Brussels / 3 & 4 February 2024

schedule

AI and Machine Learning devroom

Room: UB2.252A (Lameere)
Calendar: iCal, xCal


09 10 11 12 13 14 15 16 17 18
Sunday An Introduction to Open Source AI
 From OpenLLM-France to OpenLLM-Europe: Paving the way to sovereign and open source AI
 Building Open Source Language Models
 LinTO Studio as Your Ultimate Open Source AI-driven Media Management Solution
 LangChain From 0 To 1: Unveiling the Power of LLM Programming
 ML Guided Optimizations in LLVM
 Practical Introduction to Safe Reinforcement Learning
 Open Discussion on AI and Machine Learning
 Introducing 'Refiners' – A Micro-Framework for Seamless Integration of Adapters in Neural Networks
 Dynamic Explainability through Dynamic Causal Modeling
 AI for Developers: Treating Open Source AI as a Function
 Using Haystack to Build Custom Functionality for LLM Applications
 Using code generated by AI: issues, misconceptions and solutions
 Reducing the risks of open source AI models and optimizing upsides
 Fortify AI against regulation, litigation and lobotomies
 A Principled Component Analysis of Open Source Artificial Intelligence
 Codes Bound by Ethics: The Rising Tide of Non-Free Software Licenses in AI ecosystems
 Moving a step closer to defining Open Source AI
 Open Source AI at TechWorks, the UK trade body for Electronic Systems Engineering

Read the Call for Papers at https://lists.fosdem.org/pipermail/fosdem/2023q4/003514.html.

Event Speakers Start End

Sunday
  An Introduction to Open Source AI
 William Jones 09:00 09:30
  From OpenLLM-France to OpenLLM-Europe: Paving the way to sovereign and open source AI
 Michel-Marie Maudet 09:30 09:45
  Building Open Source Language Models
 Julie Hunter 09:45 10:00
  LinTO Studio as Your Ultimate Open Source AI-driven Media Management Solution
 Damien Laine 10:00 10:15
  LangChain From 0 To 1: Unveiling the Power of LLM Programming
 Stefano Fancello 10:15 10:45
  ML Guided Optimizations in LLVM
 Mircea Trofin 10:45 11:15
  Practical Introduction to Safe Reinforcement Learning
 Kryspin Varys 11:15 11:30
  Open Discussion on AI and Machine Learning
 11:30 12:00
  Introducing 'Refiners' – A Micro-Framework for Seamless Integration of Adapters in Neural Networks
 Benjamin Trom 12:00 12:30
  Dynamic Explainability through Dynamic Causal Modeling
 William Jones 12:30 12:45
  AI for Developers: Treating Open Source AI as a Function
 Martin Hickey 12:45 13:15
  Using Haystack to Build Custom Functionality for LLM Applications
 Tuana Çelik 13:15 13:45
  Using code generated by AI: issues, misconceptions and solutions
 Andrew Katz 13:45 14:15
  Reducing the risks of open source AI models and optimizing upsides
 Stefania Delprete, Jonathan Claybrough, Felicity Reddel 14:15 15:00
  Fortify AI against regulation, litigation and lobotomies
 Edward C. Zimmermann 15:00 15:30
  A Principled Component Analysis of Open Source Artificial Intelligence
 julia ferraioli 15:30 16:00
  Codes Bound by Ethics: The Rising Tide of Non-Free Software Licenses in AI ecosystems
 Niharika Singhal 16:00 16:15
  Moving a step closer to defining Open Source AI
 Stefano Maffulli 16:15 16:45
  Open Source AI at TechWorks, the UK trade body for Electronic Systems Engineering
 Jeremy Bennett 16:45 17:00