USING GENERATIVE AI & MACHINE LEARNING IN THE ENTERPRISE + STARTUP SHOWCASE
AUSTIN + ON-DEMAND | March 20-21, 2024
ARIEL GAMIÑO
Lead AI Engineer at Trajector Medical
At the forefront of artificial intelligence, Ariel Gamiño leads natural language processing initiatives at Trajector, leveraging large language models to optimize the company offferings. An educator at heart, he shares his passion by teaching a data analytics bootcamp at The University of Texas at Austin McCombs School of Business.
Ariel is a published author with Manning Publications, having written a series teaching students how to build recommendation systems. Ariel boasts an impressive educational background, with a Bachelor of Science in Computer Science from The University of Texas at Austin, a Master of Software Engineering from Harvard University, and Master of Science in Predictive Analytics from Northwestern University.
Driven by a passion for education and community, Ariel leverages his leadership expertise as President of the Harvard Extension Alumni Association to cultivate a spirit of collaboration among the organization’s expansive network of over 40,000 members worldwide. Based in Austin, Texas, Ariel is committed to advancing AI and inspiring future generations of data scientists, especially those from underrepresented backgrounds.
Through mentoring and teaching underserved minority groups through local organizations, he increases diversity and inclusion across the tech community. His dedication to empowering others fuels his efforts to help shape the next generation of innovators and create a more equitable future.
Watch in-person: March 20 @ 3:50 – 4:20 PM CT
Leveraging LLMs for Text Augmentation in NLP Tasks
In this talk the speaker delves into the innovative use of Large Language Models (LLMs) for enhancing natural language processing (NLP) applications. This talk offers insights into how synthetic data, generated by state-of-the-art LLMs, can be used to augment text datasets, thereby significantly improving the performance of NLP tasks such as sentiment analysis, text classification, and topic modeling. The presentation will explore practical examples, showcasing how LLMs can expand and enrich text data, providing more context and depth for complex NLP tasks. Special emphasis will be placed on how this approach addresses challenges associated with data scarcity, imbalance, and bias in traditional datasets. Attendees will learn about the methodologies for efficiently generating high-quality synthetic data and integrating it into various NLP tasks, paving the way for more robust and accurate AI systems.