Computing, Data, Algorithms, and, Problem Solving

@pieroleo
Computing, Data,
Algorithms, and,
Problem Solving
Pietro Leo
Executive Architect - IBM Italy CTO for Artificial Intelligence
IBM Academy of Technology Leadership
Head of IBM Italy Center of Advanced Studies
A guest lecture prepared for
Information
Technology & Digital
Strategy
@pieroleo
www.pieroleo.com

@pieroleo
@pieroleo
www.pieroleo.com

@pieroleo
ALGORITHMS
My dentist told me how to perfectly clean
your teeth…..
COMPUTING
Me, a toothbrush, toothpaste and water…
PROBLEMS
I have to brush my teeth….
DATA
Teeth can get sick, caries, tartar…
@pieroleo
www.pieroleo.com

@pieroleo
Source: Grush video
@pieroleo
www.pieroleo.com

@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING
• QUANTUM LAWS
• DIGITAL
• ANALOG
High
Complexity
• WISDOM
• KNOWLEDGE
• INFORMATION
• NUMBERS
• TALK WITH
A DIGITAL
HUMAN
• DGITAL
TRANSFOR
MATON
• ASK FOR A
LOAN
• 2+2=4
• MACHINE
LEARNS
WITHOUT A
TRAINING
• SUPERVISE AND
TRAIN A
MACHINE
• PROGRAM
Low
Complexity
Dimensions of IT systems and their complexity
@pieroleo
www.pieroleo.com

@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING ALGORITHMS PROBLEMSDATACOMPUTING
1950-1960 1960-1970
1970-1980
1980-1990
1990-2000
2000-2010
2010-….
IT Complexity waves
• QUANTUM LAWS
• DIGITAL
• ANALOG
• WISDOM
• KNOWLEDGE
• INFORMATION
• NUMBERS
• TALK WITH
A DIGITAL
HUMAN
• DGITAL
TRANSFOR
MATON
• ASK FOR A
LOAN
• 2+2=4
• MACHINE
LEARNS
WITHOUT A
TRAINING
• SUPERVISE AND
TRAIN A
MACHINE
• PROGRAM
@pieroleo
www.pieroleo.com

8
Data
We live
in a
cloud
of data
@pieroleo
www.pieroleo.com

@pieroleo
www.pieroleo.com
City
Lifestyle
ZIPcode
Costal vs Inland
Generation
Location Family Size
Gender
Income Level
Age
Loyalty & Card
Activity
Revenue Size
Life Stages
Education
Legal status
Sector
Marital status

@pieroleo
www.pieroleo.com
Subscriptions
Wish List
Size of Network
Check-ins
App usage duration
Number of Apps on Device
Device Usage
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

Non è possibile visualizzare questa immagine.
@pieroleo
www.pieroleo.com
Sentiment
Tone
Euphemisms
Hedonism
Extroversion
Face Recognition
Openness
Colloquialism
Reasoning Strategies
Language Modeling
Dialog
Intent
Latent Semantic Analysis
Phonemes
Ontology Analysis
Linguistics
Image Tags
Question Analysis
Self-transcendent
Affective Status

12
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) @pieroleo
www.pieroleo.com

@pieroleo
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
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
@pieroleo
www.pieroleo.com

@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 signs Social
Interaction
Affinity (retail)
Sleep Pattern
@pieroleo
www.pieroleo.com

@pieroleo
16
>80% Unstructured Data
+ External Data
“Untouched” Data
+ Stream of Data
Enterprise Data Machine Data People Data
@pieroleo
www.pieroleo.com

@pieroleo
Data is there and we need to make the best out of it
@pieroleo
www.pieroleo.com

@pieroleo
We produce and consume Data for a specific purpose
@pieroleo
www.pieroleo.com

@pieroleo
Source: http://datacoup..com
Value of Data
Pietro Leo's
Second
Income!
@pieroleo
www.pieroleo.com

@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
Source: http://www.pnas.org/content/114/38/10166.full
New kind of prediction microscope: With the DNA raw data you could
predict some individual traits

Investment
in digital is
a matter of
survive for
companies
@pieroleo
www.pieroleo.com

Investment
in private
Fintech
companies
increase 10x
in past 5
years >20B

Credits: http://www.arkive.org/whale-shark/rhincodon-typus/
What is
Fintech?
27
@pieroleo
www.pieroleo.com

Fintech are « UNBUNDLING » tranditional banks
@pieroleo
www.pieroleo.com

« 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
@pieroleo
www.pieroleo.com

« UNBUNDLING » logistics
Source: https://www.cbinsights.com/blog/startups-unbundling-fedex/
@pieroleo
www.pieroleo.com

The
biggest
taxi
company
do not
own cars
@pieroleo
www.pieroleo.com

The largest
accommodation
company owns
no real estates
@pieroleo
www.pieroleo.com

Marriott CIO
acknowledged Airbnb
as a new competitor
@pieroleo
www.pieroleo.com

35
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
@pieroleo
www.pieroleo.com

Automation will bring big shifts to the world of work, as AI and
robotics change or replace some jobs, while others are created.
Source: McKinsey & co: Jobs Lost, Jobs created: workforce transactions in a time of automation, 2017
By 2030, 75 million to 375 million workers will need to
switch occupational categories.
60% percent of occupations have at least 30% of
constituent work activities that could be automated
@pieroleo
www.pieroleo.com

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

38
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
@pieroleo
www.pieroleo.com

39
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
@pieroleo
www.pieroleo.com

Assistant
Tools
Collaborator
Coach
Mediator
Emerging types of Cognitive Systems
Augment Decision Making is opening to new forms of
collaboration between humans and machines
to solve problems
@pieroleo
www.pieroleo.com

Assistant
Tools
Collaborator
Coach
Mediator
Emerging types of Cognitive Systems @pieroleo
www.pieroleo.com

@pieroleo
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

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

46
The Medical Sieve § Build a fast anomaly detection
engine
– Quickly filters irrelevant images
– Highlights disease-depicting regions
– Flags coincidental diagnosis
§ Intended as a radiology assistant
– Clinicians still do the diagnosis
– Machine reduces workload
– Machine performs triage/decision
support
Given history of the patient and images of
a study
Is there an anomalous image here?
If so, where is the anomaly ?
Describe the anomaly
The Medical Sieve
@pieroleo
www.pieroleo.com

@pieroleo
www.pieroleo.com
Conversation

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

@pieroleo
www.pieroleo.com
I amgoing to
NewYorknext
May
Man
Walking, go
around
vest
Whereand When
will you beusing
thisjacket?
I'll find a jacketthatfits
thoseconditions. Areyou
looking for a men'sor
women's jacket?
Okay, Igotit. What
will you use thisjacket
for?
Whatstylesareyou
looking for?
@pieroleo

@pieroleo
www.pieroleo.com
Conversation Intents Entities Dialogues
Personality 5Five Personality Traits Needs Values
Language Tone Emotion Social propensities Language styles
Translate Conversational News Custom TranslationPatents
Language Deep
Understanding
Speech-to-text
Custom pronunciations Voice Transformation
Expressive Voice
Voice synthesis
Keyword Spotting Telephony Broadband
Vision Face Recognition Image Similarity Image Classification
Custom eyes
Keyword Extraction,
Entity Extraction,
SentimentAnalysis,
Concept Tagging,
Relation Extraction,
Taxonomy Classification,
Author Extraction…..
Custom Analysis
WATSON
Kind of skills
Source: https://www.ibm.com/watson/developercloud/services-catalog.html
@pieroleo

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

53
2 solutions that address the biggest needs in
Diabetes Care
Integrated & personalized diabetes
care program with coachingservices&
risk stratification for healthcare
systems to help high risk/at-risk
individualswith diabetesimprove their
lives & reduce the cost of care
Personalizeddiabetes mobile
companionwith real time
glucose insights for individuals
with diabetes to help make
daily diabetesmanagement
easierand more effective
Medtronic
Turning PointBridging the gap
in between doctor
visits enabling
Self-Management
& Better Care
53© 2016 International Business Machines Corporation
@pieroleo
www.pieroleo.com

