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It is an interesting presentation, but I do NOT understand Katie's explanation about how they were going to minimize the bias [to "see" already predicted black

It is an interesting presentation, but I do NOT understand Katie's explanation about how they were going to minimize the bias [to "see" already predicted black hole visualization] while creatively interpreting inputs from sparsely placed telescopes around the earth. Do you understand Katie's explanation?

cheekybillberry 8y ago
I wondered this as well. It seems like a significant problem when doing this sort of thing.
QuotedForTruth 8y ago
Disclaimer: this is based on watching the talk and some basic machine learning knowledge. Im no expert. They have a sparse set of data that is part of an image. They have trained a model to look at the sparse set and make an educated guess about what the full image looks like. They do this by feeding it full images. The full images you feed into the model thus have an effect on the final image generated. In order to see how large that effect is, they trained different versions of the model with different sets of complete images. Some were images of what we thought a black hole looked like. There is potential that this heavily influences the model and ensures that the output looks like what we expect it to, even if that isnt actually true. They also trained the model with non-blackhole images. Since the output of the model was approximately the same, this indicates that the resulting output picture doesnt look like what we think a black hole looks like just because it was trained with black hole images. It likely really looks like that. The model doesn't need to be told what a black hole looks like. The sparse measurements combined with knowledge of how sparse data can be combined to form a generic image is enough. The model learned that the sparse data is not likely pure noise, instead there are shapes and lines and gradients that relate the sparse data points to each other. Her analogy of sketch artists is good. If you have a functionally complete description and give it to 3 sketch artists from different cultures who are used to different looking people, they will still draw the same person. However if your description isnt actually detailed enough, their sketches will significantly differ as they use their existing knowledge and bias to fill in the gaps with what they think is likely.
- jamestenglish 8y ago
  Not OP, but I too am confused. I understood the sketch artist analogy but that didn't seem related to this point: >They also trained the model with non-blackhole images. Since the output of the model was approximately the same, this indicates that the resulting output picture doesnt look like what we think a black hole looks like just because it was trained with black hole images. It likely really looks like that. If you are feeding non-blackhole images in and getting blackhole results out, wouldn't that be indicative of an over-trained model? Her other analogy was we can't rule out that there is an elephant at the center of the galaxy, but it sounds like if you feed a picture of an elephant in you'll get a picture of a blackhole out?
  - pricecomstock 8y ago
    From what I understand, the training input images are just to establish the relationship between sparse data points and full image, regardless of subject matter. Since they were getting the black hole picture out of the trained model regardless of how it was trained, it's likely that the model was producing accurate results of what the "camera" was pointed at. If they had pointed it at an elephant, the model would have produced a picture of something elephant-like because it was somewhat accurately reconstructing a full image from sparse data points.
    - dennisgorelik 8y ago
      > getting the black hole picture out of the trained model regardless of how it was trained Did they try to feed random noise into their trained image builder? I suspect that the output of that trained image builder is always the same "black hole", even with random noise as an input.
      - QuotedForTruth 8y ago
        Probably not with random noise. With random noise there is literally no connection between pixels. With any actual picture there are connections. Like for instance a pixel is more likely to be the value of its neighbor or nearly so than any random value. This follows from the fact that the pictures are of actual objects with physical properties that determine the value of the pixel that maps to them. Most of the image can be characterized by continuous gradients with occasional edges. I think if you trained with random noise you would get random noise output.
MagicPropmaker 8y ago
I had the same issue. It seemed like with many plausible solutions, there is some bias in the image. I agree there are some reasonable constraints, like it should be energetic stuff in a sphere around a dark sphere, but how many solutions would have fit that criteria?
distant_hat 8y ago
If you want to understand that, do not listen to TED talks. They have a terrible format and are designed to make people feel smart rather than impart knowledge.

cheekybillberry 8y ago

I wondered this as well. It seems like a significant problem when doing this sort of thing.

QuotedForTruth 8y ago

Disclaimer: this is based on watching the talk and some basic machine learning knowledge. Im no expert. They have a sparse set of data that is part of an image. They have trained a model to look at the sparse set and make an educated guess about what the full image looks like. They do this by feeding it full images. The full images you feed into the model thus have an effect on the final image generated. In order to see how large that effect is, they trained different versions of the model with different sets of complete images. Some were images of what we thought a black hole looked like. There is potential that this heavily influences the model and ensures that the output looks like what we expect it to, even if that isnt actually true. They also trained the model with non-blackhole images. Since the output of the model was approximately the same, this indicates that the resulting output picture doesnt look like what we think a black hole looks like just because it was trained with black hole images. It likely really looks like that. The model doesn't need to be told what a black hole looks like. The sparse measurements combined with knowledge of how sparse data can be combined to form a generic image is enough. The model learned that the sparse data is not likely pure noise, instead there are shapes and lines and gradients that relate the sparse data points to each other. Her analogy of sketch artists is good. If you have a functionally complete description and give it to 3 sketch artists from different cultures who are used to different looking people, they will still draw the same person. However if your description isnt actually detailed enough, their sketches will significantly differ as they use their existing knowledge and bias to fill in the gaps with what they think is likely.

jamestenglish 8y ago
Not OP, but I too am confused. I understood the sketch artist analogy but that didn't seem related to this point: >They also trained the model with non-blackhole images. Since the output of the model was approximately the same, this indicates that the resulting output picture doesnt look like what we think a black hole looks like just because it was trained with black hole images. It likely really looks like that. If you are feeding non-blackhole images in and getting blackhole results out, wouldn't that be indicative of an over-trained model? Her other analogy was we can't rule out that there is an elephant at the center of the galaxy, but it sounds like if you feed a picture of an elephant in you'll get a picture of a blackhole out?
- pricecomstock 8y ago
  From what I understand, the training input images are just to establish the relationship between sparse data points and full image, regardless of subject matter. Since they were getting the black hole picture out of the trained model regardless of how it was trained, it's likely that the model was producing accurate results of what the "camera" was pointed at. If they had pointed it at an elephant, the model would have produced a picture of something elephant-like because it was somewhat accurately reconstructing a full image from sparse data points.
  - dennisgorelik 8y ago
    > getting the black hole picture out of the trained model regardless of how it was trained Did they try to feed random noise into their trained image builder? I suspect that the output of that trained image builder is always the same "black hole", even with random noise as an input.
    - QuotedForTruth 8y ago
      Probably not with random noise. With random noise there is literally no connection between pixels. With any actual picture there are connections. Like for instance a pixel is more likely to be the value of its neighbor or nearly so than any random value. This follows from the fact that the pictures are of actual objects with physical properties that determine the value of the pixel that maps to them. Most of the image can be characterized by continuous gradients with occasional edges. I think if you trained with random noise you would get random noise output.

MagicPropmaker 8y ago

I had the same issue. It seemed like with many plausible solutions, there is some bias in the image. I agree there are some reasonable constraints, like it should be energetic stuff in a sphere around a dark sphere, but how many solutions would have fit that criteria?

distant_hat 8y ago

If you want to understand that, do not listen to TED talks. They have a terrible format and are designed to make people feel smart rather than impart knowledge.