I will begin by discussing the computational complexity of finding vacua in String theory. I will demonstrate that finding a vacuum in string theory requires us to solve nested computationally hard problems. I will then discuss some ways of getting around this complexity "hurdle" by the use of state of the art Artificial Intelligence techniques such as deep reinforcement learning and genetic algorithms. I will conclude by discussing some of my ongoing work at the Physics and Machine learning (ML) intersection. This talk will also include a lightning review of the AI concepts used.
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