19/03/2026
Part two of Rainmakers and Neural Networks explores the challenge of interpretability: how we understand systems that learn, adapt and sometimes surprise us.
Moving beyond statistics and grand theories, this piece argues for a new approach to AI understanding one that resembles rainmaking more than engineering.
Key insights:
🔹 Why humility and careful observation are our best tools.
🔹 Navigating powerful systems inspired by and Anthropic.
🔹 How to speak to complexity without pretending to control it.
👉 Read the full article here: https://haki.africa/rainmakers-and-neural-networks/
🎥 Catch up on Part Two here:https://www.youtube.com/watch?v=Bp9279Mszm0
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