Research & Business about Artificial Intelligence: A Point of View

@pieroleo
Research &
Business about
Artificial
Intelligence
A Point of ViewPietro Leo
Executive Architect - IBM Italy CTO for Artificial Intelligence
Chief Scientist for IBM Italy Research & Business
IBM Academy of Technology Leadership
Twitter: @pieroleo ---- www.pieroleo.com

@pieroleo
Image Credits: Pixie Dust by Disney - http://disney.wikia.com/wiki/Pixie_Dust
How Artificial
Intelligence is
perceived
from the
business
world, today?

@pieroleo
How relevant is Artificial
Intelligence for the business
and research, today?

@pieroleo
Source: https://www.mckinsey.com/industries/advanced-
electronics/our-insights/artificial-intelligence-the-time-to-
act-is-now
Enterprise winners will
focus on
microverticals in
promising industries
Source: www.mckinsey.com

6
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
Popular examples of Data-driven companies….

7Source: https://www.grushgamer.com/
Democratizing
with the cloud
the access to AI
is contributing to
generate new
products,
services and
new kind of
companies….

@pieroleo
8
Hungry to Learn: There is a
constant growing interest
around Artificial
Intelligence

@pieroleo
9
Source: https://www.timeshighereducation.com/data-bites/which-countries-and-universities-are-leading-ai-research
Volume of publications, China is leading
Source: Elsevier/Scopus
Country Publications
Field-weighted
citation impact
Switzerland 1,685 2.71
Singapore 2,432 2.24
Hong Kong 2,205 2.00
United States 25,471 1.79
Italy 6,221 1.74
Although, volume sometimes doesn’t
correspond to the quality
Which countries and universities are leading on AI
research?
Source: TimesHighereducation

@pieroleo
10
AI: Back to
the basic,
now!

@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING
High
Complexity
Low
Complexity
Stack of Dimensions of Information Technology (IT) systems
and their growing complexity
ANALOG
DIGITAL
QUANTUM
NUMBERS
INFORMATION
DATA WISDOM
2+2=4
ASK FOR A LOAN
DGITAL
TRANSFORMATON
PROCEDURAL
PROGRAM
SUPERVISED
REASONING

@pieroleo
High
Complexity
Low
Complexity
From a business perspective AI is THE ingredient that can push the
genetic evolution of IT from the status of a tool to a generalized
business problem solving environment
ANALOG
DIGITAL
QUANTUM
NUMBERS
INFORMATION
DATA WISDOM
2+2=4
ASK FORA LOAN
DGITAL
TRANSFORMATON
PROGRAM
SUPERVISED
REASONING
AI
REEF

@pieroleo
High
Complexity
Low
Complexity
ANALOG
DIGITAL
QUANTUM
NUMBERS
INFORMATION
WISDOM
PROGRAM
SUPERVISE
AND TRAIN A
MACHINE
2+2=4
ASK FORA LOAN
AI
REEF
AI isn’t magic — it’s a lot of hard work!
Here there are lions

@pieroleo
Stack of Dimensions of Information Technology (IT)
systems and their growing complexity
ANALOG
DIGITAL
QUANTUM
NUMBERS
INFORMATION
WISDOM
PROGRAM
SUPERVISE
AND TRAIN A
MACHINE
MACHINE LEARNS
WITHOUT A
TRAINING
2+2=4
ASK FORA LOAN
DGITAL
TRANSFORMATON
High
Complexity
Low
Complexity

@pieroleo
Why we need the help of
Artificial Intelligence to solve
problems?

Investment in
digital is a
matter of
survive for
companies,
after
digitalization
what’s next?
@pieroleo
www.pieroleo.com

Source: McKinsey
What’s next?
For agriculture
Precision
Agriculture

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

20
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

21
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
www.pieroleo.com

22
Main point: it is not only a matter of how many
data points you consider to take a decision
It is a matter of how data we have are approximating the
reality
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
AI could help Medicine to reduce
approximation

@pieroleo
Which is the current and
future role of Artificial
Intelligence in augmenting
decision making?

