is74

My method for generating startup ideas is notice the things that I hate. For example, I hate shaving, and would kill for a device that I can stick my face into and become clean-shaven one minute later. I also hate choosing the shower temperature -- I want the shower learn the temperatures I like. I also hate not knowing the definition of terms in government websites. I can't stand passwords. Etc. This feeling is my indicator of a good potential startup idea.

Many comments expressed concern about the alleged inappropriateness of the title. Even the no-free lunch theorem has been invoked, and words like SVM mentioned.

However: The original title, "Neural Networks officially best at object recognition", is much more appropriate than the current title, because it is by far the hardest vision contest. It is nearly two orders of magnituder larger and harder than other contests, which is why the winner of this contest is best at object recognition. The original title is much more accurate and should be restored.

Second, the gap between the first and the second entry is so obviously huge (25% error vs 15% error), that it cannot be bridged with simple "feature engineering". Neural networks win precisely because they look at the data, and choose the best possible features. The best human feature engineers could not come close to a relentless data-hungry algorithm.

Third, there was mention of the no-free lunch theorem and of how one cannot tell which methods are better. That theorem says that learning is impossible on data that has no structure, which is true but irrelevant. What's relevant that on the "specific" problem of object recognition as represented by this 1-million large dataset, neural networks are the best method.

Finally, if somebody makes SVMs deep, they will become more like neural networks and do better. Which is the point.

This is the beginning of the neural networks revolution in computer vision.

Actually, it does, since the difference in performance between entry #1 and entry #2 is so huge (25% error vs 15% error!), and since this is by far the hardest computer vision challenge yet!

Although life is meaningless, every person fears death and can enjoy their meaningless life.

While he was fired from Pay pal, the other two claims are factually false.

These findings should not be misinterpreted, since it is indeed impossible to reach any goals. A person in their late 20s is extremely unlikely to become an olympic swimmer or a squash player because of physical limitations. And some people will relentlessly try and never succeed.

But believing that you can change is useful because it makes it easier to persist in my efforts to change. Because if I believed that change is impossible, I'd give up on the spot.

I found that I simply cannot believe a statement like "I can get much smarter" or "I can get much better at X", but I found that I can easily (fully, honestly, without reservations) believe that "I can get a bit more smarter", or "my intelligence is sufficient for mastering this material, so I need to push harder", or "I can get at least a bit better socially." These beliefs motivate me and make it easy for me to do the work even when it looks like progress is nonexistent. This is the meaning of believing that you can change.