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Medicine and the Machine
AI Comes to Medicine: This Time, It's Serious
Jun 27, 2023
The podcast discusses the evolution and impact of artificial intelligence in medicine, from early neural networks to transformer models. It explores the development of GPT-4, a powerful language model, and the concept of superhuman performance in AI. The podcast also explores the wide range of AI applications in healthcare, raising questions about accuracy, reliability, regulation, and biases.
38:14
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Quick takeaways
- The evolution of deep learning models, from convolutional neural networks to transformers, has led to impressive advancements in performance, with GPT-4 demonstrating potential for human-like performance.
- Large language models like GPT-4 have the ability to diagnose medical conditions and provide valuable insights, but they still make errors and lack common sense, necessitating the need for regulation and public engagement in shaping their future.
Deep dives
The Evolution of Deep Learning Models: From Convolutional Neural Networks to Transformers
The podcast episode begins by discussing the progression of deep learning models, starting with convolutional neural networks and later transitioning to the transformer architecture. The speaker highlights how these models were purpose-built for specific tasks and were evaluated based on their accuracy. In 2017, the transformers architecture introduced a different approach by creating generic models trained on large corpora of data, such as words from the internet. This allowed for fine-tuning and training on more generic tasks. The speaker mentions that the increase in model size and performance over the years has been impressive and indicates that GPT-4, a large language model released in mid-March, has the potential for human-like performance on various tests.
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Discover
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