유럽의 AI와 빅 데이터 관련 동향 및

KTAG 프로젝트 통한 국제 협력 시작 경험 공유

김이화

About Myself

BSc, Comp Sci, SNU, Korea

MSc, Cog Sci, SNU, Korea

Ph.D Computational Neuroscience, Univ of Bern, Switzerland

Worked in academia and industry

Currently working as Senior AI Scientist at IQVIA

IQVIA, one of the largest CRO (Clinical Research Organizations)

Created as a merge between Quintiles and IMS Health

My role is to develop ML / AI based solutions that could help clinical trial processes more efficient

About the talk today

Method

Published reports

JRC Reports from EU

Deloitte, EU AI in healthcare

Interviews with Data Scientists from different organizations

IQVIA (Spain, UK)

SBB (Switzerland) , Prognosis (Switzerland)

Overview

Global and European comparative AI Landscape

EU AI In healthcare

Recent change in European law on AI and big data

Some use cases of AI In europe

Spanish companies using AI and big data

Swiss companies using AI and big data

KTAG project experience share

Global and European comparative AI Landscape

Top 10 world geographic areas by number of AI players Absolute vs. Relative to GDP

Absolute Relative https://publications.jrc.ec.europa.eu/repository/handle/JRC120106

AI Players in the EU

https://publications.jrc.ec.europa.eu/repository/bitstream/JRC113826/ai-flagship-report-online.pdf

Normalized thematic hotspot indicator per thematic area

EU AI in healthcare

Europe’s top challenges in implementing digital technologies

https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/public-sector/deloitte-nl-shaping-the-future-of-european-healthcare.pdf

European health’s increasing and decreasing demand and capacity

UK AI in healthcare landscape

● Benevolent – Using AI for drug discovery● The NHS AI Lab -- Accelerating the safe adoption of Artificial Intelligence

in health and care● Faculty – providing AI consulting and training up new talent with

postdoctoral fellowships to help PhDs transfer to industry

Then in terms of Big Data in the precision healthcare space there are:

● Eagle Genomics, LifeBit, Chronomics, Congenica, Nucleome Therapeutics and Genomics PLC

And in terms of Big Data for energy there is Bboxx.

Communication with Greg, Lever (IQVIA)

Recent change in European law on AI and Big data

A european approach to Artificial Intelligence

published in April 2021

Centres on excellence and trust, aiming to boost research and industrial capacity and ensure fundamental rights.

Fostering excellence in AI will strengthen Europe’s potential to compete globally.

The EU will achieve this by:

1. enabling the development and uptake of AI in the EU2. making the EU the place where AI thrives from the lab to the market3. ensuring that AI works for people and is a force for good in society4. building strategic leadership in high-impact sectors

https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligencehttps://digital-strategy.ec.europa.eu/en/library/coordinated-plan-artificial-intelligence-2021-review

Software 2.0

Software 1.0

Consists of explicit instructions to the computer written by a programmer.

Software 2.0

written in much more abstract, human unfriendly language, such as the weights of a neural network.

specify some goal on the behavior of a desirable program (e.g., “satisfy a dataset of input output pairs of examples”, or “win a game of Go”)

“software development” takes the form of curating, growing, massaging and cleaning labeled datasets

https://karpathy.medium.com/software-2-0-a64152b37c35https://www.youtube.com/watch?v=y57wwucbXR8

European strategy for data

Data driven applications will benefit citizens and businesses in many ways. They can:

● improve health care● create safer and cleaner transport systems● generate new products and services● reduce the costs of public services● improve sustainability and energy efficiency

Creation of european data space:

One of the commission’s priorities in 2019-2025 E.g. Common european health data space: Promote better exchange and access to different types of health data (electronic health records, genomic data, data from patient registries, etc.)

support healthcare delivery (so called primary use of data) And health research and health policy making purposes (so called secondary use of data).

https://digital-strategy.ec.europa.eu/en/policies/strategy-data https://ec.europa.eu/health/ehealth/dataspace_en

Artificial Intelligent Act

On April 21, 2021

the European Commission published a draft law to regulate artificial intelligence (AI) in the European Union.

The Artificial Intelligence Act (AIA) is notable for its expansive definition of AI systems, and the imposition of extensive documentation, training, and monitoring requirements on AI tools that fall under its purview.

