Dec 16, 2015

XL-Files: Yo-Yo Ma on Machine (or Massive) Learning

Xiao-Li Meng writes:

Boston’s reputation of being a hub of universities was elevated recently by the inaugural HUBweek (Hospital, University and Business), which kicked off with a forum led by Michael Sandel, the “rockstar moralist.” Amid an array of thought-provoking questions, Sandel asked if the audience would feel comfortable letting a smart machine, i.e., “a very, very good app”, trained on a large corpus of student work, to grade essays. To panelist Yo-Yo Ma, this idea is as uncomfortable as relying on “an app for parenting.” Using music teaching as an analogy, Ma explained, “The path from one note to the next is going to be different for every single human being on this planet,” because the way the second note joins the first depends on the player’s physical mechanism, neuromuscular structure, etc. Displaying his trademark passion (but without the cello this time), Ma continued: “If you have an app, I don’t care how big the data is and how great your algorithms are, it’s finite. The idea of the human spirit actually getting to something that is beyond the finite is a part of every human being, and we want to look for that in every student…” (The original remark is at about 1:30:00 in https://www.youtube.com/watch?v=urcSDiQwaNQ; and check out Conan O’Brien’s hilarious answers at 1:24:30!)

Ma’s remark touched upon two fundamental questions of possibilities, or perhaps impossibilities. The obvious one is whether a machine can ever make judgments, or more generally think, like a human. Evidently Ma’s answer would be a “no” because human judgments and emotions are too rich to be replicated fully by any “finite” machine. Indeed, machines are generally perceived as being mechanical, useful for repetitive tasks, but not for adaptive ones. The term “machine learning” (ML) therefore is unfortunate, because much of its promise builds upon the computer’s ability to process and abstract information collected from vastly many individuals and sources. Thus a smart machine like the grading app is meant to serve as a “mass brain” or “meta brain.” In that sense, it would be more apt to denote ML as abbreviating “Massive Learning” or “Meta Learning.”

This brings up the second, subtler question: Can we fully learn about an individual from studying many others? Personalized treatment sounds heavenly, but where on earth can anyone find enough (any?) guinea pigs that are exactly like me to make the promise evidence-based? Similar questions about “transition to similar” have been pondered by philosophers from Galen to Hume. But their contemporary realization injects a healthy dose of skepticism to the modern-day pursuit of fully individualized prediction and inference. Nevertheless, the availability of Big Data, aided by ever-growing computing power, is moving us increasingly close to that ideal, albeit never attainable goal (as Ma correctly emphasized).

The Holy Grail of this individualized learning of course is a balancing act: matching on more individualized attributes in constructing a proxy learning population for me increases relevance (lower bias) but decreases robustness (higher variance) due to smaller data size, but matching on fewer attributes trades lower variance for higher bias. However, such dilemmas provide excellent foundational research opportunities, especially for young talents, as detailed in “A Trio of Inference Problems That Could Win You a Nobel Prize in Statistics (If You Help Fund It)” (Meng 2014, http://www.stat.harvard.edu/Faculty_Content/meng/COPSS_50.pdf) and “There is Individualized Treatment. Why Not Individualized Inference?”(Liu & Meng, 2015, http://arxiv.org/abs/1510.08539).

The self-reference might make you think that I take myself too seriously. So let me lighten the mood by describing an amazing coincidence. While working on this XL-File on a flight, I noticed that a couple of flight attendants were very excited at spotting a passenger. The photo below should help you to conduct an individualized inference about the coincidence, or rather to infer who the individual was…

Who was sharing a flight with Xiao-Li?

Share

Leave a comment

*

Share

Welcome!

Welcome to the IMS Bulletin website! We are developing the way we communicate news and information more effectively with members. The print Bulletin is still with us (free with IMS membership), and still available as a PDF to download, but in addition, we are placing some of the news, columns and articles on this blog site, which will allow you the opportunity to interact more. We are always keen to hear from IMS members, and encourage you to write articles and reports that other IMS members would find interesting. Contact the IMS Bulletin at bulletin@imstat.org

What is “Open Forum”?

In the Open Forum, any IMS member can propose a topic for discussion. Email your subject and an opening paragraph (to bulletin@imstat.org) and we'll post it to start off the discussion. Other readers can join in the debate by commenting on the post. Search other Open Forum posts by using the Open Forum category link below. Start a discussion today!

About IMS

The Institute of Mathematical Statistics is an international scholarly society devoted to the development and dissemination of the theory and applications of statistics and probability. We have about 4,500 members around the world. Visit IMS at http://imstat.org
Latest Issue