Data Spaces Symposium
Data, AI and beyond inlanguage, cultural heritageand media sectors. Impact
on training and skills

Daniel Alonso, Georg Rehm,
Andrejs Vasiļjevs, Oscar Rey,
Sylvain Le Bon, Valentine
Charles, Sabine ZanderObjectives of the session▪ Identify potential of data and the impact of data sharing / data
spaces on each specific sector▪ Discuss about how to leverage data and data sharing to power AI/ Generative AI, in those sectors▪ Explore potential synergies and inter-sector connections,interoperability and potential joint use cases. Possible next steps

Agenda
Introduction
Daniel Alonso (BDVA)Introduction and setting-up the scene

Initial statements and panel discussion
Georg Rehm

View from language data space

Andrejs Vasiļjevs

Industry perspective, language and AI

Oscar Rey

View from media data space

Sylvain Le Bon

Industry perspective in the media sector

Valentine Charles

View from cultural heritage sector

Sabine Zander

Education benefiting from synergies. Relevance of up- and reskilling within these sectors(AI-assisted skill management)European Language Data Space

Prof. Dr. Georg Rehm (DFKI GmbH, Germany)georg.rehm@dfki.de

13-03-2024
Data Spaces Symposium, Session: Data, AI and beyond in language, cultural heritage and media
https://language-data-space.ec.europa.eu

Context: Large Language Models (LLMs)• Large language models are the most disruptive breakthrough in AI in recent history(BERT, GPT-3, ChatGPT, GPT-4 etc.)• LLMs are trained on vast amounts of training data (language data)• LLMs use dozens, some even hundreds of terabytes (trillions of tokens) of language and also
image, video, audio etc. training data
• Europe’s languages are vastly under-resourced, except English, i.e., for many languages we have asubstantial lack of data, which negatively impacts the performance of LLMs for those languages• At the same time, the global LT/NLP Market is exploding: 439.85B$ by 2030
• A concerted effort for the collection of enormous amounts of language data for all European
languages is very much needed – we need new data, fresh data, industry data to compete.

Common European Language Data Space

7

Common European Language Data Space• Type of action: procurement (CNECT/LUX/2022/OP/0026)• Budget: 6M€ (+ 2M€ if renewed)• Runtime: 36 months (+ 12 months if renewed)• Objective: Develop and deploy a European platform and marketplace for the collection, creation,
sharing and re-use of multilingual and multimodal language data

• Salient features: governance framework, technical architecture and infrastructure, openness,promotion• Stakeholders: industry, research, public administration, cultural associations, NGOs and citizens• LDS is one of the 14 official EU data space projects – focus on industry

Common European Language Data Space

8

Consortium and Subcontractors
Lead Partner and Coordinator
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH

DFKI

DE

R.C. “Athena”, Institute for Language and Speech Processing

ILSP

GR

Evaluations and Language Resources Distribution Agency

ELDA

FR

TILDE

TILDE

LV

3pc GmbH Neue Kommunikation

3pc

DE

Capgemini Deutschland GmbH

CapG

DE

CLARIN ERIC

CLARINNLBig Data Value Association (Data, AI and Robotics) AISBL

BDVA

BE

Partners and Operation Leads

Main Subcontractors

Plus legal experts (Delcade, France) and approx. 30 organisations
for the logistics of multiple country workshops
Common European Language Data Space

9

Previous Projects and Initiatives• The four core partners – DFKI, ILSP, ELDA, TILDE –have been involved in many projects, including:• META-NET (FP7, 2010-2013)

The technical development work in LDS will be
informed by ELG, ELRC-SHARE, META-SHARE.

• META-SHARE• ELRC (CEF, 2014-2023)• ELRC-SHARE• ELG (H2020, 2019-2022)• ELG Cloud Platform• ELE (PP/PA, 2021-2023)

Common European Language Data Space

10

Common European Language Data Space11

Classes of DataClass of Data

Typical Size

ProvidersIntegration into LDS

Relevance for LLMs

Small (MB, GB)

Primarily NLP/LT research:
ELG, META-SHARE, CLARIN,
ELRA, ELDA etc.

Can be easily integrated by connecting the repositories to LDS

Usually very high quality data andthus relevant for LLMs but not asbase data

Web Crawls

Very big (TB, PB)

Common Crawl (andOSCAR-processed CC
dumps), Internet Archive
dumps etc.