Sugar.IQ : Intelligent Mobile Assistant
Guardian Connect Cognitive Computing Sugar.IQ
• Real-time, smartphone CGM
with alerts
• Insulin, Meal, Activity, Context
• Standalone, for MDI patients
• Watson Health Cloud & Analytics
• Pattern recognition, insights &
Predictions
• Engagement & Gamification
• Real-time insights, coaching
platform
• Assists in daily diabetes mgmt
• Aggressive capabilities
roadmap
Insights Glucose
Forecasts
Hype-hyper
Predictions Ask
Watson
@pieroleo
www.pieroleo.com

Sugar.IQ app
@pieroleo
www.pieroleo.com

Assistant
Tools
Collaborator
Coach
Mediator
Types of Cognitive Systems
@pieroleo
www.pieroleo.com

ViTA Advisor: it is a
conversational multi-
modal agent to
support older as well
as a tool to collect
meaningful data
about the context of
an individual
ViTA : Virtual Trainer for cognitive impaired patients
Sustain
Independence
and Dignity with
affect and
purpose,
preserve and
reinforce
individuals and
social memories
Vita Memory
Coach: a system
that supports
caregivers to collect
meaningful facts and
memories of an
individual and his
context
Vita
Memories
@pieroleo
www.pieroleo.com

Memories are a bridge among generations
12/15/1Tales
@pieroleo
www.pieroleo.com

@pieroleo
Source: Cognitoys
@pieroleo
www.pieroleo.com

Dinner Planner
@pieroleo
www.pieroleo.com

Source: Soul Machine - https://www.youtube.com/watch?v=fNWjKtVWToc @pieroleo
www.pieroleo.com

64
@pieroleo
www.pieroleo.com
Source: Soul Machine - https://www.youtube.com/watch?v=0tUSsqOLZC8 @pieroleo

Sphie
Source Sophie Soule Machine - https://www.youtube.com/watch?v=JiYANsW2F5A @pieroleo
www.pieroleo.com

@pieroleo
67
There is a constant
growing interest
around Artificial
Intelligence
@pieroleo
www.pieroleo.com

@pieroleo
68
Various forms of AI works
@pieroleo
www.pieroleo.com

@pieroleo
69
AI system & sighting
General Purpose
Visual Services
Source IBM Research Computer Vision: http://www.research.ibm.com/cognitive-computing/computer-vision/
Medical Image Analysis
“a person holding a giraffe in their hand”
Video Content Analysis Image Captioning Low-power computer
vision - Gesture
Recognition
Multimodal Analysis
@pieroleo
www.pieroleo.com

@pieroleo
70
Source: IBM Research automatic sport highlights generation https://www.ibm.com/blogs/research/2017/06/scaling-wimbledons-video-
production-highlight-reels-ai-technology/ @pieroleo
www.pieroleo.com

@pieroleo
71
Source: IBM Research Food Recognition - https://www.ibm.com/blogs/research/2017/05/training-watson-see-whats-plate
@pieroleo
www.pieroleo.com

@pieroleo
72Source: IBM Research Image Caption generation paper - https://arxiv.org/pdf/1612.00563.pdf
“a blue boat is sitting on the side of a building” “a person holding a giraffe in their hand”
@pieroleo
www.pieroleo.com

@pieroleo
73
Santiago Ramon y Cajal Camillo Golgi

@pieroleo
74
Perception
Deep Reason
Classification
Explain
InterpretabilitySymbolic
Reasoning
Observe
Common-Sense
Planning
Patterns & Sub-patterns
Observation
AI Algorithms
….. ….. …..
Ethics
@pieroleo
www.pieroleo.com

