AI models need more data about biology, and OpenAI is paying to create it
Last year, the clinical trial policy analyst Ruxandra Teslo posted an idea for super-charging medical AI systems: use data from failed biotech companies. By bidding at their bankruptcy proceedings, she proposed, it might be possible to obtain detailed regulatory filings, manufacturing strategies, and safety data—information usually kept hidden as valuable trade secrets. She called these…
OpenAI's AI models require more biological data to make significant breakthroughs in curing diseases, according to a clinical trial policy analyst. OpenAI's nonprofit arm, the OpenAI Foundation, is addressing this need by funding a new initiative called Data for Public Health. This initiative aims to develop high-quality scientific datasets to improve AI's ability to contribute to medical advancements.
The foundation recently awarded $40 million to collect data on novel cancer vaccines at the University of North Carolina, Chapel Hill, and will also support OpenAdmet, a competition that challenges researchers to predict drug effects. Ruxandra Teslo's proposal to fund "biotech's lost archive" by bidding on failed biotech companies' regulatory filings and manufacturing strategies was selected for $500,000.
The data obtained from these bankrupt companies, known as common technical documents, contains essential information for AI training, enabling them to become regulatory experts and potentially accelerate the development of cures. As OpenAI's charitable efforts expand, concerns about the risks of AI systems, including the potential for a deadly bioweapon or a 10% chance of human extinction within a decade, have intensified.
OpenAI's foundation, led by Sam Altman, is on track to become the wealthiest charitable organization in the world, with a potential $250 billion in stock value from its upcoming IPO.
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