Well, I've just started my technical/research blog.
I already have a personal blog, which is (I hope) used to be read by a number of my friends. In recent time I noticed that I had begun post a lot of technical stuff my friends were not interested in. Also, that blog is in Russian, which limits its audience. So, I preferred not to change the blog policy but create a new blog. It was also Boris Yangel's amazing blog that inspired me to create mine.
Well, yes. Folks who know me might already divine that the blog is primarily about computer vision, and they are certainly right. I chose such a fancy name because computer vision is somewhat disappointing. Let me explain.
Do you remember that story when Marvin Minsky at MIT asked his student to teach a computer understand the scene retrieved from a camera during the 1966 summer break? Unsurprisingly, the student failed. Moreover, the general task is far from being solved even now. That days computers were slow, and AI scientists thought that performing logical inference is way more complicated than just scene analysis. Now, we have Prolog and a pile of different verification systems (thanks to the university curriculum -- we used some of them), but a computer is not able to recognize even simple object categories on different classes of images (e.g. relatively robust face detection was done only in early 2000s by Viola and Jones).
Actually, not only Minsky was misled. If you are a vision researcher, when you tell people about your research, they are likely to reply: "Is that all you can? Man, it's simple!" Sure, it is simple for you to figure out that it is a cow on the meadow, not a horse, but try to explain it to computer! It is actually a problem in our lab: when vision guys try to defend a Masters/PhD thesis in front of the committee (which consists of folks who do research in computer hardware, system programming etc), and the committee is usually not impressed by the results, because they think it is not too difficult.
Okay, you've got the point. Computers are blind, and we should cope with that.
Next. Do you know, what's the difference between computer vision of 1980s and modern computer vision? In 80s, they used to solve particular problems, not general ones. They could implement quite a decent vision system that performed its task, but it could not be transferred to another domain. Nowadays, such approach is no more scientific, while it is still good for engineers. Today, computer vision science is about general tasks. It became possible because of extensive using of machine learning methods (a lot of them were developed in '90s). Vision is nothing without learning now. I hope you've got this point too: Vision + Learning = Forever Together. That's why my blog cannot ignore machine learning issues.
What else? Programming language is a tool, but it can be interesting per se. I am about to finish a 5-year university programme in Computer Science, so I have been being taught different language concepts and programming paradigms, and I find it interesting sometimes. That is another possible topic.
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