There’s history here, too. In 2017, AI researchers came up with the concept of “meta learning,” where AI could search for models best suited for experiments and problem solving. The search parameters at the time were mostly specified in code, but now that can be done with natural language, Dean said.
Natural language interaction makes it easier for agents to find ways to get better, such as finding new information, specific algorithms, or distillation mechanisms. AI can be seen as a performance multiplier that frees researchers to think up new ideas. “It’s a partnership between super-capable researchers and super-capable agents,” Dean said.
More interactive LLMs: As AI technology progresses, LLMs could become more interactive with the real world, actually re-learning and updating themselves in real time and taking actions based on that new knowledge.
