Markov: Two Brothers and Chaining Hope to a Single Method for Efficiency
July 4, 2018
I am no math guy. I am no Googler. I am just an old person related to a semi capable math person named V.I. Arnold. That Arnold knew of the Markov guys because those who assisted Kolmogorov sort of kept in touch with stochastic methods.
This is recent news in math history. Andrey Andreyvich Markov died in 1922 when my uncle was a very young math prodigy. His brother Vladimir died in 1897.
Who cares?
I do sort of.
I read “Can Markov Logic Take Machine Learning to the Next Level?” From my point of view, the short answer is, “Not really.”
Machine learning requires a number of numerical recipes. Truth be told, most of these methods have been around a long time. The methods are taught by university profs and even discussed in IBM sales engineers’ briefings. (Yep, at least they were once upon a time.)
The write up explains Pedro Domingos’ insight. The article does not make clear that Dr. Domingos’ work has influenced the Google smart software effort. In fact, Google has, like Amazon, deep affection for the University of Washington. Dr. Jeff Dean, I have heard, shares a warm spot in his heart for the university.
The write up presents some of Dr. Domingos’ insights about Markov and Markov logic.
The key point for me is that as useful as the Russian brothers’ ideas are, there is more to machine learning than a single approach.
In fact, I find this statement from the article interesting:
The productivity advantages of Markov Logic may be too great to ignore. A deep learning machine that takes tens of thousands of lines of code in a traditional language could be expressed with just a few Markov Logic formulas, Domingos says. “It’s not completely push-button. Markov Logic is not at that stage. There’s still the usual playing around with things you have to do,” he says. “But your productivity and how far you can get is just at a different level.”
A few formulas. Interesting idea. How will one explain what comes out of a machine learning process if regulations about transparency for smart software become a reality?
Those who want to understand what smart software does may have to become familiar with the work of the Markov guys. That’s probably unrealistic. Therefore, figuring out how machine intelligence works is likely to be a challenge.
Now let’s get that accuracy of facial recognition systems above the 75 percent level on University of Washington tests.
Stephen E Arnold, July 4, 2018
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