The AI Evolution and the Challenge of Decision Automation

In his latest post Prof. Warren Powell contrasts “using AI to automate decisions to Tesla’s aggressive use of robotics to automate their assembly lines. It is important to understand the most common use of AI today (machine learning) with the tools for making decisions. I also talk about “failures” of AI as primarily failures in setting expectations. AI is not a revolution – it is an evolutionary process, and we have to do more than just automate what people do, and how they do it.” Link

“Artificial intelligence in the 1970’s consisted primarily of rules: “If this condition, then do …”. The problem with rule-based AI is that it does not scale to complex problems where the condition has more than a few dimensions — the classic “curse of dimensionality.” This means that rules could not solve all the lofty ambitions of AI — they are clumsy, they do not scale, and they do not adapt…. but they are still incredibly useful, and widely used in virtually all information systems. Rule-based AI did not fail; it just failed to meet unrealistic expectations.”

“Managing resources requires making decisions. The operations research community has developed powerful optimization solvers that provide perfect answers, but only if you have perfect data (and forecasts). The machine learning community developed their own decision tools under the heading of “reinforcement learning.” RL emerged when it successfully solved the Chinese game of Go, and has suddenly become the new hammer for solving all problems, including the highly complex domain of “dynamic resource allocation” ”

“More problematically has been the development of tools for making decisions over time in the presence of different sources of uncertainty. I refer to the study of sequential decision problems as “decision analytics” which builds on the tools of machine learning and a substantial literature known as “stochastic optimization.” Sadly, most of these tools come across as complex and computationally intractable, which is a problem in an industry that prizes simplicity and transparency.

Building on my career of solving problems in freight transportation and logistics, I finally cracked the code for optimizing these complex problems under uncertainty. Instead of finding optimal decisions (as is done with deterministic problems), we have to find effective “policies” which are methods for making decisions given what we know at the time. Designing policies requires understanding the structure of the problem, and the types of uncertainties that we have to accommodate.”

This entry was posted in Artificial Intelligence, Decision Optimization, Machine Learning. Bookmark the permalink.

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