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Kate Park: Data Engines for Vision and Language
The Gradient: Perspectives on AI
Exploring the Transition to Generative AI and NLP Applications
The transition from working in computer vision to leading generative AI data engine was motivated by the goal of extending the data engine framework to more models. Generative AI and language models are seen as unlocking clear value, particularly in impacting daily life. The speaker's introduction to NLP began with a project at Stanford involving training a laugh bot, an NLP model that detects humor and responds with laughter. Initially, the project utilized a non-transformer architecture which won a best student paper award in 2018. However, revisiting the project in 2022 and fine-tuning a transformer-based model proved to be significantly more effective. This experience highlighted the importance of both architecture and quality data sets in improving models, which continues to drive the speaker's interest in enhancing model performance.
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