artificial intelligence - deloitte united states · 2020-05-13 · • build multi-scale scaffold...
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Human Brain ProjectUnifying our understanding of the human brain
Co-funded by
the European Union
Marc-Oliver Gewaltig
Ecole Polytechnique Fédérale de Lausanne, Blue Brain Project, Neurorobotics
Disruptive tools and
technologies…
April 25, 2017 12
… empower
industry and/or
give rise to new
industries
(after – sometimes –
a long delay!)
April 25, 2017 13
Medicine
Information Technology
Biotech
The Vision of the Human Brain Project
April 25, 2017 14
R. Feynman : What I cannot create I do not understand
HBP is a European FET Flagship project to create and operate
collaborative research tools for experimental and virtualized brain
research, and for developing brain-derived technologies.
To understand the brain (better) we need a
• large-scale, interdisciplinary, integrating infrastructure
• for performing multi-level studies of brain and body
• from analytics and neuroscientific data by way of synthetic
modeling for partial/full brain simulation, brain reconstruction,
• and the design of new computer architectures and robots.
HBP at a Glance – Facts and Figures
April 25, 2017 15
• 10-year, EUR 1 billion Research Roadmap
(50% Core Project, 50% Partnering Projects)
• Core project 400+ scientists, 116 institutions, 19
countries
• 6 prototype research platforms
released in March 2016
• Embedded in previous and existing national and
international initiatives: Blue Brain, BrainScaleS,
Supercomputing and Modeling the Human Brain,
SpiNNaker, PRACE, etc.
• 23 industry collaborations; 121 research collaborations
with non-HBP research groups (61 with universities and
institutes in 3rd countries)
Research Branches within the Human Brain Project
April 25, 2017 16
Accelerating Neuroscience
Integrate everything we know about the
brain into computer models and
simulations.
Accelerated Future Computing
Learn and derive from the brain to build the
supercomputers and robots of tomorrow.
Accelerated Medicine
Contribute to
understanding,
diagnosing
and treating diseases
of the brain.
The HBP Platform Universe supports the science
April 25, 2017 17
Brain Simulation:
Collaborative integration of neuroscience data into
multi-scale scaffold models and simulations of brain
regions
Neurorobotics:
Testing brain models and simulations in
dynamic virtual environments
Neuroinformatics:
Organizing neuroscience data, mapping to brain
atlases
Medical Informatics:
Bringing together information on brain diseases
Neuromorphic Computing:
ICT that mimics the functioning of the brain
High Performance Analytics and Computing:
Hardware and software to support the other
Platforms
constraints
predictions
capability
Ne
uro
scie
nce
Mouse Human
Computing
Scaffold Models
Me
dic
ine
In-silico behavior
and cognition
Brain Simulation
Neurorobotics
High Performance Analytics
and Computing,
Neuromorphic Computing
Ne
uro
info
rma
tics
Med
ical In
form
atic
s
Neuroinformatics Platform
April 25, 2017 18
Brain Atlases
Brain Simulation Platform
April 25, 2017 19
Detailed reconstruction and simulation of brain regions
Markram et al., 2015
High Performance Analytics and Computing Platform
April 25, 2017 20
Simulation technology
Extending the functionality of brain
simulation codes: concepts, numerical
algorithms and software technologies
Data-intensive supercomputing
Linking extreme scale data processing
challenges to the exploitation of
scalable computer resources
Interactive visualization
Visual analysis of large-scale neural
simulation data
Dynamic resource management
Novel approaches for managing the
resources in a
supercomputer across applications
Neuromorphic Computing Platform
April 25, 2017 21
Intel Free Press, CC BY-SA 2.0
SpiNNaker BrainScaleS / HICANN
Many-Core Machine
Base Chip with stacked DRAM
18 Cores
Physical Model Machine
Base Chip
512 Neurons
115k Synapses
Neurorobotics Platform
April 25, 2017 22
Closed-Loop Simulation of Soft Biological Bodies: The HBP Mouse
Mouse body Simulated activation of S1 with
tactile stimulation
Forelimbs
Hindlimbs
Mouth
Trunk
Whiskers
Nose
Project Timeline
April 25, 2017 23
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 20232010 2011 2012
Submission of
FET proposals
for pilots
Start of
HBP pilot
Start of the ramp-up
phase
Passed
review
successfully
6 pilots
selected
Ethics Advisory
Board formed
Public
platform
release
Passed review
successfully
New Users
Transformation into a European
Research Infrastructure
New Collaborators
HBP is selected as one
of two FET Flagship
projects by the
European Commission
Operational PhaseRamp-Up PhasePilot
Summary
April 25, 2017 24
• HBP is a European Flagship project that builds an integrated ICT-based research
infrastructure for brain research, cognitive neuroscience and brain-inspired
computing:
• Gather, organise and disseminate data describing the brain and its diseases
• Build multi-scale scaffold models and theory for the brain
• Simulate the brain
• Develop brain-inspired computing, data analytics and robotics
• Ensure that the HBP's work is undertaken responsibly and that it benefits society
• The project promotes collaboration across the globe.
