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Table of Contents

To be updated by event committee. Template Event /Meeting PageWiki

Event Details

Event Website:

Please submit any questions about this event to:

Event Committee Lead: Jamil Chawki, Orange

Date:  16 September

Time: 9 am to 4 pm

Location: Paris-Châtillons, France

Venue: Orange Gardens, 44 Avenue de la République, 92320 Châtillon, France

Zoom meeting session recording: N/A

Register or RSVP for event here:

Visa Letter Request Process:

Recommended Airport: 

Two area airports provide service to Châtillon, France.

Recommended Area Hotel(s)

  • Mercure Paris Malakoff Parc des Expositions
  • Ibis budget Châtillon Paris Ouest 
  • Ibis Paris Porte de Vanves Parc des Expositions

Transportation Options: 

  • Métro line 13 : Châtillon-Montrouge
  • T6 : Centre de Châtillon
  • 388 bus: République-Liberté, Liberté, Rue Froide

Guest Parking Available: To be confirmed

Security Access: ID card or passport

Guest Wifi: To be confirmed

Event Planning 

Meeting planning notes at bottom of page.

Key Event Tasks and Deadlines:

To Do

Due Date




Preparation of event Announcement 


xJamil & Jacqueline
Registration link


xJacqueline & Ibrahim

Draft Agenda & Blog post


Final Agenda


Final AV Needs & Food and/or Beverage Sélections 


xJamil & Ibrahim
Draft Agenda

Meeting Content

Please post agenda here.




Presentation (Link)


Registration & Agenda

View file
name0-Agenda LF AI Day Paris.pdf

9:10-9:30 Welcome messageNicolas Demassieux SVP, Orange Labs Research
9:30-9:50 Building Sustainable Open Source AI EcosystemIbrahim Haddad, Executive Director, LF AI Foundation

View file
name2-Ibrahim LF AI Day Paris.pdf

9:50-10:20 Orange AI activities

Steve Jarrett VP, Orange Data & AI

10:20-10:50 NTT's Challenges of AI for Innovative Network Operation

Masakatsu Fujiwara, Project Manager, NTT Network Technology Laboratories

View file
name3-NTT LF AI Day Paris.pdf

11:10-11:40 Acumos AI – Platform Overview, Releases and Use CasesAnwar Aftab, Director, Inventive Science, AT&T Labs

View file
name4-ATT LF AI Day Paris.pdf

11:40-12:00 We make AI accessibleJamil Chawki, Intrapreneur-CEO Orange AI Marketplace

View file
name5-Orange LF AI Day Paris.pdf

12:00-12:30 Trusted AI - Reproducible, Unbiased and Robust AI Pipelines using OpenSourceDr. Margriet Groenendijk, Data Scientist, IBM Center for Open Source Data and AI Technologies

View file
name6-IBM LF AI Day Trusted AI.pdf

12:30-13:00 Activities in LF AI and AcumosSahar Tahvili, PhD. Lead Data Scientist, Global AI Accelerator, Ericsson

View file
name7-Ericsson-LF AI Paris.pdf

14:00- 14:40 Orange AI Marketplace demonstrationPhilippe Dooze, Project Technical Lead Orange Labs Networks
14:40-15:10 Open Source AI strategy Philippe Carré, Program Manager, Nokia Bell-Labs

View file
name8-Nokia LF AI Paris.pdf

15:10-16:00 Startups Panel discussion, Barriers for AI development

François Tillerot, Intrapreneur-CMO, Orange AI Marketplace:

  • Rahul Chakkara, Co-Founder, Manas AI
  • Laurent Depersin, Research & Innovation Home Lab Director, Interdigital
  • Marion Carré, CEO Ask Mona
  • Sana Ben Jemaa, Project manager Radio & AI, Orange Labs Networks

View file
name9-0-LF_AI_panel on barriers.pdf

View file
name9-1-Ask Mona.pdf

View file
View file
View file
name9-4-Orange Barriers.pdf

16:20-16:30Open discussion and Closing session

Event Photos

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Meeting Notes

3 Challenges for LF AI

  • Control the « AI Economics » (ROI optimization & Risk management when introducing AI Model)
  • Speed-up the development of end to end AI tools and the interoperability with Enterprise Data Lake
  • Setting up guidelines for Trusted & Fairness «AI»

Several types of barriers were highlighted by the panelists

  • Why to introduce AI - Difficulty to describe an AI use case and translate business benefit, not clear ROI.
    -> Supporting customers in their strategy to introduce AI and build ROI can tackle this issue
  • Project Technical or HR (skills) issues - Multiple environment/tools constrain End to End Solution, lack of talent/skills in companies, difficulty to deploy and scale.
    -> Open source solutions, in particular LF AI and Acumos AI project will facilitate mutualized approaches and multi-skills collaboration to workaround
  • Readiness (technical or mindset) - Data supply chain not ready, lack of trust
    -> trustful AI approaches and better awareness in AI capabilities (also avoiding “overselling” AI) are potential solutions to tackle this.