6 ms·
Katie gave a TED talk: https://youtu.be/BIvezCVcsYs https://youtu.be/BIvezCVcsYs
by melling 8y ago
Katie gave a TED talk:
https://youtu.be/BIvezCVcsYs https://youtu.be/BIvezCVcsYs
- anc84 8y agoYou mean Bouman?
- stronglikedan 8y agoSame person.
- CydeWeys 8y ago... you never referred to someone by their first name in informal talk??
- sorenjan 8y agoIt's a TEDx talk. In this case it's an interesting one by an expert in her field, but TEDx have a lot lower demands for participating, so try to keep the distinction clear.
- rohan1024 8y agoIt might have been on TEDx but now it's on TED YouTube channel.
- CydeWeys 8y agoI don't see TEDx anywhere on that.
- dave84 8y agoShe’s literally standing in front of a sign on the stage that says TEDxBeaconStreet.
- nvr219 8y agoMy eyes are closed that's why I didn't see it.
- CydeWeys 8y agoI meant on the channel branding, intro, etc. Usually when these things are TEDx they say so really obviously. Did this talk's status get upgraded?
- seaish 8y agoThe main TED channel occasionally reposts talks from TEDx conferences. This one should be the original posting: https://youtu.be/P7n2rYt9wfU https://youtu.be/P7n2rYt9wfU
- CydeWeys 8y agoSo this talk got upgraded then? They don't make a habit of posting the shitty TEDx talks to the main channel, I'm guessing. (And there's plenty of those.) This is definitely high quality relative to most TEDx talks, so I understand why it was upgraded.
- tendencydriven 8y agoDoes it really matter if its a TEDx or TED talk? It just appears like you're trying to diminish her achievements for literally no gain.
- dennisgorelik 8y agoIt 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 agoI wondered this as well. It seems like a significant problem when doing this sort of thing.
- QuotedForTruth 8y agoDisclaimer: 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.