Live data from Hacker News

Katie Bouman, the computer scientist behind the first black hole image

bbc.com

261–270 of 547 posts

Re: Katie Bouman, the computer scientist behind the first black hole image

#261

Earlier quoted context omitted.

I agree with you, however the difference is that her Dr is relevant to the article, and Shaq's Phd usually isnt.

Neil deGrasse Tyson has a PhD in astrophysics and isn't typically referred to as "Dr Neil deGrasse Tyson" in headlines either. Same for Carl Sagan, Stephen Hawking, and many others. Titles simply tend to be dropped in headlines and casual conversations; they certainly aren't universally used, or even close to it.

Are you at all, in any way willing to entertain a viewpoint that suggests that those gentlemen have their titles dropped possibly because they have become synonymous with their crafts and a lifetime of achievement that-at least in the case of Dr. Sagan has spanned generations (Dr. deGrasse-Tyson's work and personality on the cusp of enjoying the exact same), and their names closely associated with a deep personal connection to the dissemination of science as a form of consumable entertainment (that also happens to inform) and that this maybe serves as an important distinction between someone who is appreciating their first bit of notoriety for their scientific accomplishments?

I personally think they should all be addressed by the titles they've worked lifetimes to earn, that anyone who holds a formal title such as Doctor should be addressed as such in a non-casual/non-informal environment, but I'm also willing to entertain that this is a possibility for why the difference may exist between Dr.'s Sagan, deGrasse-Tyson, and Bouman. And yes, there are probably, most likely others that are far less nuanced and charitable.

Would you be willing to entertain that viewpoint?

Re: Katie Bouman, the computer scientist behind the first black hole image

#263

Earlier quoted context omitted.

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?

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…

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?

Re: Katie Bouman, the computer scientist behind the first black hole image

#264

I've read comments on Hacker News for many years, often finding them a useful source of additional information and insight into details from whatever the linked piece is. Sometimes these threads are full of only subtly veiled hatred and leave me with a feeling of disgust. This thread is one of those. There have been countless threads over the years where a man gets the credit for something a team has worked on and th…

[flagged]

> I'm copying my comment I made on reddit

Please don't. HN threads are supposed to be for people conversing, not copy-pasting.

Re: Katie Bouman, the computer scientist behind the first black hole image

#265
Her story is trully inspiring! She seems like a really likable person, has been hard working, with great results, making a major contribution. The photo with her and the hard drives is amazing and I am sure she will inspire many to enter science.

However, I think to call her "the woman behind the first black hole image" is a hyperbole. It makes it sound as if she was _the one person_ responsible that all this came about. -- But that is not the case. Arguably, there are others who have contributed as much if not more. This is what makes me somewhat feel that this focus on her is not quite fair.

Coverage in mainland Europe has been different so far: Prof Falcke gets a lot of credit for the image/project. Falcke is heading one of the major teams that contributed to the project. In fact, many here attribute the conception of the project to him. But how many in the English speaking sphere have heard or will ever hear about Falcke? Why is that?

My personal guess is that the reason for this is: 1) The Anglo-american media were looking for inspiring EHT scientists from the English-speaking world. 2) Bouman fit that description best.

So, imv, something like "The inspiring story of Katie Bouman" and some credit to some of the other major figures like Falcke would have been fairer.

Re: Katie Bouman, the computer scientist behind the first black hole image

#266
Reading some of the comments in here and in previous texts, I think everyone should try to heed to the following guidelines:

- When an individual/team's work is emphasized by their biological characteristics, it is often meant for clicks or to drive emotions (positive & negative)

- When that happens, ask yourself whether the author of the paper did it for nefarious reasons or not.

- If the cause doesn't seem nefarious, ask yourself whether the society around you has outgrown the biases towards X biological characteristics

- When interacting with others, do not base your actions and thoughts on their biological characteristics.

- Celebrate, debate and criticize the work that the individual/team did, the work the author of the article did and the comments.

Re: Katie Bouman, the computer scientist behind the first black hole image

#267

Earlier quoted context omitted.

Nice 4chan-style image and completely uninformed and idiotic comment. the large majority of those 850K lines of code were machine generated, models, or docs. Go back to whatever alt-right hole you crawled out of.

Citation definitely needed. The software is "ehtim", here are the contributors: https://github.com/achael/eht-imaging/graphs/contributors He says: > I wrote ehtim (eht-imaging) as a python framework for implementing regularized maximum likelihood imaging methods on EHT data. In the last two years, it has evolved into a flexible environment for manipulating, simulating, analyzing, and imaging interferometric data and…

and also doing stuff like computing Fourier matrices:

https://github.com/achael/eht-imaging/commit/40665b2f4c5a220...

But who's counting, right? Seems that you have to not only manage the team writing the software, but you also have to write the entire software to get credit.

Re: Katie Bouman, the computer scientist behind the first black hole image

#268
post #228
post #54

Earlier quoted context omitted.

In my experience this is common for this type of news about "prodigies" on HN. I remember the same types of reactions a few years ago about an article about a child who made the headlines (it even prompted a response by pg IIRC). Was it Malala? I can't remember. I think it's just that many people feel threatened or inadequate when they (naturally) compare themselves to these people. It's tempting to put them down so…

the closest one that comes to mind is the teenager who was credited with that article summarizing algorithm that I think Yahoo or someone ended up buying. My memory is pretty hazy on it, but in that case it seemed more like a group of researchers actually made it and I'm not 100% certain how he was connected. I remember that one getting a bit of "hey, what a second" kind of comments about it. I think in this particul…

You were probably thinking about the startup Summly, which was founded by a 16 yo [0]. Summly eventually got bought by Marissa Mayer while at the helms at Yahoo! for $30 million [1].

0: https://news.ycombinator.com/item?id=3399377

1: https://news.ycombinator.com/item?id=5442290

Re: Katie Bouman, the computer scientist behind the first black hole image

#269
post #251

Earlier quoted context omitted.

Gross double standard. Would you write: > the fact that this man has a doctorate ... No, you would not. We are a long way from equality if the fact that she happens to have a vagina becomes a huge talking point. Everyone defines her as a girl or woman, but this is a scientist. Dr. Bouman is enough.

Can I ask how you know what I would or wouldn’t do and what my sensibilities are on this topic?

A most favorable reading of your post has you:

- dragging in gender by the hairs (as if it is relevant) - trying out the trope that people are denying her title, because she is female.

If anything, I see you contributing to sexism, even though I believe your intentions to white knight are good (though likely biologically inspired to enhance your chance of mating).

Re: Katie Bouman, the computer scientist behind the first black hole image

#270

Earlier quoted context omitted.

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…

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…

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.
Post reply on HN