@pieroleo
75
Perception
Deep Reason
Classification
Explain
Reasoning
Observe
Common-Sense
Planning
Observation
AI Algorithms
….. ….. …..
Ethics
Deep
Neural
Learning
@pieroleo
www.pieroleo.com

@pieroleo
76
Deep learning basic process
Forward Propagation
Backward Propagation
Multiply + Add
Sigmoid
SoftMax
reLu
Multiply + Add
Errors
@pieroleo
www.pieroleo.com

@pieroleo
77Source: https://arxiv.org/pdf/1710.08864.pdf @pieroleo
www.pieroleo.com

@pieroleo
78
Perception
Deep Reason
Classification
Explain
Reasoning
Observe
Common-Sense
Planning
Observation
AI Algorithms
….. ….. …..
Ethics
See: https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-innovation-equation.html
@pieroleo
www.pieroleo.com

@pieroleo
79
Source: https://www.ibm.com/watson/products-services/
Conversation
Integrate diverse
conversation
technology into
your application.
Knowledge
Get insights through
accelerated data
optimization
capabilities.
Vision
Identify and tag
content then
analyze and extract
detailed information
found in an image.
Speech
Convert text and
speech with the
ability to customize
models.
Language
Analyze text and
extract meta-data
from unstructured
content.
Empathy
Understand tone,
personality, and
emotional state.
Practical exercise: explore how an AI set of basic services looks like
@pieroleo
www.pieroleo.com

@pieroleo
“Nature isn’t classical, dammit, and if you
want to make a simulation of nature, you’d
better make it quantum mechanical, and
by golly, it’s a wonderfulproblem, because
it doesn’t look so easy.”
-Richard P. Feynman
NATURE ISN’T CLASSICAL, DAMMIT,
AND IF YOU WANT TO MAKE A SIMULATION OF
NATURE, YOU’D BETTER MAKE IT QUANTUM
MECHANICAL, AND BY GOLLY, IT’S A
WONDERFUL PROBLEM, BECAUSE IT DOESN’T
LOOK SO EASY.”
RICHARD P. FEYNMAN
“

Quantum Algorithms
A quantum computer can solve certain problems much faster.
Deckwith
99 black cards
1 red Ace Problem:
find red Ace
Average-casescenario:
Classical Algorithm: 51 card draws
GroverQuantum Algorithm:10 card draws
@pieroleo
www.pieroleo.com

Quantum Algorithms
A quantum computer can solve certain problems much faster.
Deck#1
100 black cards
Deck#2
50 black cards
50 red cards
Problem:
which deck?
Worst-casescenario:
Classical Algorithm: 51 card draws
Deutsch-JozsaQuantum Algorithm:1 card draw
@pieroleo
www.pieroleo.com

Exponential speed-up:
A task taking 300 years (233
seconds) on a classical computer
might take a minute (~ 30 seconds) on a quantum computer
The problem of multiplication vs factoring:
937 x 947 = N (easy)
887339 = p x q (harder)
Modulus (1024 bits):
de b7 26 43 a6 99 85 cd 38 a7 15 09 b9 cf 0f c9
c3 55 8c 88 ee 8c 8d 28 27 24 4b 2a 5e a0 d8 16
fa 61 18 4b cf 6d 60 80 d3 35 40 32 72 c0 8f 12
d8 e5 4e 8f b9 b2 f6 d9 15 5e 5a 86 31 a3 ba 86
aa 6b c8 d9 71 8c cc cd 27 13 1e 9d 42 5d 38 f6
a7 ac ef fa 62 f3 18 81 d4 24 46 7f 01 77 7c c6
2a 89 14 99 bb 98 39 1d a8 19 fb 39 00 44 7d 1b
94 6a 78 2d 69 ad c0 7a 2c fa d0 da 20 12 98 d3
1024bit public key:
= p × q
→ just short of impossible
Shor’s algorithm jumpstarted the interest in quantum
computing
Classical Record: 320 digits (𝟐 𝟏𝟎𝟔𝟏; >300 CPU years)
exp(𝐶 𝑏./0)
𝐶 𝑏0
Quantum Algorithms
@pieroleo
www.pieroleo.com

