Listen: How did the 1955 plan for AI go? Surprisingly well!

In the summer of nineteen fifty-six, a small group of scientists gathered at Dartmouth College for a study of what they called artificial intelligence. They believed that every aspect of learning and intelligence could be precisely described and simulated by a machine. They even hoped to make a significant advance in just one summer. Seventy years later, we can see how far we have come, and where we still stumble. Today, computers easily handle natural language, solve complex math olympiad problems, and generate human-like reasoning. But they do this through statistical prediction and massive scale, rather than the precise logical rules the founders imagined. In some areas, machines are even helping to build themselves. Artificial intelligence assistants now write the vast majority of the code for their own systems. Yet, true self-improvement remains out of reach. We still do not have a machine that can autonomously design its own successor. Physical understanding is another hurdle. While modern vision and robotic systems can translate sight into movement, their internal world models are fragile. Change a background color or a minor detail in a scene, and their abstract understanding often falls apart. The pioneers of nineteen fifty-five thought computer capacity was a minor issue compared to programming. Ultimately, it was massive computing power and scaled-up neural networks that finally unlocked the doors to artificial intelligence.