Assistant
Tools
Collaborator
Coach
Mediator
Emerging types of augmenting
decision making
AI is augmenting decision making and is opening to new forms of
collaboration between humans and machines to solve problems
@pieroleo
www.pieroleo.com
Passive
Role
Active
Role

Assistant
Tools
Collaborator
Coach
Mediator
AI is augmenting decision making and is opening to new forms
of collaboration between humans and machines to solve
problems
@pieroleo
www.pieroleo.com
decision making
Passive
Role
Active
Role

Gutman, Codella, Celebi, Helba, Marchetti, Mishra and Halpern, “Skin LesionAnalysis toward Melanoma Detection:A
Challenge”, Int. Symposium on Biomedial Imaging (ISBI) 2016
• Deep Learning for skin lesion image
analysis
• Trained on dermoscopic images of
melanoma and other skin cancers
• Automates analysis of images of
skin lesions
• Extracts clinical features
• Segments lesions
• Predicts disease
• Reports disease score
• Searches for similar lesions
Tool: Skin Lesion Image Analysis for Melanoma Detection

Assistant
Tools
Collaborator
Coach
Mediator
AI is augmenting decision making and is opening to new
forms of collaboration between humans and machines to
solve problems
@pieroleo
www.pieroleo.com
decision making
Passive
Role
Active
Role

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

Personalized diabetes mobile companion
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

Collaborating to have an healthy regime

Assistant
Tools
Collaborator
Coach
Mediator
AI is augmenting decision making and is opening to new
forms of collaboration between humans and machines to
solve problems
decision making
Passive
Role
Active
Role

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
Leo, D’Onofrio, Sancarlo, Ricciardi, De Petris, Giuliani, Peschiera, Failla, Renzi and Greco, “ViTA:
Virtual Trainer forAging”- FAAL 2017

Coaching our memory but also memory
2/8/18Tales

Source: IBM & Marchesa Cognitive Dress https://www.ibm.com/blogs/internet-of-things/cognitive-marchesa-dress/
& https://www.ibm.com/watson/stories/dress.html
How you perceive see yourself How others see you
A Cognitive Dress mediates you

Computers help to
Organize & Find
Information
Make betterdecisions
four our business
Big Data
Decision
Lakes
Invent New Products
& Markets
Augment Intelligence
Machine Learning(Visual,
Multimodal learning,…)
NaturalInterfaces…
Industry-driven decisions
Augment Problem Solving
IntelligentMaterials
Newform of Computing
After mediation Active Intelligence is at the horizon
Source: See Pietro Leo, 2017 - https://pieroleo.com/2017/10/05/active-intelligence-is-at-the-horizon/

@pieroleo
38
Example challenges IBM is tackling requiring tech innovation
Media & Entertainment Regulatory Compliance Industrial - Maintenance
Customer Care Marketing / Business IoT
Is my organization compliant with
latest regulatory documents
Guide me through fixing
malfunctioningcomponents
Summarize the strategic intent
of a company based on recent
news articles
Bot that can guide a user
through buying the right
insurance policy
Retail
Find rust on electric
towers, using drones
Healthcare
Visual Inspection
Improve the accuracy
of breast cancer
screening
Predict yieldof fieldbased
on images and sensor data
Create highlights of sports
events

@pieroleo
39
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 attempts to make easy the access to AI: think in terms of AI
microservices (or business building blocks) ready to use

@pieroleo
ANALOG
DIGITAL
QUANTUM
NUMBERS
INFORMATION
WISDOM
2+2=4
ASK FORA LOAN
PROCEDURE
DGITAL
TRANSFORMATON
High
Complexity
Low
Complexity
PROGRAM
SUPERVISED
REASONING

@pieroleo
41
Perception
Deep Learning & Reason
Classification
Explain
InterpretabilitySymbolic
Reasoning
Observe
Common-Sense
Planning
Patterns & Sub-patterns
Observation
AI Algorithms
….. ….. …..
Ethics
@pieroleo
www.pieroleo.com

@pieroleo
42
Perception
Classification
Explain
Reasoning
Observe
Common-Sense
Planning
Observation
AI Algorithms
….. ….. …..
Ethics
Deep
Neural
Learning
@pieroleo
www.pieroleo.com

@pieroleo
43
Various forms of AI works
Kind of problems where Artificial Intelligence is generating a relevant
business impact

@pieroleo
44
AI & Computer Vision
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
45
Source: IBM Research automatic sport highlights generation https://www.ibm.com/blogs/research/2017/06/scaling-wimbledons-video-
production-highlight-reels-ai-technology/

@pieroleo
46
Source: IBM Research Food Recognition - https://www.ibm.com/blogs/research/2017/05/training-watson-see-whats-plate

@pieroleo
47
r - 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
Rennie, Marcheret, Mroueh, Ross & Goel, “Self-Critical Sequence Trainingfor Image Captioning.” CVPR 2017

@pieroleo
Recognizing products on a supermarket shelf for planogram
compliance
Karlinsky, Shtok, Tzur, and Tzadok, “Fine-grainedrecognitionof thousands of objectcategories with single-example training”, CVPR-2017
Challenge:
Fast detection and recognition of thousands
of object categories while training on one
example per category
Approach:
Non-parametric probabilistic model for
initial detection, CNN-based refinement and
temporal integration (where applicable)
Results:
Achieving state-of-the-art performance on
existing retail benchmark and new dataset
that we curated

@pieroleo
49Source: https://arxiv.org/pdf/1710.08864.pdf

@pieroleo
50
Perception
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
Key topics in research Learning & Reasoning to support business problems
Making Learning More Human-
Like
People learn by trail and error without a lot of labeled
data. We learn continuously throughout their lives,
remembering what we’ve learned and leveraging it for
new tasks.
Interpretability
Explaining AI decisions is crucial forcustomers,
government and regulators, enterprises.
Optimization
Beyond back-propagation
Neuro AI
Novel AI approaches based on brain function including
plasticity, attention, memory, reward processing,
motivation
Deep Document Understanding
People can access the accumulated knowledge of
humanity directly, by reading, viewing and listening.
And they can apply that knowledge directly to new
tasks.
Conversational Knowledge
Acquisition
Acquiring, Applying and Accumulating knowledge
during collaboration with humans.
Multi-step Reasoning
Humans can combine inputs and knowledge from
multiple sources to solve sub-problems and larger
complex tasks
Reliable, Approximate
Reasoning
Human reasoning can be exact and it can be flexible,
AI systems need to be able to span this range

@pieroleo
5
2
Video Face Extraction
12 Jun 2016
21:40 – 22:00
Video Time Tagging
Cleveland,
OH
Video Geotagging
Face Identity Attributes
Woman,
20-30
Face Expression
Pensive
Face Extrinsics
Full hair, blond,
no glasses, no
hat
Video Object Finding
Segmented
Objects
Bicycle:{
Colour:gray,
Brand:Raleigh,
Pose: inverted}
Object Recognition
Multimedia Retrieval
To: find examples of scenes in videos with sets of objects
fitting descriptions in a list L
• Retrieve candidates videos
• For each video, and object type
• Use appropriate extractor to find
spans with that object
• Segment those objects out
• Run attribute extraction on each obj o
giving a
• Remember span and o if a satisfies
any description in L
• Remember span if it contains objects
satisfying all descriptions in L
To: Answer a query x for user u,
• Identify the languageof x, l,
• Use languageto logic for l on x to make an equivalent
query y,
• Reason to answer y, yielding answers z
• Use logic to language to turneach zi into language l
equivalent ai
• Assemble ai into list a
• Find a convenient display d for u
• Display x and the list a on d
English to Logic
What’s the
population of
Auckland?
(nInhabitants
Auckland ?nu)
Language ID
定シエムチ曜
玲ロ危氏47貫
っを数満え形
60弘90健ル
がぽぞ逮
Japanese {jp}
Logic to English
(nInhabitants
Auckland 1e6)
Auckland has a
million people.
The popn of
Auckland is 1m.
Problem Solving Methods
Machine Reasoning
Multi-step reasoning for Skill Composition

@pieroleo
53
Source: https://www.ibm.com/blogs/think/2017/01/ibm-cognitive-principles/
Source: https://www.research.ibm.com/software/IBMResearch/multimedia/AIEthics_Whitepaper.pdf
www.pieroleo.com