Any company with EU market exposure that develops or wants to adopt machine-learning-based software will be affected by the AIA.

https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206https://datainnovation.org/2021/05/the-artificial-intelligence-act-a-quick-explainer/

Artificial intelligence act : Risk based approach

Dark Pattern AI: Dark patterns Dark Patterns are tricks used in websites and apps that make you do things that you didn't mean to, like buying or signing up for something.

https://www.darkpatterns.org/https://techcrunch.com/2021/01/14/cant-figure-out-how-to-end-your-amazon-prime-sub-these-complaints-could-help/https://uxdesign.cc/dark-patterns-powered-by-machine-learning-an-intelligent-combination-f2804ed028ce

Dark pattern by UX designer vs. AI

Facial recognition

Although faces are peculiar to individuals, they are also public, so technology does not, at first sight, intrude on something that is private.

One big difference between faces and other biometric data, such as fingerprints, is that they work at a distance.

Privacy issue:

Facebook’s bank of facial images cannot be scraped by others, but the Silicon Valley giant could obtain pictures of visitors to a car showroom, say, and later use facial recognition to serve them ads for cars.

Even if private firms are unable to join the dots between images and identity, the state often can.

https://www.michalkosinski.com/

Manipulation:Cambridge Analytica data scandal

Using FB’s like information, could collect traits of people

People were segmented to different groups

Targeted Ads were given to change voting behavior of people

https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.iafrikan.com%2F2018%2F03%2F22%2Fpsychographics-the-behavioural-analysis-that-helped-cambridge-analytica-know-voters-minds%2F&psig=AOvVaw1E9grzGyWHToPotM0jsPXt&ust=1625688847848000&source=images&cd=vfe&ved=0CAsQjhxqFwoTCLD_oIahz_ECFQAAAAAdAAAAABAJ

https://www.pnas.org/content/110/15/5802

Employment: Spanish law on algorithm transparency

Glovo

Recognition of employment status of riders

Algorithm transparency : Need to inform workers’ legal representatives about the mathematical or algorithmic formula determining their working conditions

Other digital platforms will have to make available to trade unions as algorithm, or any artificial intelligence of sorts, which

may have an impact on such conditions - including individuals’ access to, and maintenance of, employment and their profiling.

https://socialeurope.eu/spains-platform-workers-win-algorithm-transparencyhttps://english.elpais.com/elpais/2018/11/23/inenglish/1542976796_159932.htmlhttps://english.elpais.com/economy_and_business/2021-05-12/spain-approves-landmark-law-recognizing-food-delivery-riders-as-employees.html

Education

AI is guiding admissions, grading homework, and even teaching in college campuses.

- Baylor university, Boston university, and others use personality-assessment software from Kira Talent to score applicants on traits. Such as openness, motivation, and ‘neuroticism’. Human administrators make final call on who gets accepted.

- Australia’s Deakin University developed Genie, a chatbot that monitors student behaviours and locations. If it determines that a would-be scholar is dawdling in the dining hall, for instance it will send a message to get back on-task.

Deeplearning.ai The Batch newsletter, June edition

Some use cases of AI in europe

Spanish companies using big data and AI

Carto : SaaS (Software as a Service) cloud computing platform that provides GIS, web mapping, and spatial data science tools

Glovo : Glovo (a misspelling of Globo, meaning balloon in Spanish) is a Spanish quick-commerce start-up founded in Barcelona in 2015

Savana : Founded in 2014, Savana® is an international medical company that has developed a scientific methodology that applies Artificial Intelligence (AI) to unlock all the clinical value embedded within the free-text of Electronic Health Records (EHRs)

Cabify: Cabify is a Spanish ridesharing company which provides vehicles for hire via its smartphone mobile app.

Bbva: Banco Bilbao Vizcaya Argentaria, S.A., better known by its initialism BBVA, is a Spanish multinational financial services company based in Madrid and Bilbao, Spain.

Conversation with Raimundo, Gordejuela (Senior AI Scientist at IQVIA)

BBVA

Some examples of data based solutions are BBVA Valora,

a tool that allows users to find out the fair rental

or purchase price estimates for any given property or

housing unit, which accumulates 125,000 monthly searches since September

2016.

https://www.bbva.com/en/bbva-doubles-down-on-its-data-push-with-the-creation-of-the-ai-factory/

BBVA valora

SBB

Swiss railway system (The world’s densest network)

Swiss public transportation ecosystem

SBB train ticket could be purchased from local vendors

33000 employees

Public company (50% owned publicly )

Erik Nygren, Product Manager Analytics Service AT SBB Roland Schaeffer, Energy management expert ,Data Analytics SBB AB

SBB (https://www.sbb.ch/en/)

Specialized in public transportation search

SBB

Consumer facing and Digital asset management

SBB: Digital Asset Management

E.g. Transmission line, power plant, etc.

