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Generative AI is going to change how we do things across the entire economy, including the fields Harry covers on the show, namely healthcare delivery, drug discovery, and drug development. But we’re still just starting to figure out exactly how it’s going to change things. For example, AI is already speeding up the process of discovering new biological targets for drugs and designing molecules to hit those targets—but whether that will actually lead to better medicines, or create a new generation of AI-driven pharmaceutical companies, are still unanswered questions.
One thing that’s for sure is that generative AI isn’t magic. You can’t just sprinkle it like pixie dust over an existing project or dataset and expect wonderful things to happen automatically. In fact, just to use the data you already have, you have to you may have to invest a lot in the new infrastructure and tools needed to train a generative model. And that’s the part of the puzzle Harry focuses on in today's interview with David Buniatyan. He’s the founder of a company called ActiveLoop, which is trying to address the need for infrastructure capable of handling large-scale data for AI applications. He has a background in neuroscience from Princeton University, where he was part of a team working on reconstructing neural connectivity in mouse brains using petabyte-scale imaging data. At ActiveLoop, David has led the development of Deep Lake, a database optimized for AI and deep learning models trained on equally large datasets.
Deep Lake manages data in a tensor-native format, allowing for faster iterations when training generative models. David says the company’s goal is to take over the boring stuff. That means removing the burden of data management from scientists and engineers, so they can focus on the bigger questions—like making sure their models are training on the right data—and ultimately innovate faster.
For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast
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