Invited Talks

Language-based AI is now ubiquitous, and user expectations for intelligent machines are scaling along with it: we expect machines to understand us, predict our needs and wants, do what we enjoy and prefer, and adapt as we change our moods and minds, learn, grow, and age. Physical AI, in the form of robotics, is the next major AI challenge, and it is not ready to leap into our daily lives yet. While massive investment is focused on functional behavior of humanoid robots (perceiving the world, moving around, and manipulating objects), human-robot interaction (HRI) is relegated to an afterthought. It is assumed that once a robot can move around and do things, it will be useful and wanted, yet over 25 years of research in HRI tells us otherwise. While the needs for human-centered services continue to grow, research and development is minimal. This talk will discuss how bringing together robotics, AI, and machine learning for long-term user modeling, real-time multimodal behavioral signal processing, and affective computing is enabling machines to understand, interact, and adapt to users’ specific and ever-changing needs. We will overview methods and challenges of sparse and noisy heterogeneous, multi-modal, personal interaction data and of creating expressive agent and robot behavior toward understanding, coaching, motivating, and supporting a wide variety of user populations across the age span (infants, children, adults, elderly), ability span (typically developing, autism, anxiety, stroke, dementia), contexts (schools, therapy centers, homes), and deployment durations (from weeks to 6 months) through socially assistive robotics. We will discuss the challenges of understanding what we humans want from interactions with machines vs. what we need vs. what we are getting, and how those distinctions are shaping the future of not just AI and ML but society at large.

As AI capabilities skyrocket, openness plummets: the scientific community and broader public knows little of how frontier models (including open-weight models) are trained.  I will describe Marin, a radically new way of doing model development, inspired by true open-source software. Every experiment is done in the open, and anyone can suggest ideas, review, and even run experiments through GitHub, providing a better way of doing science that improves on preregistration, reproducibility, and peer review. I will discuss a selection of scientific results that have emerged from Marin, including new optimizers and scaling laws. We hope that Marin will be a platform for the community to participate in the development of frontier AI.

Behavior is everything we do. With age and experience, infant behavior becomes more flexible, adaptive, and functional. More intelligent. How do infants acquire intelligent behavior? Babies are learning while developing. Advances in motor skills expand infants’ interactions with the environment—the parts of the environment they “touch” with eyes, hands, and body. In the course of everyday activity, infants acquire immense amounts of time-distributed, variable, error-filled practice for every type of foundational behavior that researchers study. Practice is largely spontaneous, self-motivated, and frequently not goal directed. Formal robot models suggest that infants’ natural practice regimen—replete with variability and errors—is optimally suited for building an intelligent behavioral system that responds adaptively to the constraints and opportunities of continually changing bodies and skills in an ever-changing world. I propose that open video sharing will speed progress toward understanding behavior and its development.

I will present our latest research on Artificial Intelligence for Open Science at the Center for AI at the Universidad de los Andes, Colombia (CinfonIA). We will focus on our ongoing collaborative projects in scientific disciplines such as robotic surgery, spatial transcriptomics, drug discovery, geology, and nature conservation.