Accelerating drug discovery with AI: Insights from Isomorphic Labs
Gradient Dissent: Conversations on AI
Navigating Input Granularity in NLP and Biology Data
Exploring the shift towards lower granular input representations in NLP and biological data to accommodate larger data sizes, enabling scalable architectures and extracting high-level structures. Emphasis on the significance of minimal input structure and larger datasets for enhancing model capabilities in drug discovery and language processing. Discussing challenges of data representation, the need to incorporate priors, and the potential opportunities of leveraging large language models for cross-domain scientific applications.
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