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Comment by marcinzm | Hacker News Reader

I find his comment about hallucinating faces in the snow amusing given that humans hallucinate faces in things all the time. And then either post it to Reddit or have a religious experience.

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Yes, that is explicitly part of the point Doctorow is making. It’s why the essay mentions the fact that humans see faces in clouds, etc. Humans typically know when they are “hallucinating” a face, and ML algorithms don’t. When humans see a face in the snow, they post it to Reddit; they don’t warn their neighbor that a suspicious character is lurking outside. This is the distinction the essay draws.

People perceive nonexistent threats all the time and call the police. The threshold is simply higher than current AI but that’s a question of magnitude rather than inherent difference. Fine tune a reinforcement model on 5 years of 16 hours a day video and I’m sure it will also have a better threshold.

There is general knowledge about the world for humans to know that there isn’t a giant human in the sky no matter how good the face looks.

Train it with as many images as you want and as long as a good enough face shows up, the model is going to have a positive match. The entire problem is it’s missing that upper level of intelligence that evaluates “that looks like a face, could it actually be a human?”

>There is general knowledge about the world for humans to know that there isn’t a giant human in the sky no matter how good the face looks.

Is there? Humans used to think the gods were literally watching them from the sky and the constellations were actual creatures sent into the night. So this seems learned behavior from data rather than some inherent part of human thinking.

>Train it with as many images as you want and as long as a good enough face shows up, the model is going to have a positive match.

So will a human if something is close enough to a face. A shadow at night for example might look just like a human face. Children will often think there's a monster in the room or under their bed.

Children will think there is a monster under their bed based on no evidence at all. That speaks to something beyond object recognition that happens at a different processing layer.

Humans do not need to be trained on billions of images from around the globe to semantically understand where human faces are not expected to appear. Modern AI can certainly recognize faces really well with that level of training now, but it still doesn’t even understand what a face is (i.e. no model of reality to verify its identification against).

But very seldom do they do that because of a hallucination.

Well, we seem to experience such things in a split second, and then we correct ourselves. We use some kind of reasoning to double-check suspicious sensory experiences.

(I was thinking of this when I was driving in a new place. Suddenly it looked like the road ended abruptly and I got ready to act, but of course it didn't end and I realized that just a split second later.)

> Given that humans hallucinate faces in things all the time

Pareidolia: https://en.wikipedia.org/wiki/Pareidolia

Those humans don't typically believe those hallucinated faces belong to people though nor do they call the cops.

You don't think a person has ever called the police because they hear a noise they thought was an intruder, or saw someone or something suspicious only in their mind? People make these kind of mistakes too.

Of course, but the consistency of the false positive is the issue. An able-minded person can readily reconcile their confusion.

Then that’s a question of training data.

The problem with this line of reasoning is that it can be used as a non-constructive counter to any observation about AI failure. It’s always more and more training data or errors in the training set.

This really is a god-of-the-gaps answer to the concerns being raised.

No, my point is that if two systems show very similar classes of errors but at different thresholds with one trained on significantly more data than the more likely conclusion is that there isn't enough data in the other.

Don't most high-end machine learning solutions have more training data than a human could consume in a lifetime?

I don't think there is a realistic way to make that comparison.

For consideration, our brains start with architecture and connections that have evolved over a billion years (give or take) of training. Then we are exposed to a lifetime of embodied experience coming in through 5 (give or take) senses.

ML is picking out different things, but it's not obvious to me that models are actually getting more data then we have been trained on. Certainly GPT has seen more text, but I don't think that comparing that to a person's training is any more meaningful than saying we'll each encounter tens of thousands of hours of HD video during our training.

They aren't very similar errors, ML solutions are equally accurate as humans in at a glance performance but longer and humans clearly wins. I'd say that the system is similar to humans in some ways, but humans have a system above that which is used to check if the results makes sense or not, that above system is completely lacking from modern ML theory and it doesn't seem to work like our neural net models at all (the brain isn't a neural net).

An ML system generally can reconcile (and also avoid) this kind of confusion, with present technology. The example is more a question of responsible implementation than of a gap in the state of the art.

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