> I am genuinely surprised that I am even slightly offended by this... its not a rational choice.
Racism is not rational. You feeling offended could have more to do with your own prejudice being exposed (the joke worked) than feeling sorry for the arabs that may have been offended by this (there is, as of yet, no sign pointing to that).
Humor uses stereotypes. That's why the owner of the Kwik-E-Mart is an Indian illegal immigrant named Apu Nahasapeemapetilon. If such humor makes one uncomfortable, do you really think that is out of empathy for the Indians? I think it is more likely that one has a thin skin or does not want to see their own stereotypes confirmed.
Also the context is important here (see the Comodo Hacker). The choice for a foreign name and an untrustworthy-sounding business name was not willy-nilly. Finally: Have a hacker tip of the day https://www.youtube.com/watch?v=bAQqrnX7BsM
I thought you were asking me a (loaded) question. The advice given was not "never use Python 3 until...", that was a suggested reply to people telling newcomers to go for Python 3. Perhaps too tongue-in-cheek, but coming from someone who taught millions of people Python, worth noting.
You ignore people who say that Python 3 is the future, because it will make you happy and more productive when you can run modern libraries that have a 2.7 requirement. Or you don't ignore them, and get into endless debates about how almost all libraries support 3, and that you need to spend just 1 in 20 days of your time worrying about getting your code to work, instead of... programming. But if that makes you happy, you should do that.
It's not an act. LPTHW is my Bible. I retake the courses every Sunday. Saying that I should hop on the Fortran train for stating that even advanced fearless Python users opt for 2.7 is heretics!
It is good advice, because it works. You can give reasonable explanations for Python 3, involving politics, the future, and dead languages, while still having your students be confused when Google releases TensorFlow with only 2.7 support. Hard language is his style. He loathes anything that gets in the way of being a productive programmer. See: http://programming-motherfucker.com/
> A programmer may try to get you to install Python 3 and learn that. Say, "When all of the Python code on your computer is Python 3, then I'll try to learn it." That should keep them busy for about 10 years. I repeat, do not use Python 3. Python 3 is not used very much, and if you learn Python 2 you can easily learn Python 3 when you need it. If you learn Python 3 then you'll still have to learn Python 2 to get anything done. Just learn Python 2 and ignore people saying Python 3 is the future.
Besides, judging from the number of DARPA-sponsored projects that require Python 2.7, the future is not going anywhere soon.
Thanks for the links to interesting papers. I really liked the Breiman paper. I did not try to qualify either machine learners and statisticians as bad or good, just pointing out a difference in their approaches to problems.
I do not know enough about statistics to make a (negative) quality statement about it. I know a bit more about machine learning though, and there I also see things like: Picking the most favorable cross-validation evaluation metric, comparing to "state-of-the-art" while ignoring the real SotA, generating your own data sets instead of using real-life data, improving performance by "reverse engineering" the data sets, reporting only on problems where your algo works, and other such tricks. I believe you when you say much the same is happening for statisticians.
Maybe it was my choice of words (careful, sober). I think its fair to say that (especially applied) machine learners care more about the result, and less about how they got to that result. Cowboys, in the most positive sense of the word. I retraced where I got the cliff analogy. It's from Caruana in his video "Intelligible Machine Learning Models for Health Care" https://vimeo.com/125940125 @37:30.
"We are going too far. I think that our models are a little more complicated and higher variance than they should be. And what we really want to do is to be somewhere in the middle. We want this guy to stop and we want that statistician to get there, together we will find an optimal point, but we are not there yet."
I don't think the paper asked for that. Relevant quote:
> machine learning is more concerned with making predictions, even if the prediction can not be explained
very well (a.k.a. “a blackbox prediction”)
So in your example: an algo may explain that a car slows down, before taking a turn, because else it would likely crash. It may even get to a threshold ("under these weather conditions, anything over 55Mph is unsafe when taking a turn of such and such degree"). Statistics can help with that.
Welling is not asking for deep learning models to explain why a person got a cancer, but to explain its reasoning when it diagnoses a person with cancer ("I am confident, because in a random population of a 1000 other patients, these variables are within ..."). Statistics can help with that. It aligns with their mind set and tool set.
Regulations are cheated even with these kind of explanations, but that is for another story (black box models may provide some plausible deniability).
For the security people at ad networks: The URL was
whatsapops.com,
the mobile device was Android latest, the region was The Netherlands. It's not a virus on the device itself:
- No app installs since setting up the device a year ago.
- A lot of internet forum chatter with people using clean factory resets and getting the same problem.
