Here’s a tweet (addressed to Judea Pearl), “If you could get rid of all the spurious non causal correlations in a machine-learning model, you would be left with only the invariant ones which would in turn allow you to understand causality relationships.”
Let’s rephrase that: If you could identify only causal connections in a model, you could understand cause.
True. But of the same value as saying If we could only create a model that predicts perfectly, we’d have a model that makes perfect predictions.
Identifying which connections are in the causal path of some observable, and which are connections are merely spurious, is I think what AI researchers would admit is their holy grail.
They’ll never find it.
At least, not in any general sense.
The first problem is the notions of cause many are using isn’t right. The second is that grasping a causal power is an activity of the intellect, and machines don’t have intellects.
Oh, sure, they’ll be able to program machines to automate tasks that people first know how to do, and where the causal powers of things are at least roughly understood. License plate readers do a reasonable job, as does, to a lesser extent, facial recognition. A lot of human minds, or rules derived from human reasoning, goes into these algorithms. Nothing wrong with that. Indeed, it’s just the right thing. But it’s not AI discovering the cause, it’s non-artificial intelligence.
There are myriad causes of a pixel firing a certain strength on a CCD. And for its neighbor pixels firing or remaining quiescent. One of the causes is the shape of the letters in the reflected light off a plate or face. Others are dirt, rain scatter, etc., etc., etc. You can’t know with certainty you have only the reflection isolated, and the other stuff eliminated. You’ll be left with a model—a predictive model, thank the Lord—in the end.
Well, it’s the same in any model. The hope is that only the right “stuff” is measured to predict the observable. But if we knew we only had the right stuff, then we’d know the cause of the observable, and then we don’t have AI, we have physics. In other words, AI is just statistical modeling. But predictive statistical modeling, which is good.
Facial recognition is big. I’m not up on facial recognition tests in real environments. I mean I don’t know how accurate they are in non-laboratory conditions. The picture heading this post gives some indication (from here; only a database test, I believe). There are reports like this:
The closely watched NIST results released last November concluded that the entire industry has improved not just incrementally, but “massively.” It showed that at least 28 developers’ algorithms now outperform the most accurate algorithm from late 2013, and just 0.2 percent of all searches by all algorithms tested failed in 2018, compared with a 4 percent failure rate in 2014 and 5 percent rate in 2010.
That 0.2% is fantastic, of course. That sounds like a planned database test, and not a field test. Any algorithm ought to do good in database tests, or it’s not worth talking about in public.
A field test is harder. Say, point a camera at the airport security line and see how many bad guys on the wanted list are discovered. Accuracy can’t be assessed unless you have actors playing the roles, since in a real line you won’t know who you missed; only false positives are recorded. You’re back in dirty license plate territory. Seems in real tests the accuracy is closer to coin flipping.
Here, too, the idea of skill is paramount. Probably not a lot of bad guys go through any given line. Suppose it’s 1 in a 1000. So if you guessed for every single person “Not a bad guy”, you’d be right 999 out of a 1000, an accuracy of 99.9%. Wonderful!
No, the model—and it is a model—stinks. Any fancy-dancy AI algorithm must beat 99.9% or it’s useless. If it can’t beat it, then we’re better off guessing “Not a bad guy.”
Of course, you might be willing to accept some extra false positives in exchange for not missing real bad guys. Better to inconvenience a few travelers than let some bad guy fly to Hollywood and harass the indigenous populants. There are formal ways you can account for this asymmetry (ahem).
Anyway, performance is surely not what’s reported in the press.
We’ll discuss much more about AI/stats and cause later.
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