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@pieroleo
The Era of Artificial Intelligence
Lecture 3
Pietro Leo
IBM Italy Executive Architect and thought leader for Artificial Intelligence
Chief Scientist for IBM Italy Research & Business
IBM Academy of Technology Leadership
Member of ISO/SC42 Artificial Intelligence Standardization Committee
www.pieroleo.com
@pieroleo
Why do we
need the help
of Artificial
Intelligence?
@pieroleo
AGRICUTURE AUTOMOTIVE HEALTHCARE
@pieroleo
Investment in
digital is a
matter of
survive for
companies,
after
digitalization
what’s next?
@pieroleo
Source: McKinsey
What’s next?
For agriculture
Precision
Agriculture
@pieroleo
What’s next?
For agriculture
Precision
Agriculture
@pieroleo
For Science,
Big Data is the
microscope of
the 21st century
Wine DNA Tracing
@pieroleo
www.pieroleo.com
@pieroleo
Source: Cornell University - Maize kernal infected with Aspergillus flavus, which produced
aflatoxin.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
www.pieroleo.com
@pieroleo
9
Mapping the
microbiome
will protect
us from bad
bacteria.
Microbiome Analysis
@pieroleo
10
Food safety inspectors around the
world will gain a new superpower:
the ability to understand how
millions of microbes coexist within
the food supply chain. These
microbes—some healthy for
human consumption, others not—
are everywhere –in foods at farms,
factories, and grocery stores.
The ability to constantly and
cheaply monitor the behaviors of
microbes at every stage of the
supply chain represents a huge
leap in food safety.
Microbiome Analysis
@pieroleo
11
Preliminary scientific evidences
https://researcher.ibm.com/researcher/view_group.php?id=9635
By sequencing the genomes of the
microbiome, or community of microbes,
present in the food we eat, IBM
researchers as well as partnering
organizations like Mars, Inc., Bio-Rad, and
Cornell University are turning the corner to
a new and more predictive kind of food
testing.
This new regimen may allow inspectors to
identify dangerous pathogens inhabiting
food with better sensitivity well before they
make anyone sick.
This rapidly evolving field at the
intersection of big data and microbiology
is built upon the technology of next
generation sequencing (NGS), which
researchers are using to amass an
unprecedented reference database of
genomes through an IBM-led partnership
called the Consortium for Sequencing the
Food Supply Chain.
Consortium for Sequencing the Food Supply Chain
Preliminary scientific evidences
@pieroleo
12
#twinning:
Farming's digital
doubles will help
feed a growing
population using
less resources
Digital Farm
@pieroleo
13
Creating a digital twin or a
“virtual model” of the world’s
farms could help ready
agriculture for new challenges
by democratizing farm data,
allowing those in agriculture to
share insights, research, and
materials, and communicate
data on farmland and crop
growth across the planet, and
connect and cross-reference
with the food supply chain.
Digital Farm
@pieroleo
14
Preliminary scientific evidences
https://ibmpairs.mybluemix.net/
IBM PAIRS Geoscope
IBM PAIRS GEOSCOPE is a
platform, specifically designed
for massive geospatial-temporal
data (maps, satellite, weather,
drone, IoT), query and analytics
services. It frees up data
scientists, developers from the
cumbersome processes that
dominate conventional
geospatial-temporal data
acquisition and preparation and
provides search-friendly ready
access to a rich, diverse, and
growing catalog of historical and
continuously updated geospatial-
temporal information
@pieroleo
15
https://www.ibm.com/blogs/research/2018/09/smarter-farms-agriculture/
IBM Watson Decision Platform
for Agriculture
A platform that combines data, satellites,
mobile phones and sensors with AI
capabilities to collect and analyze
unstructured, visual data about agricultural
land use, from soil chemistry and water
supplies, to crop diseases, equipment usage
and availability, impending rainstorms, heat
waves, and cold streaks - all to deliver on the
promise of improved food quality and safety.
• Yield History and Forecast for Corn
• Disease & Pest Indicators for Corn
• High Definition Normalized Difference
Vegetation Index (HD-NDVI) for Crop Health
Monitoring
• High Definition Soil Moisture (HD-SM)
Hello Tractor
Preliminary scientific evidences
@pieroleo
16
Dinner plate
detectives: AI
sensors will
detect
foodborne
pathogens at
home.