Next to mining for phone numbers, they try to sucker you into installing a 12 Euro a week app. Very similar to the porn-redirects ads from DoubleClick that were plaguing mobile devices and tablets around 2012. This page also abuses the vibrate API and was successful in disabling the back key by working with a redirect-chain.
Working with both statisticians and pure machine learners on the same task, I did notice some tendencies, presuppositions and modus operandi that were different (beyond being a specialized subset). Like said in this position paper, machine learners like to throw computation and parameters at the problem, where statisticians are more careful and sober. As an analogy, a statistician will approach a cliff very carefully, stomping the ground to make sure it is sturdy enough to carry a human. They'll approach the edge of the cliff 'till they have their p-measures and that is their model. Machine learners will jump head-first off the cliff and when you listen you can hear them yell: Cross-validatioooohhhh... as they plummet down.
I like the complement with cryptography. I would add another coding method: compression - Approximating the simplest model with explanatory power.
As we start to use AI/ML for more tasks, the need for model interpretability rises. We expect doctors to explain their gut feelings, much like we expect computer vision models that detect disease to explain their findings and have a (theoretically sound) estimate of confidence.
SVM's were so popular, pretty much because they had a firm theoretical basis on which they were designed (or "cute math" as deep learners may call it). As Patrick Winston would ask his students (paraphrasing): "Did God really meant it this way, or did humans create it, because it was useful to them?". Except maybe for the LSTM, deep learning models are not God-given. We use them because, in practice, they beat other modeling techniques. Now we need to find the theoretical grounding to explain why they work so well, and allow for better model interpretability, so these models can more readily be deployed in health care and under regulation.
An interesting paper at NIPS aims to combat the lower quality of Turkers with gamification. Instead of yes/no answers they offer a choice of: absolutely sure, yes, don't know, no, absolutely not. Monetary reward is doubled if they answer a question correctly with a high certainty, and it is zero'd when they are wrong.
Thanks for the link to the paper. I think the older criticisms still stand:
- They created their own data set. Instead of a general Q-A system, they may have overfit to this particular task and question types.
- They set the human intelligence benchmark by using Mechanical Turk. This may not be representative of true human intelligence (given the lower quality of Mechanical Turkers).
As a future work, I hope they look at the work the Allen AI institute is doing with Aristo [1]
There is a current challenge [2] to beat 8th graders on a standardized 8th grade science exam. Here the data set is made by someone else, the questions are closer to real-life questions (vs. simple analogies), progress and results can be compared to other research teams and the human benchmark is set by the actual performance of 8th graders.
For human intelligence, next to accuracy and speed, we also care about simplicity. This system nails accuracy and speed, but it may be beaten by someone who has never read the entirety of Wikipedia. A deep net trained on millions of words and their relations may be too complex for this task (it uses up a lot of energy to train).
As to computer intelligence being different than human intelligence: I once nearly aced an aptitude test where I had access to a search engine. The test involved programming languages I never programmed a single line in. Yet, searching for keywords in the question, combined with keywords in the answers, I could give correct answers, merely by comparing page count statistics. Like the robot in Searle's room, I was merely pattern matching, without a real understanding or insight into the questions asked. The result of my test leaked out on the workfloor, and for weeks I was a headline wonder (having beaten all the senior engineers' scores), without really deserving it.
1 - Arrogance is confidence projected outwards (in a way that looks ugly)
2 - People who are arrogant do not ask much questions. Arrogant people are unpleasant to interact with.
3 - Coping mechanism for little internal confidence. Alternatively, nobody told them they are projecting or they can't see this for themselves. In pathetic cases they can not help themselves.
4 - No, not always bad. In some roles it is necessary to exude confidence and a better-than-thou attitude. But when you are not a general, king, pope, or professor, then people will not take kindly to such behavior.
You can avoid being arrogant, the same way you can (try to) avoid being stupid, Andrey Kolmogorov: "Every mathematician believes that he is ahead of the others. The reason none state this belief in public is because they are intelligent people.". Also realize that there is only one way to be perfectly correct, but a possibly infinite ways of being wrong.
Racism is not rational. You feeling offended could have more to do with your own prejudice being exposed (the joke worked) than feeling sorry for the arabs that may have been offended by this (there is, as of yet, no sign pointing to that).
Humor uses stereotypes. That's why the owner of the Kwik-E-Mart is an Indian illegal immigrant named Apu Nahasapeemapetilon. If such humor makes one uncomfortable, do you really think that is out of empathy for the Indians? I think it is more likely that one has a thin skin or does not want to see their own stereotypes confirmed.
Also the context is important here (see the Comodo Hacker). The choice for a foreign name and an untrustworthy-sounding business name was not willy-nilly. Finally: Have a hacker tip of the day https://www.youtube.com/watch?v=bAQqrnX7BsM