• The next HBP Summit will be held in Glasgow in October 2017
29
Deloitte Digital Series
April 2017
30
To bring the best of data science to every risk
office
James mission is
Artificial Intelligence
31
Artificial Intelligence is a reality today
32
So why isn’t AI into credit risk?
33
Well, because....
4. It is hard to find
the right talent
2. Regulatory
constraints
1. It is a time
consuming and
complex process
3. Cannot rely
on black
boxes
34
What is the solution?
Credit Risk AI
Easy to use by
experts
Regulation Ready
Easy to integrate
Some banks are already building their own AI!
35
The time is now
Higher cost of risk
Lose market share
time
Pioneers Early adopters Early majority Late majority LateAdoption
tiers
Effort to catch up increases as adoption spreads
This is
where we
are today
Ad
op
tion
of A
rtific
ial in
telli
ge
nce in c
red
it r
isk
36
James is the first credit risk AI and he helps risk officers
4. He is proactive,
autonomous and
easy going
2. He is compliant with
Basel Committee
directives
1. He automates time
consuming processes
3. He provides you with
intelligible state-of-the-art
algorithms
(plays well with other softwares
and platforms)
James is your credit risk AI.
37
James brings the best of data science to every risk team
1. State of the art
modeling techniques
2. Basel-compliant
validation reports
3. Seamless model
deployment
4. Proactive model
monitoring
38
James has a recognized experience in the credit risk
space
Runs & Operates successfully at:
Tested with positive results at:
40
Gini Index (discriminating
capacity)
Lender 1 Lender 2 Lender 3
BenchmarkUsing James
50
60
70
630 mln AUM
Default Rate: 2%
842 bln AUM
DR: 11.13%
4258.3 mln AUM
DR: 1.77%
39
What results can you obtain?
1. State of the art
classification algorithms
2. Best optimization and
validation techniques
3. Easy model management
4. Automate model validation
5. Automated performance
reporting
Reduce default rate
Up to 30%
Increase acceptance rate
up to 10%
James provides
Team of experts in
Artificial intelligence
provides
Results obtained
Best machine learning credit
risk support
On-demand data cleansing
On going analysis of
monitoring alerts
On-demand reporting
40
Goal:
The results speak for themselves
To decrease the default rate without impacting the
acceptance rate.
Default rate
incumbent model
2,69%
Default rate
James model
2,44%
Reduction in
default rate
9,3%
1.5M (aprox.)
