A Path to Strong AI - with Jonathan Mugan

A Path to Strong AI - with Jonathan Mugan

Actions and Detail Panel


Date and time


To Be Announced - Austin

In Person and Online

Austin, TX 78705

View map

Did you miss Jonathan Mugan's Keynote at Data Day Texas? Here's an encore presentation

About this event

A Path to Strong AI

The abstract:

We need strong artificial intelligence (AI) so it can help us understand the nature of the universe to satiate our curiosity, devise cures for diseases to ease our suffering, and expand to other star systems to ensure our survival. To do all this, AI must be able to learn condensed representations of the environment in the form of models that enable it to recognize entities, infer missing information, and predict events. And because the universe is of almost infinite complexity, AI must be able to compose these models dynamically to generate combinatorial representations to match this complexity. This talk will cover why models are needed for robust intelligence and why an intelligent agent must be able to dynamically compose those models. It will also cover the current state-of-the-art in AI model building, discussing both the strengths and weaknesses of neural networks and probabilistic programming. The talk will cover how we can train an AI to build models that will enable it to be robust, and it will conclude with how we can effectively evaluate AI using technology from computer-animated movies and videogames.

This talk, which was first presented at Data Day Texas 2020, is based on an article Jonathan published in The Gradient.

A Path to Strong AI - with Jonathan Mugan image

Jonathan Mugan (website / Linkedin) is a researcher specializing in artificial intelligence, machine learning, and natural language processing. His current research focuses in the area of deep learning for natural language generation and understanding. Dr. Mugan received his Ph.D. in Computer Science from the University of Texas at Austin. His thesis was centered in developmental robotics, which is an area of research that seeks to understand how robots can learn about the world in the same way that human children do. Dr. Mugan also held a post-doctoral position at Carnegie Mellon University, where he worked at the intersection of machine learning and human-computer interaction. One of the most requested speakers at the Data Day Texas conferences, he recently also spoke on the topic of NLP at the O’Reilly AI conference, and is the creator of the O’Reilly video course Natural Language Text Processing with Python. Dr. Mugan is also the author of The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion.

Share with friends