Are Human Experts Less Prone to Catastrophic Errors than Machine-Learned Models? • Hacker News Board

Re: Are Human Experts Less Prone to Catastrophic Errors than Machine-Learned Models?

#11

Post by tndalpaul » Sun, May 25, 2008, 5:07 PM UTC

kurtosis wrote: post #7

It's possible to think of the process that airline safety regulators have gone through where they have observed crashes and guessed about the cause and then suggested fixes as a type of learning problem. For goog's case it would be fun to try and build a supervised learning system whose sole purpose is to try and identify the queries which a human observer would consider a "catastrophic failure" I've also heard of st…

"I've also heard of stories where decision trees supposedly outperformed human cardiologists in making diagnoses. (I'm skeptical of this claim but let's assume that it's true)"

If you substitute "Statistical Prediction Rules(SPRs)" for "decision trees" in your statement, then it's true in every tested field of medicine. Read the FuturePundit article (URL below) and it's links. The system _always_ outperforms the experts in it's field, without fail within the domain of expertise. Even the _best_ experts. Always.

Re: Are Human Experts Less Prone to Catastrophic Errors than Machine-Learned Models?

#12

Post by kurtosis » Sun, May 25, 2008, 5:23 PM UTC

tndalpaul wrote: post #11

kurtosis wrote: post #7

It's possible to think of the process that airline safety regulators have gone through where they have observed crashes and guessed about the cause and then suggested fixes as a type of learning problem. For goog's case it would be fun to try and build a supervised learning system whose sole purpose is to try and identify the queries which a human observer would consider a "catastrophic failure" I've also heard of st…

"I've also heard of stories where decision trees supposedly outperformed human cardiologists in making diagnoses. (I'm skeptical of this claim but let's assume that it's true)" If you substitute "Statistical Prediction Rules(SPRs)" for "decision trees" in your statement, then it's true in every tested field of medicine. Read the FuturePundit article (URL below) and it's links. The system _always_ outperforms the expe…

This article is very interesting, thank you for posting it. I haven't read it yet, but it seems possible that this review suffers from a kind of selection bias, in that the only SPR's which have appeared in the literature are those which were more successful than human experts. This does not mean that a widespread switch to SPR's would outperform human experts, because the average implementation could be inferior to human experts. Consider how difficult it is for IT departments to follow best practices in security.

another aspect of these systems which people may find repugnant is that they allow one to weigh the costs and benefits of different treatment decisions in a consistent way. I personally feel, after watching people I loved suffer in an oncology ward that denying treatments which are very expensive but have small probability of success would be a good thing, but I know other people don't feel this way and there would be outrage.

Re: Are Human Experts Less Prone to Catastrophic Errors than Machine-Learned Models?

#13

Post by msg » Sun, May 25, 2008, 5:52 PM UTC

This comment will be a little roundabout, but it has a real conclusion.

It seems like the subject of this Google Tech Talk keeps coming up over and over again.

http://video.google.com/videoplay?docid=-2469649805161172416

Here's a summary. Yann Le Cun discusses his research on deep learning. The basic problem with standard learning, SVMs, neural networks and the like is that they are limited to coming up with shallow templates for classification, with more or less fancy versions of linear and nonlinear combinations of weights. The number of such templates you need in a highly dimensional space like computer vision is exponential (think about how much data you need to represent one object at different combinations of distance, lighting, orientation, focus, etc.).

Instead, he suggests, we need to learn how to get past shallow template-matching and train, essentially, features of features or networks of networks. This gives us a shot at discovering highly abstract features of the data we have and doing real learning.

If you have any interest in the subject, I strongly suggest you carve out an hour for the video.

TD-Gammon is another example. It is a learning program that plays backgammon. Training from simple board features, it eventually derived what you might call first-order expert features by doing shallow learning. But, when similar first-order expert features (presence of an anchor, etc.) were added by hand to the initial state space, TD-Gammon derived deeper expert features to the point that it and similar programs became better than the best players in the world.

The point here is that there was no way to transition from the shallow features to the deep features without recoding the system from scratch. That obviously won't scale to the kinds of AI problems that are interesting.

Big finish: The advantage humans have over computers (at the moment) is that we work on those multiple levels of abstraction all the time. We do deep learning in every field of human endeavor. That's what the deeply connected neural networks in our brain are all about. In fields where such expertise is possible, humans have it all over the computers. In fields where brute-force calculation can win, the computers have it all over the humans.

What that implies to me is that we can't train computers to think as deeply about catastrophes as we do. It requires a new paradigm of learning to get the computer to that point.