Potential upside
per year
41
james.financeNew York +1 (347)
305-9110
London +44 20 3287
4132
Lisbon +351 912 250
990
João Menano
Henry White@pixoneye
CONTENTS
1
3
4
2
Intro Deep Learning and evolution of Computer Vision
Current user understanding on mobile devices
Pixoneye’s Computer Vision capabilities
Pixoneye’s Product solutions and Use cases
1950 1960 1970 1980 1990 2000 2010 2020
AI
MACHINE
LEARNING DEEP
LEARNING
LANDSCAPE
1950’s a broad concept
established - can
machines one day think
like humans?
one path of AI, rather than
trying to hard code or
develop a theoretical
model teach by exampleis a branch of machine learning based
on a set of algorithms that attempt to
model high level abstractions in data
deep learning is the primary driver and
the most important approach to AI and
will drive enterprise
Computer Vision
The aim is to imitate the
functionality of human eye
and brain components
responsible for your sense
of sight
This can provide essential
data to process, analyse
and utilise in fields ranging
from transport to facial
recognition to marketing
COMPUTER VISION
[2008] Image Detection
[2010]Image
Recognition
What’s in the image: Friends,skiing, on the slopes, snow,outdoors
Tags: Snow; Winter; Skiing;Couple; Friends; Mountains;Holliday.
[2013] Image Understanding
Demographics:Location: London User: Male 25-30Marital Status: EngagedFamily: children 0Relationship: Female 25-30Work environment: business/Casual
Lifestyle:Past-time: Cycling – 30% | Hiking –30% |Rugby – 30%| BBQ – 10%Fashion: CasualIncome level: 5/6 (0-8) Pets: Dog – 1 Breed: Great daneInterests: Cycling, Beach, Rugby, Friends, Social events.Relevant details: Traveler, young couple, outdoor lifestyle
[2015-2016] CONTEXTUAL UNDERSTANDING
NO ONE KNOWS THEIR
MOBILE CUSTOMERS…
THE CURRENT MOBILE MARKETING PROBLEM
…AND THEREFORE, NO ONE CAN
TARGET OR RECOMMEND TO
THEM EFFECTIVELY
81% Of companies say they
have a holistic view of
their mobile customers
UNDERSTANDING
CUSTOMERS
v22%Of consumers on mobile
say the average retailer
understands them as an
individual
PERSON
1
?
PERSON
2
?
Male Married twice Grown Children Young Grand Children
English Countryside Holiday in Alps Extensive Travellers
Born 1948 Dog Lovers Sports Cars Fanatics
Wealthy
What You Know
54
Why personal galleries?It is effectively a data set along a timeline
OFFLINE
understanding
documents real life
People take >250
photos each month
The average camera
1,500 photos and 24
videos
<2% of images are
shared on social
media
0 50 100 150 200 250 300 350 4000
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6
Am
ou
nt
of
imag
es
Categories
31
ContextualUnderstanding
of personalgalleries
HOW IT WORKS…
24
Engaged
Skier
Cyclist
Dog owner
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2
3
4
5
6
Am
ou
nt
of
imag
es
Categories
Engaged Skier Cyclist Dog Owner
FEATURE VECTOR
3 Main products
Analytics:
150 Characteristics
1
Recommendation
Engine
2
Case Study - Advertising Optimization Capability
“Before meeting Pixoneye we typically generated
sales from 4 in 1,000 digital ad impressions by
targeting pet owners. Using the Pixoneye
technology, we generated 40 in 1,000 digital
impressions by targeting cat and dog owners
specifically.”
0 50 100 150 200 250 300 350 4000
1
2
3
4
5
6
Am
ou
nt
of
imag
es
Categories
Triggers:
Life changing
events
3
Case Study 1
User 1 User 2 User 3UserNEW
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1
2
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Am
ou
nt o
f im
ag
es
Categories
0 50 100 150 200 250 300 350 4000
1
2
3
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5
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ou
nt o
f im
ag
es
Categories0 50 100 150 200 250 300 350 400
0
1
2
3
4
5
6
Am
ou
nt o
f im
ag
es
Categories
0 50 100 150 200 250 300 350 4000
1
2
3
4
5
6
Am
ou
nt o
f im
ag
es
Categories
ACCURATE DECISION MAKING USING PIXONEYE’S AI
92%
Henry White@pixoneye
Thank You