2025-11-05 –, AI
Wouldn't it be amazing to know what your cat feels? Is your dog a lover or a fighter? When does a cow’s moo signal sickness? Today, in the age of Artificial Intelligence (AI), these questions are no longer science fiction. The desire to understand animals is as old as humanity itself, with stories of people speaking to animals etched on cave walls. In this talk, we present three studies where machine learning (and deep learning) were used to answer these very questions, taking a bold step toward fulfilling this ancient dream. There will be code and data on-screen so you will be able to try on your own pet.
In this talk, we explore how AI is transforming our ability to understand animals’ emotions, behaviors, and health. The focus is on three real-world studies that demonstrate how machine learning and deep learning models, aiming at an applicative talk. To this end, the objective is to showcase how modern AI techniques, particularly computer vision, supervised learning, and temporal data analysis, can be designed for different animals in a multimodality setting - video, audio, and sensor data. The talk begins with a short introduction to the field and quickly moves into the technical dimension of developing such models today. Each of the three studies will be explored through the full pipeline: experimental design, data collection challenges, model development, validation, and interpretation of results. Specifically, live code snippets, annotated datasets, and practical demos will make the talk hands-on and accessible, providing attendees with actionable tools and frameworks they can explore independently.
Background Knowledge: Basic familiarity with machine learning concepts is required. The talk is accessible to interdisciplinary audiences from computer science, mathematics, cognitive ethology and assumes relatively basic AI background as the methods are well-known and the novelty lies in their unique usage.
Key Takeaways:
How AI can detect animal emotions, behaviors, and health states
Practical considerations in designing AI studies with animals
Ethical and technical challenges unique to animal data
Hands-on code and data resources for further exploration
Previous knowledge expected
Prof' Teddy Lazebnik is a applied mathematics and computer science researcher with extensive experience leading RnD teams in both the life sciences and financial domains. Over the past decade and a half, Teddy has honed his software development skills, including nine years of experience managing development teams of up to fourteen professionals. Teddy has a proven track record in system architecture, developing production-ready algorithms, and collaborating with clients. His expertise includes bio-physical simulations, big data analysis, and data analysis for information systems. His research is focused on applying advanced mathematics and computer science to the life sciences and socio-economic domains, covering areas such as AI-driven personalized treatment protocols, drug discovery, eXplainable AI, socio-economic systems modeling and simulation, and optimal policy detection from financial data.