AI, GenAI, LLMs, ML and Data Meetup

March 21, 2024
6:00 pm
Merantix AI Campus (Berlin)
Organiser
Matthias Richter
Machine Learning Engineer
Guest speakers
Jules Talloen
Machine Learning Engineer
Arne Vandendorpe
Machine Learning Engineer

Welcome to the monthly in-person AI meetup in Berlin, co-hosted by ML6. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

Agenda:

* 6:00pm~6:30pm: Checkin, Food/drink and Networking

* 6:30pm~8:00pm: Tech talks and Q&A

* 8:00pm: Open discussion and Mixer

Speakers/Topics:

Arne Vandendorpe / ML Engineer @ ML6

A Computer vision engineer's perspective on 3D data.

Gaussian splats have taken the field of 3D reconstruction by storm and have prompted an avalanche of papers that build on top of it. Next to that, ever more accurate zero-shot models that work with the depth modality, like Depth Anything, are steadily emerging. In this talk, we'll dive into our way of working to leverage these recent breakthroughs in our computer vision projects.

Jules Talloen / ML Engineer @ ML6

Beyond Text: Exploring the World with Large Multimodal Models

Large language models have revolutionized our ability to interact with computers through text. But the world is rich with more than just words. Join us as we delve into the exciting world of Large Multimodal Models (LMMs)! These powerful AI systems can understand and process information from various sources, including images, audio, and even video. We'll explore how LMMs are pushing the boundaries of AI, unlocking new possibilities for communication, creativity, and problem-solving across different fields. Get ready to discover how AI is learning to see, hear, and understand the world just like we do!

Kashif Rasul / Researcher @ Huggingface

RLHF via Direct Policy Optimization

In this talk I will go over the Direct Policy Optimization approach of RLHF which is a new method from Stanford researchers to learn to steer LLMs from human preference data without the need to learn an explicit reward model. I hope to go over the approach and show how to use it on your own data via the implementation in Hugging Face’s TRL library.

🎟️ Register here to secure your spot!

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