S4E6: MIT’s James DiCarlo on Reverse-Engineering Human Sight with AI
Sep 6, 2023
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Neuroscience professor James DiCarlo from MIT discusses how artificial intelligence can be used to understand human sight. He compares convolutional neural networks to the human brain's visual system and explores the potential applications of AI in healthcare. They also discuss using diagnostic imaging to reveal hidden health clues and the impact of AI on clinical insights.
Reverse engineering the visual system using AI tools can help understand and predict how humans see.
Leveraging machine learning algorithms with eye and ECG data provides valuable insights for healthcare applications.
Deep dives
The Power of Machine Learning in Understanding Human Vision
Machine learning and engineering mindset can be used to understand human vision. Professor Jim DiCarlo explains that reverse engineering the visual system allows for understanding and predicting how humans see. There are three ways to demonstrate understanding: explaining observed data, predicting future data, and fixing broken systems. By building and optimizing convolutional deep neural networks, researchers can align them with the brain's visual system and gain insights into human vision. The goal is to go from molecules to minds, using AI tools to uncover hidden information about diseases and develop diagnostic and therapeutic interventions. This approach opens up possibilities for brain-machine interfaces, mental health interventions, and more.
Using Eye Images to Predict Age, Gender, and Smoking Status
Images of the back of the eye, called fundus images, contain rich information that can be leveraged by machine learning algorithms. The topology of the eye's landscape can predict age, gender, and smoking status. Similarly, electrocardiograms (ECGs) can be used to predict age and gender. These unexpected correlations between eye and ECG data and personal information provide insights into the potential of using intelligent human vision applications in healthcare. These applications could lead to earlier disease detection, personalized interventions, and improved patient outcomes.
Transforming Eye Image Analysis into Clinical Actionability
While eye and ECG data can reveal valuable insights, the challenge lies in making these insights clinically actionable. For example, predicting age alone might not have a significant impact on patient care. However, by examining the discrepancy between predicted and actual age, valuable biomarkers of aging can be identified. Turning eye and ECG data into biologic age indicators can provide personalized health assessments and guide treatment strategies. This highlights the importance of coupling medical knowledge with machine learning tools to maximize the clinical impact of intelligent human vision applications.
Unlocking the Potential of Machine Learning in Healthcare
Machine learning, particularly computer vision, offers a powerful tool for understanding human vision and its applications in healthcare. By harnessing the hidden information within eye and ECG data, researchers can make strides towards earlier disease detection, personalized treatment plans, and improved patient outcomes. The ability to bridge the gap between molecules and minds using AI tools allows for the exploration of innovative diagnostic and therapeutic interventions. As we continue to develop and optimize these intelligent human vision applications, the future holds great potential for revolutionizing healthcare practices.
Season 4 of our Theory and Practice podcast investigates the powerful new world of AI applications and what it means to be human in the age of human-like artificial intelligence. Episode 6 explores what happens when AI is explicitly used to understand humans.
In this episode, we're joined by James DiCarlo, the Peter de Florez Professor of Neuroscience at Massachusetts Institute of Technology and Director of the MIT Quest for Intelligence. Trained in biomedical engineering and medicine, Professor DiCarlo brings a technical mindset to understanding the machine-like processes in human brains. His focus is on the machinery that enables us to see.
"Anything that our brain achieves is because there's a machine in there. It's not magic; there's some kind of machine running. So that means there is some machine that could emulate what we do. And our job is to figure out the details of that machine. So the problem is someday tractable. It's just a question of when."
Professor DiCarlo unpacks how well convolutional neural networks (CNNs), a form of deep learning, mimic the human brain. These networks excel at finding patterns in images to recognize objects. One key difference with humans is that our vision feeds information into different areas of the brain and receives feedback. Professor DiCarlo argues that CNNs help him and his team understand how our brains gather vast amounts of data from a limited field of vision in a millisecond glimpse.
Alex and Anthony also discuss the potential clinical applications of machine learning — from using an ECG to determine a person's biological age to understanding a person's cardiovascular health from retina images.
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