Big Five dimensions
Emotional Range, Consciousness, Openness,
Introversion/Extroversion, Agreeableness,
Big Five facets
(30 sub dimensions)
Needs
Structure, Curiosity, Challenge, Ideal, Stability
Values
Stimulation, Tradition, Helping others, Taking
pleasure in life, Achievement
Tone
Analyzer
API

Platform
Curate
Training data
Conversation
API
Tone
Analyzer
API
Document
Conversion
API
Discovery
API
Personality
Insight
API
Nat Language
Understanding
API
Compare &
Comply
API
Visual Recognition
API
Nat Language
Classifier
API
DLaaS
API
Speech
API
Knowledge
Query
API
AI
= Affective Service
Expressiveness
GoodNews, Apology, Uncertainty
Voice Transformation
Young, Soft
Custom: Pitch, pitch range,, glottal tension,
breathiness, rate timbre (sunrise, Breeze)

Platform
Curate
Training data
Conversation
API
Tone
Analyzer
API
Document
Conversion
API
Discovery
API
Personality
Insight
API
Nat Language
Understanding
API
Compare &
Comply
API
Visual Recognition
API
Nat Language
Classifier
API
DLaaS
API
Speech
API
Knowledge
Query
API
AI
= Affective Service
Emotions
joy, fear, sadness, disgust, anger
Target Emotions for
Entities
(24 main types of entities)
(433 subtypes)
Custom entities
Keywords

Augmented intelligence pietro_leo_sole24_ore_school

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