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“When I left you, I was but the learner. Now I am the master.”
— Darth Vader, speaking to Obi-Wan Kenobi in Star Wars: Episode IV — A New Hope (1977).
Who trains your newest technician when you hire one? Your service manager? A more senior technician? You? Whoever it is, it’s not the new technician themselves.
Who trains your newest AI large language model (LLM)? Initially it’s the model maker. But who improves upon that? You? Unlikely. That model maker? Likely. But do they monitor the performance of your instance?
What if it was another LLM? What if a “more senior” LLM could improve smaller LLMs in its environment? What if it could improve itself without human help?
That will sound good to many people. Others will be scared out of their wits.
Recursive Self-Improvement (RSI)
You’ve likely read or heard about Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI).
AGI is defined as a hypothetical type of artificial intelligence that can match or surpass human capabilities across any cognitive task, learn independently, and solve unfamiliar problems without task-specific reprogramming.
ASI is defined as a hypothetical form of artificial intelligence where machine cognitive capacity, reasoning, creativity, and problem-solving skills surpass the best human minds across virtually all fields.
RSI is defined as a process where an artificial intelligence system analyzes, optimizes, or redesigns its own architecture, code, or training methods to create a more capable successor
It would sound like RSI could lead to ASI or AGI, but it’s not necessarily the case. There are still hurdles to clear before ASI or AGI can be achieved, and many are working on it. That’s about as much as you can tell customers when they ask. Even the greatest AI experts disagree about when or whether these will happen.
