> how do we identify these people who add nothing positive — or not enough positive — to our lives?
The dude invokes bayes theorem (the theorem that is trending right now) to solve such a basic issue? His grandmother can give him better advices than Bayes.
ML or more generally mathematics do not cause anything. People who misuse mathematics are to blame here. Some fields are simply using tools they don't understand and this predates ML advances by decades. Thinking of stats use in psychology and medicine for instance.
This trend of presenting ML are some kind of magic powder is ridiculous. I blame hyped presentations by influential ML scientists for this.
> Turns out it can disentangle pretty much any set of data.
All the example I have seen (including your links) are variants of face generation algorithms. Any ideas on how this could be useful beyond image generation in some style? Specifically for (data) science?
Sorry if this is a naive question.
Edit:
By "variants of face generation algorithms" I mean any image generation really.
The original Karras et al 2018 paper did both cars and cats, which aren't faces. Worked very well, unsurprisingly. (ProGAN also did well on those, though it was the faces everyone paid attention to.) Look at the samples in the paper or the Google Drive dumps, or at the interpolation videos have posted on Twitter.
Aside from the original work, on Twitter, people have done Gothic cathedrals very well, graffiti very well, fonts very well, and WikiArt oil portraits not so well. On Danbooru2017 full anime images (linked in my thread), one person has... suggestive blobs but has only put 2-3 GPU-days into it and we aren't expecting much so early into training. skylion has been running StyleGAN on a whole-body anime character dataset he has, and the results overnight (on 4 Titans) are pretty impressive but he hasn't shared anything publicly yet.
Thanks! The wait on training is killing me, though. I've been doing large minibatch training to try to fix the remaining issues in the anime face StyleGAN and it's frustrating having to wait days to see clear improvement. Checking GAN samples is so addictive and undermines my ability to focus & get anything else done. I'm also eager to get started on full Danbooru image training, which I intend to initialize from skylion's model - whenever that finishes training...
Haha, having to work around the computation limits are welcoming! It feels like building web apps back in the late 90's again. These days we have so much memory and disk space at hand it doesn't even feel like a challenge anymore.
I forgot one failure case: a few hundred/thousand 128px pixel art Pokemon sprites. StyleGAN seems to just weakly memorize them and the interpolations are jerky garbage, indicating overfitting. (No GAN has worked well on it and IMO the dataset is too small & abstract to be usable.)
no not naive at all. this method isn't specific for just extracting features from faces. it can disentangles features from any kind of images. in fact, the next dataset i might train on is on flowers (or birds)
OK, my point is what could be done beyond generating images in some style? Can we generate interesting mock data given a database for instance (of course this is exactly what you did in a way, but I have in mind e.g. a database containing some numerical/categorical features known to a specific accuracy)?
You can use GANs to generate fake data based on stuff like particle accelerator data or electronic health records. Whether you can use StyleGAN specifically is unclear. What's the equivalent of progressive growing on tabular/numeric data? Or style transfer?
Could be used to generate building plans or other schematics (pretty sure of no use though). Could certainly be put to good use generating pornographic images.
Surprisingly: Viber. Viber Out works great for calling out to 50 countries, and most text communication is on eMail/Slack/iMessage/etc
It's been a few days of Viber and already I see several pluses:
* Putting a call on speaker takes one tap and doesn't hang my phone, unlike Skype which pops up the native iOS sound output device chooser for some reason.
* Easy to see call history & times under a given contact (Skype did a weird center-aligned thing that made the information super-difficult for human eyes to parse)
* Easy to edit contact name info (Skype made this like 5 menus deep)
* Easy to set/remove contacts as favorites (Skype hid this somewhere new every few weeks)
I used skype extensively in its pre-Microsoft era as pretty much all the academics i know. It was synonymous of exciting discussions with collaborators. It had something special which set it apart from usual calls. A kind of special space for discussions.
Last time i used it it took me a day to figure out what my username was. Had to search forums, ask on the internet, and finally navigate the awful microsoft website back and forth to finally find a childish live:xxxxq122 username (MS blocked my 10+ years account because it didnt like the date of birth i provided in a hurry. I was “too young” to use the service). I have no idea now what happened to basic things like a contact list. The new interface should be studied in design schools on all the things that shouldn’t be done.
I find myself using whatsapp or gtalk for meetings now. Having to call collaborators using the same device/service i use with my family. Thanks Microsoft.
> It's not that hard people. Stop believing everything you're told about how "hard" something is.
There are still many problems in physics and mathematics which are considered "hard" (e.g., dark energy, Riemann hypothesis, etc). Can we crack them by simply adopting your positive mindset?
I don't think the "you can do anything" mindset works in real life. It helps self-help book authors sell their stuff, but it's not a good strategy to live by. (Incidentally, this reminds me of Key & Peele's "You can fly" sketch).
What does work though is this: advanced formal education in a topic. Once you have that you can start thinking on how to solve some simple open problems. And if you are lucky and turn out to be extremely smart, you may be able to tackle more challenging problems. Some amount of self confidence may also you to keep going but doesn't make you a genius overnight.
Simply going to a mindset where things are 'not hard' is closer to delusion than it is to anything else.
In academia we get often emails from people who solved quantum gravity (e.g. using fire), show us how einstein is wrong (e.g. using a pendelum), etc. I'm pretty sure they also convinced themselves to "Stop believing everything they're told about how "hard" something is"
Oh man, that reminds me of an experience I had in college. I was working with the aerospace department on their fusion reactor (I was just writing software to help them process data from it, not involved in the science itself). My boss kept getting calls from crackpots who'd go on and on and on about their bogus theories, and how they were being shut out of the mainstream by small minded fools, etc etc.
It was pretty frustrating. He was too nice a guy to tell them off or even cut them off quickly.
My advice to any crackpots who are really sure they're actually geniuses: Get into the stock market (with a SMALL investment). If you're as smart as you think you are, you can find an angle and turn $100 into $1,000,000 or more, and then if anything it'll be GOOD that nobody ever believed in you. I've run across arbitrage opportunities that would have made me fiendishly rich if I'd noticed them sooner myself, believe it or not. Just be careful and don't mess with box spreads.
No, but even those from "left field", if genuine, tend to take the time and care to write things up properly, to use the nomenclature of the field, to address obvious potential concerns up front.
If you're asserting something that's likely to encounter resistance, it's worth being clear and careful.
> Feynman concluded: “for my money Fermat’s theorem is true”.
> "the main job of theoretical physics is to prove yourself wrong as soon as possible."
Great example of the main difference between mathematicians and theoretical physicists .
This reminds me of another magician, Enrico Fermi, who was also an extremely good mathematician but didn't pursue rigor or precision for the sake of it: 20% was good enough precision for him for most cases.
I feel like it is a very "physics" motivated approach to at least "investigating" this theorem. Calculating probabilities is a line of thinking Feynman would be familiar with (quantum mechanics). Physics is often responsible for mathematical development, while they are different, they complement each other. It's nice to see different perspectives, and how ideas are connected.
> > Feynman concluded: “for my money Fermat’s theorem is true”.
> > "the main job of theoretical physics is to prove yourself wrong as soon as possible."
> Great example of the main difference between mathematicians and theoretical physicists.
Actually, I'm not sure I agree: even before Wiles's proof, almost every mathematician would have been willing to wager, at least conversationally, on the truth of FLT; and mathematicians also are in the business of proving themselves wrong as soon as possible. The only catch is that we don't count an inability to prove yourself wrong as a proof that you're right ….
The difference lies in the fact that absolute rigor to assess truths is not as fundamental in theoretical physics as it is in mathematics. Uncertainty is accepted. Physics puts a premium on empirical results and intuition over the more formal treatments common in mathematics (many important results/tools are not mathematically well-defined e.g. Feynman path-integral in d > 1).
Agreed! I didn't mean to claim that there isn't a difference, for there is a wide one; only that the two particular quotes chosen seemed (unlike most other things Feynman said!) not to illustrate them.
