Last Wednesday I attended the Buck Institute’s workshop on AI and Longevity, where speakers from several different organizations discussed how they were using machine learning to advance different aspects of medical discovery. Uses for AI in aging include creating new drugs, finding new uses for existing drugs, and discovering new biomarkers–all of which are important for improving the current drug development cycle, which is long and expensive.
The Buck Institute itself was established in 1999 and remains the only independent anti-aging research center in the world. Its goal is to fight the diseases of aging by approaching them holistically, as opposed to traditional organ-focused research.
All talks used a machine learning technique called neural networks. In broad strokes, neural networks can do three different things:
All neural networks are “trained” on sample data before being given problems to solve. For example, the classification network would be fed thousands of pictures labeled either “cat” or “not cat”. The regression network would be fed hundreds of homes with their statistics and sale prices (and the amount of training data necessary is shrinking all the time). The trained network is then fed the same type of data (a picture of a cat, housing statistics)
Article originally posted at