@pieroleo
Traveling Salesman Problem
- Visit all cities just once
- Choose the shortest path
- Come back to starting point
17 x … x 5 x 4 x 3 x 2 x 1 = 17! =
355’687’428’096’000 possible paths
A quantum computer can explore all routes
simultaneously while a classical computer
has to try them sequentially. 18 selected cities in Switzerland
16 ausgewählte Städte in der Schweiz
@pieroleo
www.pieroleo.com

@pieroleo
Molecular Dynamics, Drug Design & Materials Simulation Challenges
This laptop could simulate a 25 electron system,Titan a 43 electron system
but no classicalcomputereverbuilt could simulate a 50 electron system
exactly.
Caffeine is a moderately sized molecule. It
is more complex than water, but simpler
than DNA or proteins.
The complexity involved to simulate its
energy, structure and interactions is beyond
the capability of any computer with current
technology.
To simulate Caffeine on Quantum might
take 160 Qubits, doing so with classical
with current methods require around
10^48 bits (as there are atoms on planet
Earth)

@pieroleo
Some of the earliest examples of these include
optimization, chemistry, and machine learning.
Quantum
Computing
holds the
potential to
solve today’s
intractable
computational
challenges

@pieroleo
How quantum mechanics laws (such
as superposition, entaglement,
interference, etc..) could help to build
new computational methods to solve
these problems more efficiently?
@pieroleo
www.pieroleo.com

@pieroleo
0
1
Quantum
QUBIT
‘1’
‘0’
‘0’ + ‘1’
Classic
BITS
Superposition: each qubit in 2 states simultaneously
How Quantum laws
can speedup
Information
representation?
@pieroleo
www.pieroleo.com

@pieroleo
Combination of Quantum and Classical Computing

@pieroleo
50 qubits
20 qubits
https://www.ibm.com/blogs/research/2017/11/the-future-is-quantum/
https://www-03.ibm.com/press/us/en/pressrelease/53374.wss
@pieroleo
www.pieroleo.com

@pieroleo
Whereas a 56-qubit
quantum computer can
theoretically perform 2^56
operations simultaneously,
IBM's accomplishment
involved dividing this task
into 2^19 slices that each
essentially consisted of
2^36 operations. This
strategy meant the
researchers only needed
about 3 terabytes of
memory for their simulated
quantum computer.
In contrast, earlier in 2017,
a 45-qubit simulation at the
Swiss Federal Institute of
Technology in Zurich
required 500 terabytes of
memory. @pieroleo
www.pieroleo.com

@pieroleo State-of-the-art quantum
computing innovation continues at IBM
Research
Published in the journal Nature in September, 2017
@pieroleo
www.pieroleo.com

Source Kate Crawford #NIPS2017

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

@pieroleo
Source:
http://www.ted.com/talks/sherry_turkle_alone_together
Sherry Turkle:
Connected, but alone?
These days phones in our pockets are changing our
minds and hearts offer us three gratifying fantasies
and 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

@pieroleo
100
Source: https://www.ibm.com/blogs/think/2017/01/ibm-cognitive-principles/
Source: https://www.research.ibm.com/software/IBMResearch/multimedia/AIEthics_Whitepaper.pdf
@pieroleo
www.pieroleo.com

https://www.partnershiponai.org/

@pieroleo
@pieroleo
Pietro Leo
Executive Architect
IBM Italy CTO for Artificial Intelligence
Head of IBM Italy Center of Advanced Studies
IBM Academy of Technology Leadership
Grazie!
www.pieroleo.com

Computing, Data, Algorithms, and, Problem Solving

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Similar to Computing, Data, Algorithms, and, Problem Solving

Similar to Computing, Data, Algorithms, and, Problem Solving (20)

More from Pietro Leo

More from Pietro Leo (15)

Recently uploaded

Recently uploaded (20)

Computing, Data, Algorithms, and, Problem Solving