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

Video

April 25, 2017 25

Link

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

jam@crowdprocess.com

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

1

2

3

4

5

6

Am

ou

nt

of

imag

es

Categories

31

ContextualUnderstanding

of personalgalleries

HOW IT WORKS…

24

Engaged

Skier

Cyclist

Dog owner

0 50 100 150 200 250 300 350 4000

1

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

0 50 100 150 200 250 300 350 4000

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

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