Challenge due to their size (hardto transfer, hard to preprocess,hard to store; must be close to
the HPC)

Indispensable due to their sizeand coverage – but: high level ofnoise, massive need for preprocessing

New, fresh datafrom industry andother organisations

Arbitrary size,
ideally as large
as possible

Publishing houses, media
companies, libraries, callcentres, broadcasters etc.;
also: Media Data SpaceCan be easily integrated by connecting these organisations to LDS

Especially high quality data or
domain-specific data or data
covering specific languages andthus highly relevant for LLMs

Regular Corporaand Language
Resources

Common European Language Data Space12

Next Steps• LDS is in full swing: technical development, promotion, dissemination, governance etc.
• Collaboration with DSSC and ALT-EDIC• Collaboration with European projects, e.g., HPLT, OpenGPT-X, OpenWebSearch• Collaboration with data spaces, especially Media and Cultural Heritage• Collaboration with EuroHPC• Very important next step:• Adoption of LDS by industry and other organisations

Europeanstreaming
platform fornational news
accessible in all
EU languages• Identify and make available new and fresh language data, especiallyfrom industry and covering all European languages and modalities• Most important synergy between LDS and other data spaces: newand fresh language data, especially media and broadcast data

STUDY
Panel for the Future of Science and Technology
EPRS | European Parliamentary Research Service
Scientific Foresight Unit (STOA)
PE 740.249 – June 2023

Common European Language Data SpaceENNational or regional streaming platform

National or regional streaming platform

National or regional streaming platform

National or regional streaming platform

Broadcaster A

Broadcaster B

Broadcaster C

Broadcaster D

Regional users
consume media
in A’s language

Regional users
consume media
in C’s language

Regional users
consume media
in B’s language

Regional users
consume media
in D’s language

Media programmes (MDS) andtranscriptions and their translations(LDS) need to be interlinked. The
transcriptions and translations canalso be stored in the MDS, if
deemed appropriate.

European Media Data SpaceIf all European broadcasters deposit their programmes in the European Media
Data Space, the European streaming platform can retrieve them from there.

European streaming platform

Media Catalogue

Search and Retrieval

User Modelling

European
Language Grid

ASR, Transcription

Recommender System

Video Analysis

Annotations

Key technical idea not shown in this figure: media data is not stored
in the platform but rather played out from the original broadcasters‘ repositories.
Additionally, the European media platform clients add the subtitles, captions or
audio tracks it generated while processing the corresponding broadcast once
(afterwards the media file can be discarded in the European streaming platform).

Machine Translation

Web, mobile app, smart tv app(localised into all European languages)

The European streaming platform
initially makes use of ELG ASR, MT
and other remote services (later tobe replaced by customised models)Media
Processing
European
Language Data
SpaceTranscripts and translations ofprogrammes are stored in theEuropean Language Data Spaceand will be used continuously to
improve the ASR, MT etc. services
that need to be developed andcustomised for the European
streaming platform

European users
consume media intheir own languages

Common European Language Data Space

14

Common European Language Data Space

Thank
you!

Prof. Dr. Georg Rehm (DFKI GmbH, Germany)georg.rehm@dfki.de
A Common European Language Data
Space – funded under contract LC01936389 with the European Union.

13-03-2024
Data Spaces Symposium, Session: Data, AI and beyond in language, cultural heritage and media
https://language-data-space.ec.europa.eu

Data Spaces Symposium

Towards sustainable language dataand services infrastructure

Andrejs Vasiļjevs

Co-Founder and Board Member, TILDE
Member of the Board of Directors, BDVA

“

Generative AI is powered by large language models (LLMs).
Language means culture, and we’ve got a very diverse set ofcultures and languages in Europe. Therefore, one cannot only
speak about global generative AI applications. They should also
be localized into the local context and culture. And if anything,
Europe is quite a unique place in which to do so.

Source: KcKinsey&Company, “Leveraging generative AI in Europe: The opportunities andchallenges”

FP7, 2010-2013

A Network of Excellence consisting of 60 research centres
from 34 countries dedicated to building the technologicalfoundations of a multilingual European information societyCEF, 2014-2023
Identifying,collecting, andsharing
language datafrom public
administrations

H2020, 2019-2022

The platform for European language technologies, resources,
and services, fostering collaboration and innovation acrossthe Europe's language technology sector.

CNECT, 2023-2026 (2027)

Federated language data sharing infrastructure aimed at supporting the digital language equality in the European Union
by facilitating access to, sharing, and use of language data and services.

