2010= tracking brac uni coalition - deepest sgirls dg alumni

 At 35 priscilla chan has imo the toughest job in the world -possibly the largest philanthropy spend ever as leader of foundation czi of hers and facebook founder zuckenberg

she is a heroic pediatrician and her family immigrated to boston; her hq is part of facebook campus mountain view down road from stanford 

czi has an ai biohub on the campus and connects labs it sponsors across stanford, san francisco, berkeley; there is every reason to assume she can win-win with fei-fei li, melinda gares and indeed all the women of the moments of lift revokution that melinda gates has oublished and ai connected since starting her own foundation around 2016

i understand some people are wary of young leaders of big foundations -its a really hard job- but everything is convreging

biotech

the 3 sister towns stanford's alto, mountain view and santa clara

in a way whether the whole of america connects sdgs around the world depends on open learning models of these 3 towns- i am more worried that many other us universities will not be up to the cooperation oppoerrtunity of transformation of education - todate the demand from 2016 un sdg4 reviews that ed is nit fit for ourpose of millennilas as sdg generation has not been responded to by much of us education - yet good ai and good media llms are going to be tested between now and 2025 like no other time- good luch to all us humans and to pc

-here is newsletter sequence of pc czi uodating from august 2023

I'm always energized when I meet with teams across CZI to review our work as an organization. Earlier this summer, we held strategy reviews to reflect on the lessons we’ve learned and to discuss how we can build on the progress we’ve made. From these conversations, it became clear that the last several years have prepared us to leverage new, exciting technologies like artificial intelligence (AI) to accelerate progress across our core focus areas.   In the scientific field specifically, researchers are already using AI to turn decades of scientific research into breakthroughs about the biomolecules — like proteins — that keep our bodies running. And with large language models and machine learning technologies advancing so quickly, AI can help us do so much more.   AI can assist us in analyzing the various types of cells in our body and how they interact — which we believe is key to demystifying disease and helping scientists cure, prevent, or manage all diseases by the end of this century.    Before that can happen, researchers need to shift from using AI to create predictive models of molecules to using it to create predictive models of cells. It's like transitioning from tracking the orbit of a single planet to predicting the interactions of an entire solar system. We’ve helped the science community reach the point where that shift is possible.    We’re building one of the largest computing clusters in the world for non-profit life science research, with the goal of using it to create a new type of "virtual cell." It would give scientists worldwide access to digital models that could predict the behavior of any cell type and how it may respond to different conditions. For example, researchers could predict how an immune cell will react to an infection, or what’s happening at the cellular level when a child is born with a rare disease.    Watch our video to learn how we’re accelerating progress on important scientific questions about how our cells work.