Why do Kernel functions had to be positive semi-definite?

In my machine learning class, my professor explained that a kernel function must be symmetric and psd. I understand that kernels represent the inner product of the feature vectors in some Hilbert space, so they need to be symmetric because inner product is symmetric, but I am having trouble understanding why do they need to be positive semi-definite.