What would preeminent 20th century geographer Halford J. Mackinder say about the coming revolution in artificial intelligence and its impact on our current ideological war?
What happens when a post-Trump, reputationally-bruised United States, and improved generative models (the technology behind "deepfakes") collide head-on?
In world of weaponized drones piloted by algorithms, what new strategic opportunities arise?
I'm beginning to write about the intersection of artificial intelligence and geopolitics.
A detailed derivation of Mean-Field Variational Bayes, its connection to Expectation-Maximization, and its implicit motivation for the "black-box variational inference" methods born in recent years.
Deriving the expectation-maximization algorithm, and the beginnings of its application to LDA. Once finished, its intimate connection to variational inference is apparent.
Stochastic maximum likelihood, contrastive divergence, negative contrastive estimation and negative sampling for improving or avoiding the computation of the gradient of the log-partition function. (Oof, that's a mouthful.)
A pedantic walk through Boltzmann machines, with focus on the computational thorn-in-side of the partition function.
Introducing the RBF kernel, and motivating its ubiquitous use in Gaussian processes.
A thorough, straightforward, un-intimidating introduction to Gaussian processes in NumPy.