Chris von Csefalvay

"It often takes years to create a viable antibody test as accurate as PCR-based testing. But in less than six weeks, biotech companies—approached by the U.S. government through the White House-created public-private partnership—have already seen their efforts bear fruit. This is a tribute to the incredible creative potential of the biotech sector, but it also shows the power of free enterprise, unshackled by government bureaucracy. It took more than America’s best scientists to rise to the occasion: it took a regulatory regime to let them do so."

"Hesiod’s description of Pandora’s box reminds us that though we live in a world of danger, where infectious diseases continue to maim and kill millions, especially across the developing world, we are not without hope. Part of that hope is our ability as humans to bring mathematics, genomics, data science, statistics, and computational science to bear on this problem and call these altogether rather disparate disciplines into humanity’s service against disease. Infectious disease modeling is part of that wider story of hope."

"Today, the efforts waged to curb the COVID-19 pandemic may be the first example of a large-scale, global data-driven response to a worldwide crisis, and as such perhaps the first war of data science."

"Computational models of infectious disease can make all the difference in our response to pandemics. As habitat loss and climate change make zoonotic spillover events increasingly more likely, COVID-19 is almost certainly not the last major pan- demic of the 21st century. In fact, it is reasonable to assume that such outbreaks will become increasingly frequent. Computational models can be powerful weapons in our fight against pandemics."

"We may think of maps and mapping as an objective process, but that would be an illusion. What gets mapped, and more importantly, what does not, is a product of various social, economic, and political phenomena. Quite apart from border disputes and contentious sovereignty, mapping also reflects political priorities. Creating the survey data that can be used in maps is expensive, and large-scale mapping endeavors are typically the preserve of states, whose ability to deliver that data often depends on resources that compete with other governmental priorities. This is true especially in resource-constrained settings."

"To me, agentic AI has always been about large scale emergence and self-organisation. This isn’t a matter of degree but qualitatively different. If you can draw it on a whiteboard, it may well be useful, possibly make decent dinner reservations, but it will not ever exhibit meaningful emergence (if you don’t believe me, try to draw every neuron of a modern convolutional neural network). It may be useful, but it’s not what I mean when I talk about agentic AI."