ALT-EDIC

European Digital
Infrastructure consortium

The European Alliance for Language Technologies▪ ALT-EDIC will pool Member States’ funding and other resources in a flexible andefficient way, to invest in transformative work on language data and language
technologies.
▪ European Digital Infrastructure Consortium (EDIC) is a new mechanism for multi-country projects ofcritical importance, adopted by European Commission as part of AI Innovation Package on Jan 24,
2024▪ The ALT-EDIC’s mission is to develop a common European infrastructure in Language
Technologies, particularly Large Language and other foundational AI models. It seeks
to improve European competitiveness, increase European data and other relevant
resources and uphold Europe’s linguistic diversity and cultural richness:▪ Language Data
▪ Existing language tools and language models▪ New Language tools and Large Language Models▪ Evaluation, certification, and normalization▪ Language Ecosysem

Open platform that provides services, tools, and resources to advance the useof language technologies tailored to the nation's linguistic needs.

Towards sustainable language
data and services infrastructure

National and local initiatives

ALT-EDIC

Data Spaces Symposium
TEMS:Use cases description.

Sylvain Le Bon
Startin’ BloxPILOT 1: B2B exchange platform for fact-checking
and news content
Pilot description: Sharing news content produced by certified
fact-checkers, news media and other media organizations
including other B2B stakeholders.
Objective: Increase visibility/exposure and/or knowledgetransfer, and/or monetization through republication or AI use.
AI: Enhance news verification tools by utilizing TEMS as areliable source for fact-checking and news content in AI
generative tools, such as ChatGPT.

Media and Techs involved: AFP, APA, DPA, EDMO (European
Digital Media observatory), VRT, NISV.
333
3PILOT 2: Marketplace for content and services
Pilot description: Distributed marketplace integrated in TEMS
Data Space for content syndication and media monitoring
services.
Objective: Gain visibility and attracting larger media outlets forsyndication, leading to increased revenue opportunities.
AI : Enhance the efficiency of searching and acquiring datasets
within EMDS, providing users with more relevant andpersonalized content recommendations.

Media and Techs involved: APA and (indirectly) Austrian
publishers
3
34
4

PILOT 3: Next level DRIVE – The Digital Revenue Initiative

Pilot description: TEMS as a centralized environment for thecollection, sharing, and analysis of data related to media trends
and personalization services.
Objective: Enhance media companies' capabilities inunderstanding media trends and providing personalized digitalnews products
AI : AI-driven personalization service for enhancing the user
experience by delivering more tailored and relevant news
content to individual end-users.
Media and Techs involved: Deutsche Presse-Agentur andSchickler, other newspaper and media publishers
3
35
5

PILOT 5: Collaboration in Production Chain
Pilot description: TEMS as an decentralized data exchange
framework as a service for the audiovisual production
ecosystem
Objective: Enhancing interoperability and tracking of metadata
associated with audiovisual works.
AI : Ensure the preparation of data sets of good quality that
could be exploited for algorithms’ training, thanks to the betterdata tracking. AI based tools will contribute to enrich descriptive
metad0ata linked to an AV work, enabling a better”findability”.
Media and Techs involved: Cap Digital, CST, France Télévisions,France Télévisions Studio, INA, PROCIREP, SPTD // ISAN-IA,
Lum::Invent, Startin’Blox, Perfect Memory.
3
36
6

PILOT 6: B2B exchange platform for content syndication

Pilot description: Sharing media content(text, audio, and video)
produced by community and independent media organizations
including other B2B stakeholders.
Objective: Increasing visibility/exposure and/or knowledgetransfer, and/or monetization through republication.
AI :Facilitate the cross-language search functionality, allowing
personalized recommendations, enabling the automatic
generation of transcripts and translations, etc.
Media and Techs involved: Display Europe, Krytyka Polityczna,
OktoTV, Cultural Broadcasting Archive (cba.media)
3
37
7

PILOT 7: Syndication platform for written works

Pilot description: Creating interoperable connectors,
standardizing data exchange, and addressing challenges in thedigital publishing sector (Writings)
Objective: Enhance the competitiveness of media and cultural
publishers in the digital sector.
AI: Enhancing intellectual property value&promoting
transparency and ethics
Media and Techs involved: Publishers and Press Agencies in the
media and cultural sectors
3
38
8

PILOT 8: Market and co-creation place for 3D
environments for virtual production
Pilot description: European market and co-creation platform
within TEMS, facilitating the discovery, collaboration, andstorage of diverse 3D data types.
Objective: Support the development and re-use of innovative
media formats in virtual production
AI : AI to enhance the discovery and utilization of 3D data,
optimizing the creation and co-creation processes for innovative
media formats in virtual production
Media and Techs involved: VRT, Arctur, Sound Holding.
3
39
9

