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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 o

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.

starmftronajoll 5y ago
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.
- marcinzm 5y ago
  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.
  - kortilla 5y ago
    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?”
    - marcinzm 5y ago
      >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.
      - kortilla 5y ago
        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).
  - user-the-name 5y ago
    But very seldom do they do that because of a hallucination.
- kzrdude 5y ago
  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.)
itisit 5y ago
Those humans don't typically believe those hallucinated faces belong to people though nor do they call the cops.
- version_five 5y ago
  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.
  - itisit 5y ago
    Of course, but the consistency of the false positive is the issue. An able-minded person can readily reconcile their confusion.
    - marcinzm 5y ago
      Then that’s a question of training data.
      - foobiekr 5y ago
        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.
        marcinzm 5y ago
        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.
        Dylan16807 5y ago
legrande 5y ago
> Given that humans hallucinate faces in things all the time Pareidolia: https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia

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.

starmftronajoll 5y ago

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.

marcinzm 5y ago
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.
- kortilla 5y ago
  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?”
  - marcinzm 5y ago
    >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.
    - kortilla 5y ago
      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).
- user-the-name 5y ago
  But very seldom do they do that because of a hallucination.
kzrdude 5y ago
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.)

itisit 5y ago

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

version_five 5y ago
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.
- itisit 5y ago
  Of course, but the consistency of the false positive is the issue. An able-minded person can readily reconcile their confusion.
  - marcinzm 5y ago
    Then that’s a question of training data.
    - foobiekr 5y ago
      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.
      - marcinzm 5y ago
        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.
        Dylan16807 5y ago

legrande 5y ago

> Given that humans hallucinate faces in things all the time Pareidolia: https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia