A Deep Dive Into Neural Networks and Interpretability

Author(s): Mirko Peters

Originally published on Towards AI.

This post explores how AI models operate, focusing on convolutional neural networks, the challenge of interpretability, and the implications for our future. Uncover the secrets behind how machines learn and the necessity of comprehending their decision-making processes.

This member-only story is on us. Upgrade to access all of Medium.

Imagine this: you’re staring into the eyes of someone, searching for hidden truths. A scientist once trained an AI to do just that, revealing heart risks and biological gender simply through eye scans. Astonishing, right? This blog will unravel how these AI systems function behind the scenes, much like peering through the kaleidoscope of neural networks. We’ll take you on a journey from the known to the unknown, shedding light on the complexities that make AI tick — and tick dangerously close to misunderstanding.

Have you ever considered how AI can analyze images and make predictions? It’s pretty wild! For example, in 2018, researchers trained an AI model to examine images of people’s eyes. This AI wasn’t just looking for flaws; it assessed risks for heart conditions. Remarkably, it could even determine the biological sex of individuals with high accuracy. Can you believe that? It shows how AI can unravel complex relationships in ways we might not expect.

AI systems like this rely on deep learning. Instead of learning from explicit instructions, these models observe data and find patterns on their own. They don’t need… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI