We derive a fundamental uncertainty principle of meaning that links precision to complexity. Demonstrating that power and precision within AI always comes at the cost of explainability. Thus, great advances like ChatGPT or Dall-E must come with close to trillion parameters. Indeed, explainability has never been a viable path forward for human-like intelligence, but how did we build trust without it? We use the tool of evolution: the test of time. Giving AI the chance to earn our trust through the test of time. Our great challenge lies in applying rigorous tests of time to this new AI as we are advancing almost a billion times faster than evolution did in creating human intelligence. We're reaching a pivotal moment. In fact, this could be our greatest test of time yet.
MIT researcher specializing in foundation models and generative AI, previously at Stanford. Serial entrepreneur and co-founder of Unbox AI, a leading company innovating in foundation models and generative AI for behavior-driven businesses. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at [ Ссылка ]