Previously: Scheduled maintenance e.g. every 2 year check up

Approach: Understand / Detect problem

Digital twin e.g. Digital power plant

Fields force support

Transmission line problem - Support smart devices

Send drone to see where problem is / Image segmentation and detection of track automatically to find problems in advance

SBB: Consumer facing

E.g. (1) Augmented reality application at Zurich station

Information overlaid on mobile phone

: Collaboration with Google

(2) Train occupation prediction model

- Give users suggestions

(3) Information about journey

: Inform about changes in real time

(4) Repair and clean App

SBB : Data

- SBB Open data https://data.sbb.ch/pages/home/

: core data (e.g. Ticket sales data, traffic data, schedule data, etc.)

Open Data is data that a company makes available to third parties for secondary use free of charge and in machine-readable form. … All genuine innovations based on high-quality data from SBB ultimately increase the attractiveness of public transport in Switzerland.

- Internal Data related Challenges

Not all data are shared between teams.

If the information from sales platform (search data ) was available , search request might already give clue to predicting the train occupancy on that day : Search + Sales data

->This could be used for example to suggest alternative connection (e.g .1 hour later)

SBB - How business problems and AI solutions meet

Ways that business problems get solved:

(1) Business units escalate issues

-Negative customer review -> Business owners

(2) Innovation department

-Come up with ideas

What does it mean to solve business problem (from tech perspective)

: Involvement of business

: If ideas come, then need to connect with business models

DS needs to create value for customers (Gartner conference)

SBB : Use case (1)

Repair and Clean App

Problem: It takes time for the problems to get solved.

Method: Predictive maintenance. Users report problems using App.

Solution:

Version1: Questionnaire to report -> Problem: People give up On an application, by taking photo and answering questionnaire

Version2: The process made simple. Taking photo, anomaly detected automatically(image recognition using deep learning), Sent with location information: Currently under POC

https://news.sbb.ch/artikel/75013/defekte-und-verschmutzungen-einfach-per-sbb-mobile-app-melden

SBB : Use case (2)

Problem: Previously, on average 20 years in the job -> Nowadays, on average 4-5 years in the job -> Expertise is lost

Augmented reality

Solution: AI, AR to help gather that knowledge

Method: Using MS Hololens

Maintenance using augmented reality glasses

Gain: Maintenance using guidance from AR helped even untrained persons to work at similar level

SBB : Use case (3)

Problem: Optimize scheduling

Previous method - White board: People workshop, required draw and test

Suggested method: Digital app that lets you draw schedule, test, computation of travel time at the same time.

Initial response: Reluctant to change by users who are experts in scheduling

Result: In the time where 1 schedule could be made, now 5 schedules can be made

Important processes: Discuss together with the stakeholders, Showing it is not automating their job. It is helping them.

Lesson: Involve people

Prognosix

Swiss based start up working on predictive modeling

Transition from food industry warehouse optimization to healthcare recently

3 steps of applying technologies

(1) Business intelligence

(a) Visualization of data - bring people closer to data (2) Forecasting

(a) Not necessarily big data but from different sources (b) Intuitive solution that users can understand e.g. RF , Regression tree, clustering, etc.

(3) Include forecasting in the existing process (Process integration)(a) Additional data required to integrate forecasting into client’s process

Conversation with Peter Kauf, CEO of the company

KIAT work

회사 DTonic

시공간 빅데이터 처리

Webpage: https://www.dtonic.io/

설립: 2014 년 7월

직원수 :

46 명 (연말까지 80 명으로 늘릴 계획)

매출 :

작년 23억 (영업 이익 11억)

올해 120억 (영업 이익 30억) 예상

결과 (1) - (1) DTonic’s participation in the local seminar that enabled them to seek

business opportunities in Spain- (2) Beia International Consultant, MOU 체결 - (3) Ongoing conversation with Biomedical NMR Laboratory, CSIC-UAM,

Madrid, Spain (Prof. Sebastian Cerdan) - Initiated and coordinated biweekly ongoing conversations between the two parties

- aimed at joint R&D project proposal - Collaboration Topic: Analysis of human NMR data to help doctor in diagnostic decision

making

결과 (2)

Coordination of biweekly seminar with UAM

Lessons learned

Trust is crucial, and might need time to build.

Collaboration between business (domain expert) and technical expert (AI / ML) is key.

Processes come before technology.

Thank you for your attention