@pieroleo
ANALOG
DIGITAL
QUANTUM
NUMBERS
INFORMATION
WISDOM
PROGRAM
SUPERVISE
AND TRAIN A
MACHINE
MACHINE LEARNS
WITHOUT A
TRAINING
2+2=4
ASK FORA LOAN
PROCEDURE
DGITAL
TRANSFORMATON
High
Complexity
Low
Complexity

@pieroleo
55
http://science.sciencemag.org/content/345/6197/668
IBM Research Brain Chip - http://www.research.ibm.com/articles/brain-chip.shtml
In 2014, IBM presented 1M spiking-neuron chip with a scalable communicationnetwork and interface. The chip has 5.4
billion transistors, 4096 neuro-synaptic cores and 256 million configurable synapses.
Neuromorphic Computing – IBM True North
Source: Introduction - https://www.youtube.com/watch?time_continue=3&v=jqI0L44yFEo
www.pieroleo.com

@pieroleo
56
Source: IBM Research Gesture recognition at Low power devices - https://www.ibm.com/blogs/research/2017/07/brain-inspired-cvpr-2017/
Source Video: https://www.youtube.com/watch?v=g08IW-qRomM
Trained a spiking neural network to recognize 10 hand gestures in real-time at 96.5 percent accuracy
within a tenth of a second from the start of each gesture, while consuming under 200 mW – much lower
power than frame-based systems, which use traditional processors.

@pieroleo
57
• 64 million neurons
• 16 billion synapses,
• Processor
component will
consume the energy
equivalent of a dim
light bulb – a mere 10
watts to power.
Source: IBM Research new TrueNorth project update https://www-03.ibm.com/press/us/en/pressrelease/52657.wss
U.S. Air Force Research Lab Taps IBM to Build Brain-
Inspired AI Supercomputing System
www.pieroleo.com

@pieroleo
58
In-memory Computing with 1 Million Devices for
Applications in AI
Source IBM Research Phase Change Memory: https://www.ibm.com/blogs/research/2017/10/ibm-
scientists-demonstrate-memory-computing-1-million-devices-applications-ai/
IBM Research demonstrated
that an unsupervised
machine-learning algorithm,
running on one million phase
change memory (PCM)
devices, successfully found
temporal correlations in
unknown data streams.
When compared to state-of-
the-art classical computers,
this prototype technology is
expected to yield 200x
improvements in both
speed and energy
efficiency
Source: http://www.nature.com/articles/s41467-017-01481-9
PCM Device Collocated Memory and computing

@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
“

@pieroleo
Intersection of AI & Quantum Computing
How can AI accelerate the development of quantum
computers?
How will quantum computers speed up the training
of AI models?
IBM Quantum Environment
www.research.ibm.com/ibm-qx
doi:10.1038/s41534-017-0017-3
Optimization Chemistry Machine Learning

61
CLOSING
What are next challenges for Artificial Intelligence for
supporting complex business problems?

Source Kate Crawford #NIPS2017
Transparency
& Trustworthiness

@pieroleo
FUTURE
Multi-Task, Multi-Domain Intelligence
Automated Application Development
Continuously-Adapting Applications
Human-Like Task Learning
Implicit + Explicit Memory
Explainable
Continuously Learn & Adapt
Induce Rules & Processes; Infer Solutions
Modality Independent
Automatically-Constructed Architectures
Hybrid Infrastructure
Acceleration via Novel Devices & Materials
Dynamic Data
Information Represented by Knowledge
TODAY
Single-Task, Single-DomainIntelligence
Human-Guided Application Development
Static Applications
Data-Defined End-to-End Tasks
Implicit Memory
Opaque
Train and Deploy
Program Control
Uni-Modal
Static Algorithm-Specific Architectures
Traditional Infrastructure
Deep Learning Acceleration
Static Data
Information Represented by Data
AI for Business requires Research innovations
RESEARCH
ALGORITHMS
PROBLEMS
DATA
COMPUTING

@pieroleo
@pieroleo
Pietro Leo
Executive Architect - IBM Italy CTO for Artificial Intelligence
Chief Scientist for IBM Italy Research & Business
IBM Academy of Technology Leadership
Grazie!
www.pieroleo.com

Research & Business about Artificial Intelligence: A Point of View

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Research & Business about Artificial Intelligence: A Point of View