Future Science - Science of the Future

In the opening paragraph of Die Frage nach der Technik,  Martin Heidegger describes science and technology in the following sequel "Questioning builds a way. [ ]  The way is one of thinking. All ways of thinking, more or less perceptibly, lead through language in a manner that is extraordinary."  For a long time I have liked this description as particularly fitting for the mathematical and physical sciences (and I did not  read the rest of the book because I found it incomprehensible). These three sentences were quite clear - questioning builds a way towards understanding and understanding, miraculous and extraordinary, leads to discovery and innovation. This scheme lays foundation for the modern scientific method where the branches of the  Tree of Knowledge represent the ways of a conscious mind.

So is there another way to obtain knowledge? one that is not preceded by understanding but where the existing knowledge is leveraged to move between  points  where a human mind cannot follow? Perhaps, if machines are involved.

One possibility is offered by the Data Science and it is fairly simple. We can gather colossal amounts of data and extract knowledge from them. Neither task can be done without sophisticated tools. Consider Large Synoptic Survey Telescope - it is a marvel of modern astronomy that (when operational) will collect about 30 terabytes of data per night. This data will contain information about planets, supernovas, dark energy, and much more, and this is all possible because the Universe observed at this scale appears to be quite simple.The fact that we can simultaneously track a relatively small number of variables on a huge number of objects through frequent snapshots is all it takes to make discoveries that thirty years ago were not thought to be possible. Another such tool is the Kepler planet finder telescope.  With the marginal cost of finding new planets well below 1M a piece, commercial applications become possible…

When the setting is more complicated simple statistical tools  tend to fail. Consider the  Human Genome Project that was supposed to provide critical advances in medicine. Unlike in astronomy, the number or variables is very large and the data set is not big enough. In the end, fundamental advances in statistical reasoning are necessary.

Computational Science is another alternative, and the premise is simple - use the existing knowledge to compute and model the nature. In many cases it is the only way to gain some understanding of how the nature works. However, the methodological underpinnings of this process are inherently shaky, and when the stakes are high the results are questioned. Whether modeling of the climate or designing nuclear weapons the conclusions from computational models are difficult to verify and open to criticism. No wonder, even when only four arithmetical operations are concerned, the computer implementation may be deeply flawed as this incident shows. 

For most of the other things, computational methods are very successful.  Doron Zeilberger wrote:

Don't Ask: What Can The Computer do for ME?, But Rather: What CAN I do for the COMPUTER? This is a Faustian bargain but hopefully it will take some time  before we get there. Nevermind, consider Inverse Symbolic Calculator and play with it a little. As this article  illustrates the computer sometimes knows more math than you do.  Other examples abound - consider Eureqa or software developed by Ayasdi. Eureqa will "discover" Newton's Second Law of Motion based on the results of a countertop experiment, and Ayasdi's  software is likely to surprise you as well. Machine learning algorithms such as automatic translation between languages, Google's Page rank's calculations are all well known commercial applications  where a great deal of computational power goes to produce results that cannot be in any sense verified by a paper and pencil calculation.

So in the end do not  think that the battle is over and all goes to  Computational  Data Science and Engineering, Large Hadron Collider  and other projects that need electricity but no coffee.  Enjoy this and cheer up for human brain that operates on 1 watt of green energy.

Foreign language

I like photography. I like taking pictures, editing them, cataloguing, evaluating and labeling. Yet I have never paused to think what is the nature of photography, and how it relates to other things. So when my daughter gave me Camera Lucida by Roland Barthes I took the opportunity to step up to a new level. And while this is what happened in the end, the whole experience left me puzzled and uncertain.

 I read the book on a long flight - it is fairly short and it consists of about forty vignettes comprising two parts of the book. The writing style is peculiar, I was annoyed with the sentence structure, abundance of quotes and references, prolific use of latin, italicized words and more. All along I had the impression that the book represents a trip that my mind does not want to take, yet I glided from one sentence to another with relative ease. The main feature of the book is that while I understood most of the words, I hardly understood any of the sentences. After finishing the book, I have glanced at randomly selected phrases and I could not honestly say that I could parse them unambiguously . One would think at this point that this could not have been a positive experience. Au contraire, and here is why and how.

 The subject of the book is of course photography and it takes it from the first principles. There is no doubt that the author knows what he is talking about, although a reader like myself cannot grasp the meaning at some level. The discussion is akin to looking at a painting from a distance of one inch and establishing the language and physical evidence. The jig-saw puzzle of different bits and pieces is slowly absorbed by your mind and in end the picture emerges. I doubt if it is the same picture for every reader, and in this respect the book mimics the subject itself. This type of thing is common when watching someone paint or draw, listening to the music, reading poetry or watching a movie that was edited in a fancy way. However, I have never seen it, or even thought possible, in a scholarly work.