Colloquium Talks

This talk will introduce a probabilistic model representing commonsense knowledge using a learned latent space of geometric embeddings -- probabilistic box embeddings. Using box embeddings makes it possible to handle commonsense queries with intersections, unions, and negations in a way similar to Venn diagram reasoning. Meanwhile, we show limitations with current Large Language models with their (commonsense) reasoning ability. 

I present three of our recent works: 1) Discourse-aware generation models for automatic social media moderation and mediation, 2) Sign language processing, and 3) Equitable and human-like dialogue generation models based on learning theory. Finally, I describe my research vision: Building inclusive and collaborative communicative systems and grounded artificial intelligence models by leveraging the cognitive science of language use alongside formal methods of machine learning.

This talk will introduce a probabilistic model representing commonsense knowledge using a learned latent space of geometric embeddings -- probabilistic box embeddings. Using box embeddings makes it possible to handle commonsense queries with intersections, unions, and negations in a way similar to Venn diagram reasoning. Meanwhile, existing evaluations do not reflect the probabilistic nature of commonsense knowledge. To fill in the gap, I will discuss a method of retrieving commonsense related question answer distributions from human annotators and a novel method of generative evaluation. 

 In this talk, I will share our recent study on developing a deep-learning-based object detection system on a field-programmable gate arrays (FPGAs) platform for driving applications.

On September 9th, we are hosting a joint Computer Science Colloquium / Research, Ethics and Society Initiative Seminar, entitled Thoughts of a Reformed Computer Scientist: On the Nature of Real and Artificial Intelligence, given by Dr. James Morris, MBA, PhD (Emeritus Professor of Computer Science, Carnegie Mellon University).

Dr. Michael White is a Professor in the Department of Linguistics at The Ohio State University. His research has focused on NLG in dialogue with an emphasis on surface realization, extending also to paraphrasing for ambiguity avoidance and data augmentation in the context of Ohio State's virtual patient dialogue system.

Sameh Gobriel is a senior research scientist at Intel Labs where he leads a team to drive research enabling future Intel products to be best-in-class in performance using software and hardware optimizations.