On Science: passion towards solving a problem must come from within

I'm currently in Chicago while traveling to Vancouver (NIPS 2009 conference) where I'll be defending my research during Tuesday's poster session. Instead of delving into the computational challenges that motivate my research, I want to take a step back and criticize what (sometimes? often?) happens during the publishing cycle.

According to me, good research starts with the passion to solve a particular problem or address a specific concern. Quite often, good research will raise more questions than it successfully solves. Unfortunately, when we submit papers to conferences we are judged on the clarity of presentation, level of experimental validation, as well as overall completeness. This means that the publishing cycle quite often promotes writing "cute" papers that have little long term impact in the field and can only be viewed as thorough and complete due to their narrow scope. This is why we should not solely rely on peer review nor cater our scientific lives towards pleasing others. Sometimes being a good scientist means breaking free from the norms that the world around us rigidly follows, sometimes publishing too often skews our research focus, and sometimes falling off the face of the earth for a period of time is necessary to push science in a new direction.

I want to challenge every scientist to follow their dreams and attempt to solve the problems they truly care about and not just attempt to please peer review. Maybe some think that the perversion of science (that is evaluating scientists by the number of publications they have) is okay, but in my book a scientific career which produces a single grand idea is superior to a career saturated with myriad "cute and thorough" papers. I'm not particularly upset with the progress of Computer Vision, but I think more people should ponder the negative consequences of pulling the publish-trigger too often.

Linguistic Idealism

I have been an anti-realist since a freshman in college. Due to my lack of philosophical vocabulary I might have even called myself an idealist back then. However, looking back I think it would have been much better to use the word 'anti-realist.' I was mainly opposed to the correspondence theory of truth which presupposes an external, observer independent, reality to which our thoughts and notions are supposed to adhere to. It was in the context of the Philosophy of Science that I acquired my strong anti-realist views, (developing my views while taking Quantum Mechanics, Epistemology, and Artificial Intelligence courses at the same time). Pragmatism -- the offspring of William James -- was the single best view which best summarized my philosophical views. While pragmatism is a rejection of the absolutes, an abandonment of metaphysics, it does not get in the way of making progress in science. It is merely a new a perspective on science, a view that does not undermine the creativity of the creator of scientific theories, a re-rendering of the scientist as more of an artist and less of a machine.

However, pragmatism is not the anything-goes postmodern philosophy that many believe it to be. It is as if there is something about the world which compels scientists to do science in a similar way and for ideas to converge. I recently came across the concept of Linguistic Idealism, and being a recent reader of Wittgenstein this is a truly novel concept for me. Linguistic Idealism is a sort of dependence on language, or the Gamest-of-all-games that we (humans) play. It is a sort of epiphany that all statements we make about the world are statements within the customs of language which results in a criticism of the validity of those statements with respect to correspondence to an external reality. The criticism of statements' validity stems from the fact that they rely on language, a somewhat arbitrary set of customs and rules which we follow when we communicate. Philosophers such as Sellars have gone as far as to say that all awareness is linguistically mediated. If we step back, can we say anything at all about perception?

I'm currently reading a book on Wittgenstein called "Wittgenstein's Copernican Revolution: The Question of Linguistic Idealism."

Computer Vision Courses, Measurement, and Perception

The new semester began at CMU and I'm happy to announce that I'm TAing my advisor's 16-721 Learning Based Methods in Vision this semester. I'm also auditing Martial Hebert's Geometry Based Methods in Vision.

This semester we're trying to encourage students of 16-721 LBMV09 to discuss papers using a course discussion blog. Quicktopic has been used in the past, but this semester we're using Google's Blogger.com for the discussion!

In the first lecture of LBMV, we discussed the problem of Measurement versus Perception in a Computer Vision context. The idea is that while we could build vision systems to measure the external world, it is percepts such as "there is a car on the bottom of the image" and not measurements such as "the bottom of the image is gray" that we are ultimately interested in. However, the line between measurement and perception is somewhat blurry. Consider the following gedanken experiment: place a human in a box and feed him an image and the question "is there a car on the bottom of the image?". Is it legitimate to call this apparatus as a measurement device? If so, then isn't perception a type of measurement? We would still have the problem of building a second version of this measurement device -- different people have different notions of cars and when we start feeding two apparatuses examples of objects that are very close to trucks/buses/vans/cars then would would loss measurement repeatability.

This whole notion of measurement versus perception in computer vision is awfully similar to the theory and observation problem in philosophy of science. Thomas Kuhn would say that the window through which we peer (our scientific paradigm) circumscribes the world we see and thus it is not possible to make theory-independent observations. For a long time I have been a proponent of this post modern view of the world. The big question that remains is: for computer vision to be successful how much consensus must there be between human perception and machine perception? If according to Kuhn Aristotelian and Galilean physicists would have different "observations" of an experiment, then should we expect intelligent machines to see the same world that we see?

More Newton's Method Fractals on Youtube

I've posted another cool fractal video. I used ffmpeg and imagemagick to get the screenshots from an OpenGL C++ program running on my Macbook Pro.