It’s a special time in the evolutionary history of computing. Oft-used terms like big data, machine learning, and artificial intelligence have become popular descriptors of a broader underlying shift in information processing. While traditional rules-based computing isn’t going anywhere, a new computing paradigm is emerging around probabilistic inference, where digital reasoning is learned from sample data rather than hardcoded with boolean logic. This shift is so significant that a new computing stack is forming around it with emphasis on data engineering, algorithm development, and even novel hardware designs optimized for parallel computing workloads, both within data centers and at endpoints.
A funny thing about probabilistic inference is that when models work well, they’re probably right most of the time, but always wrong at least some of the time. From a mathematics perspective, this is because such models take a numerical approach to problem analysis, as opposed to an analytical one. That is, they learn patterns from data (with various levels of human involvement) that have certain levels of statistical significance, but remain somewhat ignorant to any physics-level intuition related to those patterns, whether represented by math theorems, conjectures, or otherwise. However, that’s also precisely why probabilistic inference is so incredibly powerful. Many real-world systems are so multivariate, complex, and even stochastic that analytical math models do not exist and remain tremendously difficult to develop. In the meanwhile, their physics-ignorant, FLOPS-happy, and often brutish machine learning counterparts can develop deductive capabilities that don’t nicely follow any known rules, yet still almost always arrive at the correct answers.
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