We have here someone who doesn't/never work/ed on HEP but on something so remote from it that I would find it hard to even call it physics sometimes. She goes on a sudden crusade against HEP and all its (prominent) practitioners who spent years working on it. She uses some facts we all agree on (uncertainty about the future, etc.) then twists them in a way that makes it look as if the whole HEP community is part of a huge conspiracy to deceive the public. Our truth warrior then courageously exposes them in her ... blog. BS.
On the other side how the hell can she justify her salary and grants to taxpayers? Why isn't she doing some biology or something? It's all so incoherent.
I used to read her when she was less crazy. But I really can't stand her anymore ... it's just too much.
It's all very strange. Two of the most popular and active bloggers in HEP are totally crazy and politically extreme (although in opposite extremes). Blogging seems to be an unhealthy activity for physicists.
With apologies for Appeal to Authority: Paul Graham elucidates the low value of Ad Hominem in his post How To Disagree: http://www.paulgraham.com/disagree.html
It's not as though the author's biases or background are completely irrelevant; but discussing them is unhelpful without additional clarifications on the mistakes, ignorances, or dishonesties alleged.
Imagine a different context: maybe I have a strong opinion as a lay citizen on campaign finance issues. It's all well and good for someone to enter saying "I'm a political operative/lobbyist/etc, and you don't know what you're talking about"; but it's a zero-information statement until they describe what they know that I don't (which would be just as helpful and pertinent if I had turned out to be an expert anyway).
> but it's a zero-information statement until they describe what they know that I don't
I don't think it's a zero-information statement at all. If S. Weinberg tells me that my physical arguments are wrong but he doesn't have the time to say how/why, then it's certainly a non-zero information statement and I'll scrutinize my line of thoughts thoroughly after that. Dismissing this as a zero information statement would be pretentious from my part. The same goes for you against the expert in political finances.
It seems that there's an underlying assumption in your argument that we're all equal and equally capable of having opinions on anything unless someone comes to us, and spends time thoroughly showing us why we are wrong. Or that we are all correct until proven wrong. This is problematic because 1/ we're not all equal, and acknowledging that we dont know everything is important, 2/ it's unlikely that there's always an expert around willing to spend time educating us everytime we feel the need of commenting on things we dont know, and 3/ we may not comprehend why we are wrong by lack of proper education.
I’ve heard a lot of the terms from that post but never read it. it’s extremely awesome and thanks for sharing. Was particularly inspired by this line:
You don't have to be mean when you have a real point to make. In fact, you don't want to. If you have something real to say, being mean just gets in the way.
It's a public discussion of an important matter, and these tend to go best when people present arguments based on their positions. When someone like you fails to do this, then I tend to assume that it is because they lack such.
I don't see a thorough debunking to be honest. The first commentor points to the practical results of particle accelerators. The Vox article (and the blog author) acknowledge this fully. But all examples that are given are the result of past accelerators, where we had reasonable expectations to find new technology because we were still exploring the standard model. This is not the case for a larger accelerator, and this is Hossenfelders point.
The second commenter follows a similar logic. He doesn't seem to engage the point that we don't expect the discovery of something new, he seems to suggest that scientists should build bigger accelerators simply because we can.
But he must know that this is not how science works. Chemists don't just simply perform all imaginable chemical reactions just to completely map the space of chemistry. We allocate scarce resources like time and money to experiments that, according to our best models, may yield promising results. We have ideas for experiments like this in physics, they just happen to not involve a larger accelerator. Scientists are not blind cartographers.
That is not at all how science works. Models yield predictions about the world, not "results" as you suggest. We design experiments to test if those predictions, and thus the models, are correct. Often, new "results" are found when a model fails, and that is why we test boundary conditions.
There is no way of predicting which line of work is likely to yield new physics as you suggest there is.
Neither of those twit sequences directly address the points made. Yes, a larger collider would probe the Higgs field with more precision. It would lead to more precise measurements of what is already known. The question at hand is "is it worth 20 billion dollars"? And if it is, why are all the press releases surrounding the proposal hyping up new physics?
The first link is not very compelling. Nobody disputes that particle physics has produced useful derivates for humanity in the past. The question is whether it will in the future, and whether they will be worth $20B.