Data Spaces Symposium
Europeana and the Data Space
for Cultural Heritage

Valentine Charles

https://www.europeana.eu/en/dataspace-culturalheritage

DATA SPACE IN NUMBERS
Millions of items and thousands of collaborations

+

42

FNL

EE
CA
LV

DRI

LT

JHN
EF
ES

Accredited aggregatorsin the Europeana
Aggregators’ Forum

EUS
DC4EU

APE

TIB

FBC

DCJM
KUL
MIM

MUS

DDB EFGCDL

MOT BNF

MS

SK

ElocalKP

MuM
OU= National Aggregator

MSS FHINP

NUKKR= Domain Aggregator

NBS
PS

BNP

CAT
MCD

PHC

= National Aggregator &National Library

FH
ICCU

EIE

CY
Europa [Material cartográfico] : Nach den vorzüglichsten Hülfsnitteln, Götze, Johann August Ferdinand, 1773-1819 Biblioteca Digital de Madrid Spain, Public domain

EUROPEANA.EU AND APIs SUITEAI TO SUPPORT THE USE AND ACCESS TO CULTURALHERITAGE● AI to support the enhancementof data quality● AI to support the engagement ofusers on Europeana.eu● Build capacity around AI(AI4Culture platform to belaunched this year)

Data Spaces Symposium
German Gaia-X education
dataspace project MERLOT

Dr. Sabine Zander, imc

German Gaia-X education dataspace project MERLOTMarkEtplace foR LifelOng
educaTional dataspaces andsmart service provisioning(MERLOT)▪ Funded by: German Federal Ministry for
Economic Affairs and Climate Action▪ Coordinator: imc AG▪ Consortium partners: 11

TU Kaiserslautern▪ Runtime: January 2022 – December 2024

© imc

© MERLOT - MarkEtplace foR LifelOng educaTional dataspaces and smart service provisioning
Gefördert durch das Bundesministerium für Wirtschaft und Klimaschutz
aufgrund eines Beschlusses des Deutschen Bundestages

47

The need for upskilling and reskilling

The 2023 World Economic Forum's Future of Jobs Report predicts that
61 percent of employees will have to be retrained by 2027.

Workers want to reskill. 77% are ready to learn new skills or completely
retrain (PwC’s Global Workforce Hopes and Fears Survey 2023).
Gartner research shows that HR technology leaders have identified
skills management as one of the most important HR technologies(2023 survey).
© MERLOT - MarkEtplace foR LifelOng educaTional dataspaces and smart service provisioning
Gefördert durch das Bundesministerium für Wirtschaft und Klimaschutz
aufgrund eines Beschlusses des Deutschen Bundestages

48

Lifelong learning needs data (AI)© MERLOT - MarkEtplace foR LifelOng educaTional dataspaces and smart service provisioning
Gefördert durch das Bundesministerium für Wirtschaft und Klimaschutz
aufgrund eines Beschlusses des Deutschen Bundestages

49

From data silos to data spaces
Personal education related
data is highly vulnerable,
therefore locked in data silosof institutions

AI education revolution throughpersonal adaptive tools andassistance depends on accessto data in secure data spaces

Learning
progress

Skills/competencies-Careergoals© MERLOT - MarkEtplace foR LifelOng educaTional dataspaces and smart service provisioning
Gefördert durch das Bundesministerium für Wirtschaft und Klimaschutz
aufgrund eines Beschlusses des Deutschen Bundestages

50

MERLOT Project Goals
1.
2.
3.
4.

5.

© imc

From data protection to data sovereignty for
individuals
Guarantee data sovereign data sharing fororganizations
Interoperability of data and services
Sustainable dynamic development of theeducation data economy and enabling datadriven business models
Development of innovative education
services© MERLOT - MarkEtplace foR LifelOng educaTional dataspaces and smart service provisioning
Gefördert durch das Bundesministerium für Wirtschaft und Klimaschutz
aufgrund eines Beschlusses des Deutschen Bundestages

51

Open call for the deployment of thecommon European data space for skills
DIGITAL-2024-CLOUD-DATA-06-SKILLS-Data Space for Skills (deployment)
The awarded proposal will integrate, test and deploy the data space for skills, allowing participants to makedata available and accessible, as well as sharing it, in a controlled, simple and secure way.
Opening date: 29 February 2024

Deadline date: 29 May 2024, 17:00 (CEST)➢ Simple Grant – 50% funding rate

➢ Budget: 3 Million EUR➢ Submit questions in the F&T portal: https://european-union.europa.eu/contact-eu/write-us_en

Open call for the deployment of thecommon European data space for skills• Expected outcomes: deployment of a Data Space for Skills, including
the technical infrastructure, a governance mechanism, continuous
maintenance, usage monitoring, helpdesk, sustainability beyond theend of the project, fostering engagement, and three use cases.
• Targeted stakeholders: education institutions, HR organisations,employment agencies, public employment services, guidance
providers, IT developers, trade and industry associations, alliances andsocial partners, other private and public actors

Thank you!