61 karma · joined June 13, 2016
Instead they present another causal factor for creating startups and just implicitly deny that other factors exist.
I think Quartz is basically the archetype of fake news at this point.
I strongly disagree with this line of reasoning. One's ability to have impact through altruism is closely linked to one's knowledge of where they're intervening. Denmark could easily have more impact in helping Greenland because they know more about what things need to be done in Greenland and can more easily get feedback on whether they've improved things. If you send money to a country that you have no connection to, how do you know that the money is being used effectively? How do you know that you're solving the right problem? How do you make improvements to your spending unless you're getting continual feedback?
There are limits to this of course and I think it can be okay to donate to international charity, but I think the impact of a well-targeted and knowledgable charity is much more certain.
Yes.
What do people mean by this exactly? I've heard some people make the case that the exact boundaries between ethnic groups can be socially constructed, which makes sense to me (like how one draws a hard line between two groups which have some admixture).
But the idea that ethnicities have nothing to do with biology and is a purely social phenomenon sounds like a lay-person's misunderstanding of the above claim. For example, it should be obvious that using a sperm/egg donor can lead to a child of a different ethnicity even if the socialization is kept the same (which you can't necessarily do perfectly in reality, but still).
I think why people review is: -You should be reading more papers anyway and we like doing it, so why not take it as an obligation? -I assume that eventually it helps one to get selected to be an area chair or something along those lines.
I think maybe the real mistake is that someone who is buying a bunch of copies doesn't really need the item to be recommended to them. The recommendations are more helpful for things that you can't find in the history.
I probably would suspect minimal effect unless there were (1) a clear mechanism of action, (2) really strong associations even after adding controls, (3) a causal study, perhaps on non-humans.
What about twin studies?
In general in generative models, you have some "true data distribution" P and an estimator distribution Q.
The goal is to make P and Q the same, generally by minimizing some divergence between them.
The actual objective is defined as being between the actual distributions P and Q, but because we only have so many data points, we define an empirical loss that just uses the real observations from P. So if the model makes Q just memorize the samples from P, then it actually hasn't made P and Q similar, it's only minimized the empirical loss.
One practical way to get around this with GANs is to train a conditional GAN instead of an unconditional GAN, and then run the conditioned generation task on held-out samples from the validation set. Another good and perhaps more general solution is to train an inference network and to generate reconstructions on held out data points. If they look totally different, then the model is probably not very "representative".
1. If you train a conditional GAN to do image inpainting (for example, left to right), it should be quite apparent the degree to which the model is copying and pasting the training set - by running the model with "given" parts from the test set.
2. I disagree that an ideal GAN could just output the training set. I think the right conceptual framework is that any generative model is trying to produce a distribution similar to the data distribution, and we try to accomplish this by using samples from the data distribution. So if the model memorizes the training set, then it isn't actually that close to the true underlying data distribution. In likelihood-based models (for example the usual generative RNN) you can test this by evaluating likelihood on a validation set.
I think that if the robot is really transparent and there's a human inspecting, then people will be willing to live with it.
Reviewing your lease could be tricky because it could involve lots of external variables, cultural differences, changes over time, interactions with the law, and so on.
So expect the winner to be, paradoxically, someone who is completely obedient.
In any case though my guess is that a human neuron accomplishes way more than a hidden unit in a NN so it may be fair to view that as a lower-bound.
I actually think that intelligence test questions have highly correlated difficulty when given to people from different cultures (even more different than US/China). I.e. there's broad agreement in terms of what questions are hard/easy.
For example, in the social sciences it's common to look for "instrument variables" which are random and can't be affected by anything else. If you can find these types of variables then you can pretend that you're looking at the results from an experiment.
There are also methods like discontinuity analysis, which argue that if your system is discontinuously effected by a variable, but the causes will effect the variable in a continuous way, then you can look at changes around that specific point as being causal.
My view is that Machine Learning is a deep field which could take decades to fully appreciate, but which is also easily accessible, especially if one is interested in building applications.
I think that a good litmus test for ML expertise is asking someone what % of all NIPS papers from last year they'd be able to understand well enough to reproduce after a quick reading.
https://nips.cc/Conferences/2016/AcceptedPapers
Even though I have ~7 years of experience doing ML, I would say that I could probably only fully appreciate ~10% of all NIPS papers.