> Blogging seems to be an unhealthy activity for physicists.
This kind of observation is valuable, I think, even if it's only statistically true. Certain disciplines encourage practitioners to work within conventions and with capabilities that closely match the activity of blogging (or being on Twitter). For other disciplines, tweeting/blogging is very far from the core competencies, so practitioners who do pursue it are more likely to be outliers.
The other, not-so-neutral aspect of this is the narcissism problem. People who do a lot of personal PR are more likely to be narcissists—and this is a more negative indication in disciplines where self-promotion is an anomaly.
I agree. I've come to the same realization recently when I started following the work of some CS researchers. I was (and still am) amazed to see how active they are on the internet (here, twitter, medium, youtube, github, blogs, etc.)
In the far more conservative physics community, there are (essentially) only two ways of communicating that are acceptable: writing academic papers, or delivering academic talks. Online presence is seen with suspicion.
I am not sure if this is a good or a bad thing.
>People who do a lot of personal PR are more likely to be narcissists—and this is a more negative indication in disciplines where self-promotion is an anomaly.
In my experience many of the CS people who are most active are basically doing it for career visibility reasons and much of what they post is either highly misleading or exaggerated to the level of clickbait. But it depends who we are talking about.
I was thinking specifically of the AI/ML folks. Many top researchers from universities, google, open ai, fair, etc. are super active online. I don't think they do it for career visibility.
There's a cargo cult blogging thing going on in ML, where you also have a large number of tutorials written by variably-competent people written not to inform, but to look good. As I'm sure you know, the results are fairly mixed.
For a reasonable rebuttal of her arguments, please take a look at this article on slate [1]. The main argument against her article is that progress in science is not just measured by how many particles you've discovered this year.
(disclaimer: former HEP physicist here)
That article is nowhere near a rebuttal of Hossenfelder. It makes a semi-decent case that the LHC was not a failure, which no one is disputing AFAICT. It doesn't even attempt to argue that a new, bigger accelerator is a good idea... unless you count the mere juxtaposition of the hypothetical new accelerator with the non-failure of the LHC, which is a despicable, manipulative form of persuasion.
Then there's this:
> Finding out that there are no particles where we had hoped tells us about the distance between human imagination and the real world.
Which is Not Even Wrong. It sounds like it was generated by a Markov bot trained on quotes from Neil deGrasse Tyson.
Yes, and that she has a book to promote with the same thesis doesn't help matters... It makes it look even more like a selfish cry for attention and ultimately money. She has something interesting to say, but to say that the Higgs Boson measurement was somehow well predicted is... odd. One could also make similar arguments against the Gravity Wave observatories, but now that we've actually seen (and continue to see) events, that is silly.
Really, there was a golden age of particle physics when it was easier to theorize and find things. Maybe the allocation of money should be different, but that has more to do with politics and how governments make funding decisions. How do they view the benefits of what we learn building an LHC, collecting, and analyzing the data? You can argue about whether those benefits matter to physics... but Tim Berners-Lee had to work somewhere.
Gravitational wave observatories are not the same thing at all. They're a new kind of telescope, and new cosmology and astrophysics results are going to keep coming in. Confirming yet another prediction of GR is icing.
I would second your observations. The blog seems to whining about things without substance or any constructive propseal. It even runs the risk of bashing those who dedicate their life in exploring nature. It is stromgly recommended that the German lady think more maturely ...
Sure. First off, note that if you read her 2002 work about gender, it often relies on a(n in) famous "bridge study". In 2002, she mentioned it as "publication forthcoming"; 17 years later, it still hasn't been published.
But a general good overview of her statements regarding to blame, and why they fail, can be found on LanguageLog [1]. I'd also recommend searching LanguageLog, as there's other articles discussing how she often overstates the conclusions, and how the media takes it ever farther. If you're on Reddit (can't access it currently on work WiFi), search in /r/linguistics and /r/badlinguistics to find even more criticisms of it (and other Linguistic Relativity research).
The dude invokes bayes theorem (the theorem that is trending right now) to solve such a basic issue? His grandmother can give him better advices than Bayes.