I recently watched The Minds of Modern AI, a roundtable featuring Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Fei-Fei Li, Bill Dally, and Jensen Huang — the laureates of the 2025 Queen Elizabeth Prize for Engineering.
Each shared a deeply personal aha moment that not only shaped their careers but also laid the foundation for today’s AI revolution.
🔹 Yoshua Bengio had two defining moments. As a graduate student, reading Geoffrey Hinton’s early papers made him realize there might be simple, universal principles, like the laws of physics, behind human intelligence. Decades later, after ChatGPT’s rise, he felt a different kind of awakening: what happens when machines understand language and goals, but we can’t control them? That question led him to shift his entire research toward AI safety and ethics.
🔹 Bill Dally’s insight came from solving the “memory wall” problem in the 1990s, leading to stream processing, the foundation of GPU computing. Another came in 2010, when he repeated Andrew Ng’s “cat experiment” using Nvidia GPUs. The results convinced him that GPUs should power deep learning, a turning point that changed Nvidia’s trajectory.
🔹 Geoffrey Hinton’s revelation dates to 1984, when he trained a tiny neural network to predict the next word in a sentence. It learned representations that captured meaning, a primitive version of today’s large language models. It took 40 years of compute and data to realize that early vision.
🔹 Jensen Huang saw parallels between chip design and neural networks, both built on structured, scalable representations. When researchers from Toronto, NYU, and Stanford simultaneously showed him early deep learning results, he recognized the pattern and committed Nvidia to building the “factories” of modern AI.
🔹 Fei-Fei Li realized in 2006 that algorithms weren’t enough — data was the missing piece. That insight led her and her students to create ImageNet, a dataset of 15 million labeled images that became the catalyst for deep learning. Later, as Google Cloud’s Chief Scientist, she had a second epiphany: AI is a “civilizational technology,” one that must remain human-centered, inspiring her to co-found Stanford’s Human-Centered AI Institute.
🔹 Yann LeCun, fascinated by how intelligence can self-organize, was determined to make machines that learn on their own. His early collaborations with Hinton in the 1980s laid the groundwork for convolutional networks and, decades later, self-supervised learning, the foundation of how LLMs now learn from data without explicit instruction.
What struck me most is how intuition, persistence, and cross-disciplinary thinking shaped their breakthroughs. None of them planned these moments — they followed curiosity and kept exploring what others might have missed.
What was your own “aha moment”, the insight that changed how you see your field?
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