Dinner plate
detectives
@pieroleo
17
https://www.ibm.com/blogs/research/2018/05/ai-authentication-verifier/
IBM Research is currently creating
bacteria-detecting sensors that would be
a next-generation extension of IBM’s
Crypto Anchor Verifier. This optical
device, which is currently being tested by
businesses from drug stores to
construction companies, uses AI and
machine learning techniques to analyze
microscopic features and “read” the
wavelengths emitted by different
substances and objects.
After scanning a material, a verifier
records its unique wavelength and
microscopic details on the blockchain,
comparing its fingerprint to that of other
identical substances. Soon, we’ll be able
to stop them in sub-seconds
IBM's Next-generation Crypto Anchor Verifier
Preliminary scientific evidences
@pieroleo
AI-enabled
value
opportunity for
Automotive
Industry
According to McKinsey2025
the potential incremental
value that will accumulated
by AI in Automotive industry
will be
USD 215 billions
(+1.3% per year)
@pieroleo
Self drive vehicle announcements
@pieroleo
TESLA: Autopilot 1 billion of miles
@pieroleo
Waymo 10 milions of miles
@pieroleo
https://techcrunch.com/2018/05/24/uber-in-fatal-crash-
detected-pedestrian-but-had-emergency-braking-disabled/
Uber in a fatal crash, Mar 18
@pieroleo
@pieroleo
@pieroleo
Cloud + AI + Connected Car = Predictive Maintanance
@pieroleo
@pieroleo
AI
Ding sun bao 2.0: standardize Car Damage Assessment
@pieroleo
Example of Use Case: Car Damage Assessment System
from Ding sun bao
@pieroleo
Example of Use Case: Car Damage Assessment System
from Ding sun bao
Estimated damage: slightly deformed left-rear fender
Repair cost: 40$
@pieroleo
30
Body Mass Index (BMI)
Mass (weight - Kg) /
height (cm) x height (cm)
18.5 < NORMAL BMI < 24.99
Adolphe Quetelet, 1832
@pieroleo
31
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 relative “simple” signal
@pieroleo
32
AI could help Medicine to reduce
approximation
(this is largely valid to all kind of industries....)
The BMI - Body Mass
Index is an approximation
of our health status, it is
inherently a proxy or a
condensed information of
a huge quantity
physiological parameters
Bottom line: it is not only
a matter of how many
data points you consider
to take a decision,
It is more a matter of
how large is the data set
you have that
approximates the reality
@pieroleo
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
@pieroleo
34
Source: Bipartisan Policy Center,
“F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)
@pieroleo
35
Image source: http://personalexcellence.co/blog/ideal-beauty/
Source: Bipartisan Policy Center,
“F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)
Human Digital Twins
@pieroleo
36
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 & 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
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
DNA
Proteome
Microbiome
Clinical/Biochemical
Data
Steps
Nutrition
Genetics
Runs
X-rays (CT scans)
sound (ultrasound),
magnetism (MRI),
Radioactive (SPECT, PET)
light (endoscopy, OCT)
Environment
Bio-Images
Source: Bipartisan Policy Center,
“F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)
Human Digital Twins
@pieroleo
37
Source: http://www.bloomberg.com/video/meet-the-world-s-most-connected-man-
Vs~LzkbkR7yhjza~7nji1g.html
Meet the World's
Most Connected
Man
@pieroleo
38
Source: http://www.bloomberg.com/video/meet-the-world-s-most-connected-man-
Vs~LzkbkR7yhjza~7nji1g.html
@pieroleo
6 Terabytes
60%
Exogenous Factors 1.100 Terabytes
0.4 Terabytes
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
@pieroleo
Leveraging Exogenous Data for Chronic Care (Type 2 Diabetes;
Primary & Secondary Prevention)
60%
Exogenous Factors
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
Glucose Monitoring
Calorie Intake
Stress Levels
Physical Activity
Other vital signsSocial
Interaction
Affinity (retail)
Sleep Pattern
@pieroleo
www.pieroleo.com
@pieroleo
Source: http://datacoup..com
Value of Data
Pietro Leo's
Second
Income!
@pieroleo
Source: http://fdna.com/blog/pmwc_duke/
Personalizing Medicine with Artificial Intelligence and
Facial Analysis
Presented by Omar Abdul-Rahman, MD, University of Nebraska Medical Center
Precision Medicine World Conference (PMWC)| September 24-25, 2018 | Duke University
@pieroleo
Thanks!
Pietro Leo
IBM Italy Executive Architect and thought leader for Artificial Intelligence
Chief Scientist for IBM Italy Research & Business
IBM Academy of Technology Leadership
Member of ISO/SC42 Artificial Intelligence Standardization Committee
www.pieroleo.com

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Era of Artificial Intelligence Lecture 3 Pietro Leo

  • 1. @pieroleo The Era of Artificial Intelligence Lecture 3 Pietro Leo IBM Italy Executive Architect and thought leader for Artificial Intelligence Chief Scientist for IBM Italy Research & Business IBM Academy of Technology Leadership Member of ISO/SC42 Artificial Intelligence Standardization Committee www.pieroleo.com
  • 2. @pieroleo Why do we need the help of Artificial Intelligence?
  • 4. @pieroleo Investment in digital is a matter of survive for companies, after digitalization what’s next?
  • 5. @pieroleo Source: McKinsey What’s next? For agriculture Precision Agriculture
  • 7. @pieroleo For Science, Big Data is the microscope of the 21st century Wine DNA Tracing @pieroleo www.pieroleo.com
  • 8. @pieroleo Source: Cornell University - Maize kernal infected with Aspergillus flavus, which produced aflatoxin.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 www.pieroleo.com
  • 9. @pieroleo 9 Mapping the microbiome will protect us from bad bacteria. Microbiome Analysis
  • 10. @pieroleo 10 Food safety inspectors around the world will gain a new superpower: the ability to understand how millions of microbes coexist within the food supply chain. These microbes—some healthy for human consumption, others not— are everywhere –in foods at farms, factories, and grocery stores. The ability to constantly and cheaply monitor the behaviors of microbes at every stage of the supply chain represents a huge leap in food safety. Microbiome Analysis
  • 11. @pieroleo 11 Preliminary scientific evidences https://researcher.ibm.com/researcher/view_group.php?id=9635 By sequencing the genomes of the microbiome, or community of microbes, present in the food we eat, IBM researchers as well as partnering organizations like Mars, Inc., Bio-Rad, and Cornell University are turning the corner to a new and more predictive kind of food testing. This new regimen may allow inspectors to identify dangerous pathogens inhabiting food with better sensitivity well before they make anyone sick. This rapidly evolving field at the intersection of big data and microbiology is built upon the technology of next generation sequencing (NGS), which researchers are using to amass an unprecedented reference database of genomes through an IBM-led partnership called the Consortium for Sequencing the Food Supply Chain. Consortium for Sequencing the Food Supply Chain Preliminary scientific evidences
  • 12. @pieroleo 12 #twinning: Farming's digital doubles will help feed a growing population using less resources Digital Farm
  • 13. @pieroleo 13 Creating a digital twin or a “virtual model” of the world’s farms could help ready agriculture for new challenges by democratizing farm data, allowing those in agriculture to share insights, research, and materials, and communicate data on farmland and crop growth across the planet, and connect and cross-reference with the food supply chain. Digital Farm
  • 14. @pieroleo 14 Preliminary scientific evidences https://ibmpairs.mybluemix.net/ IBM PAIRS Geoscope IBM PAIRS GEOSCOPE is a platform, specifically designed for massive geospatial-temporal data (maps, satellite, weather, drone, IoT), query and analytics services. It frees up data scientists, developers from the cumbersome processes that dominate conventional geospatial-temporal data acquisition and preparation and provides search-friendly ready access to a rich, diverse, and growing catalog of historical and continuously updated geospatial- temporal information
  • 15. @pieroleo 15 https://www.ibm.com/blogs/research/2018/09/smarter-farms-agriculture/ IBM Watson Decision Platform for Agriculture A platform that combines data, satellites, mobile phones and sensors with AI capabilities to collect and analyze unstructured, visual data about agricultural land use, from soil chemistry and water supplies, to crop diseases, equipment usage and availability, impending rainstorms, heat waves, and cold streaks - all to deliver on the promise of improved food quality and safety. • Yield History and Forecast for Corn • Disease & Pest Indicators for Corn • High Definition Normalized Difference Vegetation Index (HD-NDVI) for Crop Health Monitoring • High Definition Soil Moisture (HD-SM) Hello Tractor Preliminary scientific evidences
