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A controlled study that started as a question about machine “creativity” and ended as a clean result about length generalization — one strong negative finding, one positive flicker, and a methodological trap worth naming.
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We have a memory problem in AI. If you’ve built applications with Large Language Models, you’ve hit the wall: the context window. The moment your conversation exceeds the token limit, the model forgets. The beginning of the chat falls off a cliff, and the AI loses who you are. The industry’s standard fix is RAG (Retrieval Augmented Generation). We chop up data, embed it, and stuff it into a vector database like Pinecone or Chroma. It works. But RAG has a hidden cost: linear growth. To store 1 million facts, you need 1 million vectors. Your database grows with every interaction.
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For over half a century, a single, deceptively simple scenario has served as philosophy’s favorite moral laboratory: the trolley problem. We all know the setup, originally conceived by Philippa Foot in 1967 and later refined by Judith Jarvis Thomson. A runaway trolley is about to kill five people. You can pull a lever to divert it, but doing so will kill one person on a side track.
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It all started with a simple walk to class. Back at university, there were two distinct pathways to the lecture hall. Both were the exact same distance, had the same view, and took the same amount of time. Yet, I noticed something strange: people rarely picked a path at random. One route always seemed to pull more people than the other.
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AI is a wonderful, powerful tool. For someone like me, with a mind constantly buzzing with ideas, possibilities, and new cases, it acts as a “know-it-all buddy” to bounce those ideas off of. It’s an incredible accelerator. But, as you might have guessed, a know-it-all buddy can be dangerously wrong and, worse, doesn’t know when it’s wrong.
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Valet parking is a classic test of human multitasking. It’s not just about driving; it’s a high-pressure logistical puzzle that combines spatial reasoning, memory, and strategic decision-making against a ticking clock. This complexity makes it a fascinating and challenging problem to solve with Artificial Intelligence.