“We then collected those corrections and then basically fed them back into the training data, retrained the models. They then got better.”
♫
Said on The Leadership in Insurance Podcast - Insurtech & Innovation
“We trained models on internal data and then predicted on external data… total number of tests … like 13 million across 500 protein targets.”
Ian Quigley · Latent Space: The AI Engineer Podcast · Lab-in-the-Loop at Compute Speed - Ian Quigley, Leash Bio
Play from 56:20
56:20
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What was described
Train models only on internally generated data and then evaluate them on large external molecule collections without ever training on those external sets
13 million tests across ~500 protein targets
Said nearby on the same subject
“We will license core ai models from really deep resourced company and then train those models on client data ... without having to hire 300 people”
♫
Said on InsTech - insurance & innovation
“Collect some data, train a model, then deploy; a little bit of data (couple hours) is enough for many tasks”
♫
Said on Latent Space: The AI Engineer Podcast
“One is to have an external set of things that get to look at all of the model's actions and subject them to a symbolic, clearly stated constraint.”
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Bill Gates
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