  • 16. @pieroleo 16 Dinner plate detectives: AI sensors will detect foodborne pathogens at home. Dinner plate detectives
  • 17. @pieroleo 17 https://www.ibm.com/blogs/research/2018/05/ai-authentication-verifier/ IBM Research is currently creating bacteria-detecting sensors that would be a next-generation extension of IBM’s Crypto Anchor Verifier. This optical device, which is currently being tested by businesses from drug stores to construction companies, uses AI and machine learning techniques to analyze microscopic features and “read” the wavelengths emitted by different substances and objects. After scanning a material, a verifier records its unique wavelength and microscopic details on the blockchain, comparing its fingerprint to that of other identical substances. Soon, we’ll be able to stop them in sub-seconds IBM's Next-generation Crypto Anchor Verifier Preliminary scientific evidences
  • 18. @pieroleo AI-enabled value opportunity for Automotive Industry According to McKinsey2025 the potential incremental value that will accumulated by AI in Automotive industry will be USD 215 billions (+1.3% per year)
  • 20. @pieroleo TESLA: Autopilot 1 billion of miles
  • 25. @pieroleo Cloud + AI + Connected Car = Predictive Maintanance
  • 27. @pieroleo AI Ding sun bao 2.0: standardize Car Damage Assessment
  • 28. @pieroleo Example of Use Case: Car Damage Assessment System from Ding sun bao
  • 29. @pieroleo Example of Use Case: Car Damage Assessment System from Ding sun bao Estimated damage: slightly deformed left-rear fender Repair cost: 40$
  • 30. @pieroleo 30 Body Mass Index (BMI) Mass (weight - Kg) / height (cm) x height (cm) 18.5 < NORMAL BMI < 24.99 Adolphe Quetelet, 1832
  • 31. @pieroleo 31 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 relative “simple” signal
  • 32. @pieroleo 32 AI could help Medicine to reduce approximation (this is largely valid to all kind of industries....) The BMI - Body Mass Index is an approximation of our health status, it is inherently a proxy or a condensed information of a huge quantity physiological parameters Bottom line: it is not only a matter of how many data points you consider to take a decision, It is more a matter of how large is the data set you have that approximates the reality
  • 33. @pieroleo 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
  • 34. @pieroleo 34 Source: Bipartisan Policy Center, “F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)
  • 35. @pieroleo 35 Image source: http://personalexcellence.co/blog/ideal-beauty/ Source: Bipartisan Policy Center, “F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013) Human Digital Twins
  • 36. @pieroleo 36 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 & 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 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 DNA Proteome Microbiome Clinical/Biochemical Data Steps Nutrition Genetics Runs X-rays (CT scans) sound (ultrasound), magnetism (MRI), Radioactive (SPECT, PET) light (endoscopy, OCT) Environment Bio-Images Source: Bipartisan Policy Center, “F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013) Human Digital Twins
  • 39. @pieroleo 6 Terabytes 60% Exogenous Factors 1.100 Terabytes 0.4 Terabytes 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
  • 40. @pieroleo Leveraging Exogenous Data for Chronic Care (Type 2 Diabetes; Primary & Secondary Prevention) 60% Exogenous Factors 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 Glucose Monitoring Calorie Intake Stress Levels Physical Activity Other vital signsSocial Interaction Affinity (retail) Sleep Pattern @pieroleo www.pieroleo.com
  • 41. @pieroleo Source: http://datacoup..com Value of Data Pietro Leo's Second Income!
  • 42. @pieroleo Source: http://fdna.com/blog/pmwc_duke/ Personalizing Medicine with Artificial Intelligence and Facial Analysis Presented by Omar Abdul-Rahman, MD, University of Nebraska Medical Center Precision Medicine World Conference (PMWC)| September 24-25, 2018 | Duke University
  • 43. @pieroleo Thanks! Pietro Leo IBM Italy Executive Architect and thought leader for Artificial Intelligence Chief Scientist for IBM Italy Research & Business IBM Academy of Technology Leadership Member of ISO/SC42 Artificial Intelligence Standardization Committee www.pieroleo.com