Innovative Deep Learning Architecture for Fluid Dynamics, MIT’s Working Group on Generative AI, and Open-Sourced Biomedical AI Model

SineNet by Texas A&M University and the University of Pittsburgh addresses temporal misalignment in fluid dynamics through deep learning. MIT launches a working group on generative AI and the work of the future. Researchers at Stanford and Databricks open-source BioMedLM, a 2.7 billion parameter GPT-style AI model trained on PubMed text. Plus, the rise of cybernetic doppelgängers and their impact on AI in various industries.

Sources:
https://www.marktechpost.com/2024/03/31/sinenet-by-texas-am-university-and-the-university-of-pittsburgh-innovates-pde-solutions-addressing-temporal-misalignment-in-fluid-dynamics-through-deep-learning/
https://news.mit.edu/2024/mit-launches-working-group-generative-ai-and-work-of-the-future-0328
https://www.marktechpost.com/2024/03/31/researchers-at-stanford-and-databricks-open-sourced-biomedlm-a-2-7-billion-parameter-gpt-style-ai-model-trained-on-pubmed-text/
https://ytech.news/en/the-rise-of-cybernetic-doppelgangers-how-digital-twins-propel-ai-to-new-heights/

Outline:
(00:00:00) Introduction
(00:00:41) SineNet by Texas A&M University and the University of Pittsburgh Innovates PDE Solutions: Addressing Temporal Misalignment in Fluid Dynamics Through Deep Learning
(00:03:54) MIT launches Working Group on Generative AI and the Work of the Future
(00:06:25) Researchers at Stanford and Databricks Open-Sourced BioMedLM: A 2.7 Billion Parameter GPT-Style AI Model Trained on PubMed Text
(00:09:49) The Rise of Cybernetic Doppelgängers: How Digital Twins Propel AI to New Heights