We’re excited to announce DBDS OPEN HOUSE 2025 – and we’d love for you to join us!
Date: May 6, 2025
Time: 4pm-6pm
Location: Edwards Building, 300 Pasteur Drive, 3rd floor, Stanford, CA 94305
An algorithm that learned from tens of thousands of nutrition prescriptions for premature babies could reduce medical errors and better identify what nutrients the smallest patients need.
You may remember that Nima presented this work at the C&C in January.
Read it here: https://med.stanford.edu/news/all-news/2025/03/prematurity-nutrition0.html
Abstract: Recent breakthroughs in artificial intelligence (AI) are increasingly driven by systems orchestrating multiple large language models (LLMs) and other specialized tools, such as search engines and simulators. So far, these systems are primarily handcrafted by domain experts and tweaked through heuristics rather than being automatically optimized, presenting a substantial challenge to accelerating progress. The development of artificial neural networks faced a similar challenge until backpropagation and automatic differentiation transformed the field by making optimization turnkey. Analogously, here we introduce TextGrad, a versatile framework that performs optimization by backpropagating LLM-generated feedback to improve AI systems….
Read it here: https://www.nature.com/articles/s41586-025-08661-4
Abstract: Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop Machine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences…..
Read it here: https://www.science.org/doi/10.1126/science.adp2407