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This goes a little too far. For traditional ML, sure, you need lots of deep statistical knowledge. But the fact is that deep learning is different: it's mostly

This goes a little too far. For traditional ML, sure, you need lots of deep statistical knowledge. But the fact is that deep learning is different: it's mostly a black box. no one understands exactly what they're doing, how they're biased, and how exactly these models understand things differently than humans, whether you have a PhD in statistics or not. Because of that, doing deep learning consists of a bunch of cobbled together heuristics for getting good results and probing the model to give a human an intuition for whether it's learning correctly. The tricks and tips for steering that black box have mostly been developed in the last decade: it is not a super deep well. These tricks and heuristics are like the knowledge needed to be a technician in a nuclear facility, not the knowledge needed to build the nuclear facility in the first place. It's not nothing, to be sure, but unless you're a researcher developing new novel architectures, a very shallow understanding of the statistics will go a very long way.

zozbot234 5y ago
It's no different, statistical knowledge is still needed to draw the best possible inferences out of the combination of limited data points and prior general information with varying strengths/confidence levels attached. Not to mention that loosely "black box" methods have a long history of their own in non-parametric and semi-parametric stats, so it's not like deep learning is doing anything radically different.
etangent 5y ago
Even so, there are procedures, protocols, and best practices for working with (and validating) black boxes, acquiring which may require time, skill, and patience.
- habitue 5y ago
  Sure, but it's finite, reasonably circumscribed, and honestly not that mathy. I mean, even the example given by the OP about the tanks is super well known (apocryphal[0]) and doesn't require math knowledge to avoid. You just have to have heard of this kind of failure mode [0] https://www.gwern.net/Tanks https://www.gwern.net/Tanks
  - FridgeSeal 5y ago
    > You just have to have heard of this kind of failure mode Yes exactly. You have to be aware of it, you have to know what it entails and what can cause it and how to diagnose and fix it. That’s the other half of the domain knowledge, and just “autoML-ing it” or following some set of prescribed steps won’t necessarily get you that solution.
igorkraw 5y ago
The "deep learning is a black box" meme is about 5 years past it's due date. It's not as tight as for convex models but we do understand what's going on inside, just not perfectly yet.
- habitue 5y ago
  I think we're talking about different levels of understanding. For things like convex optimization, we have optimality results. For deep learning we have "try to stop training early because it tends to get overfit if you run it too long", "increase the number of parameters in the transformer to magically get uncanny impressions of written text out". These are not the same kind of understanding. If I hand you a 175B parameter language model, are you really contending we know what's going on in there? At a mechanical level, sure, it's tensor products and activation functions, but that's like saying we know how human brains work because the standard model is very predictive.
  - igorkraw 5y ago
    Well, the thing is, we do know how human brains work, just not perfectly. A black box is something you can't look inside and don't understand, which we left behind years ago. We can even interpret parts of it. And if you hand me a 175B parameter language model without skip connections I can tell you exactly what will happen because we understand what role they play and how attention decays rank. Note I'm not saying we perfectly understand everything, or that our understanding is as solid as that involving convex models or linear regression. But "black box" just isn't true anymore and just adding unnecessary mystique. We are somewhere between Newton and the Lord Kelvin and Faraday era of physics, no longer in the ancient alchemist days
  - l33t2328 5y ago
    Is it actually tensor products? I thought it was some multilinear map that factors through the tensor product, but my knowledge is limited.
hooande 5y ago
Not sure why you're making a distinction between ML and deep learning here. both of them can be black boxes. Calculating the area of a square can be a black box if all you know is how to plug numbers into the formula. A big part of machine learning is looking at weights and outputs to make sure the results are sane and that you have an understanding of what's going on. This is true no matter what algorithm you use to make predictions.

zozbot234 5y ago

It's no different, statistical knowledge is still needed to draw the best possible inferences out of the combination of limited data points and prior general information with varying strengths/confidence levels attached. Not to mention that loosely "black box" methods have a long history of their own in non-parametric and semi-parametric stats, so it's not like deep learning is doing anything radically different.

etangent 5y ago

Even so, there are procedures, protocols, and best practices for working with (and validating) black boxes, acquiring which may require time, skill, and patience.

habitue 5y ago
Sure, but it's finite, reasonably circumscribed, and honestly not that mathy. I mean, even the example given by the OP about the tanks is super well known (apocryphal[0]) and doesn't require math knowledge to avoid. You just have to have heard of this kind of failure mode [0] https://www.gwern.net/Tanks https://www.gwern.net/Tanks
- FridgeSeal 5y ago
  > You just have to have heard of this kind of failure mode Yes exactly. You have to be aware of it, you have to know what it entails and what can cause it and how to diagnose and fix it. That’s the other half of the domain knowledge, and just “autoML-ing it” or following some set of prescribed steps won’t necessarily get you that solution.

igorkraw 5y ago

The "deep learning is a black box" meme is about 5 years past it's due date. It's not as tight as for convex models but we do understand what's going on inside, just not perfectly yet.

habitue 5y ago
I think we're talking about different levels of understanding. For things like convex optimization, we have optimality results. For deep learning we have "try to stop training early because it tends to get overfit if you run it too long", "increase the number of parameters in the transformer to magically get uncanny impressions of written text out". These are not the same kind of understanding. If I hand you a 175B parameter language model, are you really contending we know what's going on in there? At a mechanical level, sure, it's tensor products and activation functions, but that's like saying we know how human brains work because the standard model is very predictive.
- igorkraw 5y ago
  Well, the thing is, we do know how human brains work, just not perfectly. A black box is something you can't look inside and don't understand, which we left behind years ago. We can even interpret parts of it. And if you hand me a 175B parameter language model without skip connections I can tell you exactly what will happen because we understand what role they play and how attention decays rank. Note I'm not saying we perfectly understand everything, or that our understanding is as solid as that involving convex models or linear regression. But "black box" just isn't true anymore and just adding unnecessary mystique. We are somewhere between Newton and the Lord Kelvin and Faraday era of physics, no longer in the ancient alchemist days
- l33t2328 5y ago
  Is it actually tensor products? I thought it was some multilinear map that factors through the tensor product, but my knowledge is limited.

hooande 5y ago

Not sure why you're making a distinction between ML and deep learning here. both of them can be black boxes. Calculating the area of a square can be a black box if all you know is how to plug numbers into the formula. A big part of machine learning is looking at weights and outputs to make sure the results are sane and that you have an understanding of what's going on. This is true no matter what algorithm you use to make predictions.