April 16 - AI, ML and Computer Vision Meetup en Español
27 attendees from 5 groups hosting
Details
Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision en Español .
Date, Time and Location
Apr 16, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!
Uncertainty in Large Vision-Language Models and Computer Vision
What if we train a model to classify dogs and cats, but it is later tested with an image of a human? Generally the model will output either dog or cat, and has no ability to signal that the image has no class that it can recognize.
Machine learning models by default do not provide estimates of their confidence or uncertainty, which hinders their use in applications involving humans. Possible solutions is the use of Bayesian Neural Networks or similar models.
In this talk I will show research applications of neural networks with uncertainty quantification, covering Computer Vision, Large Language Models and Vision-Language Models. This includes super-resolution, frame generation, verbalized uncertainty, robustness to corrupted inputs, and input uncertainty.
About the Speaker
Dr. Matias Valdenegro is Tenured Assistant Professor of Machine Learning at the Department of Artificial Intelligence, Bernoulli Institute, University of Groningen since 2022. He studied Computer Science, Autonomous Systems, and Electrical Engineering in Chile, Germany, and Scotland, holding a PhD from Heriot-Watt University on a thesis in detecting marine debris in sonar images. As a Researcher at the German Research Center for Artificial Intelligence in Bremen he conducted research in Computer Vision and Uncertainty Quantification from 2018 to 2022.
Deep Generative Modeling for Multimodal Human Trajectory Prediction
In this talk, I plan to show how deep generative models can be used as powerful multiple-hypothesis predictive models, in human trajectory prediction. This kind of problem arises in particular in autonomous driving. I will show a few works we have done in the past and a few ongoing works in my team.
About the Speaker
Jean-Bernard Hayet studied my engineering degree at Ecole Nationale Supérieure de Techniques Avancées (ENSTA) in Paris, and obtained my master degree in artificial intelligence at University Paris VI. I got my Ph.D. degree from University of Toulouse in 2003, at LAAS-CNRS, in Toulouse.
Cuando el conocimiento es Open la Innovación se acelera
En esta charla mostraremos cómo, cuando el conocimiento es abierto, la innovación se acelera al volverse accesible para cualquier colaborador y no solo para unos pocos expertos. Presentare Promptotyper, una plataforma creada por Innovaitors que integra modelos open source y librerías como LangChain y LangGraph para habilitar soluciones agénticas que guían desde el planteamiento del problema hasta el prototipado.
A través de agentes expertos en innovación, los equipos pueden estructurar retos empresariales y avanzar hacia soluciones en áreas como automatización (por ejemplo con n8n), prototipado de aplicaciones web y analítica de datos. El enfoque democratiza el “saber hacer” innovación en empresas de Latinoamérica, reduciendo la fricción y aumentando la velocidad de aprendizaje y ejecución. Al final, verás cómo convertir el expertise en un sistema reutilizable que escala capacidades de innovación en toda la organización.
About the Speaker
Alejandro Uribe es científico de datos, cofundador de Innovaitors y consultor en industria 4.0. Magíster en Inteligencia Artificial (U. Javeriana), profesor en la U. Externado e investigador en IA en la Javeriana, con 6 años desarrollando soluciones de IA y analítica de datos.
From Using Open Source to Contributing: A Practical Guide to Getting Started
Open source is one of the best ways to learn faster, build real experience, and grow your career, but many people don’t know how to start. In this talk, I share a very practical approach to contributing to open source, based on real experience. We’ll cover how to choose the right project, understand large codebases, start with small contributions, and communicate clearly with maintainers. Using FiftyOne as a real example (but keeping everything general), I’ll show how small actions like fixing docs, improving tooling, or opening a simple PR can lead to long-term impact, visibility, and growth.
About the Speaker
Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow, Docker, and OpenCV. I started as a software developer, moved into AI, led teams, and served as CTO.