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The 2nd AI Winter (https://en.wikipedia.org/wiki/History_of_artificial_intelligence#Bust:_the_second_AI_winter_1987.E2.80.931993) is over (or was it the 3rd AI winter?). Let's celebrate.

Upcoming events (1)

Generating Natural-Language Text with Neural Networks

TBA North Central Austin

This is a joint meetup with our friends at the Austin-ACM-SIGKDD meetup. (https://www.meetup.com/Austin-ACM-SIGKDD/) We will be raffling off two tickets to Data Day Texas 2020 at the meetup. This is the north side encore presentation of Jonathan Mugan's talk: Generating Natural-Language Text with Neural Networks The last time Jonathan offered this talk, it was held downtown. So, many people couldn't make it. Hence, the 2nd encore. Jonathan has revised and augmented this talk considerably since originally giving it at Data Day 2017. Even if you already saw it at Data Day, expect quite a bit fresh content and insight this time. Abstract Automatic text generation enables computers to summarize text, to have conversations in customer-service and other settings, and to customize content based on the characteristics and goals of the human interlocutor. Using neural networks to automatically generate text is appealing because they can be trained through examples with no need to manually specify what should be said when. In this talk, we will provide an overview of the existing algorithms used in neural text generation, such as sequence2sequence models, reinforcement learning, variational methods, and generative adversarial networks. We will also discuss existing work that specifies how the content of generated text can be determined by manipulating a latent code. The talk will conclude with a discussion of current challenges and shortcomings of neural text generation. About the speaker Jonathan Mugan (http://www.jonathanmugan.com/) 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 (http://datadaytexas.com/) conferences, he recently also spoke on the topic of NLP at the O’Reilly AI (https://conferences.oreilly.com/artificial-intelligence/ai-ny-2017/public/schedule/speaker/273569) conference, and is the creator of the O’Reilly video course Natural Language Text Processing with Python (https://player.oreilly.com/videos/9781491976470). Dr. Mugan is also the author of The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion (https://www.amazon.com/Curiosity-Cycle-Second-Preparing-Technological/dp/0692022120).

Past events (15)

The Texas AI Summit

AT&T Executive Education and Conference Center

Photos (10)